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Graphics Processing Units (GPUs) consist of thousands of identical cores, each designed to operate independently on massively parallel tasks, which can be subdivided so that each core works independently. This design differs from that of the traditional Central Processing Unit (CPU), which is composed of a smaller number of highly complex cores, with sophisticated control logic, large amounts of hierarchical cache memory, and advanced mechanisms such as out-of-order execution, branch prediction, and deep pipelines. This architecture is optimized to minimize latency in the execution of sequential tasks and to handle diverse and dependent instruction streams. GPUs, on the other hand, are designed to keep data and processing as local as possible within their multiprocessors, reducing data movement, increasing throughput, and maximizing performance in highly parallelizable workloads. Computational Fluid Dynamics (CFD) is ideal for GPU architecture because everything is done locally in the computational cell, eliminating the need for communication with distant cells. Reducing the distance between electrons brings three main benefits: simulations can be run faster; power consumption per simulation is much lower; and the hardware footprint is much smaller. The ability to run Simcenter STAR-CCM+ simulations on GPUs is not new, but Simcenter STAR-CCM+ 2602 took a major step forward by adding Volume of Fluid (VOF) and Multiphase Mixing (MMP) to the list of native GPU solvers. The multiphase capability supported on GPU in this version is impressive, including phase change models such as evaporation, boiling, and cavitation, acceleration techniques such as implicit multi-steps, and support for multiple regimes with MMP-LSI. Here are some examples of the benefits that GPU execution can bring to a variety of applications. Run faster tank sloshing simulations Solver: VOF; Mesh: Uniform static mesh of 5.6 million pixels (AMR not yet compatible with GPU); Time step: 5e-4s with dynamic substeps (target CFL 0.5); Motion: Sinusoidal lateral motion; CPUs used: 192 CPU cores (AMD EPYC 7532); GPU used: 1 NVIDIA RTX 6000 Ada The first example is a case of liquid oscillation in an automotive fuel tank. As engineers, we want to know how the center of gravity shifts as the tank oscillates, due to the loads it transfers to the vehicle, and the effect this will have on the vehicle's stability and dynamics. Liquid oscillation is also a concern in cryogenic applications, where boiling occurs frequently, which is also addressed in this version. In Simcenter STAR-CCM+ , the same solver was used for both CPU and GPU versions, meaning that if the cases converge well, identical results can be expected. In the tank oscillation example, this is exactly what is observed, with the free surface motion over time being almost identical (as in real experiments, VOF transient cases are stochastic in nature and therefore no run will be completely identical to the point where every drop coincides). The center of gravity motion shows good agreement with the experiment, both in CPU and GPU runs. CPU GPU The advantage of running the program on the GPU becomes more evident when execution times are compared. A single GPU was significantly faster than 192 CPU cores. In fact, it would take 251 CPU cores to match the GPU's speed (a metric known as CPU core equivalence). When comparing power consumption, the benefits of the GPU are clear, as it uses only 19% of the CPU equivalent, reducing operating costs and carbon footprint. Speed up propeller cavitation simulations Solver: VOF plus Schnerr-Sauer cavitation model; Mesh: 4.4 million clipping static mesh (focused on the region near the propeller); Time step: 5e-6s with 3 volume fraction substeps; Motion: MRF; CPUs used: 160 CPU cores (Intel Xeon Gold 6248); GPU used: 1 NVIDIA Tesla V100 The next example is a marine propeller operating in a condition where cavitation is expected. This gives us the opportunity to test some of the advanced physics features included in the GPU VOF in this version. In this case, the Schnerr-Sauer cavitation model was used. The model predicts the growth and collapse of vapor bubbles due to low pressure on the propeller surface. These bubbles coalesce to form larger vapor pockets that fill the tip vortex and move downstream, forming a classic helical pattern. The results of this simulation on CPUs and GPUs are shown below. They are identical, as expected. CPU GPU The single GPU completed the execution in about 70% of the time it would take for the 160 CPU cores, which is equivalent to 231 CPU cores. As in the previous example, the energy consumed to complete the execution is also much lower, with the GPU consuming only 35% of the energy used by the CPUs. Accelerate marine resistance predictions: Kriso Container Ship (KCS) Solver: VOF plus VOF waves Mesh: 28M clipped static mesh Time step: 0.02s Movement: None CPUs used: 512 CPU cores (AMD EPYC 7532) GPUs used: 2 NVIDIA RTX 6000 Ada Still on the topic of maritime applications, the next simulation is a drag calculation for the Kriso container ship (KCS) test case. Accurate drag prediction in these examples requires precise capture of free surface waves both around the vessel and downstream. This simulation is possible on GPUs thanks to VOF wave support in this version. CPU GPU Once again, the CPU and GPU results are indistinguishable. Comparing execution time, both GPUs were slightly slower than 512 CPU cores, resulting in an equivalent of 214 CPU cores. The GPU's power consumption was only 30% of the CPU cluster's consumption. Run E-Motor cooling studies faster Solver: MMP-LSI; Mesh: Static polyhedral mesh of 4.16 million iterations; Time step: Adaptive time step with a maximum CFL target of 2 and 10 substeps; Motion: Rigid body motion (with intersection based on metrics and distance from the PDE wall); CPUs used: 160 CPU cores (Intel Xeon Gold 6248); GPU used: 1 NVIDIA Tesla V100 The last example is an electric motor similar to those found in electric vehicles. These motors require cooling with a dielectric fluid (oil) which, in this motor, is injected through fixed inlets on the top of the machine over the copper windings. Optimizing cooling in an electric motor is fundamental to maximizing performance and efficiency. This simulation uses Multiphase Mixture Modeling (MPM) with Large Scale Interface (LSI) to allow the coexistence of resolved jets and dispersed mixtures of sub-grid droplets. The simulation also includes relative motion (Rigid Body Motion with sliding interfaces). CPU GPU The results again show excellent agreement between CPU and GPU execution. In this example, the single GPU was slightly slower than the 160 CPU cores, resulting in an equivalent of 124 CPU cores and power consumption equivalent to 65% of that of the CPUs. This is not as good as in the other examples due to the need to re-intersect the sliding mesh at each time step (this is a non-local operation and therefore less suitable for GPUs). Even so, it still represents a very significant speed gain. Take your multiphase simulations to a new level of speed and efficiency with the power of GPUs in Simcenter STAR-CCM+ . CAEXPERTS can help you implement, optimize, and extract maximum performance from this technology in your projects. Schedule a meeting with our experts and discover how to accelerate your results, reduce computational costs, and innovate with greater confidence. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

A strong rise of the interest in green hydrogen production The demand for today and the future is for true zero-emission power. Alternatives must be found to replace fossil fuels. Currently, batteries are a solution for automotive. Unfortunately, they are not suitable for many applications due to limitations with storage capacity, lifetime, charge constraints and environmental concerns. Therefore, green hydrogen production (produced for instance by electrolysis, using renewable electricity) is identified as a promising solution for long-term zero-emission renewable energy storage. In 2019, the power generated thanks to hydrogen had the order of magnitude of the power delivered by a modern nuclear plant. And for a few years, hydrogen consumption rose rapidly. This trend will continue to grow significantly as many countries have recently committed large investments to increase hydrogen production and usage for transportation, energy or the industry. Fig. 1: Evolution of hydrogen consumption Most hydrogen is still produced from fossil fuels, which means that new infrastructures must be developed with the following challenges: Green hydrogen production, without any CO₂ emissions. Water electrolysis is one solution, using clean electricity generated for instance from wind turbines, solar panels, wave converters or a combination of these. The improvement of the system performances, reliability and efficiency in order to reach an acceptable price for the produced hydrogen The storage of the hydrogen. As this gas has a poor mass energy density in ambient conditions, it is usually compressed or liquefied for storage. Fig. 2: Hydrogen production plant So, how do we address these challenges, capture the behavior of a hydrogen production plant and each of its subsystem? A model combining all subsystems to evaluate global performances Green hydrogen production simulation within Simcenter Amesim is the solution. It makes it possible to capture the complete process of green hydrogen production, predict interactions between subsystems and the global performances. Fig. 3: Hydrogen production plant model in Simcenter Amesim Let's now move on to analyzing the example of electricity generated from 3 different green sources: Wind turbines Solar panels Wave converters The electric power is used to power an electrolyzer generating hydrogen. The hydrogen is finally compressed in order to store it in high pressure tanks, ready to be used, refuel vehicles or to be transported. Wind Turbines The wind turbine model takes into account the number of wind turbines we wish to use, the definition of the turbine geometry (especially the propeller diameter, the angle of inclination…), the generator performance, the losses of the subcomponents and the control of the propeller pitch. Fig. 4: Wind turbine model This model makes it possible to predict, for instance, the electric power and the mechanical power of the turbine depending on the wind transient speed. Fig. 5: Wind turbine model results Solar panels The solar panel model is taking into account the number and geometries of cells and panels, the transient operating conditions: considering the evolution of the sun position and the impact of clouds and the definition of the solar array performances. Fig. 6: Solar panels model It makes it possible, for instance to predict the electric power delivered by the solar panel, depending on the transient irradiation power on the cells. Fig. 7: Solar panels model results Wave Generator To predict the performance of a wave generator, a highly detailed multiphysics model was initially built. This model reproduces the detailed architecture of the system, considering the sizing and behavior of the subsystems: the piston, valves, hydraulic motor and generator, an accumulator, piping, etc. The model takes into account transient operating conditions with variable wave frequency and amplitude. This model is accurate and useful for the detailed design and optimization of the wave generator. However, for long-duration simulations, it remains slow. Then, in a second stage, starting from the accurate model, a reduced model was built using the Simcenter Amesim Neural Network Builder tool. The Neural Network Builder allows training a reduced model easily and quickly, generating the corresponding Amesim model that will run very quickly. In a validation simulation, the reduced wave generator model managed to reproduce the results of the initial model with a 94% confidence level, with a significantly shorter simulation time. It's truly amazing! Fig. 8: Wave generator model reduction This reduced-scale model can then be used to predict the electrical energy generated by the wave generator, depending on the frequency and amplitude of the wave, with the performances we need in our green hydrogen production system model. Fig. 9: Wave generator model results Electrolyzer The electric power generated by solar panels, wind turbines and wave generators is combined and used by the electrolyzer. This will convert water into O₂ and H₂. In this model, performance and reaction rates are predicted thanks to the polarization curve provided as a parameter, the number of cells, and the active area of ​​the cells. Fig. 10: Electrolyzer model This makes it possible to predict the electric power used by the electrolyzer, the hydrogen instantaneous flow it will produce and the corresponding average mass you can produce per day. In this example, you can produce approximately 9 kg of hydrogen per day. You can also see that, with the sizing of the subsystems, the wave converter produces 88% of the electrical energy, the solar panels 4%, and the wind turbine 7%. Fig. 11: Electrolyzer model results Hydrogen storage Finally, the hydrogen is compressed in the hydrogen storage model. This model is based on pipes, a compressor with its control, controlled valves and several tanks. The valves control allows the 1st tank to fill until the pressure reaches 750 bars. The 2nd tank is filled next and finally the 3rd. Thermal exchanges occurring between hydrogen, the pipes and the tanks are taken into account. The simulation stopped when the pressure had reached 750 bars in each of the 3 tanks. Figure 12: Hydrogen storage system model Thanks to the model and simulation, it is possible to predict that, under the defined operating conditions, the 3 tanks can be filled in 42 days. It is also possible to clearly understand how quickly the pressure and mass of hydrogen increase, as well as the evolution of the gas temperature inside the 3 tanks. The compression of hydrogen to 750 consumes part of the energy generated by the solar panels, wind turbines, and wave generators. This, ultimately, reduces hydrogen production. Thanks to the simulation, it can be estimated that the compressor consumes about 6% of the electrical energy. Fig. 13: Hydrogen storage system model results Conclusions In conclusion, green hydrogen production simulation within Simcenter Amesim can definitively help address the challenges of green hydrogen production. The extensive multi-physics simulation platform makes it possible to model complete systems Sizing the different subsystems, considering various operating conditions is beneficial It makes it possible to better integrate subsystems and improve the overall performances and ROI Provides you with a better understanding of the system global behavior With system simulation, you can better design your system but also evaluate virtually and improve your control strategies You can finally select the right design at the 1st attempt, reducing risks of errors and accelerating your projects Finally, Simcenter Amesim , thanks to generic models and libraries makes it possible to address clean hydrogen production but also many other applications. We can mention briefly for instance the following ones: Design of hydrogen tanks integrated in a vehicle or an aircraft, considering high pressure or cryogenic tanks, simulation of scenarios as refueling or hydrogen extraction. Evaluation of the performances of aero-engine and gas turbines, analyze the bleed impact on multi-stage compressors, focus on engineering questions analyzing model for off-design and transient assessment. Design of hydrogen combustion engines, adapt the injection systems and controls, charging systems, combustion controls and after treatment systems. Fuel cells design and integration with the air and hydrogen supply, the power electronics, the thermal management and controls. Fig. 14: Examples of Simcenter Amesim capabilities for other applications about hydrogen About the author: Patrice Montaland is a Business Developer for Simcenter Amesim . He first gained experience about simulation, fuel cells and hybrid vehicles as an engineer working in the automotive and the hydrogen industries. Patrice joined Siemens 14 years ago, he is now working very closely with the Simcenter Amesim development team, with a real motivation for better addressing the industry new challenges. Patrice strongly believes in the benefits of system simulation for designing green hydrogen production systems and improving the usage of the hydrogen in systems as for instance fuel cells thanks to a fast and comprehensive multi-physics modeling approach. Ready to advance your green hydrogen production more efficiently and safely? Schedule a meeting with CAEXPERTS and discover how simulation solutions, such as Simcenter Amesim , can help your company optimize systems, reduce risks, and accelerate results in clean energy projects. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

For any vehicle, poorly handled water can be a problem, one that is very well known to today’s automotive engineers. Whether a light drizzle or a deep flood, water impacts almost all aspects of a car design, from driver visibility and component longevity to vehicle safety and overall performance. For years, tackling these issues meant costly, time-consuming physical tests. Today, however, car designers are equipped with an ever-growing suite of tools to simulate and mitigate excess water flow and ingress concerns. This growing tool set, coupled with the continuous advancement of high performance computing (HPC), has solidified simulation as the “must have” option for the modern engineer, boosting cost and time savings early in the design cycle. You’ve Got Options To help navigate the simulation waters, Simcenter software and services offers both RANS and SPH method-based analysis tools providing efficient combinations of fast, high fidelity solutions. Let’s take a deeper look at what each option can provide. RANS (Reynolds-Averaged Navier-Stokes) RANS methods are continuum-based, grid-dependent Computational Fluid Dynamics (CFD) methods that solve time-averaged equations for fluid flow, meaning they model the effects of turbulence rather than resolving every turbulent eddy directly. A RANS solver treats fluid as a continuous medium and solves for averaged quantities like velocity, pressure, and temperature on a fixed mesh. RANS methods have several strengths: Computational efficiency for steady-state flows: Generally more efficient for steady-state or quasi-steady flows, making them suitable for larger domains and longer simulation times. Bulk Fluid/Air Flow Prediction: Excellent for predicting overall water behavior with aerodynamic interaction of the vehicle (multiphase). Pressure Prediction: Provide stable, accurate pressure fields used in coupled deflection and stress predictions, ie – FSI (Fluid/Solid Interaction) or FEA/CFD coupling. SPH (Smoothed Particle Hydrodynamics) SPH methods are mesh-free, Lagrangian particle-based methods. Instead of solving on a grid, the fluid is represented by particles that move with the flow, carrying properties such as mass, velocity, and pressure. These particles interact with their neighbors within a defined smoothing length, and fluid properties are reconstructed using kernel-based interpolation from these particle interactions. SPH methods also have several strengths: Computational efficiency for transient flows: Naturally excel at simulating highly transient, violent, free-surface deformation, splashing, sloshing, and complex free-surface dynamics without numerical diffusion. No meshing: Eliminate the need for complex meshing and remeshing, simplifying setup for intricate geometries, moving components, and scenarios involving breakup or water fragmentation. Localized water ingress and component-level interaction: Well-suited for detailed analysis of water ingress through vents, drains, seals, and small openings, as well as direct water interaction with sensors, cameras, and air-intake pathways. Let’s explore some common water management issues comprehensively tackled using Simcenter Fluids and Thermal Software and Simcenter Engineering and Consulting Services solutions. Ensuring Clear Vision and Driver Safety Everyone knows good visibility while driving is paramount. Even a mild compromise to a driver’s point of view can be aggravating or at its worst downright dangerous. Luckily there are a number of ways simulation can help your car maintain a clear view on a rainy day. Windshield & Wiper Optimization Using multiphase CFD modeling, engineers can precisely predict how wiper blades remove water under various rain and wind conditions. This allows for the optimization of blade design, speed, and sweep angle, and helps mitigate issues like water flow and streaking from A-pillars. More advanced coupled simulations (air and water) even analyze aerodynamic effects to ensure wipers remain effective at high speeds, reducing noise and vibration issues from wiper chatter. Side Mirrors & Windows Side view mirrors are an essential component of safe driving, one whose design must comply with varying government guidelines (minimum sizes and angles) as well as aerodynamic engineering constraints (minimal drag). For these reasons, a side mirror’s surface area tends to be relatively small and can easily become compromised by water. Fortunately, advanced CFD modeling techniques exist to mitigate these problems before they occur on the road. Detailed side mirror simulations often require a hybrid approach using different multi-phase models to approximate different solution domains; air flow, bulk water, water droplets & water film. Transition between domains is dictated by a user defined criteria (ie – droplet impingement, film thickness or blob diameter). Hydroplaning & Tire Analysis A properly performed tire analysis can enhance vehicle safety through optimized tire tread designs and significantly reduce physical prototyping time. These complex simulations use advanced methods to model the interactions between tires, water, and air in order to predict when and how a tire loses traction. They can be highly challenging simulations due to the moving and deforming tire geometry, transient flow behavior and the level of detail required to achieve acceptable accuracy. These challenges can be overcome with Simcenter STAR-CCM+ using a combination of Volume of Fluid (VOF), dynamic or overset meshing and coupled FSI solvers. Navigating the Deep: Water Wading and Component Protection Large accumulations of water are common after heavy rainfall, particularly in low-elevation areas, coastal communities and poorly draining roadways. Manufacturers know these events will occur over a vehicle’s lifetime and as a result, controlled water wading tests have become a standard requirement of most vehicle validation programs, particularly for electric vehicles where e-component protection is critical. Water wading places simultaneous demands on splash management, sealing integrity, and dynamic water channeling around the vehicle. The unique architectures of both combustion-engine and battery-electric platforms create very different exposure paths for water, from air-intake and vent systems to underbody components, high-voltage enclosures, and thermal management hardware. Understanding how water moves, accumulates, and interacts with these systems during a wading event is essential early in the design process, long before physical testing begins. Underpanel Deflections and Stress Whether from reckless driving or simply not paying attention, there is a good chance you will impact a large, heavy mass of water at high speeds with your car. You may not realize it at the time but this unfortunate event not only produces excessive water splashing but it can also lead to significant structural damage around the underbody of your car. Large panel deflections can produce unwanted stress or contact between components. So, although counterintuitive, a vehicle’s underbody must be designed for impact. Engineers start the analysis by calculating high speed wading impact loads. These loads are then used as input conditions to predict underpanel deflections. The deflections correspond to mount point stresses which can lead to material fatigue and failure. The entire workflow is a coupled analysis called Fluid-Structure Interaction (FSI), and is essentially a two-part process with two separate simulation disciplines: CFD – A transient, multiphase (air/water) simulation calculates hydrodynamic pressure loads on the vehicle’s underbody as it wades (can include aerodynamic effects). FEA – Pressure loads are mapped onto a structural model and a Finite Element Analysis Solver is used to compute deflection, stress and strain of the underpanels and connecting components. Taming the Spray: Fortifying Durability and Component Protection We all remember childhood bike rides, drifting mindlessly between the road, sidewalk and puddles of rain. And it’s not until you are home when you realize that your back is completely soaking wet from the tire spray (more poor vehicle water management). Water spray from a BMX bike could be seen as an acceptable nuisance (or even kind of fun), however, water spray from a motorcycle or car must be taken more seriously. It can be corrosive, destructive or even dangerous. Fortunately, there are several ways simulation can help mitigate the impact of tire spray early in the design cycle before the vehicle hits the road. Driver Visibility & Safety Spray simulations help engineers design components like wheel arches, underbody panels, and mudguards to minimize spray, improving driver visibility and addressing aero-acoustic and aesthetic issues. Component Contamination A spray analysis can predict areas of high soiling on components like headlights, taillights, and radiators, allowing for optimized placement and the development of anti-adhesion coatings. Corrosion and Component Failure Dynamically simulating spray can also help engineers understand how corrosive substances like road salt affect vulnerable parts of a car or motorcycle over time, allowing for protective design measures. Camera and Sensor Degradation For Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, a high-fidelity simulation can be critical for eliminating poor designs. Water droplet and film predictions help determine the optimal placement of cameras and sensors to minimize exposure to rain, mud, and dirt, and can aid in the design of effective cleaning systems. Ingress Protection of Electronics Ingress Protection (IP) Testing has become a critical part of modern vehicle validation as more electronics are packaged in exposed or splash-prone locations. Standards such as IEC 60529 and ISO 20653 define liquid ingress requirements for enclosures and road-vehicle electrical equipment (e.g. inverters, ECUs, sensors, battery pack housings, and connectors). It covers water-exposure scenarios that range from basic drip and spray protection (IPX1) to high-pressure, high-temperature washdown conditions (IPX9), representing progressively more demanding ingress protection requirements for automotive components. Simulation is particularly effective for studying these behaviors because ingress failures are often driven by highly transient, localized water motion rather than steady flow. Short-duration events such as splash impingement, and impact-driven exposure can be analyzed to understand water pathways and accumulation. Simulation can also reveal how water enters components like latch mechanisms, while assessing the impact of vent and drain placement and identifying where water accumulates and repeatedly wets critical regions. Optimizing Performance and System Functionality Bulk water simulations also fine-tune how water interacts with complex vehicle systems. Cowl Assembly Drainage and HVAC Ingress Control The cowl assembly plays a critical role in vehicle water management, acting as a primary collection and redistribution zone for rainwater, spray, and runoff from the windshield and hood. At the same time, it often houses or feeds sensitive systems such as HVAC air intakes, cabin air filters, wiper mechanisms, and electronic components. During heavy rain or car wash events, the cowl experiences highly transient inflow, localized pooling, and rapid drainage demands. Poorly managed water behavior in this area can lead to ingestion into air-handling systems, water accumulation near electrical components, noise issues, or long-term durability concerns. Simulation enables engineers to study these complex, time-dependent behaviors early in the design process. Transient water accumulation, overflow, splash-back, and interaction with grilles, screens, and drain paths can be visualized and quantified under repeatable conditions. This allows teams to evaluate cowl geometry, drain sizing and placement, and baffle effectiveness before physical prototypes are built. By understanding how water moves through the cowl under realistic loading scenarios, designers can reduce ingress risk, improve robustness, and avoid costly late-stage design changes. Hood and Tailgate Water Run-off Run-off from the hood, decklid, and tailgate is a key aspect of vehicle water management, as water naturally follows surface geometry and will migrate toward gaps, edges, and interfaces if not intentionally guided. During rain and car-wash conditions, water films and rivulets form on exterior panels and detach at edges, hinges, lamps, and latch regions. Simulation allows engineers to visualize these run-off paths under controlled conditions and evaluate features such as gutters, lips, channels, and drip rails that direct water away from openings and user touchpoints. This helps reduce water dumping during tailgate opening, limits repeated wetting of critical interfaces, and improves overall robustness before physical prototypes are built. Tank Sloshing Tank sloshing is a challenge in vehicle design, particularly in applications involving partially filled tanks such as fuel, coolant, or washer-fluid reservoirs. During braking, acceleration, cornering, or operation on uneven roads, liquid motion inside these tanks can become highly dynamic and chaotic. Uncontrolled sloshing can influence vehicle dynamics, introduce transient loads on tank walls and mounts, contribute to load shifting and slosh-induced roll moments, and generate noise. These effects are especially important in larger vehicles and systems where fluid volumes are significant and operating conditions vary widely. Simulation provides a practical way to study and manage sloshing behavior early in the design process. Engineers can evaluate the influence of tank geometry, fill level, and internal features such as baffles under repeatable driving scenarios. This enables rapid optimization of baffle layouts to improve vehicle stability, reduce transient structural loading, limit fluid-induced center-of-mass movement, and mitigate slosh-related roll moments before physical testing begins. Digital Drops, Real Impact Vehicle water management is an intricate challenge, extending far beyond the backyards and driveways of our daily lives, and practical solutions may not always be obvious. Water mitigation requires insight, creativity and innovation – all of which the Simcenter portfolio of software and services can help provide. With effective simulation, the unpredictable becomes calculable, transforming engineering from a reactive, trial-and-error process into a proactive, predictive science. Today’s engineers can innovate faster, build safer, and deliver more reliable products, ensuring a safer, drier, and more comfortable experience for everyone, regardless of the weather. Discover how simulation can transform the challenges of water management in vehicles into opportunities for innovation and performance. CAEXPERTS  can help your team apply advanced solutions with tools like Simcenter STAR-CCM+ to improve designs, reduce prototyping costs, and accelerate development. Schedule a meeting with our experts and see how to implement efficient simulations in your engineering process. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Introduction Electric vehicles are at the center of today’s developments in the automotive industry, and as the heart of an electric vehicle, the battery plays a pivotal role in powering the vehicle. It’s a crucial and valuable component. However, its significance goes beyond functionality only – the battery’s weight, cost, and vulnerability to damage make it an area of concern for EV owners. In contrast to vehicles with internal combusting engineers, electric vehicles often have the battery integrated in so-called skateboard platforms which are popular in the automotive industry as they integrate the battery pack into the vehicle’s floor. This design provides numerous advantages but also poses significant risks for electric vehicle owners. With the increasing prevalence of speed bumps on our streets, there is a growing concern about the potential damage these obstacles can cause to the battery pack located underneath the vehicle. Carelessly traversing a speed bump at high speeds can result in expensive repairs or even the need to replace the entire vehicle. The battery pack of an EV caught fire after being damaged in an accident ( source ) In a press article published by Reuters Information in March 2023 , Christoph Lauterwasser, the managing director of the Allianz Center for Technology, emphasized the increasing number of battery damage cases and underscored the importance of handling batteries with utmost care. The repairability of battery packs has become a critical consideration for automakers, with some, like Ford Motor Co. and General Motors Co., focusing on making repairs easier. However, Tesla Inc. has taken a different approach, introducing a new structural battery pack in their Texas-built Model Y that experts have deemed “zero repairability”. The impact of battery damage extends beyond individual vehicle owners. Even prominent car rental companies like SIXT and HERTZ have seen a decline in the number of electric vehicles in their rental fleets due to the high repair costs associated with battery damages, particularly those resulting from collisions. This information, reported by Reuters in January 2024 , highlights the significance of protecting EV batteries to ensure their longevity and minimize repair expenses. In this blog, we'll take a closer look at the challenges and solutions related to protecting your electric vehicle's battery. Using Simcenter Amesim , we'll demonstrate how you can better understand the risks posed by collisions and uneven roads, and explore strategies to protect this critical component. Battery modelling in Simcenter Amesim Simcenter Amesim offers very valuable capabilities when developing and integrating components in electric vehicles. It allows you to assess key performance characteristics like range, vehicle performance, thermal performance and battery lifetime. In this blog, we will show demo models developed in Simcenter Amesim that help to analyze the impact of an electric vehicle’s battery on a speed bump, depending on the velocity of the car and the height and shape of the bump. Virtual proving ground The test ground is generated using the Ground Designer tool and the battery pack of the skateboard platform is meshed using mesh-sphere contact submodels. Speed bumps are increasingly used to ensure low speeds in urban areas. Depending on the country, they have many shapes, heights and widths; several standards exist and are more or less respected. The test ground used in this demo includes 3 types of speed bumps: Semi-circle speed bumps Sinusoidal speed bumps Trapezoidal speed bumps For each speed bump type, 5 different heights are tested: h1 = 5 cm, h2 = 7.5 cm, h3 = 10 cm, h4 = 12.5 cm and h5 = 15 cm. This test ground is then created in Simcenter Amesim using the embedded ground designer app. Sequence with a multitude of speed bumps Several parameters are exposed in the ground designer app, giving the possibility to modify certain parameters of this proving ground. Proving ground creation in the ground designer app Proving ground parametrization in the ground designer app Vehicle model The electric powertrain is modeled using the IFP Drive library and the chassis model uses mainly the vehicle dynamics library components. Powertrain model This model includes a VCU, the electric motor, the battery system, a charger and a simple thermal model of the battery and its cooling system. The braking system includes a simple hydraulic system and a basic thermal model. The friction coefficient is calculated as a function of disc temperature using the following table: Friction coefficient against temperature The final model sketch includes a detailed chassis 15 DoF model of Tesla chassis and Pacejka tires models. The new automatic tuned driver model will follow the trajectory on the defined bumpy ground. Final full vehicle model Integrated battery pack The battery pack is meshed using 12 mesh-sphere contacts. These contacts form a very simple grid to mesh the battery pack, as shown below. Battery pack mesh The contact detection uses the (BVH) Bounding Volume Hierarchy approach. The first contact detection is at the (BV) Bounding Volume level. If a contact appears between the BV of the sphere and the end BV of the BVH, a contact detection is computed between the sphere and the triangle inside the end BV. The next picture shows 3 spheres in contact with one or several triangles. The sphere 1 has only 1 plane contact with triangle 1 The sphere 2 has 5 point contacts with triangles 1, 2, 3, 4, and 5 The sphere 3 has 2 segment contacts with triangles 3 and 4 Each sphere has only one reaction force Sphere Mesh Contact detection Results The Tesla is then driven on the test ground at five different speeds: low speed: 5 m/s (approximately 18 km/h or 11 mph) normal speed: 10 m/s (approximately 36 km/h or 22 mph) high speed: 15 m/s (approximately 54 km/h or 34 mph) very high speed: 20 m/s (approximately 72 km/h or 45 mph) The first three speed profiles are not totally constant to more accurately define the behavior of a human driver when approaching a speed bump. If the vehicle travels at 5, 10, and 15 m/s over the bumps, it goes faster on the flat sections, it brakes before passing the bump and accelertes just after (see curves below). No Contact at low and normal car speed No Contact at low and normal car speed Several plots are created to study the behavior of the vehicle. The mesh-sphere contact submodels include a “contact point number” variable which can be used to monitor the occurrence of a contact. By summing this variable for all contacts, it is easy to detect whether the battery has hit the ground, as the sum will be greater than 0. For low speed and normal speeds, one can observe that the battery doesn’t touch the ground. By contrast, when speed exceeds 50 km/h or 30 mph, i.e. the usual speed limits in urban areas for many countries, one can observe that some contacts are detected while driving on the highest speedbumps. Several contacts are found at high speeds Other plots can be used to locate these contacts. One can clearly see that the most affected area is the front of the battery pack. This kind of study can help to identify the weakest points of the battery and position reinforcement plates. Localization of contact points Finally, the mesh-sphere contact submodel enables the estimate of contact force and penetration during the impact in order to determine whether the battery pack would be severely damaged, as well as to size reinforcement plates. As an example, here is a comparison of penetration and force on the front of the battery pack between 15 m/s (in blue) and 20 m/s (in red). Penetration and forces comparison – front contact points A 3D Animation helps the user visualize where the contact points on the battery pack occur. Conclusion This blog demonstrates how the multiphysics simulation capabilities from Simcenter Amesim can help automotive engineers analyze the possible contact shocks against the battery pack, depending on different chassis designs. The couplings on the same simulation sketch of the chassis, the powertrain, and the battery pack geometries allow us to have the right virtual analysis of the damage caused. Some modular, split battery modules with reinforced walls on some battery pack parts could be defined using this simulation model. As the next step, engineers could consider designing active suspension systems using ADAS sensors and controllers to change the stiffness of the suspension. After the detection of the obstacle (speed bumps, potholes…) by cameras or lidars, the ADAS controllers will interact with the active suspension sub-system to increase the stiffness just before the bump to avoid damage to the battery. All of this is possible with Simcenter Amesim . Protect one of the most critical components of electric vehicles with the power of advanced simulation. CAEXPERTS can help your team use Simcenter Amesim to analyze impacts, optimize chassis design, and reduce the risk of battery damage during the development phase. Schedule a meeting with our experts and discover how to apply these engineering solutions to make your electric vehicle projects safer, more efficient, and more economical. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Deliver instant predictions with AI. Accelerate multiphase CFD with GPUs. Boost performance across CPU–GPU resources. Speed-up vehicle wading simulations with SPH solver. Plus, many more. AI‑powered Geometric Deep Learning (GDL) in Simcenter STAR‑CCM+ 2602 enables instant predictions to evaluate design variants in real time and accelerate early‑stage decision‑making. This release also brings GPU‑native support for Volume Of Fluid (VOF) and Mixture Multiphase (MMP) solvers, along with improved multi‑GPU scaling that significantly reduces turnaround time across multiphase and large‑scale CFD workloads. New GPU flexibility, including AMD support on Windows and smarter CPU/GPU co‑utilization, helps extract full value from engineers’ hardware, regardless of operating environment. Foundational accuracy improvements, such as enhanced prism‑layer meshing and fully conservative implicit mixing planes, strengthen numerical robustness and confidence in results. Application‑specific advances, from vehicle wading with SPH to more efficient transient automotive aerodynamics, further reduce engineering cycle time. Together, these upgrades allow to simulate more designs, with higher fidelity, on more hardware configurations, ultimately enabling faster, more confident engineering decisions. Faster convergence, increased accuracy and robustness with Enhanced Quality Prisms Complex geometries often force prism layers to retract, especially near concave and convex corners, thereby degrading near‑wall mesh quality and slowing convergence. CFD engineers frequently face “hanging node” transitions that complicate boundary layer continuity and make it harder to achieve robust turbulence resolution at the wall. In Simcenter STAR‑CCM+ 2602 , activate the Enhanced Quality Prism layer mesher to generate thicker, better‑behaved layers and preserve layer integrity in problematic corners. This will reduce prism retraction and eliminate hanging‑node topologies at layer transitions, which stabilizes residuals and accelerates solver convergence. This improvement translates to cleaner boundary‑layer representation and more trustworthy wall‑shear, heat‑transfer, and separation predictions. Across aerospace and automotive use cases, gain robustness on challenging surfaces and cut the number of restarts required to reach targets. The net effect is a smoother residual drop and earlier asymptotic behavior, which shortens the path to engineering decisions. Ultimately, achieve faster convergence with increased accuracy and robustness. Full conservation at implicit mixing planes with improved solver stability When rotors and stators couple through implicit mixing planes with poor conformality, energy conservation suffers and total pressure/temperature errors creep into turbomachinery predictions. Those inaccuracies can mask true stage performance and compromise design choices. In multi-stage configurations, these errors compound, undermining the confidence engineers require to optimize compressors and turbine designs. With Simcenter STAR-CCM+ 2602 , apply an enhanced boundary value update strategy together with imprint binning at the mixing plane to enforce conservation more faithfully. Stabilize the solver across mesh types and lower the error on total pressure and total temperature by an order of magnitude, while maintaining full compatibility with existing simulation workflows. The result is more reliable predictions, robust solver convergence, and the precision needed when simulation informs critical design decisions. No workflow changes required – just better physics and better answers. Instant external aerodynamics predictions Early design stages require fast solutions to quickly assess multiple variants and guide key decisions. Having rapid insight at this phase enables broader exploration and quicker alignment on optimal concepts. With Simcenter STAR‑CCM+ 2602 , Geometric Deep Learning (GDL) capabilities are integrated directly into Simcenter STAR-CCM+ Design Manager, removing friction between simulation and prediction. Train predictive models that evaluate performance in minutes, using only a minimal set of simulations or leveraging existing CFD datasets. Working directly within the familiar Design Manager environment avoid external data transfers and keep iteration fluid and intuitive. Dual‑prediction by comparing side by side CFD results and AI inference give confidence to explore the design space. As GDL predictions replace hours‑long CFD loops, focus high‑fidelity simulations only on the most promising concepts. This approach compresses early design phases while preserving accuracy where it matters most. The result is rapid external aerodynamics predictions that enable decision‑grade design iteration at a glance. Leverage existing simulation results Engineers often sit on years of high‑value simulation results from previous projects, yet these datasets remain underused when it comes to accelerating new AI‑driven workflows like the abovementioned GDL. Re‑running simulations solely to generate training data slows adoption of predictive methods and duplicates past effort. With Simcenter STAR‑CCM+ 2602 , import existing simulation results directly into Design Manager to reuse them as training data for Geometric Deep Learning (GDL) models. Leverage proven datasets, whether from legacy projects or single standalone runs, without changing your established CFD practices. Using Design Manager’s automated workflows, generate consistent post‑processing across imported results, ensuring data quality and comparability. Compare multiple simulation files side by side in a unified environment, making trends and sensitivities easier to identify. This capability extends the value of historical CFD investments while accelerating surrogate‑model creation. As a result, build predictive GDL models faster, with less manual effort and fewer new simulations. Ultimately, exploit existing results to scale AI‑driven design exploration with confidence. Get faster results for multiphase applications Volume Of Fluid (VOF) and Mixture Multiphase (MMP) cases, especially those involving free surfaces or phase change, previously had long run times, offering less opportunities for users to make design changes. With Simcenter STAR-CCM+ 2602 , multiphase simulations can now be executed on GPUs using GPU-native VOF and MMP solvers, representing a major step forward in multiphase simulation acceleration. This capability includes support for phase change and surface tension models, broadening the set of industrial scenarios which can be covered on GPU. It also integrates acceleration techniques such as implicit multi-step and supports multiple regime through MMP-LSI (Large Scale Interface). Because the same solver is used for both CPU and GPU executions, the same results are guaranteed, provided that the solutions are well converged on each hardware type. In terms of performance, a single GPU matches the throughput of approximately 250 CPU cores for a tank‑sloshing case for example, while consuming only 19% of the energy required for the CPU run. This GPU‑based acceleration delivers substantial benefits for multiphase applications in sectors such as automotive, marine, and process engineering, where free‑surface behavior and interphase interaction are critical. The improved throughput enables broader design‑space exploration, increased design confidence, and more effective risk management. Up to 20% enhanced multi-GPU performance While GPU acceleration significantly speeds up CFD simulations, a common challenge arises when scaling to multiple GPUs: the computational overhead of data transfer and communication between devices can hinder efficiency. This often limits the practical benefit of adding more GPUs, as the speedup doesn’t always scale linearly. This bottleneck can prevent users from fully leveraging their multi-GPU hardware investments for larger, more complex simulations. With Simcenter STAR-CCM+ 2602 , leverage enhanced multi-GPU scalability. This improvement offers more proportional increase in simulation speed as additional GPUs are added, enabling greater parallelization. This delivers a noticeable increase in throughput for large and complex CFD models. Achieve faster turnaround times by effectively utilizing multiple GPUs on a single workstation or on multi-node servers. This enhanced scalability maximizes the value of multi-GPU hardware, allows extensive design exploration and accelerates the entire simulation workflow up to 20% at higher GPU counts. Maximize flexibility to leverage GPU-enabled CFD acceleration Many Windows workstations come with powerful AMD GPUs that, until now, couldn’t be used for CFD workloads in earlier Simcenter STAR-CCM+ versions. This limitation often forced engineers to move jobs to other machines or operating systems, complicating IT planning and slowing down local execution. This meant existing hardware investments were underutilized, creating a significant barrier to efficient workflow. With Simcenter STAR‑CCM+ 2602 , full AMD GPU acceleration on Windows removes this limitation. This pivotal development unlocks these devices for production simulations: run complex cases with competitive performance directly on preferred workstations. This advancement bridges the software gap and enables the full potential of AMD hardware. Broaden deployment choices and simplify IT planning across operating systems. Streamline license allocation and keep analysts productive in their preferred environment. Make more of installed hardware viable: maximize flexibility and budget efficiency by leveraging AMD GPU resources for accelerated CFD with Simcenter STAR-CCM+ 2602 . Make more out of your CPU and GPU resources In high-performance computing, especially on GPU nodes, a key challenge arises because certain simulation tasks, like surface wrapping or post-processing, remain inherently CPU-bound. This often leads to underutilized CPU resources, as a fixed CPU-to-GPU ratio can leave significant CPU power idle during GPU-intensive phases. This inefficiency slows down overall simulation turnaround and wastes valuable computing capacity. With Simcenter STAR-CCM+ 2602 , dynamically engage available CPU cores for multi-threaded tasks, even while GPUs are heavily engaged, with adaptive utilization of CPUs on GPU nodes. This ensures that CPU-bound portions of a simulation no longer cause delays or leave valuable CPU resources idle, optimizing the entire workflow. Leverage a more holistic and efficient use of the entire computing node. Experience significant speed-ups and potential computational cost reductions for simulations involving complex pre-processing or specific physics like sliding mesh. Accelerate your path to innovation with this harmonious approach, ensuring that every component, both CPU and GPU, contributes optimally to the overall simulation effort. Faster turnaround time for vehicle wading applications Traditional vehicle wading analyses involve time‑consuming, complex workflows to set up water interaction, tire–road contact, and suspension response. Achieving local resolution where it matters adds further overhead. With Simcenter STAR‑CCM+ 2602 , tackle vehicle wading using the SPH solver, refine particles around the vehicle to increase local accuracy, and resolve dynamic motion including suspension and tire‑road contact. Analyze backplate mechanical stresses and predict wetting, bringing structural and hydrodynamic considerations together. This streamlined approach reduces overall turnaround while improving fidelity where forces and splashes peak. The simplified setup lowers the barrier to routine wading assessments across variants and trims. The payoff is faster turnaround time for vehicle wading applications. Ready to transform your CFD simulations with AI and GPU acceleration? Schedule a meeting with CAEXPERTS and discover how to practically apply the most advanced features of Simcenter STAR-CCM+ to dramatically reduce response time, increase accuracy, and empower your engineering decisions with greater efficiency and confidence. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

A game-changer in renewable energy. This blog post highlights how Simcenter System Simulation helps addressing your industrial Solar Photovoltaic (PV) challenges. SolarPV systems are complex systems since they’re often combined with other surroundings like residentials, fast charging stations for electric vehicles, BESS (battery energy storage systems) or microgrids, up to space stations and satellites. It requires many different physics inside (electrics, thermal, semiconductors, …) with important aspects of system integration, controls, up to the techno-economic analysis to be successful (OPEX/CAPEX, weather conditions, load balancing). With its costs declining and technology advancing, more organizations are turning to solar as a leading option for power generation. Solar Photovoltaic tends to become the first source of renewables energies worldwide, before wind turbines hydroelectric powers or nuclear. This is an amazing increase of its installed capacity. Solar power is today mandatory to ensure great successes in the decarbonization path towards a more sustainable world. Let’s see how System Simulation is driving the digital transformation to tackle all your Solar Photovoltaic challenges. Solar photovoltaic (PV) is key in many industries Solar Photovoltaic (Solar PV) is a technology for converting the sunlight (solar radiation) into electricity with semiconductors. Solar photovoltaic converts solar radiation into electricity Solar photovoltaic systems are today present in all industries, from stationary applications (residentials, …) up to mobility (space, marine, …) and for sure in the Energy sector with its always keep-growing part in the power generation from renewables (sun, wind, hydropower, waves, heat, …). All industries need SolarPV for decarbonization Below is an example of a satellite power system represented in Simcenter Amesim , part of the Simcenter System Simulation portfolio. The digital twin with System Simulation helps for the preliminary sizing of the solar panel and battery pack to reach the requirements. Alternation of eclipse (shade) and daylight provides the solar irradiance in geostationary orbit Just to say, as claimed by the “Internation Energy Agency” (IEA), that the solar PV is set to become the largest renewable energy source by 2029. There has been exponential growth in the deployment of photovoltaic solar energy, with its global capacity now growing at a historic pace. From 2018 to 2023, it tripled. Renewable electricity generation with solar PV to become the largest renewable source Between 2024 and 2030, the solar PV technology is expected to account for 80% of the growth in global renewable capacity. While solar PV is planned to become soon the largest renewable source, surpassing both wind and hydropower, which is currently the largest renewable generation source by far. Top things to know While solar PV is a well-known technology for years, it’s quite recently that the interest grew up in System Simulation to better predict its behavior, either focusing on the component itself or regarding its complete integration within larger systems with advanced controls and fluctuating scenarios (weather conditions, energy demand within the day, energy price evolution, smart adaptative controllers, …). From a solar cell to a photovoltaic (PV) system With Simcenter Amesim , you can predict the solar panel energy production to help reaching great successes for the energy transition. You can size the solar panel from the number of solar arrays, the number of cells and the single cell area. You can enter the city you’re located or its GPS position, the weather conditions to consider if it’s cloudy or not, the ground reflection coefficient or the turbidity factor. Then you’ll get the energy produced over the day or over the months so you’ll know the results for all the seasons. Photovoltaic power generation potential – Source: globalsolaratlas.info Simcenter Amesim is typically used for system integration combining the different subsystems involved like {solar PV + controls + power electronics + microgrids + residentials + consumers}. With Simcenter Amesim , you can address and solve all these challenges from solar panels to smart integrated systems: 📐 Sizing and performances ♻️ System efficiency ❄️ Thermal management, cooling 💧 Green Hydrogen production 🔋 BESS (battery energy storage systems) 🔌 Connection to the grid/micro-grid, power converters, inverters 💡 Maximum Power Point Tracking (MPPT) 🧭 Panel trackers, best solar spots 🏠 Industries: residential, water-heaters, ships, cars, EVs, airplanes The users can drag and drop the predefined components (no coding) to assemble them together to get their complete systems. While the execution is very fast, it takes only few seconds/minutes of CPU-time to compute the complete day / week / year with economical aspects included. Why Simcenter Amesim is perfect for solar photovoltaic Well, Simcenter Amesim offers all what you need to investigate your solar photovoltaic systems. It comes with off-the-shelf libraries of physical components (electrics, thermal, …) that can be combined in one environment, also with great solvers to reach advanced integration and real-time system monitoring. Up to the virtual commissioning with advanced control strategy testing before the real system exists. You have great benefits using the solar photovoltaic components to: set up the model parameters from datasheet information scale up the PV cell easily into solar panel or array account for the temperature and solar irradiance on the electrical performance of the PV cell get solar irradiance according to GPS coordinates, altitude & date Temperature-dependent efficiency calculations for different irradiance values There’s indeed a huge variability in the configurations due to the different locations (Berlin, Xinjiang, Mexico, Abu Dhabi) and weather conditions (summer, winter), that directly impact the system performances for the local implementation onsite. That’s the reason why users need to explore solar PV digitally to reduce expensive testing time and gain confidence in their products early in the design cycles. City selection worldwide, or with (variable) GPS positions from coordinates Practically there are many challenges to solve. They come from the usual sizing or thermal management / cooling to improve its efficiency, up to more advanced analysis like the control strategy development with “Maximum Power Point Tracking” (MPPT) optimization, the integration with BESS (battery energy storage systems) or the connection to the grids/micro-grids with power electronics components (converters, inverters, …). Typical challenges for solar photovoltaic It’s also easy to address the complete Green Hydrogen Production over days/months of operations. Also generating automatically some optimal controllers to take into account the weather forecast or the energy price evolutions with Artificial Intelligence (AI) thanks to Reduced Order Models (ROM), Neural Networks (NN) or Reinforcement Learning (RL). Solar panels production over 12 months Complete system integration Let’s go through a couple of examples with Solar photovoltaic systems integrated into larger installations. Just to show how we can go from solar panels to smart integrated systems thanks to the multiphysics and scalable digital twins. So that engineers can handle complex interactions between different physical phenomena and model the complete energy conversion chain from solar radiation to electrical output. It practically allows them to match the dynamic load profiles, to optimize the peak power, to achieve the appropriate cost-effective system dimensioning. While combined with the built-in powerful analysis tools coming with Simcenter Amesim , users can investigate their energy yield predictions, the performance ratio calculations, or the power loss analysis, all with some detailed parametric studies. Going up to the monitoring of the system efficiency in real-time over some short (minutes) to long periods (months) of simulated time. What a valuable achievement! For example, the model shown here predicts the performances of the system depending on the meteorological (weather) conditions and the localization of the system. Green energy production with different sources of renewable energies including solar PV After running quick simulations executed in few minutes for the complete 1 year (12 months), several architectures or component sizing choices can be rated to select the most efficient and profitable designs. Solar panels production with all variables accessible For the solar panels, you can consider the solar azimuth (side angle), the solar altitude (solar incidence / horizontal), the irradiation power from sun to surface [W], the radiative power from ambient to surface [W], or the surface inclination. To access any types of results like the solar panel electric power [W], the photogenerated current density [A/cm²] or the solar panel electric current [A]. And when zooming, you can even follow the evolutions during the current day / night. Another application is the sustainability and energy efficiency in the Data Center industry. Today they are responsible for up to the 3% of the global electricity consumption and they will reach 4% by 2030. That’s why the market decided to take actions towards a reduction of their carbon footprint and to make the energy consumption more efficient, also due to expected tighter regulations. One possibility is the consider renewable energies for power generation, typically with solar panels. Data Centers – Conventional 480V AC electric power distribution with PV panels integration With Simcenter Amesim , you can properly size the solar panel arrays based on the location and cloud coverage. Here, three different cities are compared simply by selecting them from a menu and we see that the solar radiation in Paris at a specific date and hour is almost the half than in Tokyo or New York. It’s really nice to know this upfront, before you’ll try to adapt all parts onsite! Regarding the electric power distribution architectures, their performance can be improved using high-efficiency transformers and converters, with the deployment of renewable energies and the loads balancing. All these aspects need to be checked and it’s where System Simulation can definitely help to take the right decisions. And to finally verify virtually that your new system design is fully compatible with the electric load, and that everything can be supplied properly to the grid. Then you can even go one step further in the analysis for predicting the controller parameters appropriately depending on weather forecast and streamed data. It’s the new challenge to come, so that you could improve your right decisions for OPEX (operating expenses) and CAPEX (capital expenditures). Grid supervisory control, combining AI, weather forecast and streamed data It’s a good way to manage real-world scenarios, including partial shading (cloud cover variations, temperature fluctuations, seasonal changes, load demand variations). And in some extend, to investigate the grid disturbances during huge variations of boundary conditions and cascading events. System Simulation is the right approach to use for a large audience going from solar PV system designers, energy system integrators, research institutions, utility companies or energy consultants. Just to say that System Simulation is perfect and well appropriate to reach nice achievements for sustainability through solar PV in your company. System simulation plays a crucial role To conclude, let’s summarize how System simulation plays a crucial role in the techno-economic assessment of solar photovoltaic (PV) systems. Solar industry to reduce carbon footprint You can address some key aspects like: Performance prediction Design optimization Environmental impact analysis Economic analysis. Overall, system simulation provides a comprehensive framework for evaluating the technical and economic feasibility of Solar Photovoltaic projects, helping stakeholders make informed decisions about investments and operations. Energy segmentation – System Simulation in all industries In summary, embracing Solar Photovoltaic systems in product development requires a strategic approach, early-stage considerations, and advanced tools like physics-based digital twins to navigate the complexities and leverage Solar Photovoltaic (PV) as a competitive advantage. System Simulation definitely helps being successful in your Solar Photovoltaic journey thanks to digitalization. Ready to take your solar photovoltaic energy projects to a new level of performance, efficiency, and predictability? CAEXPERTS can help your company apply the power of System Simulation and digital twins to confidently size, optimize, and integrate photovoltaic systems from the early stages. Schedule a meeting with our experts and discover how to accelerate your energy transition with greater safety, lower cost, and better results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Why NVH performance matters The electric vehicle (EV) revolution is here, promising a cleaner, more efficient future for transportation. But as the roar of internal combustion engines fades, a new challenge emerges: the intrinsic quietness of Electric Drive Units (EDUs) brings the subtle sounds and vibrations of other components, especially the geartrain, into sharp focus. For automotive engineers, this means NVH (Noise, Vibration, and Harshness) performance is more critical than ever. In the fiercely competitive EV market, OEMs and TIER1 suppliers are under immense pressure to innovate rapidly. This intense competition demands: Faster Time-to-Market: Consumers expect cutting-edge features in new models, quickly. Cost Efficiency: Maximizing efficiency to reduce product development costs and protect shrinking margins. Digital Transformation: A strong drive towards increased digitalization and reduced reliance on costly physical prototypes. These factors collectively force companies to significantly accelerate their product development cycles. The NVH bottleneck Accurately predicting geartrain NVH performance – and ultimately replacing expensive physical prototype testing – demands high-fidelity time domain simulation. However, this type of simulation has traditionally been incredibly computationally-intensive, leading to: Long Turnaround Times: Design studies can take days or even weeks. Late-Stage Validation: Often relegated to the later stages of the development cycle, limiting its impact on initial design decisions. This bottleneck significantly hinders the agility and effectiveness of the geartrain development process. The new Motion solver Enter the new 2512 release of Simcenter 3D Motion , featuring the groundbreaking Modern Motion Solver for geartrain time domain simulation. This isn’t just an update; it’s a leap forward. This is exactly the reason why we invite our customers to test this new capability, see the note below. Engineered for Speed: Built on a cutting-edge C++ code architecture, the Modern Motion Solver efficiently tackles even the largest and most complex models. Scalable Performance: It fully leverages parallel processing, allowing a single time domain simulation to be solved across multiple threads simultaneously. Proven Results: Extensive tests on a suite of industrial geartrain models demonstrate performance improvements up to 5 times faster compared to the 2412 release – all while maintaining the same unparalleled accuracy. Imagine the possibilities: evaluating significantly more designs, exploring a wider parameter space, and bringing innovative EV geartrains to market faster than ever before. Want to accelerate the development of electric vehicles and overcome NVH challenges much more efficiently? Schedule a meeting with CAEXPERTS and discover how Simcenter 3D Motion's new Modern Motion Solver can significantly reduce simulation time, increase accuracy and think about innovation in your projects. Our team is ready to present, in practice, how this technology can transform your results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Introduction Electrification is rapidly reshaping the commercial trucking industry, promising cleaner and more efficient transport solutions. Driven by new regulations [ 1 ], the electrification of heavy-duty vehicles (HDVs) and their trailers represents is essential to decarbonize freight logistics [ 2 ]. One emerging innovation is the electrified drive axle, or e-axle, integrated into heavy-duty vehicles (HDVs) and their trailers to provide regenerative braking and additional traction. But introducing this technology fundamentally alters the dynamic characteristics of the entire vehicle, bringing new challenges in vehicle stability, a critical safety aspect for heavy-duty trucks. Our recent simulation-based study dives deep into these challenges, analyzing how electrified trailers interact with existing vehicle control systems like ABS and ESP. The goal: to identify potential stability risks including jackknifing, shaking, and roll-over [ 3 ], and uncover how a smart supervisory control system could help ensure safe operation across all driving conditions. Methodology To investigate the complex dynamics of an e-trailer system, a detailed multi-body simulation model was developed using Simcenter Amesim . Simcenter Amesim is a powerful platform for multi-domain system simulation, enabling the modeling of mechanical, hydraulic, pneumatic, thermal, and electrical components within a single environment. 2.1 Vehicle Model Description The simulated vehicle configuration consists of a 3-axle articulated vehicle: a 2-axle tractor unit coupled with a 1-axle semi-trailer featuring an electrified drive axle. The multi-body template model VDCAR22DOF01 was employed, which is specifically designed to account for critical stability issues in articulated vehicles, including: Jackknife: The acute angle formed between the tractor and trailer. Shaking: High-frequency oscillations of the vehicle body. (Trailer swing) Roll-over: Lateral instability leading to vehicle overturning. Standard tractor and semi-trailer combination Tractor and semi-trailer combination with driven trailer electrical axles 2.2 Control Systems The model incorporates realistic chassis stability controllers for both the tractor and the trailer: Tractor Unit: Equipped with ABS (Anti-lock Braking System) and ESP (Electronic Stability Program) [ 4 ]. Trailer Unit: Equipped with ABS and TCU (Traction Control Unit). The e-axle’s regenerative braking and electrical traction capabilities are integrated with specific blending strategies. The interactions between these e-axle controls and the conventional chassis stability controllers are a central focus of the analysis. Simcenter Amesim model sketch Traction blending “parallel” strategy (e-trailer machine vs. truck engine) 2.3 Driver Model An advanced driver model, utilizing Model Predictive Control (MPC) techniques, was implemented. This robust control strategy ensures accurate path following and effective sway control of the trailer, providing a realistic representation of driver inputs during various maneuvers. 2.4 Test Track Definition The simulations were conducted on a virtual test track based on a real-world road section in Croix-Rousse, Lyon, France. This challenging route includes significant ascending and descending slopes, which are crucial for evaluating the e-axle’s performance during regenerative braking and traction phases. The varying gradients and curves allow for the assessment of vehicle stability under diverse load conditions and driving scenarios. Selection of the road path with the Route planning Tool, used in the Track Import Tool Visualization of the road track Results and Discussion The Simcenter Amesim simulations provided comprehensive data on electric machine performance, battery state of charge (SOC), and overall vehicle dynamics. Electric Machine Torque at trailer axle, and battery State Of Charge (SOC) 3.1 Electric Machine Performance and Battery SOC During the ascending sections of the test track (e.g., 0-150 seconds), the e-axle required significant traction torque to assist the tractor. Conversely, during important descending sections (e.g., 250-300 seconds), the e-axle effectively engaged in regenerative braking, leading to a notable increase in the battery’s SOC. This demonstrates the e-trailer’s potential for energy efficiency and reduced reliance on friction brakes. Rapid torque sign switches (from traction to regeneration) were observed before and after overcoming road cornering, indicating dynamic operation of the e-axle. 3.2 Vehicle Dynamics and Pathological Situations The simulations revealed several critical “pathological” situations that underscore the stability challenges introduced by e-axles. One such instance occurred during a combination of significant driver steering input and braking action, where a trailer wheel was observed to be on the verge of losing contact with the road. This scenario, if unmanaged, could lead to a roll-over event. A crucial observation pertained to the interaction between the electric machine controls and the chassis stability controllers. In certain cases, particularly during rapid torque switches or intense braking/traction demands, the individual actions of the ESP on the tractor unit, ABS on the trailer, and the e-axle’s torque control were not harmonized. The absence of a high-level supervisory control system to coordinate these conjoint actions was identified as a significant risk factor. Without such a supervisor, the study indicated that a complete instability of the truck + trailer vehicle could foreseeably occur at higher velocities, highlighting a critical safety concern. Trajectory, target velocity and steering angle 3.3 Implications for Control Strategy Development The findings emphasize that simply integrating an e-axle with independent control strategies for regenerative braking and traction is insufficient. Effective integration requires a sophisticated, higher-level supervisor that can intelligently blend the e-axle’s operations with the conventional chassis stability controllers. This supervisor would need to dynamically adjust torque distribution, braking effort, and traction forces across all axles to maintain overall vehicle stability under varying road conditions, driver inputs, and e-axle operational modes. Chassis controller status (ESP and ABS) Conclusion The findings from this study highlight a key takeaway: electrified trailers hold great promise, but they require sophisticated coordination between new and legacy control systems to keep trucks stable and safe. A high-level supervisory control system is essential to avoid dangerous scenarios such as wheel lift and rollover, especially at highway speeds. If you’re involved in vehicle design, control system development, or fleet safety management, this research offers valuable insights to guide your work. Staying ahead in the electrification journey means embracing integrated solutions that prioritize stability. Referências [1] United Nations Economic Commission for Europe (UNECE). Global Forum for Road Traffic Safety (WP.1). Available at: https://unece.org/transport/publications/consolidated-resolution-road-traffic-re1 [2] European Automobile Manufacturers’ Association (ACEA). Commercial Vehicles: Decarbonisation. Available at: https://www.acea.auto/fact/commercial-vehicles-and-co2/ [3] G. G. P. Van Der Heijden, H. B. Pacejka, and J. M. J. Van Der Knaap, “Dynamic behaviour of articulated vehicles,” Vehicle System Dynamics, vol. 20, no. sup1, pp. 294-307, 1991. (General reference for articulated vehicle dynamics, not specific to e-trailers, but foundational). [4] Bosch Global. ABS and ESP: The history of vehicle safety. Available at: https://www.bosch-mobility.com/en/mobility-topics/safety-for-all-road-users/driver-assistance-systems-for-commercial-vehicle/ Want to understand how simulation can support the safe development of electric trailers and advanced control strategies? Schedule a meeting with CAEXPERTS and talk to our experts about how to apply Simcenter Amesim to assess stability, integrate control systems and reduce risks from the early phases of the project. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The new Simcenter FLOEFD 2512 software release as of December 12, 2025 is now available in all its CAD-embedded CFD variants, and also the Simcenter 3D embedded variant. This release delivers focused improvements for fast sealing of geometry for internal flow analyses for all general purpose CFD applications through to multiple enhancements for electronics thermal analysis workflows. Please read on below to explore every new feature grouped under key Simcenter pillars, or scan the topic list on the left hand side to shortcut to the sections that interest you most. Auto-sealing of CAD geometry for internal flow CFD analysis There are many reasons for performing internal flow CFD simulations depending on modeling application or computational efficiency. For any internal flow scenario, you want to seal geometry as quickly and efficiently as possible For complex CAD assemblies with hundreds or thousands of parts, this can become a time consuming challenge to make the geometry watertight. You spend your time first, locating gaps and openings, and then sealing them manually with geometry. Such approaches also mean you are making modifications or additions to the CAD model that need to be tracked and removed before you hand back modified product geometry to design and manufacturing teams after you finish your analysis tasks. CFD tools typically have features or utilities to help you. Simcenter FLOEFD users will be familiar with Simcenter FLOEFD’s existing automatic fluid volume recognition strengths and tools such as “leak tracking” to identify paths between faces which is helpful in many instances. In Simcenter FLOEFD 2512 , a step change in auto-sealing for internal flow CFD tasks has been delivered to engineers in the form of enhancements to “Close Thin Slots” and “Fill Thin Slots”. This feature is accessible when you are utilizing Mesh Boolean approach to geometry handling and meshing, that is particularly suited to complex assemblies and varying quality found in CAD models. You can now retrieve the internal fluid region automatically for non-watertight models more easily, efficiently, and without altering CAD geometry. How do you control this new automatic mesh-enabled sealing ? Users configure “Fill Thin Slots” approach in 2 ways starting from the ribbon bar: Within the mesh group, select either global mesh or local mesh and then in mesh settings you activate close “Close Thin Slots”  then specify the “Maximum Heigh of Slots to Close” and other parameters.ers. Within the Insert group, select Source> Fill Thin Slots which opens a dialog. You then select in the graphic area faces on either side of the slot, or choose relevant bodies to consider their faces Set Solid material set the desired material (or allow the default setting). Set the parameter for “Maximum Heigh of Slots to Close” Choose between options: Fill Thin Slots only ( A conservative approach where solid material is applied within the slot) Fill within slots and openings (A default approach that is a more comprehensive sealing approach where solid material is applied in the slot and also extends into the fluid domain by one computational mesh cell to seal the opening fully) Additional notes: Simcenter FLOEFD chooses material properties from nearby to the gap to preserve close thermal model accuracy, or alternatively users can set a default value. There is an option in the mesh viewer to view where where cells are inserted by selecting a mesh plot of “closed thin cells”. How do you seal thin gaps in assemblies to prepare CAD geometry for CFD internal flow studies? Find out about the new approach in Simcenter FLOEFD 2512 by watching this short video on how seal an automotive lighting CFD model for thermal analysis where there are many gaps to seal in the CAD assembly for an internal flow case. The approach uses the method starting from the “Fill Thin Slots” dialog opened from the insert group Sources>Fill Thin Slots in the ribbon. It also shows how you can view where solid material cells are added using using the mesh viewer. Faster BCI-ROM extraction: Setting specific HTC ranges A new way to set specific heat transfer coefficient (HTC) ranges has significantly shortened the reduced order model extraction time for BCI-ROM models. Below are results comparisons for one example model where setting suitable ranges of HTC for each boundary condition is compared to setting a single common range value. In this case, extraction is 8 times faster and memory peak usage was more than halved. Shorter solve time for your models with thousands of two-resistor (2R) or Network Assembly components Thermal engineers frequently leverage components modeled as two-resistors (2R) or Network Assemblies in their models. Calculations for models with hundreds or even thousands of these is now much faster through software optimization implemented in Simcenter FLOEFD 2512 release. This enables the preparation stage of analysis to take significantly less time and use less memory. A Simcenter FLOEFD model containing DIMM memory card assemblies on a motherboard with components modeled as two-resistor (2R) type components. Download Simcenter FLOEFD 2512 and evaluate your own models with 2R and Network Assembly components to see what kind of speed you can get. For one example model, containing many 2R components, a comparison was carried out with the results shown below. A speed-up by a factor of 6 was realized for the solver time compared to the prior 2506 release and a significant reduction in peak memory usage achieved by a factor of more than 3 and a half. A useful update to scripting in EDA Bridge for PCB data processing To reduce time correcting script errors that are typically only discovered at runtime when transferring from EDA Bridge to Simcenter FLOEFD that force project restarts, scripts are now validated before running to catch errors and prevent disruption. Reduced order modeling: BCI-ROM support for 2R and Network Assembly components Boundary Condition Independent-Reduced Order Models are reduced order models that operate in any thermal environment and are extracted from a 3D conduction-only model in Simcenter FLOEFD . It is now possible to extract these from 3D electronics thermal analysis models that contain 2R and Network Assembly components. As a recap on BCI-ROM model formats that you can generate in Simcenter FLOEFD : – matrices (for standalone solution) – FMU format (for system simulation use in accordance with FMI standard) – VHDL-AMS format (for circuit simulation electro-thermal modeling use) Accurate radiation modeling: Henyey-Greenstein phase function Simcenter FLOEFD has a proven history of radiation modeling for lighting applications. In the Simcenter FLOEFD 2512 release, the Henyey-Greenstein phase function has been added that enhances accuracy for modeling scattering in certain semi-transparent materials. (This is accessible via the Simcenter FLOEFD Lighting module) You select this option and define scattering coefficients in the item properties menu. A simple comparison using basic model and source and then changing the scattering coefficient value of a plexiglass material illustrates scattering below. Easier sub model re-use: Rebuild of sub-projects “From component” Many Simcenter FLOEFD users work with sub models. The previous 2506 release delivered capabilities to define project parameters in sub projects so they propagate upward into your main CFD model. Building on that release and efforts to deliver more library driven re-use of components and sub projects, in the new 2512 release there is now a built-in function to select rebuild of all sub-projects at the same time when this is required. This eliminates time overhead of manual rebuild of each sub project. View tabular results: maximum temperature column in Component Explorer If you are working with a complex thermal model and want to more quickly retrieve maximum temperatures of all components you can now do so in Component Explorer (as a convenient alternative to assigning many Volume Goals) . A new column provides maximum temperatures of all solids with minimal effort and does not slow down the solver. You can also export value to Microsoft Excel. You can also export a useful matching table of component names, input data and resulting maximum temperatures automatically leveraging Batch Results Processing. As a reminder, Component Explorer received significant updates in the 2412 release (LED and 2R model creation, surface power listing/summation and more) and 2506 (status and temperature column) release, so please do go back and review prior release blogs and documentation. Updated FMU graphical editor makes connections clearer Users are starting to leverage multiple FMU based components in their Simcenter FLOEFD projects more and more since the introduction of “FMU as a feature“. To aid the understanding of the connections to the project and to connect multiple FMU’s to each other more easily, an FMU graphical editor has been introduced. You can more easily map goals to inputs and interpret connectivity between FMUs as complexity increases. Automation in Simcenter FLOEFD 2512: Added new capabilities in EFDAPI Automation of simulation tasks continues to be a popular topic with users. EFDAPI, the API introduced back in  Simcenter FLOEFD 2312 , continues to be developed based on consistently received user feedback. Key new EFDAPI capabilities added in Simcenter FLOEFD 2512 : Switch geometry recognition to Mesh Boolean Apply default radiation surface Switch between Global and Face coordinate systems For NX users performing PCB thermal analysis: PCB Exchange – EDA Bridge integration update For customers using Xpedition and NX CAD software, there are linkages for close MCAD-ECAD collaboration enabled by PCB Exchange.  As of this release, you can now author the set up of a PCB thermal simulation for Simcenter FLOEFD for NX , right from within the PCB Exchange workflow. This incorporates the ease of PCB data processing for thermal modeling purposes using EDA Bridge accessed through its integration into PCB Exchange. Components and libraries: XTXML export of features (for 2R and Network Assembly) XTXML export for component editing was introduced in the last 2506 release which allows users to import models from Simcenter FLOEFD Package Creator utility, make adjustments to the models and then save the models in XTXML format to libraries. Manually created detailed models can also be exported in XTXML format. The Simcenter FLOEFD 2512 enhancement now has the option to export models of 2R and Network Assembly components. This enables you to build up a component library quickly. A note for CATIA V5 users. You can now edit XTXML files exported from Package Creator or create new XTXML library items with Simcenter FLOEFD for CATIA V5, as was introduced in version 2506 for other variants. Avoid the risk of losing EDA files To reduce the risk of losing EDA files from EDA Bridge for your many projects, often stored in separate folders, an option has been introduced to store EDA files nested to the main assembly. So users can choose from 3 options in settings now: 1) Model folder – files are stored next to main assembly (default option) 2) Sub-folder – files are in a separate folder next to main assembly 3) Specify – choose permanent path to the folder Want to apply these new features of Simcenter FLOEFD to your own models and gain momentum in your next simulations? CAEXPERTS can show you, in practice, how to use the new automatic cooling features, thermal resource optimizations, and model hydration to reduce setup effort, solution time, and memory usage. Schedule a quick meeting with our team and see how to extract maximum performance from your CFD and thermal analyses. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Simcenter Systems Simulation 2511  has just been released, introducing new capabilities that accelerate innovation across industries such as automotive, aerospace, heavy equipment, and turbomachinery. This latest version empowers engineers with smarter modeling workflows, AI-based documentation assistance, and enhanced productivity in Simcenter Amesim , Simcenter Flomaster , and Simcenter System Analyst. Together, these updates help users work faster, manage greater complexity, and seamlessly integrate simulation throughout the product development lifecycle. Simcenter X This release reinforces Simcenter Systems as a key pillar of Siemens’ digital transformation strategy, with continued focus on cloud enablement, AI-driven assistance, and user productivity across system simulation workflows. Simcenter Amesim can now also be offered as part of Simcenter X , Siemens’ flexible Software-as-a-Service (SaaS) multi-domain simulation suite. Simcenter X Advanced combines the trusted capabilities of Simcenter Amesim with the power of cloud entitlement, empowering teams to simulate multi-physics systems with scalable performance and instant access through a cloud-managed desktop. Simcenter X Advanced – Cloud-managed desktop Simcenter X Advanced offers a secure, cloud-managed desktop that enables users to access Simcenter Amesim without the burden of license setup and management. This deployment option simplifies IT administration, accelerates onboarding, and scales efficiently across distributed engineering teams. Simcenter X AI Chat Assistant – Instant answers, built into the cloud environment For customers using Simcenter X Advanced , the integrated AI chat assistant delivers contextual answers with direct links to documentation. Supporting multiple languages helps teams find information faster, streamline troubleshooting, and remain productive across global simulation environments. Simcenter Systems 2511 The Simcenter Systems 2511 release strengthens the portfolio with key improvements in electrification, cloud accessibility, and user experience. Engineers benefit from enhanced modeling accuracy, faster setup, and improved visualization features that support system-level design across industries. From streamlined parameter management to more efficient simulation workflows, Simcenter Systems 2511 empowers users to innovate with greater speed and confidence. Electrification Electrification remains a driving force in the Simcenter Systems portfolio, and this release continues to expand capabilities for battery modeling, integration, and optimization. With Simcenter Amesim 2511 , engineers can now design and validate battery packs more efficiently, leveraging improved workflows for parameter import and thermal management. These innovations support electric vehicle (EV) and energy storage applications, helping teams accelerate the transition toward sustainable and high-performance electrified systems. Battery Battery pack assistant – Seamless electrical parameter integration Defining accurate battery cell parameters can often be tedious and prone to error. With Simcenter Amesim 2511 , part of the Simcenter Systems Simulation portfolio, battery designers and integration teams can now automatically import cell electrical parameters directly from a validated database or an existing model into the battery pack assistant. This new capability streamlines the setup process, ensuring that simulations start from trusted data while minimizing manual input errors. Engineers can reuse existing models, resize them efficiently to match target capacity, and evaluate pack architectures earlier in the design cycle, all within a unified battery design environment. Whether applied to EV battery modeling or stationary energy storage systems, this update accelerates pack design and improves accuracy. Battery pack assistant – Capture heat gradients where they matter most Controlling temperature gradients within a battery pack is crucial for ensuring safety, optimal performance, and extended durability. In Simcenter Amesim 2511 , the battery pack assistant introduces a new capability that allows users to thermally discretize pattern groupings in any direction (X, Y, or Z) to better capture critical heat variations across packs. This flexible approach enables engineers to model a wide range of cell technologies, from prismatic to blade cells, adapting the size and orientation of thermal discretization to each design’s unique behavior. With just one click, users can generate detailed thermal models that accurately reflect real-world gradients, ensuring accurate simulation results. For battery designers and integration teams working in the automotive, mechanical, or energy storage sectors, this enhancement provides the control needed to refine thermal management strategies and improve the accuracy of electrification simulation workflows. Chassis engineering Electric motorcycle demo model – Analyze ride and handling with ease Designing an electrified motorcycle chassis that strikes a balance between ride and handling performance can be a complex process. With Simcenter Amesim 2511 , engineers now have access to a new electric motorcycle demo model, built using the 3D mechanical library and integrated with vehicle dynamics driver models. This demo serves as a fully modular starting point for creating and analyzing electrified motorbike architecture. Users can quickly explore different layouts and optimize the position of heavy components, such as the battery pack and e-motors, to ensure stability and control across varying speeds and trajectories. The real-time capable model helps motorbike manufacturers and suppliers study ride and handling dynamics in curves, evaluate design trade-offs, and accelerate development from concept to validation. Ground designer – Create a batch from parameters Testing vehicle behavior across different roads and terrain conditions is crucial for accurate chassis engineering. In Simcenter Amesim 2511 , the new ground designer batch feature introduces the ability to generate virtual proving grounds with multiple obstacle heights, shapes, and spacing, all defined through customizable parameters. This update enables engineers to create batch runs directly from any parameter in the ground designer, allowing for quick exploration of numerous test scenarios and geometries. The result: faster analysis cycles and deeper insights into vehicle dynamics performance on parametric 3D proving grounds. With this capability, automotive, off-road, and heavy equipment engineers can easily optimize suspension, stability, and control systems under varied conditions, boosting the efficiency and realism of vehicle simulation workflows. E-motors Squirrel Cage Induction Machine (SCIM) – Enhanced electro-thermal modeling Designing high-power-density electric drivetrains requires a precise understanding of thermal behavior and magnetic effects. The enhanced squirrel cage induction machine (SCIM) model in Simcenter Amesim 2511 introduces detailed loss definitions and non-linear inductance support for accurate electro-thermal simulations. This update enables engineers to analyze temperature-dependent losses that directly impact e-powertrain performance, helping to refine thermal management strategies and enhance system reliability. Through comprehensive modeling of DC, AC, and iron losses, the new SCIM component supports more accurate sizing, efficiency predictions, and component optimization. Whether used in automotive, aerospace, or industrial applications, this upgrade empowers powertrain engineers to push performance limits with greater confidence and precision. Energy and thermal management Efficient thermal management is crucial for maintaining performance and safety in electrified systems. In Simcenter Amesim 2511 , new capabilities within the Heat Exchanger Assistant simplify model generation and provide early design insights to help engineers optimize HVAC and cooling systems faster. Heat exchanger assistant – Streamline your HVAC heat exchanger design Creating detailed heat exchanger models can be time-consuming and prone to errors. In Simcenter Amesim 2511 , the heat exchanger assistant receives a significant enhancement with new capabilities that expand the range of supported geometries, including fin-and-tube and multi-core micro-channel heat exchangers. Engineers can now generate complete parameterized models with integrated 2D/3D visualization more efficiently. The updated assistant also introduces automatic sketch generation for multi-core micro-channel designs, helping HVAC and thermal engineers iterate faster and maintain consistent model structures. By simplifying model creation and providing real-time geometric feedback, this enhancement accelerates early-phase heat exchanger design and improves modeling confidence. Heat exchanger assistant – Early heat exchanger size and mass assessment Late-stage adjustments to heat exchanger dimensions or weight can lead to costly redesigns and delays. With Simcenter Amesim 2511 , engineers can now evaluate the size and mass of heat exchangers directly in the geometry definition phase, using the heat exchanger assistant. This enhancement provides real-time insights into packaging feasibility and component weight before running simulations, allowing teams to validate designs against system requirements earlier in the process. By integrating mass and dimensional data at the geometry stage, thermal engineers can make data-driven decisions faster and ensure alignment with performance targets and packaging constraints. Hydrogen Hydrogen continues to play a pivotal role in enabling clean propulsion and sustainable energy systems. With Simcenter Amesim 2511 , engineers gain new modeling tools that simplify the design and integration of fuel cell and cryogenic storage systems, helping industries such as aerospace, marine, and automotive explore the future of zero-emission powertrains. Fuel-cell turboprop demonstrator – Integrated gas, liquid, and hydrogen modeling Designing and validating an aircraft fuel-cell system requires accurate modeling of gas, liquid, and cryogenic hydrogen interactions across multiple components. The updated fuel-cell turboprop demonstrator in Simcenter Amesim 2511 provides a ready-to-use model that shows how to configure the required fluid species and phases using the common framework shared by the gas and fluid storage libraries. This demonstration gives engineers practical insight into cryogenic tank behavior, Boil-off gas (BOG) management, and fuel-cell operation throughout an entire flight cycle. It highlights how individual subsystems such as the cryogenic storage system, the fuel cell stack, and associated balance-of-plant (BoP) components interact within a complete aircraft configuration. Simcenter Amesim 2511 also introduces an improved hydrogen aging model, enabling engineers to evaluate long-term performance decay and its impact on power output, efficiency, and mission range. Together, these enhancements accelerate system-level validation and reduce setup time for hydrogen-powered aircraft studies. Strengthening the core Beyond electrification and hydrogen innovation, Simcenter Systems Simulation 2511 also strengthens its foundation with improvements that enhance accuracy, interoperability, and productivity across the modeling environment. These updates help engineers build, manage, and analyze system models more efficiently, whether they’re optimizing pneumatic and gas systems, working with advanced libraries, or improving collaboration through tighter version control. With new capabilities across both Simcenter Amesim and Simcenter Flomaster , including enhanced simulation libraries, improvements to turbomachinery modeling, and strengthened Git integration, Simcenter Systems Simulation 2511 reinforces the robustness and scalability of the platform for all industries. Gas library – Build scalable and accurate gas systems Modern gas systems require precise control, reliable modeling, and strong scalability, especially in automotive, aerospace, and industrial applications. The new gas library in Simcenter Amesim 2511 introduces a unified, real-time-capable framework that replaces multiple legacy pneumatic and gas libraries, offering greater accuracy, consistency, and flexibility. This next-generation library integrates industry-standard modeling capabilities, including ISO-6358 compliant components and Redlich–Kwong–Soave (RKS) equations of state, enabling engineers to simulate compressible flows and advanced gas behavior with improved fidelity. The library is also fully compatible with real-time export, making it suitable for hardware-in-the-loop (HiL) applications and control system validation. By consolidating and modernizing the modeling workflow for gas systems, the new gas library helps teams build, maintain, and scale large multi-domain system models more efficiently and with higher reliability. New help system – Faster access to smarter documentation Accessing technical documentation efficiently is critical for simulation engineers. The new browser-based help system in Simcenter Amesim 2511 delivers a modernized and more intuitive documentation experience. With enhanced search capabilities, familiar navigation categories, and integrated web features such as zoom, bookmarks, translation, and history, users can now find information more quickly and easily. The new help platform also offers seamless integration with Simcenter Amesim , allowing engineers to directly access relevant documentation without interrupting their workflow. NX Diagramming XML import – Simplify system model creation For fluid system engineers working on complex piping networks, CAD tools are often used to define the initial layout of 2D diagrams. However, transferring these definitions manually into a system simulation environment can be time-consuming and error-prone. The new NX Diagramming XML import capability in Simcenter Amesim 2511 bridges this gap by allowing users to automatically generate system models directly from NX Diagramming files. This functionality enables engineers to rapidly create 2D piping network definitions inside Simcenter Amesim , ensuring seamless interoperability between CAD tools and system simulation. By eliminating repetitive manual work, it accelerates model setup and provides a smoother transition from early design to performance validation, benefiting industries such as energy, oil and gas, marine, aerospace, and process engineering. 3D Scenes – Enhanced visualization for model setup and analysis Understanding model behavior visually can dramatically improve accuracy and productivity. The new 3D Scenes tool in Simcenter Amesim 2511 introduces an advanced 3D visualization environment that enables engineers to interact directly with simulation models, whether it be during building or in the simulation phase. This enhancement enables users to set parameters effortlessly by interacting with 3D objects, gain a better understanding of the system before running simulations, and interpret physical quantities through clear visual cues. With two distinct 3D viewing modes available, engineers can visualize model states before and after simulation, enabling them to validate configurations and identify potential modeling issues early. By providing intuitive visualization and interactive parameter control, the new 3D Scenes feature empowers system simulation engineers across industries to make faster, more informed design decisions. Test Execution Manager – Compare reports side by side As simulation models evolve, engineers often need to validate changes, compare results across versions, or assess the impact of updates to libraries and model parameters. Manually comparing outputs from different runs can be tedious and error-prone, especially when handling large datasets or complex systems. The enhanced compare-report feature in the Test Execution Manager streamlines this process by displaying two reports side by side in a clear, structured table. Differences in parameter values, simulation outputs, and timeseries data are automatically highlighted, making it easier to pinpoint what changed between two executions. This capability improves productivity by reducing the manual effort required for regression testing and model validation. It also strengthens traceability and transparency across simulation runs, helping engineers understand the impact of updates more quickly and make better-informed decisions. Client for Git – Large File Storage (LFS) The Large File Storage capability in Simcenter Client for Git keeps repositories lightweight by storing large files in a dedicated area. This speeds up uploads, removes file-size limits, and enhances version control efficiency. Client for Git – Delete branches from server collections As simulation projects evolve, Git collections often accumulate numerous branches — many of which eventually become outdated or unused. These obsolete branches take up server space, clutter the project history, and make it harder for teams to navigate active development lines. With Simcenter Client for Git in the 2511 release, users can now delete branches directly from server collections. This capability makes it easier to remove unnecessary or obsolete branches at the source, helping teams keep their repositories lean and better organized. By decluttering the repository, server collections become smaller and more efficient, improving performance during repository operations. It also supports better project hygiene by maintaining a cleaner and more understandable version history. Overall, this enhancement enables engineering teams to manage their branches more effectively and maintain a streamlined, professional version-control workflow. Turbomachinery simulation improvements Meeting today’s efficiency and performance targets in turbomachinery requires highly detailed and integrated modeling capabilities. As systems grow more complex, traditional approaches often struggle to capture the full dynamics of rotating components, secondary air flows, and co-simulation behaviors across tools. Simcenter Flomaster 2511 introduces several targeted enhancements to address these challenges. Internal duct and forced vortex components have been upgraded to support turbine speed data directly, enabling more realistic and precise modeling of rotating secondary air systems. A new flow-tracking capability now enables engineers to visually trace which inlet sources contribute to the flow at any given outlet, providing deeper insight into system behavior and helping to accelerate diagnostics. Additionally, FMU export has been reinforced with support for implicit iterations and improved error handling, resulting in more robust co-simulation with Simcenter 3D Thermal . Together, these improvements deliver more accurate turbine speed modeling, clearer flow-origin identification, and smoother integration into whole-engine digital workflows, supporting faster decisions and more reliable development of high-efficiency turbomachinery systems. Want to understand how Simcenter Systems Simulation can accelerate innovation, reduce the complexity of your projects, and increase the productivity of your engineering? Schedule a meeting with CAEXPERTS and discover, in practice, how to apply these advanced simulation resources to your challenges in electrification, thermal systems, hydrogen, and much more. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Capture the real-world complexity with multi-domain simulation using Simcenter X Advanced Designing complex engineering systems, such as a gas turbine, are often long-time projects where many iterations and extensive collaboration across teams, departments or organizations are needed. The traditional approach is often characterized by siloed domain workflows which additionally cost time and effort. With Simcenter X Advanced , a flexible, Software as a Service (SaaS) multi-domain engineering simulation suite, we provide you with one-license access to a comprehensive and integrated multi-domain simulation solution portfolio to address multidisciplinary complexity in one environment. Simcenter X Advanced effectively breaks down engineering silos enabling robust, physics-based, AI-augmented decision-making across multiple domains to deliver innovative and dependable engineering solutions faster. A tribute to gas turbine technology The really great thing about gas turbines, besides their beauty, is the flexibility of applications the core technology can be applied to. Gas turbines can be found everywhere in our modern daily lives, as heart for sustainable and reliable power generation, as drives in various industrial, chemical or maritime applications or designed as aircraft engines in nearly every commercial or military aircraft. Although the technological idea of a gas turbine is not new and its first applications went back to early 1900, it is still an amazing technology which we can see every day and everywhere and it affects all our lives, even if we don’t know. And today, more than 120 years later, designing a gas turbine has not become a less complex engineering challenge, independently if designed for just a few kilowatts, for hundreds of megawatts as stationery or flying applications. Designing a gas turbine is an artful interplay of physics and covering multidisciplinary engineering challenges. Since 120 years, gas turbines have marked the spearhead of engineering innovation Gas turbine development and innovation are driven by ultimate targets to become cleaner, bigger, more silent and flexible. Reducing emissions and enhancing thermal cycle efficiencies have and will remain to be main drivers of new generations of gas turbines. Since decades aero engine and gas turbine manufactures are pushing the Bryton cycle towards higher efficiencies with improvements in materials, coatings, cooling technologies, combustion and more. Faster and closer cross-domain collaboration will be the next crucial factor enabling comprehensive cross-domain and cross-disciplinary design studies to uncover and unleash hidden potential. The only limit? Carnot! Simcenter X Advanced enables integrated multi-domain simulation engineering to design a micro gas turbine Power outages, while often brief, can severely impact critical infrastructure like hospitals and data centers. To ensure uninterrupted operation, robust emergency power units (EPUs) are essential and can ultimately even decide about life and death. To fulfill their purpose, these systems, often relying on fast-starting gas turbines, need to kick in within seconds. Hybrid EPUs, combining microturbines with battery storage, offers a powerful solution, with batteries bridging the startup time and providing immediate backup. When an emergency power unit needs to take over, there is usually no acceptance for failure. Imagine, in case of a hospital or intensive care unit power backup, it even becomes live critical. An emergency power unit (EPU) is a complex multidisciplinary engineering system. A system in which various elements need to interact to provide a reliable and functional system which can operate on point, as expected, when expected and in its best-balanced performance. Break down engineering silos and boost engineering productivity Emergency power units (EPU) have one ultimate goal, they must work reliable and provide power as expected in the case of cases without any option for failure. To design an EPU systems engineering is key to size the entire EPU modules and simulate the case scenarios. System modeling allows tailored engineering towards the application cases and provides the necessary physics-based parameters that are essentially needed for sizing the integrated modules or subsystems such as the gas turbine package. Within Simcenter X Advanced engineers have direct access to powerful Simcenter system solutions such as Simcenter Amesim , which allow a comprehensive system design. In This example, the EPU has an integrated 100kW micro gas turbine. By applying the gas turbine performance application within Simcenter Amesim , engineers can directly specify the architecture of the micro gas turbine and extract relevant pre sizing design parameters for the main gas turbine components. Multi-domain engineering solves multidisciplinary challenges best Designing the micro gas turbine requires a comprehensive interplay between different technical disciplines and various multi-domain simulation techniques. As an example, flow path components which have been perfectly designed towards aerodynamic performance need be checked against their to be manufacturability but also need to be checked if they are able to withstand the transient loading during operation. Thus, structural integrity investigation need to go hand in hand with aerodynamic design considerations to be most effective and to avoid unneeded iterations. But even if the single components fulfill all requirements towards aerodynamic and structural performance, the assembly may operate in safety risk condition, e.g. due to rotational resonance. Simcenter provides a comprehensive multi-domain simulation portfolio that ideally supports turbomachinery engineers to consider all those dependencies and multi domain challenges within one CAE environment. Simcenter X Advanced provides you multi-domain simulation access to Mechanical, Computational Fluid Dynamics (CFD), Systems simulation and Multidisciplinary Design Analysis and Optimization (MDAO) under a single license Design and validation of gas turbines, from early system level via component to entire whole engine modeling. With Simcenter X Advanced , engineers get now ultimate access to a comprehensive multiphysics CAE platform via one license. With integrated PLM connectivity and data management the digital thread is not a vision anymore, with Simcenter X Advanced it becomes real. When failure is not an option: Multi-disciplinary engineering excellence with Simcenter X Advanced In the demonstrative study “When failure is not an option: Designing a micro gas turbine in context of emergency power generation”, Simcenter X Advanced for multi-domain simulation put to the test. The result: A real world engineering example of an end-to-end design workflow established with Simcenter X Advanced . system and architecture design of EPU and gas turbine sub model aerodynamic design and validation of compressor and turbine AI accelerated optimization of rotor shaft design structural design and validation of compressor, turbine and rotor rotor dynamic and self-excitation investigation, automatic creation of Campbell diagram fluid-structure co-simulation for automated cold-to-hot transformation and operational gap clearance investigation Want to understand how Simcenter X Advanced  can accelerate the design and validation of complex systems like gas turbines and EPUs, unifying all disciplines in a single environment? Schedule a meeting with CAEXPERTS  and discover, in practice, how to take your multi-domain engineering to a new level of performance and reliability. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Cement production is one of the most energy-intensive and emission-intensive industrial activities, accounting for approximately 5% to 8% of global CO₂ emissions. In the modern dry manufacturing process, the pre-calciner plays a central and critical role, being the equipment where most of the limestone calcination occurs (decomposition of CaCO₃ into CaO and CO₂) and where between 55% and 65% of the system's total fuel is consumed. The efficiency of this equipment depends on a delicate thermodynamic balance between two main reactions: fuel combustion (exothermic process) and raw material decomposition (endothermic process). Due to the complexity of multiphase flow, where solid and gaseous phases interact at high speeds and temperatures, physical experimentation on an industrial scale is extremely costly and often impractical for detailed internal measurements. In this context, Computational Fluid Dynamics (CFD), with STAR-CCM+ , emerges as an essential tool for the optimization and design of these reactors. Through numerical modeling, it is possible to predict the hydrodynamics of the flow, heat transfer, chemical kinetics, and emissions, critical factors that act in the pre-calciner. Challenges and Solutions in Pre-calciner Simulation CFD simulation of pre-calciners presents challenges associated with the multiphase, thermal, and reactive complexity of the process. The cohesion and agglomeration of fine raw meal particles significantly alter the flow and calcination efficiency, requiring advanced particulate phase models. On a larger scale, the formation of gas-solid clusters introduces heterogeneities that render homogeneous drag models inadequate. Heat transfer constitutes another critical point, due to the very thin thermal boundary layers along the walls, whose correct mesh refinement is unfeasible in complete industrial geometries. As a solution, coupling CFD with simplified mechanistic models allows for realistic thermal estimates with reduced computational cost. Furthermore, the strong coupling between exothermic and endothermic reactions imposes high nonlinearity on the system. In the context of emissions reduction, oxy-fuel combustion presents additional challenges, such as ignition delay and high CO₂ concentrations, which can be mitigated by multi-stage combustion and pre-gasification strategies, ensuring operational stability and low NOx levels. CFD Simulation of the Pre-calciner This numerical simulation study, developed in STAR-CCM+ , focuses on the detailed analysis of combustion in a large-scale cement pre-calciner, emphasizing the evaluation of the characteristics of the oxy-fuel process and its impacts on the thermal efficiency and operational stability of the equipment. The modeling was conducted using a non-premixed combustion model, incorporating the chemical kinetics of coal, devolatilization phenomena, NOx formation, and thermal radiation effects. The kinetic mechanism employed is presented in Table 1. It should be noted that, in this study, the particulate model of calcium carbonate (CaCO₃) was not considered, focusing the analysis exclusively on the combustion of the solid fuel. Reaction Equation R1 2CO+O2->2CO2 R2 C + 1.5 O2 -> 0.5 CO +0.5 CO2 R3 C + CO2 -> 2CO R4 CaCO3 -> CaO + CO2 R5 Devolatilization Table 1. Chemical Reactions The numerical model adopted the following simulation assumptions: steady-state regime, ideal gases, Eddy Break-Up (EBU) combustion model, k-ε Realizable turbulence model, thermal NOx model, and coal particles modeled in a Lagrangian fashion, with an average diameter of 50 μm. The computational mesh was strategically refined in regions of higher thermal and chemical gradients, such as the primary and tertiary air intake zones and coal injection zones. Boundary conditions were defined to reproduce real operational scenarios, including prescribed air and fuel flow rates, ensuring greater physical representativeness of the model. Figure 1. Geometry and computational mesh Figure 2 shows the temperature field inside the pre-calciner. It can be observed that the regions near the central axis of the flow reach temperatures above 2100 K, evidencing intense combustion activity. In contrast, a low-temperature zone is identified near the inlet nozzles, associated with the high concentration of air in these regions, which promotes dilution of the fuel mixture and locally reduces thermal efficiency. The analysis of the thermal field allows us to assess whether the regions of interest operate within the appropriate temperature range for the process. Figure 2. Temperature profile Complementing this analysis, Figure 3 presents the axial temperature profile along the pre-calciner. A thermal peak is observed in the first 10 meters, resulting from the initial combustion reactions of the coal, followed by a gradual reduction in temperature along the flow, as the fuel is consumed and the gases are diluted. Figure 3. Temperature plot Figure 4 illustrates the concentration profiles of CO, CO₂, and O₂ along the height of the pre-calciner. The CO₂ concentration progressively decreases as the gases rise, reflecting chemical interactions and flow dilution. CO shows peaks near the burner region, associated with centralized coal injection and incomplete combustion. Due to the low local availability of oxygen, some of the CO is not oxidized to CO₂, resulting in a lower CO₂ concentration at the equipment outlet. Figure 4. Concentration profiles: (a) CO; (b) CO2; (c) O2 Figure 5 shows the evolution of the molar fraction of the main components along the vertical axis of the pre-calciner. A significant consumption of O₂ and CO₂ is observed, concomitant with the generation of species such as volatiles, CO and H₂O, characterizing the dominant stages of the coal combustion process. Figure 5. Molar fraction plot Finally, Figure 6 presents the velocity field and streamlines, highlighting the direct influence of the pre-calciner geometry on the flow. Recirculation zones near the tertiary air inlet are prominent, as are regions of low turbulence ("dead zones"), which represent potential areas for the accumulation of unreacted material. Although these regions are associated with flow separation, they also play a relevant role in the conduction and mixing of gases. These results indicate the need for geometric adjustments to optimize the flow, reduce energy losses, and improve the overall efficiency of the equipment. Figure 6. Velocity Profile Conclusion The results presented demonstrate that the use of STAR-CCM+ as a Computational Fluid Dynamics (CFD) platform for the integrated analysis of fluid dynamic, thermal, and reactive phenomena governing the performance of cement pre-calciners is effective. The robustness of the software allowed for consistent modeling of non-premix combustion, species transport, thermal radiation, and particulate behavior, enabling a faithful representation of industrial operating conditions. The adopted approach proved particularly relevant in the context of Oxy-Fuel combustion, where the coupling between chemical reactions, heat transfer, and particulate dynamics imposes additional challenges. The use of appropriate models makes it possible to evaluate operating and design strategies, such as the optimization of air and fuel injection, geometric adjustments, and burner configurations, aiming at reducing emissions, increasing energy efficiency, and greater operational robustness. In this way, CFD simulation is consolidating itself as a strategic tool for decision-making, retrofitting, and developing new technologies in cement plants, reducing dependence on empirical testing and accelerating the transition to more efficient and environmentally sustainable processes.   References ZHENG, Qiang et al. CFD simulation of a cement precalciner with agglomerate-based drag modeling. Powder Technology, v. 436, p. 119508, 2024. ZHANG, Leyu et al. Numerical simulation of oxy-fuel combustion with different O2/CO2 fractions in a large cement precalciner.  Energy & Fuels , v. 34, n. 4, p. 4949-4957, 2020. KANELLIS, Georgios et al. CFD modelling of an indirectly heated calciner reactor, utilized for CO2 capture, in an Eulerian framework.  Fuel , v. 346, p. 128251, 2023. HAIJIAN, Dou; ZUOBING, Chen; JIQUAN, Huang. Numerical Study of the Coupled Flow Field in a Double-spray Calciner. In:  2009 International Conference on Computer Modeling and Simulation . SHU, Yixiang et al. Numerical study on oxy-fuel combustion of coal pre-gasification products in cement calciner.  Applied Thermal Engineering , p. 126901, 2025. MIKULČIĆ, Hrvoje et al. Numerical analysis of cement calciner fuel efficiency and pollutant emissions.  Clean technologies and environmental policy , v. 15, n. 3, p. 489-499, 2013. If you are looking to increase the thermal efficiency of your pre-calciner, reduce emissions, and make more informed engineering decisions, CAEXPERTS can help with advanced CFD solutions using STAR-CCM+ . Schedule a meeting with us and discover how to apply numerical simulation to optimize your cement production process and accelerate results with lower costs and greater reliability. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br