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Autonomous urban air mobility vehicles will revolutionize the urban mobility market. A digital twin is invaluable as part of an efficient development process that ensures a safe product. Safe operations are key when it comes to flying autonomous urban air mobility vehicles. Validating the flight management system for as many flight scenarios and operational conditions as possible is crucial. This is why the digital twin of the flight management system must include simulation models of both : aircraft and environment. A simulation framework for autonomous urban air mobility vehicles This study evaluated whether a simulation framework with a proven track record in the development of Automated Driving Systems for ground vehicles is able to support the development of automated flight management systems in an integrated manner. The framework includes 3 coupled software packages performing time-domain simulations. Simcenter Amesim models the flight dynamics, the propulsion systems and the navigation control loops. Simcenter Prescan models an urban environment and exteroceptive sensors that detect features in the environment (e.g. camera, lidar) Simulink® connects both, Simcenter Amesim and Simcenter Prescan . The figure below displays the simulation framework. The states of the drone are passed from Simcenter Amesim to Simcenter Prescan to position the aircraft geometry and sensors with respect to the environment. Next, Simcenter Prescan computes the sensor data and provides the reference trajectory information. The object detection and safe trajectory planning algorithms uses this input to compute a safe trajectory. This is then passed to Simcenter Amesim . Figure 1 - Simulation framework Modeling the urban air mobility vehicle and the environment To demonstrate this approach, the study makes use of an all-electric, four-seater, octocopter with realistic design parameters. The urban air mobility vehicle has 4 sets of 2 ducted counter-rotating propellers. Each propeller is coupled with an electric motor of 200kW that is based on the former Siemens SP-200D motor. A battery with a capacity of 110kWh powers the aircraft. As a result, the vehicle has an autonomy of 15min with a cruise speed of 120km/h. The Simcenter Amesim model contains 8 propeller models coupled with subsequently 8 models of phase modulated synchronous motors. Together with a 6 degree of freedom model and simplified aerodynamic model, it provides the flight dynamics behavior. The system model also includes a battery, ground contract elements and PID control loops. The PID control loops make the aircraft follow the safe trajectory. A high-fidelity model of the Siemens Perlach campus, near Munich, was implemented as an urban environment in Simcenter Prescan . The Simcenter Prescan lidar sensor model represents a high-performance commercial lidar system, providing point cloud information of the environment. Finally, a simplified obstacle detection and avoidance algorithm in Simulink was implemented. Simulating multiple collision scenarios The study simulated multiple collision scenarios to evaluate obstacle detection and avoidance functions using a simulation framework. The scenarios included a tower crane with different orientations along the planned flight path. Different trajectories were calculated depending on the crane's orientation. When the crane is positioned perpendicular to the flight path, it is detected well in advance. A gentle deflection over the crane ensured safe operations. However, when the crane is aligned with the flight path, it is detected too late. An aggressive lateral maneuver could avoid the obstacle. In this case, the system simulation revealed that the propulsion systems had to operate close to their limits. The results also show the aircraft's acceleration levels for structural evaluation and passenger comfort assessment during evasive maneuvers. Figure 2 - System simulation results of obstacle evasion maneuver This study concludes that Simcenter Amesim together with Simcenter Prescan includes all required capabilities to support the development and validation of automated flight functions of autonomous urban air mobility vehicles. This study can be expanded with: human body motion simulations of passengers modeled with Simcenter Madymo . the coupling with aircraft dynamics simulated with Simcenter 3D Motion . the prediction of fly-over noise using Simcenter 3D Acoustics . Jan Verheyen performed the above study as part of his internship at Siemens Digital Industries Software in Leuven, Belgium. Jan is a master student aerospace engineering from the control and simulation department of the Delft University of Technology. CAEXPERTS can help your company accelerate the development of autonomous air mobility vehicles with advanced simulation and digital twin solutions, ensuring safety and efficiency at every stage of the project. Schedule a meeting with us and discover how to transform your aerospace development process with Simcenter Amesim . WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Breaking barriers with a simplified electric motor design process. In the world of electric machine design and development, time is always the enemy. As the sun rises on another day at an electric mobility company, three engineers from different departments gather around a conference table, their expressions tense. The prototype deadline is just weeks away, but a critical design change has brought complexity and risk to the schedule. The team needs to modify the axial flux design to solve thermal problems, but recreating the electromagnetic (emag) model in the simulation tool would take weeks—time that is not available. This scenario is repeated daily throughout the industry. While the world rushes toward electrification, the tools and processes used to design these critical components often remain fragmented across disciplines, creating invisible barriers between the teams that most need to collaborate. The Hidden Cost of a Disconnected Project When an electrical engineer spends weeks perfecting an electric motor design in a dedicated tool like Simcenter E-Machine Design , their work represents only the beginning of a much longer journey. For that design to become a reality, it needs to undergo detailed validation, as well as thermal, structural, and vibration (NVH) analyses—each requiring the model to be manually recreated in different simulation environments. In some cases, it can take three months just to recreate a 3D axial flux machine template for more detailed analysis. This represents lost innovation time in what boils down to digital paperwork. This disconnect not only consumes time but also limits collaboration between teams. When each model recreation takes weeks, the rapid iteration and innovation cycle becomes unfeasible. Engineers end up making more conservative decisions simply because they cannot afford to explore alternatives with the necessary agility. Weaving the Digital Thread Now, imagine a scenario where the electric motor design, carefully developed in the design tool, can flow effortlessly between different tools and teams—where a single click sends the complete model, with all its complexity, directly to the next simulation environment. This is precisely the proposition of the new model transfer capability in the Simcenter portfolio. It's not just a feature—it's the digital thread that connects previously isolated tools and disciplines. After optimizing the axial flux machine design in Simcenter E-Machine Design —exploring hundreds of variants in a few hours, thanks to the equivalent circuit approach—the engineer can now transfer the complete model directly to Simcenter 3D or Simcenter STAR-CCM+ . The thermal team, in turn, receives not just data or specifications, but a three-dimensional model ready for the solver, with the appropriate mesh and complete simulation configuration. What used to take weeks now happens in minutes. Geometry, materials, and electromagnetic properties are transferred in their entirety—even complex features such as skew and end windings, which would be particularly time-consuming to recreate manually. From Concept to Validation in One Day This integrated approach completely transforms the development of electric motors. In a typical scenario, the team might identify a new axial flux machine concept in the morning, by midday the team uses Simcenter E-Machine Design to explore 500 potential configurations, optimizing efficiency and performance, and can transfer the most promising design to Simcenter 3D in the afternoon. Before the end of the day, structural validation can begin using the exact same model—without recreation, simplification, or delays. Meanwhile, the thermal team works on the same model within Simcenter STAR-CCM+ , coupling electromagnetic and cooling simulations to validate performance under real operating conditions. Identified issues can be addressed quickly, as everyone uses the same digital representation of the motor. The Human Impact More than just technical gains, this integration redefines how engineering teams collaborate. When a thermal problem arises that requires design modifications, teams can work together and validate changes in a matter of hours, instead of weeks. For engineering managers responsible for coordinating multidisciplinary teams, the impact is profound. Projects that previously needed to be carried out sequentially, with long intervals between phases, can now advance in parallel and synchronized. Rapid and continuous iterations become routine, increasing the quality and efficiency of the final products. Connecting Today's Innovation to Tomorrow's Success This functionality—now available in the latest version of Simcenter E-Machine Design 2506 —represents more than just time savings. It embodies a fundamental shift in how electric drive systems are developed, connecting the digital workflow from initial concept to detailed validation across all disciplines. For companies competing to launch the next generation of electric vehicles to market, this connected approach not only accelerates development but also enables entirely new levels of productivity and innovation that were not possible when teams worked in isolation. In the enterprise setting, as engineers leave the meeting with a new plan, there is a palpable sense of relief. Design changes can be implemented, validated across disciplines, and ready for prototype manufacturing—all on schedule. What once seemed impossible becomes simply another workday in the connected world of Simcenter tools. Eliminate the barriers between your engineering teams and accelerate the development of electric motors with a truly integrated digital workflow. Speak with CAEXPERTS and discover how to simplify your processes and drastically reduce the time between concept and validation — schedule a meeting today! WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Turbomachines are made of assemblies of different bladed disks An interesting exploitation of the periodicity of the structure is to consider cyclic symmetry sectors, instead of the whole 3D structure. When the industrial application requires the machine to be made of different stages, each with a different number of sectors, special care is required when connecting those sectors to ensure a smooth junction between the two stages. When the periodic structure deviates from its regular axisymmetry shape like at the propellers with few blades, a simulation in a rotating frame can no longer be avoided. The limitations on the non-rotating parts apply for such calculations: indeed, the non-rotating parts (stator and bearings) must be isotropic, which can be a rough assumption for industrial applications. For such cases, the Simcenter Nastran solver for Rotor Dynamics proposes a method of avoiding model limitations with the use of Coleman transformation. Indeed, with this method, bladed rotors assembled to an anisotropic stator and bearings can be computed! While this is a challenge to complete, it allows you to model the complexities and explore more possibilities when simulating and modeling rotating structures. For industrial applications like turbochargers, steam turbines, or jet engines, the assembly is made of multiple stages of bladed disks, and the assumption that the rotor has axisymmetry is not always true. Therefore, Simcenter 3D 2506 for Rotor Dynamics has expanded the use of the Coleman transformation to assemblies of multiple stages of cyclic symmetry rotors.ontagens de múltiplos estágios de rotores com simetria cíclica.   Coleman transformation for multiple stages of cyclic symmetry rotors The Coleman transformation is a solution for producing time invariant matrices in the fixed structure for cyclic symmetric rotors, as described by Kirchgassner 2016 . Campbell diagrams and stability analysis are then used to calculate the critical speeds at which resonance occurs. This method is equivalent to the Floquet method when the structure is strictly cyclic symmetric, as described by Skjoldan 2009 . . Simcenter 3D advanced capabilities for bladed rotors Simcenter 3D has taken a step further in the simulation of advanced bladed rotors applications, allowing an assembly to be computed satisfying the hypothesis of rotor dynamics calculations based on the axisymmetry or unsymmetry of the different parts of the system. It allows easy postprocessing including the production of Campbell diagrams and presents modes as output in a fixed reference frame for easy interpretation Top right, model preparation of different cyclic symmetry sectors connected at the junction; bottom right: the Campbell diagram output in fixed reference frame; left: complex mode; 57Hz at 600rpm, backward whirl. Complex modal analysis in 5 steps Step one In Simcenter 3D , prepare an associative model, where the different stages can be linked to the geometry. It will allow any changes in the geometry to be communicated to the finite element model and adapted in such a way that only the simulation has to be computed again, to account for the changes. This method is called the master model concept. Step two Prepare the finite element model of the cyclic symmetry sector of each stage, by identifying the sector as a portion of the structure that can be repeated about the rotor axis. It can contain a single blade or multiple blades. Step three Assemble the different stages at the junction Identify each junction between two connected stages. The solver will take care of the continuity of results at this junction by automatically adding the higher order harmonics. Step four Prepare the simulation Set up a complex modal analysis in the rotating frame and activate the Coleman transformation. The rotating parts modeled in cyclic symmetry will be calculated in multi blade coordinates for different cyclic waves (harmonic index). The Coleman transformation will compute time-invariant matrices that will allow results to be output in a fixed reference frame. The bearings and the stator can be anisotropic and are calculated in the fixed reference frame, allowing for the simulation of the whole assembly. Step five Post-process results in Simcenter 3D The Campbell diagram shows the evolution of the eigenfrequencies with the rotation speed, highlighting the gyroscopic effects for the relevant modes. The advantage of the Coleman transformation is the simultaneous consideration of multiple harmonic indices, which is usually not the case with simulations using cyclic symmetry. Modes at 0-diameter, or 1-diameter are output in our example. What else can cyclic symmetry do? For all these applications that show a periodic structure, cyclic symmetry is an interesting alternative to full 3D models, since it enables the use of model reduction and makes the simulation time more reasonable. But what else? let’s review what Simcenter 3D Rotor Dynamics can do with cyclic symmetry models: Consider hybrid models, that is, a model that consists of sections that are 1D, 2D, and/or 3D, it is now possible to model a rotor made of a cyclic symmetry sector, in one or multiple stages, with a connection to a 2D Fourier portion of the rotor, a 1D shaft, as well as a 3D portion of the structure. Bearings, springs, dampers, etc can be used to connect the rotor to the ground with stiffness and damping properties or to a casing. If you want to go further in the model reduction, you can create a super-element of the cyclic symmetry sectors, for one or multiple stages, using Component Mode Synthesis methods. This super-element can then be used in an assembly with bearings, in rotor dynamics solutions. The postprocessing enables you to recover the results for the original cyclic symmetry sectors and for the whole recombined structure. For bladed rotors that can have large deformations due to centrifugal loads or other types of solicitations, which might occur when the blades are long and thin, it is possible to compute a modal basis of the structure with a preliminary nonlinear prestress. The nonlinear prestress of the structure computes the equilibrium state due to large deformations, and the modal basis is computed around this equilibrium state. Afterwards, you can use that tangent modal basis in a modal frequency response to compute the vibrations of the system due to external loading. For Campbell diagram, stability study and complex mode calculations, this blog shows that Simcenter 3D Rotor Dynamics can now be used to solve multi-stage rotors modeled in cyclic symmetry, with anisotropic bearings and output results in a fixed reference frame. Want to know more about simulating rotors in Simcenter 3D ? Schedule a meeting with CAEXPERTS and see how to apply these cutting-edge technologies to gain performance, reduce simulation time and deal with real geometries and conditions much more efficiently. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Accelerate surface wrapping. Refine SPH accuracy. Assess transient passenger thermal comfort. Plus, many more. New enhancements in Simcenter STAR-CCM+ are aimed at helping you: Model the complexity Explore the possibilities Go faster Stay integrated The Simcenter STAR-CCM+ 2510 release introduces a suite of powerful new features designed to elevate your simulation workflows. You can now model greater complexity with enhanced transient thermal comfort analysis, enabling more realistic vehicle thermal management simulations. The latest capabilities empower you to explore engineering possibilities faster and more reliably, thanks to dynamic penalty update for topology optimization. Workflow speed is dramatically improved with parallelized surface wrapping, more GPU-accelerated applications, and local particle refinement for SPH, all designed to help you achieve results in less time without sacrificing accuracy. Together, these new features enable you to drive productivity, accelerate development, and make better engineering decisions with confidence. Model the complexity Capture all aspects of HVAC systems and human comfort over time Modern electric vehicles face the challenge of maximizing energy efficiency across all driving scenarios, especially during extreme temperatures, where heating or cooling the cabin and battery can significantly reduce range. Traditionally, simulating passenger comfort during transient cabin cycles has required cumbersome co-simulation with third-party tools, which limits automation and fidelity. With the new release of Simcenter STAR-CCM+ 2510 , you can now use advanced thermal comfort models, including Fiala and Berkeley models, directly in transient analyses. This means you can simulate any unsteady cabin cycle, capturing the full dynamics of HVAC systems and human comfort over time, all within a single, seamless workflow. The solution integrates natively into existing Vehicle Thermal Management (VTM) processes, eliminating the need for external coupling and enabling full automation. As a result, you gain the ability to assess trade-offs between HVAC energy use and passenger comfort duration, leading to more informed design decisions. The primary benefit is a significant improvement in user experience, enhanced automation capabilities, and increased simulation fidelity, enabling you to deliver vehicles that are both comfortable and energy-efficient. Explore the possibilities Run up to 3x faster and stable Topology Optimization with reduced user intervention Achieving optimal performance in adjoint topology optimization is often hampered by the difficulty of selecting the right penalty strategy, which can lead to overshooting constraints, optimizer instability, and increased simulation time. Manual fine-tuning of penalty factors is tedious and detracts from the usability of the optimization tool. With Simcenter STAR-CCM+ 2510 , you benefit from a Dynamic Penalty update that automatically adjusts penalties during the optimization process, leveraging the augmented Lagrangian method for each constraint. This enhancement ensures smooth convergence without overshoots, even in constraint-heavy problems, and eliminates the need for manual adjustments. You can now focus on critical design aspects rather than penalty tuning, achieving up to three times faster and more stable optimization runs. You benefit from a streamlined workflow that accelerates innovation and improves the overall user experience. Enable quick and lean analysis of Transient 3D data Large transient simulations generate massive data sets, making it impractical to interactively analyze results or capture unexpected phenomena unless analysis bodies are pre-placed. Previously, storing and analyzing full-resolution 3D results required significant storage and memory, limiting flexibility. With the latest Simcenter STAR-CCM+ 2510 release, you can now store resampled volumes in Solution History files, drastically reducing data and memory footprint while retaining all relevant qualitative information at every time step. This allows you to interactively analyze 3D transient data, leverage results in screenplays for insightful animations, and investigate any plane in the full domain after the simulation is complete. The solution combines the benefits of Solution Histories and resampled volumes, allowing for qualitative analysis without the need for the original mesh. Perform quick, lean, and comprehensive analysis of large-scale transient simulations, supporting deeper insights and faster decision-making. Go faster Leverage faster surface mesh preparation Preparing a closed, manifold surface from complex or “dirty” CAD geometry is essential, often time-consuming, especially for large models. Even with previous parallelization efforts, runtime remained a bottleneck for many users. With Simcenter STAR-CCM+ 2510 , you can now take advantage of Phase 2 of the MPI Surface Wrapper, which brings further speedup compared to earlier versions. The process is parallelized across multiple processors, reducing wrapping time by up to ~50% compared with previous releases. The performance gain enables you to increase simulation throughput or create a more refined wrapped surface for improved mesh quality. The solution delivers consistent results regardless of processor count and can prepare complex surfaces in minutes. You can move from CAD to simulation much more quickly thanks to streamlined surface mesh preparation. Speedup rotorcraft simulation with GPU power Simulating complex rotating machinery, such as rotorcraft, is computationally intensive and often constrained by project timelines. Traditional CPU-based approaches, while accurate, can occupy a lot of resources and extend turnaround time. With the 2510 release of Simcenter STAR-CCM+ , you can now leverage GPU acceleration for Virtual Disk simulations, achieving drastically shorter runtimes while maintaining equivalent accuracy. This enables you to execute multiple simulation variants or entire design exploration studies much faster, reducing energy consumption per simulation. The solution empowers you to evaluate hundreds of geometry variants or apply finer resolutions within standard project timelines, transforming design-space exploration and providing robust insights early in the design process. By speeding up rotorcraft and rotating machinery simulations, this approach helps unlock resources and boost innovation. Enable faster and easier setup of DEM multiphase interactions Setting up complex particle flow simulations with multiple DEM phases and boundary types traditionally required repetitive and error-prone configuration of interaction pairs. This process became increasingly cumbersome as the number of phases grew. With Simcenter STAR-CCM+ 2510 , you can now use user-defined templates for contact models, applying them to multiple interaction pairs simultaneously. This enhancement increases productivity and improves the user experience by allowing multiple default interaction settings, each associated with distinct sets of interaction pairs. The solution streamlines the setup process, reduces errors, and provides greater flexibility for customizing interactions. Faster and easier setup of DEM multiphase interactions allows you to focus on simulation objectives rather than configuration details. Boost simulation accuracy and efficiency with SPH particle refinement Ensuring high accuracy in Smoothed Particle Hydrodynamics (SPH) simulations previously required refining the entire domain, leading to high runtimes. This was especially challenging for applications such as gearbox lubrication or tire water splashing, where only specific regions required high resolution. With Simcenter STAR-CCM+ 2510 , you can now enable local particle refinement for fluid particles in targeted areas of your domain. You define adaptive refinement shapes, such as blocks, cylinders, or spheres, and particles are refined only when inside these shapes, then coarsened outside. This approach delivers higher accuracy where needed, with a negligible runtime penalty compared to globally fine simulations. This enables faster turnaround times without compromising accuracy, making high-fidelity SPH simulations more practical and efficient. Stay integrated Unlock more design possibilities with expanded e-machine type support Radial flux machines (RFMs) account for more than 95% of the e-machine market, and previously, simulation workflows for these machines were complex and fragmented. The lack of a uniform file format across Simcenter EMAG and e-machines solutions made it difficult to ensure seamless workflow and numerical continuity between teams. With Simcenter STAR-CCM+ 2510 , you can now import radial flux machine designs through the SimCenter Data eXchange (SCDX) file format, which is compatible across all e-machine and EMAG Simcenter tools. This solution allows you to carry both CAD and physics data in a single file, ensuring a seamless workflow and consistent results across teams. This advancement brings more design options and a smoother simulation experience, with particular relevance to the automotive sector thanks to expanded e-machine support. These are just a few highlights in Simcenter STAR-CCM+ 2510 . These features will enable you to design better products faster than ever, turning today’s engineering complexity into a competitive advantage. Discover how Simcenter STAR-CCM+ can revolutionize your simulation workflows, accelerating innovation and increasing the accuracy of your designs. CAEXPERTS can help you explore the full potential of this new version and strategically integrate it into your engineering processes. Schedule a meeting with us and see how to transform complexity into competitive advantage. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Aging affects most things on Earth, and batteries are no exception to this phenomenon. In a very peculiar way, batteries behave like "living" beings; their particles flow between two electrodes, there are chemical reactions, and even mechanical changes, such as an effect similar to "breathing" (expansion and contraction of the electrodes due to the intercalation and deintercalation of lithium during charge/discharge cycles). They are simply in operation, which generates natural wear and tear. You Can't Stop Aging: Electric Vehicle Fleets Will Age (and so will their batteries) However, when people consider buying an electric car, the number one criterion for them is range, followed by price and charging logistics (infrastructure and charging time). Concern about battery life only appears in 6th position. Today, this ranking may not be surprising, considering that most electric vehicles are bought new and aging seems to be a rather technical topic, with a wide variety of possible evolutions in battery life depending on the use of the electric vehicle. But, as electric vehicle fleets age (and with them their batteries), the used car markets begin to grow and, suddenly, for good resale prices, the lifespan and health of the batteries will certainly increase in importance (as predicted). Similarly, recycling battery cells that have reached the end of their useful life is still a very expensive and energy-intensive activity. And therefore, longer-lasting and more sustainable battery cells are already a competitive factor for those who design and sell electric vehicles and batteries. So, the time has come for OEMs and battery manufacturers to understand battery aging and develop cell designs that provide maximum lifespan. Engineers must understand not only when, but also where aging mechanisms occur. Using simulation with aging models can significantly help accelerate the prediction of the degradation trend of a given battery. Typically, 1D level simulations are used in this case, as they allow for very fast execution and can produce years of simulated data in a few hours. Throughout this article, it will be exemplified how this can be done in a simulation environment where physical phenomena are modeled, but first let's learn a little more about the causes of battery aging. The Toxic Ingredients That Cause Battery Cell Aging So, what triggers and affects aging? Battery aging has its root causes in several factors. First, unsurprisingly, time: whether the cell is being used or remains idle, time is at work allowing some internal chemical reaction to induce some performance degradation. Second is temperature: temperature has a significant impact on the battery lifespan degradation process. Storage and use at high temperature (high range of safe temperature limits) would accelerate aging. Low temperatures are better, but combined with fast charging can be recipes for other degradation effects. This leads to the third main criterion, the current applied to the cell. Basically, referring to the type of load applied to the battery. If it is used gently with smooth and low power demands, the current applied to the cell will be smooth and slowly affect aging. However, if the battery is used more aggressively, with more frequent fast charging, particularly under low temperature conditions, the accelerated degradation mode will be activated. A deeper analysis of battery cell degradation mechanisms What happens within the battery due to these effects is a combination of several degradation mechanisms: Solid Electrolyte Interface film growth: This is the slow growth of a thin, porous layer on the surface of the active material, which consumes Lithium atoms to grow. As it grows the inventory of available lithium, used for the cell operation, decreases, reducing the cell’s capacity. Also, the thickness of the SEI film creates a barrier to the Lithium ions and electrons trying to go in and out of the active material, which increases the cells’ overall electrical resistance Lithium plating. In this case, there is formation of lithium metal film on the surface of the active material, which also consumes the lithium inventory impacting the cell capacity. Loss of active material by dissolution: Active material responsible for storing lithium is dissolved into the electrolyte due to some undesired side reaction. The loss of this active materials further decreases the cell capacity. Loss of active material by mechanical cracking . The Lithium intercalation and de-intercalation process generates at each cycle some mechanical stress. Overtime parts of the active material can break down and be separated from the main electrode. This has the effect of losing ability to store lithium and further decreases the cell capacity. The bottom-line consequences of these effects are simple, your battery’s capacity will decrease, reducing your vehicle range compared to its brand-new range. And it will be less able to sustain aggressive power demands, reaching more rapidly the lower and maximum voltage safety limits, leading to the battery’s shut down. Aging takes time – that engineers don’t have That's why battery and vehicle manufacturers dedicate time and effort to characterizing these aging phenomena. But here's a challenge: the effects of aging can only be observed after several years of operation. Therefore, as you can understand, conducting tests to capture the correct degradation behavior requires an enormous amount of time and money to test the battery over the years of operation! Of course, there are some accelerated aging testing techniques, but the first results can only be seen after at least 6 months of accelerated aging tests. But to gain a competitive advantage, engineers analyzing these aging challenges need more detail; they need to further optimize the battery cells and understand not only when, but also where the aging mechanisms occur, so that they can better address degradation problems locally. EV battery cell formation (the initial charge) is a critical manufacturing step with respect to battery cell aging risks (Image: Chroma ATE). Inspection & Identification The first stage of the process. Battery cells are inspected and identified before entering the formation cycle. Formation This is where the initial charging of the cell happens — known as the “formation” process. A critical step that defines the cell’s electrochemical properties and directly impacts its lifespan . Ambient Aging Cells rest in ambient temperature conditions. This step allows the materials inside the cell to stabilize after formation. High Temperature Aging Cells are kept at elevated temperatures to accelerate aging and detect early defects. Ensures only stable cells move forward in the production line. OCV & ACR Testing Electrical testing to measure: OCV (Open Circuit Voltage)  – voltage when the cell is not under load. ACR (Alternating Current Resistance)  – internal resistance of the cell. These tests assess the performance and quality of each cell. Sorting Cells are classified based on results from electrical and aging tests. Cells with similar performance are grouped together to form consistent battery modules or packs. And it's not only aging that needs to be studied during operation: equally relevant is the initial charging process, known as formation, which is the critical final stage of manufacturing before the cells are shipped. It forms the crucial protective layer of the Solid Electrolyte Interface and therefore has a huge impact on the subsequent lifespan of the battery. Battery aging simulation There are several approaches to leveraging simulation to predict aging and the formation process. Firstly, our Simcenter Amesim systems solution, using 1D models, can be extremely efficient in rapidly generating years of aging simulation data under various operating conditions. The main advantage here is time acceleration. Physics-based aging models in Simcenter Amesim have been available since version 2410, in addition to the existing empirical aging models. In this type of simulation, each cell is represented by blocks that describe its electrical and thermal behavior—capacity, internal resistance, and heat exchange with the environment. By connecting multiple cells in series and parallel, it is possible to predict how performance and temperature evolve over time, simulating battery aging and allowing for design adjustments before moving on to more detailed 3D analyses in Simcenter STAR-CCM+ . Second, to address the need for spatial information, Simcenter STAR-CCM+ 's 3D Cell Design solution can predict aging evolution in a 3D cell geometry with resolved electrode layers. Of course, in this case, the execution time is much longer than in 1D simulations, but the user will have access to local information about where aging occurs and can mitigate these effects by changing the design or operating conditions. Thirdly, it is possible to combine 1D and 3D simulations. The 1D simulation is used to generate the very long-term aging simulation over years of physical time. Users can then extract from this discrete point the cell's State of Health (SOH) over the aging period, for example, every year. This SOH for each year can then be a starting point for a 3D simulation, where the cell is aged only for a short period, for example, 1 month of physical time, but long enough to generate the distribution of the various aging mechanisms, such as Solid Electrolyte Interphase (SEI) growth or lithium plating, as implemented in more recent versions of Simcenter STAR-CCM+ . Obviously, the 1D and 3D aging models are coupled with thermal models to capture the thermal effect on the evolution of degradation mechanisms. Finally, 3D simulations can be used to assist in predicting the initial Solid Electrolyte Interphase (SEI) layer during the manufacturing formation process. In fact, the SEI growth model can be used in the first charge of a battery cell and predict the growth of this critical protective layer. The 3D Cell Design feature can then help the user evaluate the uniform evolution of the SEI layer growth and determine the optimal point at which the layer is sufficiently thick and the amount of lithium consumed to generate it. This will help further refine the estimate of cyclable capacity. High fidelity battery aging simulation with Simcenter STAR-CCM+ Aging through parasitic side reactions with Sub-grid Particle Surface Film model Available since the release of Simcenter STAR-CCM+ 2406 , the “Sub-grid Particle Surface Film” model in the Battery Cell Designer allows simulating the cell's response to a duty cycle in relation to two of the main degradation mechanisms: The growth of the Solid Electrolyte Interphase (SEI) film The growth of the lithium metal plating film An Active Material Particle, presented at NordBatt Conference These are both parasitic side reactions which occur during the cell operation. Lithium plating is the deposition of Li-metal on the particle surface. And SEI is the film created from the reaction between the particle and the electrolyte. Due to the side reactions, the amount of cyclable lithium reduces, you can simply track the remaining lithium in the electrolyte and the active material. This should allow to check the effect on the capacity. The film resistance area (resistivity times thickness) is also a field function which can be tracked and contributes to the overall internal cell resistance. Mechanical-induced degradation with the Sub-Grid Particle Aging model Simcenter STAR-CCM+ includes "Sub-Grid Particle Aging," which focuses on degradation effects of a mechanical nature. In this case, the loss of active material due to mechanical stresses is characterized by alternating stresses during charging and discharging, i.e., the cyclic insertion and extraction of lithium from the active material particles, which can lead to the formation of cracks in the electrodes. This can cause loss of electrical contact and reduction of usable active material, leading to an overall loss of cell capacity and an increase in internal resistance. Active material particles undergoing surface cracking and loss of active material There are two types of cracks forming, represented with two model options under the “Sub-grid Particle Aging” model: First one is the “Loss of Active Material” model. It is characterized by the cracking of particles or electrode “blocks”, leading to an electrical contact loss of active material particles, making those particles electrochemically inert and no longer participating in the electrochemical reactions. These particles represent therefore a loss in cell’s capacity The second effect is the “Surface Crack Growth” model. The cyclic insertion and extraction generate cracks within the particles themselves. Those cracks expose a new surface for the Solid Electrolyte Interface (SEI) to grow, leading to Lithium consumption and therefore an overall capacity loss and internal resistance increase. Note that this model option is compatible with the “Sub-grid Particle Surface Film” model enabling the SEI growth effects simulation. Also note that, some publications on the topic suggest, that the tortuosity should increase when the surface cracks grow. A trustworthy battery aging simulation framework The abovementioned aging models were validated against experimental measurements generated during the EU commission funded project MODALIS² , which was focusing on developing physics-based aging models for the latest generation of Li-ion battery cells. This work was performed with key industrial partners specialists in the field of batteries, such as a cell maker, cathode supplier and electrolyte supplier. All that said, thanks to high physical modeling fidelity and the unique three-dimensional implementation of the models, these aging models offer you the ability to localize areas of the cell which most impacted by all types of aging. This is in theory. So let’s look at those models in action. Simulating aging cycles in 3D This first example was presented at the NordBatt conference in 2022 by my colleague Stefan Herberich from SIEMENS . A prototype cell used in the EU-funded MODALIS² project was used, and the cell is tested over several cycles with aggressive aging conditions to locate the weak areas where degradation is most dominant. The cell considered consists of 15 electrochemical layers. The discretized cell is shown below, along with some results. In total, there are approximately 200,000 finite volume cells. In particular, the thickness direction is discretized using 10 cells per anode and cathode layer and 2 cells for the separator and current collectors. The drive cycle consists of the following steps: first, charging is performed with constant current (CC), applied at a rate of 2C. C-rates indicate the ratio between the charging current and the battery capacity—at 1C, a fully discharged battery (0% state of charge, or SOC) is fully charged in 1 hour; at 2C, the current is doubled, and charging is completed in approximately 30 minutes. If the voltage exceeds 4.2 V, the process switches to constant voltage charging mode, remaining at 4.2 V until the state of charge reaches 95%. The 4.2 V limit is reached quickly. Then, the battery remains at rest for a little over 3 minutes and is then discharged to 60% state of charge, also at a rate of 2C. After another rest period, the complete cycle is repeated ten times. Interpretation of results The study provides insights into the effects of the two aging mechanisms that occur: SEI growth and the influence of lithium plating side reactions. The images show the average thickness of the SEI layer around the particle and the equivalent average thickness of the plated lithium on a particle, respectively. The corresponding results were observed on the anode plane and in a cross-section in the direction of the cell thickness. In addition to the analysis of the SEI, this study also provides important information about LAM (Loss of Active Material), which refers to the degradation or inactivation of the electrode material that participates in the electrochemical reactions. In the plane: the thermal boundary conditions are such that the highest temperatures are observed in the center of the battery cell. At this location, the temperature dependence of multiple material parameters leads to higher SEI growth rates. LAM is pronounced near the battery tabs, where the highest rates of voltage variation are observed. In thickness: As expected, SEI and LAM growth are greater near the separator. The operating conditions are such that the lithium metal, with an initially specified homogeneous profile, is dissolved more quickly than deposited, especially near the separator. SEI during the formation step The second study will be on SEI during the initial charge, also known as formation. Using the results presented in “Andrew Weng et al. 2023 J. Electrochem. Soc. 170 090523”, Simcenter STAR-CCM+ and the “Sub-grid Surface Film” model were used to replicate this study. The article describes the formation of SEI, i.e., the accumulation of a passivation layer on the graphite anode of a battery during the first charge cycles. The film layer is formed due to a side reaction of the solvent components S, ethylene carbonate (EC) and vinyl carbonate (VC), with Li+, which produces the film components P, lithium ethylene dicarbonate (LEDC) and lithium vinyl dicarbonate, and gaseous byproducts Q. Only the first 4 hours of the formation process were simulated, which is when the rapid dynamics occur and the transition from the kinetically limited regime to the diffusion-limited reaction regime takes place. The results reasonably correspond to the reference: The results demonstrate the ability to use Simcenter STAR-CCM+ in an approach to understand the SEI formation process, but also to be able to better control it and brings the potential to reduce its overall duration, which in some cases can last up to ~20 days. Want to understand how to predict and mitigate battery aging with high accuracy and efficiency? Schedule a meeting with CAEXPERTS and discover how Simcenter Amesim and Simcenter STAR-CCM+ solutions can revolutionize the development of longer-lasting and more sustainable cells for electric vehicles. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Build a scalable Battery Energy Storage System (BESS) and achieve high ROI This post focuses on the crucial role of energy storage in promoting corporate sustainability and profitability. By integrating BESS with renewable energy sources, companies can achieve significant cost savings, reduce their carbon footprint, and boost long-term profitability. We will explore how BESS industry leaders are creating digital plants, increasing flexibility, and building a competitive advantage in a rapidly changing market. If your company is ready to lead the transition to BESS, this is your roadmap. System Simulation Plays a Crucial Role System simulation plays a crucial role in the techno-economic evaluation of battery energy storage systems (BESS) in the energy sector, especially when integrated with renewable energy sources such as wind turbines and solar photovoltaic (PV) systems. Various use cases covered by BESS Here are some key aspects: Balancing Power Generation and Consumption : Peak Shaving and Load Shifting : By simulating different load profiles, BESS can be optimized for peak shaving (reducing peak demand) and load shifting (moving energy consumption to off-peak times), which can reduce energy costs and improve grid efficiency Grid Stability : Simulations can assess how BESS can be used to balance intermittent renewable energy generation with grid demand, enhancing grid stability and reliability Integration with Renewables : Energy Management : Advanced energy management strategies can be simulated to coordinate the operation of BESS with renewable generation, ensuring that energy is stored and dispatched in the most efficient way. Including the weather conditions. Energy Price Evolution : Forecasting and Optimization : System simulations can model future energy price scenarios, helping to optimize the operation of BESS for energy arbitrage (buying low, selling high). This ensures that the BESS is used in the most cost-effective manner OPEX/CAPEX : Cost Analysis : Simulations can provide detailed cost-benefit analyses, including capital expenditures (CAPEX) and operational expenditures (OPEX). This helps in understanding the financial viability and payback period of BESS projects Degradation Modeling : By simulating the degradation of battery cells over time, it is possible to estimate maintenance costs and replacement schedules, which are critical for long-term financial planning Overall, system simulation provides a comprehensive framework for assessing the technical and economic feasibility of BESS projects, helping stakeholders make informed investment and operational decisions. We will explore the initial phases of a Battery Energy Storage System (BESS) project together, focusing on some technical and economic assessments for success (OPEX/CAPEX, energy price trends, load balancing, return on investment), and moving through the different stages with Simcenter System Simulation : To calculate your customer's electricity bill Considering some weather forecasts From renewable energy (solar PV) Battery Electric Storage System (BESS) optimization and control strategy To typical results in operations based on realistic scenarios The use case here is a food processing plant near Lyon, France. Some effort was devoted to modeling the solar photovoltaic (PV) system integrated with the BESS and surrounding consumers. With its load and heating system represented over a one-year period (January to December), the digital twin considers solar PV BESS operations with different electricity tariff structures and PV or BESS unit costs. Therefore, it addresses the technical and economic value of adopting BESS in dynamic tariff structures. Although the case study was conducted in France, it is important to highlight that even more impressive results could be obtained in countries with higher solar incidence, such as Brazil. High irradiance throughout the year significantly increases the potential for photovoltaic generation, increasing BESS efficiency and reducing the payback period. Thus, the combination of high solar resources and the application of advanced simulation strategies makes the Brazilian scenario especially promising for energy storage and renewable energy integration projects. Better Design for Operational Excellence As the BESS economy gains unprecedented momentum, companies are racing to meet the growing demand for clean energy. However, scaling production while remaining profitable, sustainable, and resilient poses a formidable challenge. BESS producers and equipment manufacturers must overcome fragmented data systems, high energy costs, and supply chain complexities to stay ahead. Companies striving for operational excellence are already turning these challenges into opportunities. By leveraging digital twins and advanced simulations, they are optimizing processes, reducing costs, and improving scalability. The energy industry needs BESS to save money and reduce carbon emissions. Digital Twin for an Industrial Facility This is a distributed BESS digital twin to predict and optimize system performance with multiphysics. It includes consumers (food processing facility: 20°C) with the heating system and customer load, grid connection, solar PV with solar panels, stationary batteries, as well as a smart controller based on weather conditions and energy price fluctuations. BESS Digital Twin in Simcenter Amesim In the image above, the Heating System, Consumer Load, and Grid Energy blocks were modeled in Simcenter Amesim based on tables containing actual operating data from the industrial facility and predefined formulas for calculating grid energy consumption and costs. These blocks represent the system's energy behavior under different load conditions, providing a reliable basis for analyzing the performance and operational costs of BESS systems. The Solar Panel and Battery blocks use simplified physical models to represent the actual operation of these devices. Photovoltaic generation is simulated using historical solar irradiance and ambient temperature data, while the battery model was parameterized based on information from technical catalogs, allowing for the prediction of load states, efficiency, and available capacity. These models capture the system's energy dynamics. Finally, the Intelligent Control block integrates all this information to enable real-time decision-making. Thus, the control optimizes the energy flow between generation, storage, and consumption, seeking to reduce costs and improve overall system performance. By running the simulation, users can access all variables from the different subsystems. Thus, complete information is available, from consumer load [kW] to the electricity bill over time [€] (€1 to $1). The evolution of electricity prices throughout the year, with their daily fluctuations, was considered. Below, two price trends are shown on January 1 and July 19, to obtain information on minimum/maximum prices at different times of the year. Typical results obtained with the BESS digital twin The solar panel includes your GPS location, turbidity factor (the effect of particles, similar to smoke in the air), or cloud cover factor (for weather conditions). Cloud cover factor changes depending on weather conditions At the same time, the outside temperature changes are included from a known database, allowing users to assess its impact on the heating system, considering the factory indoor temperature setting. Temperature Evolution (ceiling, outdoor, indoor) and Air Conditioning Power This analysis allows you to calculate associated information, such as air conditioning power [W] or the energy consumption of all surrounding subsystems. This allows users to obtain a realistic power evolution to evaluate the balancing mechanism and optimal control strategies to implement in their BESS system. BESS Macroanalysis with Realistic Scenarios The industrial unit model allows for mass exploration. Analyses are completed in just a few minutes, opening the door to long and complex scenarios. Energy generation and consumption [kW] for all subsystems (the battery is inactive here) Users can practically evaluate the energy generation, storage, and consumption of all subsystems. Meanwhile, the intelligent control system manages the Energy Management System (EMS) to distribute the energy, store it in the BESS, or deliver it to the grid. Since all changes are intermittent or dynamic, a system simulation tool, such as Simcenter Amesim , is necessary to optimize sizing and control strategies. Finally, the user can access variations in energy flows over time. For example, you can check the energy generated by solar panels or brought in by the grid, as well as the energy supplied by the battery. This corresponds to the energy needed by the load, while some small levels of energy are taken from the grid or returned to the battery during off-peak periods when demand is low. Energy Flow Variations Over Time This is a major achievement! We can already observe good results thanks to the digital twin with Simcenter System Simulation . But it's possible to go much further, more technically and economically. See how you can save US$1 million over 20 years while simultaneously reducing a huge amount of CO₂ emissions, down to -17 tons of CO₂ equivalent. Save US$1 million and tons of CO₂ equivalent We will now address the business and decarbonization aspects, with the goal of demonstrating how it is possible to create a scalable forecast for BESS systems, in order to measure and replicate significant successes. The digital twin of the food processing unit is equipped with metadata to produce the relevant economic KPIs (key performance indicators) to ensure its monetization, return on investment (ROI), or payback through CAPEX (capital expenditures) or OPEX (operating expenses). Operating Costs [$k] during the Scenario The reference is the electricity bill without solar panels or BESS. It amounts to US$103,000 paid over one year. By installing the solar panels and BESS, the new electricity bill, which is now US$33,000 after one year, can be captured, with an investment of US$625,000 for the photovoltaic system and US$77,000 for the BESS. This corresponds to a savings of US$70,000 per year in OPEX, thanks to the installation. Discounting CAPEX costs, a benefit of US$698,000 is obtained after 20 years of operation. CAPEX costs are reimbursed after a 10-year payback period. Knowing that the value doubles every 15 years due to the interest rate, it can be assumed that the actual savings will reach US$1 million after 20 years. Please note that this is a preliminary calculation that shows the potential, while things like inflation and maintenance costs are not covered, which is good for a first estimate. Profitability [$k] including payback [year] Now is the right time to optimize sizing and extract maximum value from the new installation. Discover the maximum benefits and best returns in just a few clicks. A batch study was configured to vary selected parameters, defined as the number of solar panels (366, 488, 610) and the number of battery racks (0, 100, 150). It was observed that the payback period can be reduced to approximately 9 years (-11%) in the most favorable configurations, while other options can extend it to up to 12 years (+20%). Comparison of return on investment [year] depending on the number of solar panels and the number of racks Finally, for the Earth's good health in relation to climate change, it is also essential to consider the reduction of carbon emissions thanks to renewable sources, the BESS system, and smart control strategies. CO₂ emissions from the grid, load, and heating, as well as the total CO₂ reduction per year A significant reduction of 17 tons of CO₂ equivalent per year was achieved. This result represents a significant contribution to the sustainability process through decarbonization. All these achievements were achieved using the digital twin through Simcenter System Simulation . Going Further You can even go a step further, introducing a new paradigm with grid supervisory control. The latest and most innovative technologies allow the combination of artificial intelligence (AI), weather forecasting, and streamed data. This offline digital twin is converted into an executable digital twin that connects real-time performance data with accurate and well-orchestrated plant information and simulation tools, allowing you to troubleshoot critical system situations (surges during switching, etc.) or benefit even more from price and CO₂ reductions. What a great prospect! Grid Supervisory Control, Combining AI, Weather Forecasting, and Streaming Data In short, owner-operators in the global BESS business have a historic opportunity to expand their business and market share in the coming decades. The companies that will emerge as leaders in the delivery sector will be those that can overcome the complexity of BESS and turn it into a competitive advantage. System simulation definitely helps you succeed in your BESS journey thanks to digitalization, system integration, and intelligent controls. Schedule a meeting with CAEXPERTS and discover how to transform the potential of your BESS project into real results. Our experts will show you how the use of digital twins and system simulation can optimize sizing, reduce OPEX, and accelerate return on investment (ROI)—all while your company advances in the energy transition and decarbonization. Take the next step toward efficiency and sustainability: contact us today. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Challenge: achieve fast and accurate virtual prototyping The optimal multi-attribute design of transmissions remains a critical challenge for drivetrain engineers, gaining even greater significance in the era of electric vehicles. Today’s automotive industry demands powertrains that excel in multiple aspects: minimizing noise and vibrations, maximizing power density and efficiency, while ensuring unprecedented durability performance. For this purpose, transmission engineers need to design innovative transmissions that meet the multi-attribute performance criteria. Computer-Aided Engineering (CAE) allows engineers in industry to virtually prototype and optimize their next product optimization. Simulations are essential to accurately predict and optimize component behavior and the system-level transmission performance throughout the development cycle. Powerful physics-based models and simulation capabilities are available, which accurately model real-life products, perform predictive simulations and optimize the product’s performance for statics, dynamics, aerodynamics, acoustics, durability, etc. These physics-based models, for example Finite Element (FE) models, are successfully adopted in industrial development workflows. For high-fidelity simulations (e.g. detailed FE contact simulations), the model fidelity must increase to allow accurate predictions, which unfortunately also increases the model’s computation time and cost. In CAE, Artificial Intelligence (AI) and Machine Learning (ML) technologies are revolutionizing physics simulations. Among the advancements, surrogate modeling for physics simulations experiences rapid growth thanks to advanced AI techniques and architectures. Surrogate models, or reduced-order models (ROMs), provide efficient alternatives to computationally expensive high-fidelity simulations (such as detailed FE contact simulations). These AI/ML-based surrogate models, capable of providing fast and accurate predictions while maintaining high fidelity, unlock the potential for extensive and automated design space exploration, enabling engineers to efficiently evaluate thousands of design variants in a fraction of the time required by traditional simulation methods. Gears, as critical components in transmission systems, particularly benefit from such advanced simulation approaches, as their design requires careful consideration of deformation, contact and bending stresses, and durability. This blog post introduces a novel gear design analysis method3 that combines the strengths of FE and AI/ML models to achieve efficient and accurate gear stress prediction, demonstrating how this technology can be practically implemented in industrial workflows. Solution: combine powerful high-fidelity models with AI/ML The innovative gear stress analysis method3 exploits the powerful predictive simulation capabilities of FE models to generate accurate simulation data, which is then used to train an AI/ML surrogate model. Once trained, the efficient AI/ML surrogate model can accurately predict gear stresses for unseen combinations of design parameters. This combines the strengths of FE and AI/ML into a novel AI/ML-accelerated gear stress analysis capability that provides accurate predictions quickly. The simulation workflow for comprehensive gear analysis integrates the capabilities of Simcenter 3D and Simcenter Nastran software solutions, while Simcenter HEEDS serves the dual purpose of automating data generation for AI/ML model training and orchestrating multi-objective optimization studies that make use of the AI/ML model calculations. Figure 1 illustrates the automated data generation workflow, in which Simcenter HEEDS orchestrates the physics-based simulation process. The workflow streamlines the creation of gear datasets, while incorporating elements from Simcenter 3D Motion Gear Design Optimization methodologies. For each design, first the macrogeometry and microgeometry of the gear pair are evaluated before the nonlinear FE analysis (NLFEA) module generates the FE model and uses Simcenter Nastran for automated FE-based gear contact. Figure 1: Automated gear data generation workflow, involving Simcenter HEEDS as orchestrator. This workflow adopts high-fidelity physics-based simulation models, bringing several advantages to design engineers: highly accurate predictions of real-world behavior through detailed modeling of physical phenomena (such as the nonlinear contact mechanics in gear mesh), and a deep understanding of complex interactions and phenomena that could be missed in simpler models. These detailed models provide a reliable source of training data for AI models and AI model validation. To accelerate gear stress and durability analysis, our approach replaces the traditional NLFEA-based contact analysis by an AI/ML surrogate model, trained on NLFEA contact analysis results, to predict the altering gear blank and tooth root stress throughout the gear meshing cycle. The resulting AI/ML-based ROM offers significantly reduced computation times compared to the full order NLFEA-based simulations, enabling rapid design iterations. Embedding the AI/ML model within the standard gear design simulation workflows creates an AI/ML-accelerated process that remains highly accurate, while opening doors to multi-objective optimization studies (typically unachievable via traditional, resource intensive, FE models). Two additional solution elements are introduced to realize the AI/ML accelerated workflows: Simcenter Reduced Order Modeling8 that enables the creation and deployment of ROMs from simulation and test data. An upcoming Simcenter AI technology for 3D surrogate modelling that leverages operator learning techniques. Results: realize fast and accurate gear stress analysis A solution workflow has been introduced, aimed at combining the power of high-fidelity models with AI/ML models to achieve AI/ML-accelerated gear stress analysis. This section introduces a gear use case to verify the workflow vs. the objective to achieve efficient yet accurate gear stress analysis. The gear design use case is described in detail below and has four design parameters: the normal pressure angle, in range [18°-22°], the addendum coefficient of the Pinion, in range [1.00-1.30], the addendum coefficient of the Wheel, in range [1.00-1.30], and the profile shift coefficient of the Pinion, in range [0.35-0.70]. Using Simcenter HEEDS and Simcenter Nastran , 81 gear designs were created, varying the four design parameters through a level 3 full factorial DOE. The data set is split randomly into 64 designs as training set and 17 designs as test set. A transformer-based operator learning AI/ML model is trained to predict gear surface stresses, given the surface geometry and contact forces as model inputs. In this first study, we chose to have the AI/ML model learn the full 3D signed von Mises stress field, which captures both compressive and tensile behavior. The signed von Mises stress is created via postprocessing of the full 3D tensor at each node, which is stored in the NLFEA-based contact results. The model focuses on surface nodes where failures typically initiate (tooth root and contact regions), optimizing computational efficiency while maintaining accuracy for critical stress predictions3. Three approaches are evaluated for the gear design use case: FE Reference: NLFEA-based contact simulations (Reference), ML with FE Forces: AI/ML predictions using NLFEA contact forces, ML with Motion Forces: AI/ML prediction using Simcenter 3D Motion contact forces. Multibody-based gear contact forces (computed via Simcenter 3D Motion ) are used in this study as they provide a computationally efficient alternative to expensive nonlinear finite element analyses, while still maintaining sufficient accuracy in predicting contact force distributions – an aspect that will be crucial for future design optimization workflows. Figure 2 presents an analysis of the full stress field using the three approaches. The stress field predicted by the AI/ML surrogate model with FE-based contact forces as input (Fig. 2b), shows to be near-identical to that of the reference FE-based results (Fig. 2a), while the stress field predicted by the AI/ML surrogate model with Motion-based contact forces as input (Fig. 2c) shows only minor deviations from the reference results. These findings are confirmed by Figure 3, which presents the results for the tooth root stress during five mesh cycles for a point located along the middle of the face width and in the middle of the tooth root arc. Figure 2: Von Mises (signed) stress fields, computed from a) NLFEA-based contact simulations, b) ML model with FE-based forces (input), c) ML model with Motion-based forces (input), for a pinion gear example case. Figure 3: Comparison of tooth root stress for a point (middle flank, middle root arc) over the course of 5 mesh cycles (created based on the stress of 5 teeth during 1 mesh cycle) for a pinion gear example case Figure 3. Though data generation and AI/ML model training require an initial time investment, these are one-time offline processes. The resulting trained model enables rapid predictions, ideal for efficient design space exploration and optimization. The AI/ML model that was used in this study requires about 0.1 seconds to compute the full stress field for one point in the mesh cycle, while one NLFEA contact simulation requires on average about 5 minutes. Knowing that a full mesh cycle requires about 20 to 30 angular configurations per gear pair and that an industrial transmission has multiple gear pairs for which hundreds of variants are explored, the inclusion of the AI/ML surrogate models can truly accelerate gear design optimization. Conclusion and outlook This study demonstrates the successful integration of AI/ML surrogate models into gear design workflows, achieving both speed and accuracy in stress prediction. The AI/ML approach delivers results that closely match traditional nonlinear finite element analyses while being multiple orders of magnitude faster. This dramatic speed improvement, combined with maintained accuracy, opens new possibilities for comprehensive design space exploration and optimization of transmission systems. The developed workflow, supported by Simcenter tools , and validated through practical case studies, effectively demonstrates the industrial viability of AI/ML-accelerated gear and transmission design. References D. Park, A. Rezayat and Y. Gwen, “Gear design optimization for multi-mesh and multi-power flow transmissions under a broad torque range incorporated with multibody simulations”, in VDI International Conference on Gears 2022, Munich, Germany, 2022. M. Vivet, J. Melvin, S. Donders, “Advancing bevel gear contact simulation towards quiet transmissions”, Simcenter Blog, August 19, 2024. M. Vivet, D. Park, A. Scheuer, “AI/ML-accelerated gear durability analysis within gear design optimization”, in VDI International Conference on Gears 2025, Garching near Munich, Germany, September 10-12, 2025. Schedule a meeting with CAEXPERTS and learn how to accelerate gear stress analysis with solutions that combine high-fidelity simulation and Artificial Intelligence. By combining high-fidelity models and AI/ML, we help your company reduce development time, increase analysis accuracy, and accelerate innovation in transmission systems. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The industry has an actual deadline to meet: 2050. Since the adoption of the International Maritime Organization (IMO) Strategy on Reduction of Greenhouse Gas (GHG) Emissions from Ships in 2023, the maritime industry has been looking at ways to reach its goals on time. Whether modifying existing fleets or building new ships, the engineering challenges arising to comply with environmental guidelines are huge. Naval architects and design engineers need to make radical decisions faster. To do so, they turn to simulation. In this maritime context, simulation is no longer a nice-to-have tool, it’s essential for exploring every aspect of vessel performance and numerous design options. To meet your targets, you need CFD simulation tools that make it easy to set up complex cases, embed best practices, and enable automation. You also need tools that are fast to run and contain the advanced multiphysics features needed for marine simulation. The good news is that you can do all of that and more with Simcenter! Should we introduce it? Simcenter STAR-CCM+ has a full suite of tools to completely automate processes and workflows and create simulation templates. These templates allow for repeatable workflows that can be created by experienced analysts and passed on to design engineers to make engineering decisions. Simulation templates can eliminate most of the manual processes involved in building a CFD simulation via a parameterized template model by boiling down the simulation to its minimal set of inputs. From there, all meshing calculations, boundary conditions, solver settings and post-processing can be applied such that simulations are repeatable and embedded with your organization’s best practices. The templates utilize tools all built into the software, so they can be updated when desired; they are not black box tools you get from your software provider! One such template is the virtual tow tank (VTT) template. Let’s explore it a bit more, shall we? Setup time: it went so fast, I didn’t see it happening… Setting up a marine CFD simulation requires a lot of steps: importing CAD, defining mass and hydrostatic properties, creating a suitable mesh, defining solver physics… Not here. The parameterized template model automates all these settings and calculations. There is no need for scripting and the file can be reused for multiple simulations and shared with others. So, using a template ensures consistency and best practices across all simulations and the repeatability of results. Marine-focused templates guide you through set up and running and ensure your best practices are applied to every simulation. Okay… What’s next? Meshing? Traditional CFD approaches require several hours of manual setup. What size should the mesh be? How do I suitably build my boundary layer mesh? And where should I place the appropriate refinements? If you think about it, all these decisions are based on the vessel speed and size and shape of the vessel being analyzed. A set of step-by-step simulation operations is embedded into the simulation file to make these decisions automatically. The operations will determine all the meshing sizes, places for refinement, where to apply the boundary conditions and all the relevant solver settings and stopping criteria. This process is so powerful, you won’t even notice it taking place and you press a button to mesh AND run the simulation; and these two steps are seamlessly linked together. What is perhaps even more powerful, is this automated process then allows for multi-mesh sequencing (MMS). This method automatically refines the mesh, maps the previous solution to the mesh and continues the run. This process is repeated systematically until the final mesh discretization is reached. On average, this process improves the run time by a factor of 4 compared to running the model only on the final grid! Illustration of MMS sequencing grid refinement. What about post-processing? Reporting for key metrics such as resistance, trim, heave or shaft power can be set up in advance. Field data such as friction coefficients, pressure coefficients and generated wave elevation can be displayed in pre-defined scenes and exported to PowerPoint using a macro. Key information, such as local wake fraction can be exported to CSV data automatically to be read into other software packages for further analysis, if required. Layout view of various automated post-processing visualizations. Got it – faster simulation runs. What about a complete design sweep? The template is fully parametrized, meaning that any type of design exploration study can be implemented using the design manager tool in Simcenter STAR-CCM+ . You can automatically explore the design space; generate resistance and powering vs speed curves, explore effects on mass, or analyze a range of possible hull forms and more! Want to understand how CFD simulation can transform your maritime projects, reducing time and effort, and still ensure compliance with environmental goals? Schedule a meeting with CAEXPERTS and find out how to bring your naval engineering to a new level with Simcenter STAR-CCM+ . WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The role of Whole Engine Model (WEM) Predicting clearances in gas turbines is a complex and interdisciplinary task, involving transient thermal-mechanical modeling of all components within an assembly across various operational scenarios. This process, known as a Whole Engine Model (WEM), is crucial in the aero and power generation gas turbine industries. Understanding how clearances in gas turbines change over time is crucial for manufacturers. This knowledge allows them to optimize engine performance and efficiency while minimizing the risk of rubbing and other damage. The Whole Engine Model (WEM) isn’t just for assessing the steady-state condition of a gas turbine when it’s fully heated. It also helps analyze transient operations, where thermal gradients, temperature differences, and rotational loads significantly affect the gap between rotating and stationary parts. Clearances in gas turbines: understanding metal expansion Most of you probably remember physics classes and the experiments with metal utilizing the ball and ring expansion and the bimetallic strip examples, figure 1. Metal expands when heated and different metals expand at different rates. In a gas turbine, and in particular heavy duty gas turbines, there is a lot of metal that is heated at different rates and amounts resulting in axial and radial expansion over the course of the operational cycle. Figure 1. Metal expansion under thermal loads. Both images courtesy of Wikipedia Impact of clearances on performance and efficiency Gas Turbine OEMs are under tremendous pressure in a competitive market to deliver high performance and efficiency, both of which are intimately tied to the running clearances or gap between stationary and rotating parts. For example, 1mm of a clearance difference at turbine blade can have an impact of one megawatt (MW) which can power 650 houses in the United States (U.S.). That means a 500 MW gas turbine can power 325,000 homes, and just a 1 percent efficiency loss would mean powering 3,000 fewer homes. Alternatively, a study from Oak Ridge National Laboratory found that a 1 percent efficiency gain in a one-gigawatt (GW) power plant represents savings of 17,000 metric tons of carbon dioxide (CO2) per year, which is equivalent to taking over 3,500 internal combustion engine (ICE) vehicles off the road. Therefore, the clearances in gas turbines need to be as small as possible to ensure maximum power and efficiency. In the context of a rotor with a diameter of 1 to 2 meters and a length between 5 and 15 meters, the 2D WEM enables simulating and illustrating millimeter-level displacements to optimize performance and ensure component integrity. Evolution of the clearances in gas turbines On top of that, the OEMs need to ensure that no structural damage occurs as the clearances in gas turbine evolve during the start-up and shut-down of the engine. Figure 2. Example of the evolution of the clearances in gas turbine over a typical operational cycle. Graph is representative and movements are exaggerated for illustrative purposes Figure 2 is an illustrative example of what happens to one blade as an engine speeds and powers up. The cold build clearance, or gap that is present once the engine is assembled and cold, is arbitrarily set here at 2.5mm. The gap changes and we see initially it closing to a local minimum at start-up due to the centrifugal deformation and little to no thermal load on the stator side. A maximum clearance subsequently occurs as the gas turbine increases its load and more heat is added to the engine. The stator, consisting of relatively thinner parts warms up faster but as the rotor increases in temperature, we see an evolvement to a steady state running clearance. At shut down the load is switched off and we see a closure of the gap further as the stator starts to cool down before the rotor. The gap opens once the load and speed drops, and we see a gradual return to the cold build clearances as the engine cools down. Operational challenges and pinch points in gas turbines However, gas turbines have a wide range of operational scenarios, not just the idealized version representatively visualized in figure 2. Considering the current utilization of power plants as a back-up for green energy, the type’s of operation cycles the gas turbines have to endure are physically more demanding and characterized by frequent starts, stops and restarts. This has an influence not only on the lifetime of the parts, but also when and where local pinch points (tightest clearance or smallest gap) in a gas turbine occur. Figure 3 is an example of such a pinch point occurring at one blade location in the engine. One hour after shutdown, the engine is restarted and one can see how a new local minimum occurs as the rotor is still warm, the stator in comparison is cold and the centrifugal load is reapplied. Figure 3. Example of a 1 hour restart cycle. Graph is representative and movements are exaggerated for illustrative purposes Leveraging Simcenter 3D for clearance analysis Considering a typical industrial gas turbine, with approximately 15 compressor stages, and 4 turbine stages, the clearance engineer must carry out an analysis for each stage, and for all operational conditions, figure 4. This is where the WEM process within Simcenter 3D can significantly help the clearance analysis process. Reference points can be assigned within the model, for example, on the leading and trailing edges of the blades and vanes and the respective opposite locations on the stator and rotor. The transient results for the movement of these points can be read out from Simcenter 3D for further analysis in a tool like MS Excel or Matlab. This allows a clearance or mechanical engineer to build up a picture of the axisymmetric effects of the engine. Coupling these results to the non-axisysmmetric effects from other contributors (rotor-bending, casing deformation, etc), uncertainties and tolerances and the trusty engineer is able to build up a complete picture of the clearance behaviour of the gas turbine engine. Figure 4. Clearance analysis required for each blade and vane in a gas turbine. Inputs for the clearance stack-up from various contributors. This type of analysis can be used by the project team to define and optimize the performance of the engine, minimize the rubbing risks, and define the safety margins to be employed. Other decisions like abradables, squealer tips and ultimately the final manufactured and assembled state can be made with confidence using this a priori knowledge of the clearance behaviour of the gas turbine engine. Schedule a meeting with CAEXPERTS and discover how Simcenter 3D with the Whole Engine Model (WEM) can transform clearance analysis in gas turbines, optimizing performance, efficiency, and operational safety. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

In 1817, Karl Drais kicked things off by creating the Draisine, considered the starting point for the concept of the bicycle. This invention revolutionized the way people got around using only their own body strength. Since then, the evolution of bicycles has become heavily dependent on cutting-edge engineering. What characterizes today's great bicycles and components is the fact that they are the result of development processes based on rigorous simulations and testing. As in the automotive and aeronautical industries, virtual product development and advanced measurements have become fundamental to achieving superior performance, safety, and comfort in modern bicycles. Ride faster – Aerodynamics CFD and the power of GPUs Since the first Draisine a very obvious challenge in the improvement of bicycles was making them faster. Making a bicycle faster means less friction. And less friction means two things: First, lower mechanical friction in the drivetrain and between the wheels and the ground. And second, less air friction aka drag, which translates to better aerodynamics. And while tight pants can only take us so far, for high performance racing machines, CFD simulation had to conquer the world of bicycle development to make them cut through the air as efficient as possible. And to design a faster bike faster, GPU-native CFD solver technology arrived just in time as Trek Bicycle impressively demonstrated for at the Tour de France 2024. Ride more – Design space exploration But meanwhile companies like Trek not just run a one-off CFD simulation to see how a design works and then fiddle around a little with another design. No, they run automated design exploration workflows to explore the performance of hundreds of designs (that’s when cloud-hosted HPC and GPU-accelerated CFD comes in quite handy). To give you an idea of the level of care that goes into today’s optimization of race bike aerodynamics: Trek identified the optimum position of the water bottles to minimize drag, identified the optimum rider position under varying wind conditions and found the optimum drafting configuration four a team of four riders. On top these optimization studies are no longer only about one performance attribute. Taking it one step further, Trek looked at a multi-attribute optimization of aerodynamic performance and the weight of the frame. Ride Safer – Helmet impact testing SeIf you ride fast, you better ride safe. The good news is that helmets used today have improved significantly in terms of safety and comfort compared to those used in the past. This is thanks to continuous product improvement, novel materials and smart structural designs driven by homologation for certification and the customer desire for comfortable safety. Siemens professionals perform high-speed video analysis, laser head positioning, and wireless data acquisition to provide highly accurate and repeatable helmet testing. Experts can even assess the level of protection a safety helmet can offer against brain injuries caused by critical rotational accelerations. Ride cooler – Helmet aerodynamics and thermal comfort CFD Safety is one thing, but if you are on a long ride (thermal) comfort is another. But just like with vehicles, when it comes to helmets there is this constant fight between aerodynamics and cooling. Luckily, todays engineers have advanced CFD simulation at their fingertips. Below are just a few examples of how fluid dynamics combined with heat transfer enable insights to find optimum solutions in the trade-off between thermal comfort and low drag of helmets. Ride further – System simulation for ultra-lightweight hydraulic transmission Whatever your personal feelings, the success of e-bikes is actually great news. For human beings, for this planet. A lot of people who would probably have never taken the bike, would have taken their car even for short distances, would have claimed they would have been sweated all-wet, had it not been for the development of the electric bicycle to change their minds. Above all the success of the e-bike was driven by innovation in battery technology. But also the electric motor design and even rethinking transmission systems made the e-bike an interesting playground for engineers. Ride quieter – Harnessing Simcenter for testing e-bike acoustics When it comes to e-bikes range is by far not the only thing in scope of engineers. Noise is equally. And as you cannot just make an e-bike dead silent it’s about creating acoustic comfort for the rider. And guess what, leading companies are heavily investing into these acoustic experiences. Take Trek Bicycles again, they are pioneering the concept of putting sound quality on the e-mountain bike metric map as it continues to be a hot topic in the industry. Likewise, MAHLE, who use Simcenter testing solutions to streamline e-bike drivetrain noise-vibration-harshness (NVH) analysis and for end-of-line testing. Ride smoother- The grandfather of all comfort Speaking of vibration, we can't help but mention this: It is probably the most underrated part on almost any modern bicycle. Just a little bit more than an inch long, this tiny little piece has saved billions of bicyler’s a#*s over the last 130 years. If it wasn’t for Scotsman Dunlop with his tiny little invention we would probably still be riding harsh full rubber tires. But thanks to the invention of the Dunlop bicycle valve from 1891 (US patent US455899 ), we can now happily inflate our tires and enjoy the pleasure of air damping. Now, if you claim to be a bike enthusiast, can you explain how such a valve actually works? No? Well, here’s a little Fluid-structure interaction simulation that may give you some insights you will remember the next time you have to pump it… Ride braver – Simcenter for a safe low-drag monocoque wheel Ok let’s shift gears again. And in cycling shifting gears always means let’s talk Carbon. In the competitive world of cycling, innovative engineering plays a crucial role. At the edge of technology marginal gains can be the difference between winning and losing. That’s the playing field for Radiate Engineering & Design AG. Radiate’s collaboration with Scott Sports led to developing the Syncros Capital SL, a wheelset that makes every bike enthusiast go quiet in awe. The standout feature: “The Capital SL is a one-piece, or what we call monocoque, construction, meaning the rim and the spokes are fused together into a single piece,” says Frederic Poppenhäger, a partner at Radiate. This design enhances structural integrity and reduces rotational inertia, leading to better power transmission and substantial speed gains for riders. By running simulations, Radiate evaluated various rim shapes and geometries, optimizing for aerodynamics and structural performance without the immediate need for physical models. While Radiate reduced drag by 7 percent they also improved rider confidence and safety by ensuring greater stability in crosswinds. To enable a braver ride. Ride together Schedule a meeting with CAEXPERTS and discover how Simcenter solutions can transform bicycle development—from aerodynamics to comfort, safety to performance—taking your engineering to a new level of innovation. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Cryogenic storage and distribution — handling substances at extremely low temperatures — might sound like something out of science fiction, yet it plays a crucial role in many engineering applications. From aerospace rockets to medical preservation, superconducting technologies or liquified natural gas (LNG) in ships, cryogenic systems are everywhere. But how do we design these systems to be efficient with a maximum level of safety? Using thermofluid system simulation can help do just that . In this blog post, we will explore how Simcenter Amesim , a top-tier simulation platform, empowers engineers and enthusiasts alike to model and optimize cryogenic storage systems efficiently using a simulation example inspired by a very well-known NASA experiment of cryogenic tank self-pressurization. Challenges of cryogenic storage Cryogenic storage involves storing fluids (hydrogen, nitrogen, natural gas) that are gaseous in ambient conditions. Decreasing their temperature below a certain point changes them into very cold liquids, critical in certain applications. The storage of these cryogenic liquids requires specialized insulated containers, like dewars or cryogenic tanks, to maintain low temperatures, prevent heat transfer, and ensure safety. But with low temperatures come unique challenges such as thermal management, pressure buildup and boil-off. Dealing with challenges requires advanced simulation capabilities to frontload any safety issues that might occur during the product life cycle. Simulating cryogenic storage in Simcenter Amesim Simcenter Amesim offers comprehensive tools for modeling and analyzing cryogenic storage systems. It comes with a set of key components such as cryogenic tanks that enable simulations that closely represent real-world conditions, including factors like gas-liquid interaction and thermal exchange. Core capabilities: Heat and mass exchange at the liquid/gas interface Simulate the liquid and gas phases within a storage tank Model scenarios including filling and emptying, self-pressurization and boil-off Why Use Simcenter Amesim for cryogenic storage? Simcenter Amesim comes with a library of fuel cell and cryogenic fluid storage libraries. Compatibility between these multiple thermofluid libraries and the possibility to couple with detailed lumped thermal model facilitates system-wide integration, enhancing a holistic evaluation in engineering projects. With these capabilities, from the beginning of the design phase, the user can easily estimate the effect in terms of pressure buildup and temperature caused by possible heat ingress in a cryogenic tank. Thanks to the addition of a film layer at the free surface of the tank, pressures and temperatures are captured with increased precision to allow engineers to better size insulation systems. This results in 3-node cryogenic tank model – bulk, film and ullage. Three-node cryogenic tank in Simcenter Amesim As for the tank geometry, it can be of any 3D shape – thanks to the Simcenter Amesim tank CAD mapping tool, we can generate the tank liquid height as a function of volume. A practical example: NASA’s experiments of self-pressurization in a liquid hydrogen tank With the capabilities explained above, a Simcenter Amesim model can be built focusing on boil-off and self-pressurization in a cryogenic tank. This demo is based on experiments at NASA’s Lewis Research Center , exploring liquid hydrogen (LH₂) storage in a 4.89 m³ spherical tank subject to a constant heat ingress —findings published by Hasan et al . As for the setup of the experiments, the LH₂ tank was enclosed in a cylindrical cryoshroud. The shroud may be cooled with liquid nitrogen or heated above ambient with electrical resistances to maintain a certain temperature around the tank that we call Tamb in this demo. Understanding boil-off rates Three boil-off test cases were performed at 83 K, 294 K, and 350 K of ambient temperature. To cool the upper section, the tank is filled with LH₂ to 95 % capacity. The vent pressure is then gradually reduced to the backpressure control system’s operating pressure of 117 kPa. The boil-off rate is monitored until it stabilizes. The Simcenter Amesim model used to perform this test case is shown below: Hydrogen boil-off model In the above model, GH2 is vented from the tank through a relief valve. Heat is also transferred from the exterior to the vapor on one side, and from the exterior to the liquid on the other side. For each case, a heat transfer coefficient is set using a variable thermal conductance to fit the average heat flux values absorbed, as shown in the table below. The thermal conductances are piloted with the wet and dry areas to compute the heat flows. ] Experiment Ambient temperature [K] Heat flux [W/m²] Exp 1 83 0,35 Exp 2 294 2.0 Exp 3 350 3,5 The final values of the boil-off rates (expressed in SCMH) from the model, after 50 h of simulation, are pretty close, as shown below next to the steady-state boil off rates of the experiment. Boil-off rates (simulation vs experiments) Exploring self-pressurization dynamics Self-pressurization in a cryogenic tank is basically when you leave the tank sitting in “hotter” environment, LH₂ will evaporate and increase the tank pressure – which may cause a safety issue at some point, so it’s important to assess this pressure buildup in the cryogenic tank. Self-pressurization tests were conducted at different ambient temperatures: 83 K, 294 K and 350 K. The tank initial fill level was 84%, the initial pressure was 103 kPa. The goal here is to set up the model to match the time-series values of tank pressure and check whether time-series values of temperatures are within the correct ranges. Self-pressurization model In the model above, there’s no venting. LH₂ is stored and the variable thermal conductances are set to match the average heat fluxes exchanged with the ambient. Just like the boil-off tests, there are 3 self-pressurization tests, lasting respectively 20 h, 18 h and 14 h. Pressure values are shown below. Pressure values (simulation vs experiments) It can be observed above that for the pressurization tests, the pressures computed by the model exhibit a similar pattern to the tests. They deviate from the experiment by an absolute maximum ranging from 1.5 to 6 %. Conclusion As seen in the current demonstration, with a cryogenic tank model divided into three nodes — ullage, film and bulk, we can capture important phenomena such as boil-off rate and self-pressurization. For enhanced temperature predictions in ullage where different temperature layers can exist, further discretization of the upper part could prove beneficial. Cryogenic storage is a pivotal technology shaping the future of industries relying on hydrogen and other low-temperature applications. Simcenter Amesim offers not just a platform, but a comprehensive toolset to visualize, model, and optimize these systems. By leveraging its capabilities, you can significantly advance your understanding and design of cryogenic storage solutions. Ensure your cryogenic projects are more efficient and safer with the power of Simcenter Amesim . Schedule a meeting with CAEXPERTS now and discover how our expertise can help your team model, predict, and optimize cryogenic storage systems with precision and confidence. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Electromagnetic (EMAG) and electrical engineers feel at home with EMAG's simplified Computer-Aided Engineering (CAE) packages, such as Simcenter SPEED , Simcenter E-Machine Design , and Simcenter MAGNET . Computer-Aided Design (CAD) colleagues generate a defective geometry consisting only of active EMAG parts. If there are CAD problems, such as extracting geometry from an assembly or mesh errors, the design is sent back to the CAD designers, and the clock starts ticking. Can we continue like this? With the race to electrification, there is a need for more cooperation across different teams. Product designers give us “the look and the feel” of the product – the skin. CAD designers distribute real estate. CAE engineers then work within constraints to add the physics needed to optimize the product. Spending some time on communication and alignment between teams is seen as an inherent part of the process “it’s just how these things work in big companies,” is a common aggrievement. Sub-optimal processes may have been fine when electromagnetic components were an accessory to your product. However, nowadays, an electric drive is at the core of many products, and wasting time in iterations with other departments can double your development cycles and skyrocket R&D costs. Furthermore, the high competition and the lack of engineering resources make it unsustainable to keep wasting time on meaningless iterations. What about upstream CAD changes that affect the EMAG bodies? It is not unusual for an EMAG engineer working on an e-powertrain to receive a detailed CAD neutral file. Often these files, although consisting of a housing and transmission assembly, are non-associative (we will discuss what non-associative means later). The files will be missing the rotor, stator core, or both and are more visual than functional for CAE. This sets off a back and forth between the EMAG and CAD teams, who now need to clean up and simplify the geometry and verify the materials. Only after this clean-up process can an electromagnetics engineer start their real work of searching for a concept and validating the proposed design. Due to the current soaring interest rates and higher costs, Project Managers in the EV (Electric Vehicles) space are under elevated pressure since 2018, to hit vehicle delivery timelines at elevated costs if left unchecked. As a result, you may hear yours asking your team to shed weight and volume from components. As an EMAG engineer, you will find yourself crafting a way to hustle the CAD designers of their time. Rest assured; you aren’t the only one. CAD designers must reflect the new CAD updates and help CAE teams update their geometries. As you can imagine, the emails, calls, and meetings to make this happen are extensive, and the impact on the timeline to market is enormous. How to save time? A system that allows the electromagnetics engineer to automatically update their models to the latest CAD design, with minimal input from the designer, will increase productivity. You can see an example of a possible process in this short video. The video shows how to update an extracted 2D e-motor geometry from a vehicle assembly due to upstream changes to the stator outer diameter. The video begins by focusing on the electric powertrain and gradually isolates the electric motor from the rest of the assembly. Next, not only are the active electromagnetic bodies captured, but they are also associated with the assembly. Next, everything needed for the simulation is added: the air regions and remesh, the 2D geometries of the rotor, and the finite element mesh to complete it. Finally, an upstream change to the outer diameter of the stator is triggered, which you can see in the updates to the mesh. The CAD connection in the Simcenter environment allows you to focus on your work—electromagnetic analysis. Extracting and associative 2D e-motor geometry from a vehicle assembly Now let’s turn our attention to the global push for lightweighting and integrating e-powertrains. There is a need to control noise levels and get rid of the heat in ever smaller electric –motor sizes. These need more complex liquid cooling, and designs that ensure the magnets do not fly off, all of which will change the electromagnetic’s structure. How do you update the electromagnetics model with multi-physics-driven geometric changes? In a traditional workflow, the non-associated model updates made by other groups would require the EMAG engineer to repeat the workflow hustle with the design team and all its associated emails and calls. You will feel damned if you have no associativity! On top of that, the problem only gets worse by increasing the number of physics domains under consideration. E-powertrains undergo an entire range of multi-physics simulation iterations before the engineers settle on a design. These horizontal changes are mutually driven by electromagnetics, thermal, structural, and noise and vibrations (NVH) analysis. With engineers from each department working simultaneously with a non-associative tool, the possibility of an engineer working on an out-of-date model instead of the current best design is remarkably high. How do you keep track of the updated CAE geometry? Luckily, there is a reference – though subtle in a siloed environment. The design CAD is the only common link between physics, and the product requirements, such as cost, which is pegged to the volume and mass. As illustrated in Figure 1, if the design CAD is seamlessly updating, it means all the CAD and all CAE engineers are accessing the same geometry at any one time. Obviously, the updating and access are controlled in a managed environment. This means you won’t have stale results in your project meetings. For electromagnetics, geometry will reflect the recently approved upstream and horizontal changes. Figure 1 - An updated design CAD captures geometric changes driven by both upstream product requirements, and horizontal physics interactions For example, Figure 2 shows the evolution of EMAG stator parts from an efficient design based solely on electromagnetic performance to one that accommodates cooling channels in the coils. Note that there is a reduction in torque because the volume is now limited. This is because the first design contains more steel and can therefore carry more magnetic flux. As you can see in the video, you can associate the electromagnetic’s active parts with the CAD design. After a parametric study to choose the final channel width, this geometric change was introduced into CAD design. And because the geometry used for the electromagnetics simulation is associative, it was just a matter of re-running the solve with the changes Figure 2 - The effect of introducing cooling channels between coils on EMAG performance What does this really mean? In a multi-physics design process like the one required for an e-machine, having a streamlined workflow can save hundreds of engineering hours. You only spend the initial setup effort once! The rest is just on iterating the design driven by both product requirements and physics changes. The associated workflow helps overcome the compartmentalized approach and single physics bias in development. An efficient electromagnetic device may not be structurally sound or even manufacturable. By capturing what is possible through geometric constraints and physical interactions, a more realistic design is achieved. At the same time, this helps manage our own internal bias toward physics and work together toward a good balance of performance. What you think is appropriate for the physics of the design may not necessarily be good for the overall performance of the product. An integrated design process allows you to make these iterations and converge much faster! In summary, the associated process discussed in this blog reduces time wasted so products can be developed quicker. This process helps engineers and designers settle on a concept quicker by ensuring everyone is working on the most up-to-date model. While ensuring that disadvantages such as bias towards particular physics models or siloed teams do not occur. Build your next e-motor quicker, better, and in terms of engineering hours, cheaper. Are you ready to reduce rework, accelerate your development, and integrate CAD and CAE teams more efficiently? Schedule a meeting with CAEXPERTS and discover how to optimize your electric motor design with associative and multiphysics workflows—faster, more collaborative, and more cost-effective. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br