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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

Artificial intelligence (AI) has become a ubiquitous force in the engineering world, radically transforming how companies operate and how employees work. As we move further into 2025, the influence of AI will only continue to grow, ushering in a new era of increased efficiency, productivity, and innovation. Nvidia CEO Jensen Huang has predicted recently that computing power driving advances in generative AI is expected to improve “a millionfold” over the next 10 years, driven by a “fourfold” increase in computing power annually. Driving this rapid advancement is the exponential growth in computing power over the past decade. Today, the world’s leading supercomputers are capable of performing over 1 quintillion (1,000,000,000,000,000,000) calculations per second – a staggering increase from just a decade ago. This massive boost in raw computing power has enabled the training of ever-larger and more complex AI models, allowing them to tackle increasingly sophisticated tasks. Powering this AI revolution are not just traditional CPUs, but also specialized hardware like graphics processing units (GPUs) and application-specific integrated circuits (ASICs) designed explicitly for machine learning workloads. These chips can perform the parallel processing required for training and running AI models much more efficiently than general-purpose CPUs. In addition to advancements in hardware, the rise of cloud-based platforms, such as Simcenter X, has also played a pivotal role in democratizing access to immense AI computing power. Researchers and businesses can now rent GPU clusters and supercomputing resources on-demand from cloud providers, allowing them to train large-scale models without the need for massive upfront investments. Optimizing business processes with AI Simcenter Engineering Services has demonstrated that the influence of AI extends far beyond digital assistants and personal productivity. Businesses are increasingly deploying AI-powered systems to optimize core operations and decision-making. AI algorithms can analyze massive amounts of data to uncover insights that inform everything from supply chain management to marketing strategies to financial forecasting. Engineering design decisions based on heat transfer and computational fluid dynamics (CFD) simulations are no exception. AI algorithms now support the entire simulation workflow chain, from data management and part shape recognition to real-time flow and thermal results predictions and optimization. AI/ML in CFD The synergy of CFD and machine learning promises to deliver faster solutions to well-known complex industrial fluid flow and heat transfer problems – most notably, design optimization of transient problems, such as vehicle aerodynamics and passenger cabin comfort. Vehicle aerodynamics Transient aerodynamic simulations, especially those involving complex geometries, require significant computational resources in terms of processing power and memory. Running these simulations at a large scale can be computationally intensive and time-consuming. Additionally, transient simulations often require very small time increments to capture dynamic air behavior accurately. This can lead to long simulation times, particularly for problems with a wide range of time scales. Additionally, the large amount of data generated by large-scale transient CFD simulations can be difficult to manage, store, and analyze. AI algorithms address this problem by providing inferred CFD results, including pressure distributions and air velocities, enabling the calculation of drag and lift coefficients across various design parameters. The AI models are trained on a set of simulation data, which consists of point clouds extracted from simulations at both the surface and volume levels, along with scalar values such as pressure, wall shear stress, and the three velocity components. To develop accurate AI models, it is crucial to execute the training process efficiently. Simcenter Engineering Services addresses this need by employing a hybrid technique designed to cover the vast design space effectively within a set number of evaluations. Initially, a collection of design points is established. These can either be provided by the user as seeds or generated through advanced techniques like Latin hypercube sampling methods. Once established, these initial points serve as inputs to an adaptive sampling study. This study is pivotal as it intelligently selects and populates additional design points aligned with user-defined objectives. Such objectives may include the exploration of regions where there are considerable shifts in response or areas where global and local gradient variations occur. The AI model itself is principally trained using spatial data from the surface and volume mesh cells. In addition, it incorporates feature-defining scalars, such as surface normals, alongside the scalars intended for prediction by the AI model. This comprehensive approach ensures that the model can accurately predict complex engineering scalars spanning over varied design changes. The image below demonstrates an example of the training and inference stages of an AI model, showcasing its application in utilizing spatial coordinates, surface normals, and wall distance to predict engineering scalars like pressure and velocity vectors. Training workflow Inference workflow Passenger comfort Running transient CFD simulations for a vehicle passenger comfort analysis is often necessary and comes with many unique challenges. The vehicle cabins are known to have intricate geometries with many small features, such as vents, seats, and other interior components that must be captured in detail using high-resolution meshes. For unsteady flow, this meshing requirement can be quite computationally demanding. Additionally, the cabin passengers must be modeled in good detail, with separate output required for the manikin head, neck, torso, hands and feet. This level of refinement is necessary because human beings feel most comfortable within a relatively narrow range of ambient temperatures. Even small deviations outside this range can start to feel uncomfortable. Adding to the complexity is the fact that manikin surface temperatures can be influenced by very small deviations of the environment conditions, such as air flow, temperature, humidity, solar radiation, vehicle speed and acceleration. Recognizing these challenges, Simcenter Engineering Services is actively exploring reduced order models and AI/ML-based solutions specifically tailored to address challenges in passenger comfort analysis. One area of focus involves the use of reduced order models (ROM), incorporating both static ROMs and proper orthogonal decomposition (POD) interpolation. Static ROMs are used to predict key scalar metrics, like temperature or velocity, in a text-based format that provides essential quantitative insights. On the other hand, POD interpolation is employed to predict spatially varying 2D scalar fields. This approach enables the determination of key scalar metrics, such as cabin temperature and velocities, in two dimensions based on input parameters like solar loads, the sun’s relative position, and flow characteristics. Both reduced order models can be generated using simulation (and test) data for training. The combination of these two methods provides a simplified yet effective means to analyze complex data, ensuring that critical passenger comfort parameters are accurately assessed and optimized. These and other types of reduced order models can be generated using Simcenter Reduced Order Modeling tool available in the Simcenter portfolio. Complex 3D modeling AI algorithms can enhance and accelerate the model-building phase of complex 3D thermal simulations. In vehicle thermal and energy management (VTM/VEM), accurate part-to-part contact through CAD imprinting is crucial to capturing heat conduction paths throughout the vehicle assembly. Achieving high-fidelity results often requires manual review and improvement of hundreds of poor-quality imprints, leading to extended project turnaround times. To improve efficiency and reduce turnaround times, Simcenter Engineering Services is developing custom automation and “smart logic” to support the CAD imprint and review process. This leverages AI/ML-based procedures within Simcenter STAR-CCM+ . Various “modalities” can be employed for inference, as illustrated below. Once the AI model is trained, contact validity can be inferred through a custom frontend plugin using the selected modality. This approach integrates both tabular and image-based data training to derive inferences from the ML model, expediting and simplifying the modeling process. For straightforward contact validity predictions, a simple classifier model using features from a text-based file is sufficient. However, when dealing with complex contact patches where additional context, such as the identity of contacting parts, is necessary, an image-based model offers greater advantages. This model can analyze visual data to assess the spatial and structural relationships between components, providing a more nuanced understanding of contact validity. The inference pipeline for both tabular and image-based model are shown below: Inference pipeline for tabular-based model Inference pipeline for image-based model This innovative approach not only speeds up the modeling process but also enhances the accuracy and reliability of thermal simulations, ultimately leading to improved vehicle design. The path forward There’s no doubt that AI is reshaping the engineering landscape in profound ways. Savvy leaders and innovators are embracing AI as a transformative opportunity, strategically deploying AI to augment and empower their workforce. As an industry trailblazer Simcenter Engineering Services is leading by example, empowering employees and customers with AI-driven tools and capabilities, enabling the engineering community with the ability to unlock unprecedented levels of productivity, creativity, and business success. Discover how CAEXPERTS can transform your engineering projects with AI and CFD, accelerating complex simulations and optimizing critical processes. Schedule a meeting with our experts now and take the next step toward innovation and maximum efficiency. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Smoother lubrication with oil injection Oil injection offers several advantages over traditional oil bath lubrication in gearboxes. This technique allows for a significant reduction in oil consumption, while providing greater precision compared to oil bath lubrication. The process enables precise control over lubricant supply, ensuring optimal lubrication at critical points. The localized application of oil helps minimize waste, resulting in lower overall consumption. However, lubrication by injection requires a higher level of knowledge of the flow pattern and system dynamics. Moreover, it usually demands higher initial investment compared to oil bath systems. Therefore, detailed engineering insights in the early design phase of a gearbox are critical to realize optimum injector positioning and injection strategies. A viable tool to cope with this challenge early in the design of a gearbox lubrication system is Smoothed-Particle Hydrodynamics (SPH). Shifting gears with SPH fluid injection Since version 2402, Simcenter STAR-CCM+ has incorporated SPH technology into an integrated CFD environment. In previous versions, the Smooth Particle Hydrodynamics (SPH) solver already allowed simulations of powertrain lubrication in oil bath configurations. With version 2406, the functionality has been expanded to cover injection scenarios through the inclusion of specific inputs for SPH. This makes it possible to simulate oil injection with speed or mass flow imposition, as well as to define constant or time-varying distributions, such as in the lubrication system start-up process. This makes it possible to visualize the behavior of the lubricant inside the gearbox with SPH and inlet boundary conditions. This helps to validate the direction of the oil in the gear elements, ensuring efficiency and reliability in lubrication. Stay integrated with the power of Simcenter STAR-CCM+ as a platform Thanks to the integration of SPH into our flagship Multiphysics platform, you can now harness Simcenter STAR-CCM+’s full suite of quantitative data analytics capabilities in conjunction with smoothed-particle hydrodynamics. For gearboxes, you can e.g. monitor churning losses by looking at the forces and torque evolutions throughout your simulation. The torque report and graph do not depend on the surface mesh resolution. Even if you have a coarse representation of the surface mesh of your geometries, it will no longer affect the accuracy of the torque evolution. This ensures accurate results regardless of the details of the surface mesh. More applications with rotating injectors In the example above, you saw a static SPH fluid injection. Static inputs are commonly used when the flow entering the domain is not influenced by rotation. With Simcenter STAR-CCM+ , it is also possible to set up a rotating boundary condition. In injection lubrication systems, rotary injectors are commonly used to supply oil lubricant to moving parts such as gears or bearings. The rotating inlet condition ensures that the lubricant flow enters the domain with the desired rotational movement. Proper modeling of this behavior is crucial for accurate simulations of powertrain lubrication performance. This animation shows a simplified lubrication system in electronic machines. Using SPH for this application makes it easy to monitor where the lubricating oil goes with a simple workflow, compared to finite volume models. Other applications are also possible thanks to the addition of inlet boundary conditions. You can start modeling water runoff or oil leakage applications in vehicles by observing where the liquid (water or oil) enters the vehicle. You can observe how the liquid spreads to different parts of the vehicle, where it accumulates, and how it interacts with other components. To mesh or not to mesh This question has remained relevant since the introduction of SPH: to mesh or not to mesh. As in previous versions, SPH offers distinct advantages for specific applications. Traditional CFD simulations often require CAD preparation and volumetric mesh generation, which can be time-consuming for systems such as gearboxes. SPH eliminates this step by operating directly with particles, providing a more fluid experience. The combination of intuitive workflows, CAD integration, and robustness in handling movements sets SPH apart in Simcenter STAR-CCM+ . In addition, an industrial gearbox can be simulated with SPH in about 12 minutes. Thus, for initial design screening, the SPH method is ideal for use with Design Manager for design exploration, quickly and easily eliminating low-performance designs. If your goal is to visualize the oil distribution inside the gearbox and monitor agitation losses, SPH is the best candidate for this application. However, if air significantly affects the oil, or if you need to model spray inlets or observe heat transfer, or if you need to model mist (gas and liquid mixture) or model phase change or electromagnetism or other complex physics, it is recommended to use finite volume multiphase models, which are more suitable for greater accuracy. The SPH and finite volume methods complement each other and always depend on the scope of your applications and the level of fidelity required to make the appropriate choice of model to use. Explore more results from the gearbox lubrication SPH simulation interactively in your browser Surface tension modeling for SPH The surface tension model was introduced with the aim of increasing accuracy in highly dynamic free flow simulations. This functionality allows the behavior of droplets and their interaction with surfaces to be represented. Contact angles can be defined for each solid contour, enabling simulations of hydrophilic or hydrophobic surfaces. Ensure maximum efficiency in the lubrication of your gearboxes from the earliest stages of design! Schedule a meeting with CAEXPERTS and discover how to apply SPH in Simcenter STAR-CCM+ to accurately simulate oil injections, reduce losses, and accelerate engineering decisions with confidence. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

In the context of vehicle electrification, the automotive industry is taking several key steps to ensure the safety and durability of vehicle batteries. Rigorous testing protocols are in place to evaluate battery performance, thermal management and response to abuse conditions like overcharging, short circuits, and crashes. Battery ageing is being looked at intensively, and even more is battery thermal runaway, for very good reasons. The key to properly managing these requirements is front-loading the design. This means using Digital Twins to predict these effects early on in the design process, thereby minimizing the cost of physical testing and troubleshooting. But what about structural durability? Typically bolted below the body, the battery experiences the same loads as the body when the vehicle rolls over potholes, cobblestones and speed bumps. This repetitive loading will lead to accumulated damage in the structure of the battery frame and cause premature failure and high cost if not engineered appropriately. But can more be done? Can the same front-loading battery life strategy be applied using Digital Twins, allowing us to predict and understand the structure's behavior from the outset? SIEMENS simulation expert Dirk von Werne investigated this and developed a process that allows this to be achieved. This process utilizes SIEMENS testing solutions to identify appropriate load conditions and numerical simulation expertise. Besides front-loading, it allows us to understand the effect of the body to which the battery is attached. Indeed, the battery is reinforcing and interacting with the body structure. As they form an integral structure together, the loads that the battery experiences are much dependent on the stiffness and eigenmodes of the full system. This leads to complex load scenarios and responses during driving, that are not easy to replicate in component testing. The ability to include these effects in simulation allows us to engineer a better battery considering the coupling to the body, and to develop an improved accelerated test scenario for component testing. Finally, this approach also allows us to minimize the mass of the battery pack without compromising durability. Indeed, the battery is one of the heaviest (and most expensive) components of an electric vehicle. Duty cycle and loads As a starting point, you have to define the mission profile that you design your vehicle for. Standards are available to support this, but they only represent a typical usage and might not fit with a specific vehicle under development, a given target market, etc. Therefore, collecting data in relevant test conditions on a relevant vehicle should be considered whenever possible. This can be done, for instance, on a predecessor vehicle. Many scenarios can be considered: how many kilometers one will drive on the highway, in the city, or on a smaller road with cobblestones? What speed ranges to take into account? Which driver profile: comfort driver or sportive driver? All in all, finding the proper loading conditions for a vehicle under development is far from an easy task! Simcenter Testlab Mission Synthesis is a software that condenses the loading scenarios from years of operation to an equivalent accelerated test cycle. This allows you to understand the “damage potential” in many different conditions and synthesize your “test profile” much better. This can then be used for validation testing and also in a simulation context, where these loads are used as input to the Digital Twin of the structure. Mission synthesis generates a test profile Knowing the stress In the world of automotive durability, simulation processes are very well established based on decades of vehicle engineering. Using predecessor knuckle forces or actual road surface profiles as an input, you can simulate the vehicle behavior in a multi-body simulation environment, such as Simcenter 3D Motion , deriving stresses and interface forces at the battery attachment. Full-vehicle model to predict component loads A Finite Element simulation in Simcenter 3D will assess the damage and life prediction of the battery frame based on the loads at the attachment points, modal stresses to be used in time or frequency domain response analysis, and the superposition of load cases. Damage hotspots will be post-processed and their local data can be analyzed in detail. Yes, but… the battery is a very complex structure, including special materials, many connections, and some non-linearity. The battery’s structural coupling with the body structure is strong. For those reasons, applying the “classical” approach described above is not straightforward. This is where Simcenter Engineering comes in, with its experience in both testing and simulation. As Dirk explains, “You need to understand how the structure really behaves when subject to vibration loads. You can expect that a battery has a non-linear behavior, and need to find the best approach to capture it in your Finite Element model. Thanks to our large experience in combining test and simulation, we can support our customers in defining proper assumptions. Modeling guidelines can be defined based on the correlation of the physical battery with the simulation model, and adapting the model until the match is good enough. In the end, you get a Digital Twin that provides a reasonable representation of the actual battery”. The overall process for battery structural durability assessment is shown below: Workflow for battery durability analysis An optimal solution As mentioned before, there is tremendous pressure in reducing vehicle weight, in order to maximize the range. Numerical optimization enables to do this. To allow for an optimization, the durability prediction has to be fast enough to be looped in an automatic optimization process. This requires to balance the model size with the accuracy. One way to do this is to represent the Body by means of a reduced representation as a superelement. The optimization process shown below is driven by HEEDS , part of the Simcenter portfolio. A typical objective of the optimization is to reach the lightest possible battery frame while ensuring good durability performance. Typical optimization parameters include the cross sections and material thickness of the battery frame, as well as the bolt positions of the attachment to the body. All this becomes possible by parameterizing the CAD geometry in NX and building up automatic meshing, analysis and post-processing. The optimization in HEEDS is enhanced using AI. Optimizing the structure to reduce weight Conclusion In this blog post, a process for CAE-based durability analysis of an EV battery was presented. Durability simulation allows frontloading the mechanical design, optimizing the structure and developing a valid accelerated scenario for component testing. What’s in it for you? Obtain improved design earlier thanks to a CAE-based process Avoid conservative approach and reduce mass Improve and shorten battery testing Anticipate structural challenges and optimize the performance of your electric vehicle battery with a Digital Twins-based approach. Schedule a meeting with CAEXPERTS and learn how to apply advanced simulation and intelligent testing to ensure durability, reduce mass, and accelerate development with greater accuracy and lower costs. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

For a new technology to succeed it must arrive at the right time, provide a solution to a real-world problem, and solve that problem better than any other competing technology out there. That might have seemed like the case when the first electric cars started to appear in the 1880s, at last a less polluting vehicle after the coal guzzling steam cars and carrot guzzling horse drawn carriages that went before. Cities were being electrified and other electric vehicles such as trams became common. In this environment, the EV started to gain traction at the end of the 19th century (if you pardon the pun), although adoption was limited to US and European cities where electrical infrastructure existed. It was not yet to be the era of the EV however, as the initial rapid growth of the technology hit a wall in the 1920s overtaken by the Internal Combustion Engine and cheap and abundantly available oil. The electric vehicle was relegated to be forgotten in dusty archives, where it lay quietly forgotten for most of the next century… There it remained, until the elephant in the room that was the climate crisis could no longer be ignored and at last the time was right for the EV. Of course, electric vehicles are not without their challenges – mainly related to the battery and motor. In the world of Engineering Simulation, battery thermal runaway and e-motor cooling have become very hot topics (literally!). These are simulations that would have been unthinkable just a few years ago due to their complexity both in terms of geometry and Multiphysics. Consider e-motor cooling for example, it is highly complex due to the range of scales involved. Cooling jets of oil breakup into ballistic droplets and then ever smaller droplets due to the high rotational speeds and interaction with the intricate end-windings. Clouds of droplets of tens of microns or less form mixtures and even foam. Thin films of oil form on many surfaces. In other places oil pools. Oil flows through tiny gaps going where it should not. All of this happening alongside heat transfer and electromagnetics. Hybrid Multiphase Comes of Age These complex applications pose new demands of simulation, the tools of the past are no longer sufficient and the time for hybrid multiphase is here! Individual multiphase models like Volume of Fluid (VOF) and Mixture Multiphase (MMP) were designed for a single multiphase flow regime. The VOF method for example expects everything to be resolved and has no concept of a mixture, whereas Mixture Multiphase expects only mixture and no resolved free surfaces. Pick any one method for these new applications and it will be pushed beyond its assumptions and produce inaccurate results. For this reason, the move to more complex multiphase applications drives a need for hybrid multiphase. Adding Mixtures to Hybrid Multiphase In Simcenter STAR-CCM+ , Mixture Multiphase has been put at the heart of hybrid multiphase to enable applications such as e-motor cooling with three great new features: 1. Lagrangian to Mixture Multiphase sub-grid transition model The first feature is the ability to transition small Lagrangian droplets to a dispersed mixture phase. It can also be used for bubbles or solid particles, but focus on liquid droplet phases here. Lagrangian multiphase is well suited to modelling large, high momentum droplets that travel in a different direction to the surrounding gas, but when simulating the secondary break-up of droplets, you can end up with a large population of tiny droplets that follow the gas path. This is essentially a mixture and Lagrangian would not be a good model choice. With the new Lagrangian to Mixture Multiphase sub-grid phase interaction model, Lagrangian droplets can be transitioned to a mixture representation in Mixture Multiphase. This allows Lagrangian multiphase to be used only for high momentum ballistic droplets whilst the larger population of smaller droplets is transported as a mixture. This new functionality works with MMP-LSI – a model that combines the capabilities of VOF and Mixture Multiphase, using interface capturing locally where needed and allowing mixtures elsewhere. With this combination, we can now model the full cascade of multiphase scales from the resolved to the sub-grid. In the example below, we resolve the initial stages of breakup of a jet in crossflow using Large Scale Interface (LSI) modelling, before passing the resolved droplets over to Lagrangian where they continue to breakup into ever smaller droplets. Once the droplets are small enough and traveling with the surrounding gas they are passed to an Mixture Multiphase phase as a mixture. Transition is based on droplet size and Stokes number. 2. Population balance modeling for Mixture Multiphase Large Scale Interfaces (MMP-LSI) Now that we have a predictive pathway for the size of droplets produced by the breakup of large bodies of fluid, such as jets, we need a method to capture this valuable data and use it in simulations. Droplet size is an important variable in determining the transport of mixtures, and critical for physics such as evaporation or reactions. For that reason, support has been added for the S-Gamma population balance model with MMP-LSI. The S-Gamma model transports a distribution of sub-grid droplet sizes which it modifies every time Lagrangian droplets are transferred. In addition, S-Gamma models the effect of further breakup and coalescence. This is critical in applications such as e-motor cooling where jets of oil breakup into ever smaller droplets that may be 10s microns in size that are carried with the rapidly rotating air and even whipped into foam. We can see this model in action in the jet in crossflow example below where the predicted droplet size distribution from the jet breakup can be seen in terms of the Sauter Mean Diameter of the resultant sub-grid droplet population. 3. Lagrangian impingement into Mixture Multiphase continuous phase The third and final feature is another new phase interaction model – the ability of Lagrangian droplets to impinge into continuous phases in Mixture Multiphase. In hybrid multiphase simulations the same fluid can be represented by multiple different multiphase models and tracking oil droplets through liquid oil (for example) would make no sense. This new phase interaction allows Lagrangian droplets to impinge into larger bodies of fluid represented by MMP-LSI. Let’s see this in practice – below we have a fountain where the jet is initially resolved as a free surface (LSI) which then breaks up into Lagrangian droplets. The droplets then impinge into the free surface of the fountain’s reservoir, completing the cycle. Together, these capabilities allow you to simulate applications that contain mixtures or within the framework of hybrid multiphase. E-Machine cooling – A practical application of the new hybrid multiphase method Previously no toolset existed that could cover all of these multiphase regimes accurately in a single simulation in an affordable way. The marriage of these complementary technologies allows us to accurately simulate applications where before we had to suffer the inaccuracy caused by assumptions, or the expense of a high level of resolution. With hybrid multiphase we have affordable accuracy at our fingertips. The time is now right to model the complexity of electric vehicles (and other applications) with hybrid multiphase to gain the edge in performance and gain insights into your designs never before possible. See these tools being used to simulate oil cooling of an electric motor. Simulation of oil jet cooling in an electric motor becomes highly accurate yet affordable by using Hybrid Multiphase with Mixture Multiphase Large Scale Interface (MMP-LSI) including S-Gamma, Lagrangian Multiphase and Fluid Film Here, we have oil jets introduced from a rotating shaft, which has been solved with adaptive meshing and Large Scale Interfaces (LSI). The jets hit the end windings, cooling them and then breaking up into droplets. These droplets were tracked as a Lagrangian phase while sufficiently large, but as they break up, they are converted into a mixture with the diameter distribution of the droplet population modeled by S-Gamma. Last but not least, there is a Fluid Film on the out casing with impingement and stripping between the Lagrangian phase and the Multiphase Mixture. Schedule a meeting with CAEXPERTS and discover how to simulate complex multiphase phenomena - such as oil jet cooling in electric motors - with unprecedented precision and accessibility using Simcenter STAR-CCM+ . We're ready to help you turn engineering challenges into innovative solutions. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

In the world of professional cycling, attention to detail can make the difference between victory and falling behind— and that principle extends all the way to the water bottles clutched by the riders. Far from being simple vessels, these bottles embody a sophisticated interplay of ergonomics, aerodynamics, and cutting-edge engineering, design considerations that ultimately influence hydration, rider comfort, and even performance over grueling race stages. Today, the development of such seemingly ordinary products relies heavily on modern simulation technology. Bottle squeezing simulation for the perfect race bottle The newest release of Simcenter STAR-CCM+ , version 2506, brings a significant innovation to this domain with Enhanced Linear Hex Elements. This enhancement revolutionizes simulation efficiency for thin-walled structures like bicycle bottles, providing more than three time the speed for structural simulations and with that providing also a substantial speed-up for Fluid-Structure Interaction (FSI) scenarios. As a result, engineers can rapidly iterate on their designs, achieving the ideal balance of weight, flexibility, and performance needed for elite competition, helping riders stay hydrated without missing a beat. Designing a cycling bottle that excels on all these fronts requires a meticulous approach. Factors such as bottle shape, wall thickness, material choice, and nozzle design all directly impact not just the user experience, but the product’s manufacturability and cost. Mistakes in this phase can become expensive fast, as the production of advanced extrusion blow molds can reach €10,000 or more per piece. That’s why simulation platforms like Simcenter STAR-CCM+ are indispensable in today’s product development. By modeling a bottle’s structural response to hand pressure, engineers ensure that each bottle is light, resilient, and fits seamlessly into a rider’s hand and bottle cage. Activating the software’s fluid and FSI capabilities, designers can further refine flow characteristics, ensuring every squeeze delivers instant, effortless hydration with minimal strain, stage after stage. A solid grip One of the first considerations when designing a drinking bottle is how it feels in the rider’s hand. Grip is essential — cyclists must be able to grab, squeeze, and return the bottle to its cage almost without thinking, especially when fatigue sets in towards the end of a long stage. The side walls need to be as flexible as possible to allow an easy squeeze. The bottom needs to be stiffer so that the bottle springs back into its original shape to fit tightly into the bottle cage. The top of the bottle—which connects to the lid—must always remain sturdy to avoid fluid leakage. To simulate real-world use, we look at a typical squeezing event: a cyclist compresses the bottle by 10 mm on each side in just 0.15 seconds, holds that grip for 0.2 seconds, and then releases it. (We can safely ignore the pinkie finger in the load distribution, since, as any close observer of cycling will notice, it doesn’t contribute much.) Accurately capturing this interaction is crucial for delivering a product that performs under racing conditions. Simcenter STAR-CCM+ Structural Mechanics provides all the tools needed to simulate this kind of squeezing action and analyse how wall thicknesses influence the required force. According to ergonomic studies and published literature, most users find squeezing forces between 10 and 20 N comfortable during repeated use, while forces above 30 N are regarded as noticeably hard and uncomfortable. For a typical racing bottle design, our structural simulations show that a side wall thickness of 0.6 mm results in a squeeze force of roughly 8.6 N—well within this comfort zone. Reducing the wall thickness by just 0.2 mm more than halves the required force; increasing it by 0.2 mm nearly doubles it. This highlights how crucial wall thickness selection is for an ideal bottle design. Accurate bending with enhanced linear hexahedral elements (Hex8E) Modeling the bending deformation of such thin-walled structures requires appropriate mesh elements. Here, hexahedral element meshes are ideal. The new enhanced linear hexahedral elements (Hex8E) introduced in Simcenter STAR-CCM+ 2506 deliver nearly the same quality of bending simulation as higher-order Hex20 elements, but at the far lower computational cost of standard Hex8 elements. In comparison, using standard Hex8 elements in such bending-dominated problems can lead to strong locking effects, resulting in a predicted squeeze force up to 2.5 times higher than reality. Enhanced Hex8E elements, on the other hand, not only provide accurate results but also speed up the simulation—three times faster in this case and up to seven times faster for larger structures compared to Hex20 elements. With these advancements, engineers can efficiently explore and optimize the tactile experience of the bottle—making sure it’s easy to squeeze, quickly rebounds, and maintains its integrity ride after ride, all while accelerating development cycles and minimizing costly prototyping errors. Diagram Comparing thicknesses with 0.6mm Hex8 vs Hex8E vs Hex20, and then 0.4mm and 0.8mm Hex8E as a function of time, together with Finger motion Normalized solve times for structure-only simulation with varying number of processes (NP) Understanding the Fluid-Structure balance A particularly critical factor in water bottle design is the amount of pressure a rider must exert to get water flowing. At the tail end of a grueling race, Tour de France cyclists have limited energy to spare, and the last thing they need is to struggle with a stubborn bottle. It is vital that bottles require as little force as possible to deliver a satisfying water flow. Not only does this minimize the time riders spend drinking—time during which their focus is inevitably split from the road—but it also reduces physical strain, allowing them to maintain maximum concentration and performance until the finish line. When the bottle is squeezed, both the water and the trapped air pocket inside are compressed, resulting in a flow of water out of the nozzle. The exact behavior—how much water is expelled for a certain squeezing force—depends not just on the applied hand pressure, but also on the intricate relationship between the nozzle geometry, bottle wall flexibility, and the ratio of air to water in the bottle. This interplay of structural and fluid dynamics is complex and cannot be fully captured without a comprehensive multiphase Fluid-Structure Interaction (FSI) simulation. One of the significant advantages of using Simcenter STAR-CCM+ for this process is its seamless integration of structural and fluid modeling. To transition from a structural simulation to a full FSI setup, only the fluid domain needs to be defined and added to the previously established structural model. Advanced features like dynamic stabilization, FSI traction residual, and dynamic stabilization residual ensure that simulations remain robust and accurate, even under the fast, transient loads experienced during real-world bottle use. Importantly, as pressure builds within the bottle, it not only drives fluid out, but also increases the needed squeeze force and also causes further deformation of the plastic structure—making a two-way coupled FSI approach essential for realistic results. The results of such FSI simulations, as shown in diagram 4, illustrate a key point: in addition to the force needed just to deform the plastic, riders must also overcome fluid resistance as water is forced through the nozzle. In this case, the force needed to squeeze the bottle with fluid inside is more than 4 times higher than without the fluid in place. For truly optimized performance, both the structural design of the bottle and the geometry of the nozzle must be considered together, ensuring maximal water flow at the lowest possible squeezing force. Water Mass Flow as a function of Squeeze Force from FSI simulation From Design to Manufacturing Once the optimal design for the cycling bottle has been finalized, attention turns toward manufacturing—specifically, ensuring that the desired shape and carefully chosen wall thicknesses can actually be achieved in production. With the finite element–based computational rheology capabilities of Simcenter STAR-CCM+ , engineers can simulate the blow molding process itself. This capability allows for the virtual prediction of material flow and the resulting thickness distribution within the final bottle—long before any physical tool is cut. In the latest release, Simcenter STAR-CCM+ 2506 , new contact modeling features have been introduced—enabling simulation of interactions between the expanding bottle and the mold during forming. This ensures that critical geometric features and thicknesses are faithfully reproduced in manufacturing, helping to minimize costly trial-and-error iterations on the shop floor. By seamlessly integrating product design and process simulation, Simcenter STAR-CCM+ accelerates innovation from the digital workspace to real, race-ready products. Racing to the top – One perfectly engineered squeeze at a time So as the grand peloton races across France this July, every squeeze of a water bottle is backed by sophisticated engineering and simulation. What appears simple at first glance is, in reality, the result of advanced digital design and optimization, supporting the world’s greatest athletes in their pursuit of victory, one perfectly engineered squeeze at a time. Do you want to turn even the smallest details of your product into performance differentials? Schedule a meeting with CAEXPERTS and find out how simulation with Simcenter STAR-CCM+ can take your design - from cycling bottles to highly engineered components - to a new level of precision, efficiency and innovation. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The latest release of Simcenter HEEDS 2504 empowers users to continue to discover better designs, faster! This release advances Simcenter HEEDS ’ best-in-class design space exploration technology by offering enhanced surrogate modeling, new visualization features, AI-accelerated workflows, and expanded integration options that together create a seamless digital thread across the product development lifecycle. New enhancements in Simcenter HEEDS 2504 enable you to: Model complexity by streamlining surrogate modeling Explore possibilities with enhanced data-mining and visualization tools Go faster with workflow automation and AI-enhanced search Stay integrated with updates to existing portals and the addition of new portals Model the complexity Simcenter HEEDS 2504 introduces a new approach to surrogate modeling with a consolidated Surrogates tab. This centralized environment simplifies how engineers create, evaluate, and apply surrogate models throughout their design exploration process: Unified Workspace: Access all surrogate modeling capabilities through a single, intuitive interface that streamlines the entire process from data preparation to model application Automated Best Surrogate Selection: Eliminate guesswork with intelligent surrogate model recommendations based on comprehensive statistical analysis of your dataset Advanced Outlier Detection: Identify problematic data points through sophisticated algorithms and clear visual indicators, ensuring surrogate models accurately represent underlying physics Comprehensive Comparison Tools: Evaluate multiple modeling approaches simultaneously with side-by-side performance metrics and visual diagnostics These enhancements make advanced surrogate modeling accessible to engineers of all experience levels while providing the depth and flexibility demanded by simulation experts. The result is faster development of accurate meta-models that can dramatically accelerate design space exploration while maintaining high confidence in results. Explore the possibilities Simcenter HEEDS 2504 boosts insight and discovery with enhanced data-mining and visualization tools to transform data into insight. The updated Boruta influence plot offers a clearer and more intuitive view of variable sensitivity, for faster and more efficient analysis. New visual effects, including blurred lines, polygon fills, and added statistical overlays in parallel plots, let you interpret trends at a glance and dig deeper into the story your data is telling. These upgrades empower faster, more focused decision-making to explore what’s possible. Go faster Simcenter HEEDS 2504 accelerates design space exploration with smarter workflow automation and a more efficient user experience. Tagging has been made easier with smarter file grouping based on parent analyses. Now a tree structure during tagging makes greater transparency and visualization streamlines the tagging process allowing for more rapid tagging, especially for larger workflows. Script tagging now supports Python print statements for easier debugging and transparent troubleshooting. A new customizable auto-save feature helps prevent data loss while building workflows, and full support for high-DPI monitors ensures a clean and consistent interface across all screen resolutions. Simcenter HEEDS AI Simulation Predictor has been upgraded for more efficient parallel resource utilization allowing for maximization of simulation resources. Additionally, enhanced messaging provides real-time feedback during training and prediction, giving users more transparency on progress, as well as better troubleshooting information. Stay integrated Simcenter HEEDS 2504 boosts integration across your simulation workflow with enhancements to existing portals along with the addition of new portals. Existing portals such as Simcenter Amesim , NX , Simcenter FLOEFD , and GTI have been updated for improved performance and flexibility. Simcenter Amesim has been re-organized and enhanced to encompass more options. NX has been enhanced to allow for custom journal introduction into various parts of the execution process, aligning with the Simcenter 3D portal. A new Simcenter FLOEFD portal has been created with direct API integration, unlocking greater parallelization capabilities and the potential for usage with Simcenter HEEDS AI Simulation Predictor . The previous FLOEFD Parametric portal (non-API based) is still available as well for flexibility, depending on user-need. The GTI portal has been extended to support *.glx file results and the latest versions of GTI. New portals for Ansys Discovery and Aspen HYSYS Simulation Workbook further expand Simcenter HEEDS ’ connectivity, ensuring smooth, streamlined workflows. Discover how to accelerate your innovation with the new Simcenter HEEDS 2504 ! Speak to a CAEXPERTS expert and see for yourself how improvements in surrogate modeling, advanced visualization, AI, and integration can transform your development process. Schedule a meeting and take your engineering to the next level. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Render photorealistic visuals faster. Speed up design optimization. Mesh complex assemblies efficiently. Simulate bending structures quickly. And more. The latest release of Simcenter STAR-CCM+ 2506 delivers significant enhancements across CFD simulation workflows, enabling you to tackle complex problems with greater efficiency. With improved performance in meshing, solver technology, and new visualization capabilities, users will experience easier set-up, higher accuracy, faster simulations, and compelling results representations. The release introduces GPU acceleration for particle simulations and photorealistic rendering, enhanced bending capabilities for structural analyses of valves, and improved contact modeling for manufacturing applications like blow-molding. New features like voltage-driven solid coils for electric machine simulation and efficient gear system configuration in conjunction with Smooth Particle Hydrodynamics (SPH) expand Simcenter STAR-CCM+ applicability across industries. Optimized algorithms for surface remeshing and thin parts meshing ensure more robust and faster convergence for challenging geometries. Unlock higher fidelity and flexibility for e-machines analyses Engineers working with electric machines often struggle with accurately modeling current density redistribution through solid coil cross-sections, leading to less accurate performance predictions. With the latest release of Simcenter STAR-CCM+ 2506 , you can now utilize a new dedicated excitation coil model specifically designed to simulate voltage-driven solid conductors. This approach provides higher fidelity and better usability of both the Finite Element and Harmonic Balance Finite Element Magnetic Vector potential solvers. The new capability allows voltage-driven solid conductors to be modeled natively in both time and frequency domains, eliminating previous limitations. This enhancement enables you to simulate a whole new family of electromagnetic analyses in e-machines, delivering more accurate predictions of performance and efficiency. Simulate bending structures up to 7x faster Standard linear hex elements are known to be too stiff in bending deformation, exhibiting the “locking” effect that requires more expensive element formulations. The latest version of Simcenter STAR-CCM+ introduces enhanced linear hex elements that provide the accuracy of quadratic elements at the computational cost of linear elements. You can now experience up to 7x faster simulation of thin structures with bending deformation, as demonstrated in a structure-only simulation with 83,282 elements. This advancement is particularly valuable for applications involving thin-walled components under bending loads, such as sheet metal parts, thin structural members, and lightweight designs. The enhanced formulation maintains accuracy while significantly reducing computational requirements, allowing for more design iterations and larger models within the same computational budget. Optimize complex manufacturing processes with advanced contact modeling Manufacturing processes like blow molding or film/sheet extrusion often involve components constrained or manipulated by adjacent parts, making accurate simulation impossible without contact modeling. Simcenter STAR-CCM+ 2506 introduces contact modeling with unmeshed constraining geometry parts for the viscous flow solver, opening possibilities for detailed simulations of a whole new category of manufacturing applications. You can now define constraining geometry parts as stationary or moving via rigid body motion, with walls that may be slip, partial slip, or no-slip. The free surface may contact and be constrained by parts, allowing for realistic simulation of complex manufacturing processes. This advancement enables engineers to accurately predict material behavior during forming processes, leading to optimized designs and reduced physical prototyping. Improve meshing of thin parts for faster convergence Quality meshing of thin structures is critical to simulation convergence for many applications, and has traditionally been challenging to achieve efficiently. The latest release of Simcenter STAR-CCM+ 2506 features a new thin volume mesher designed to enhance detection and handling of thin geometries throughout the computational domain. You can now generate meshes where a larger percentage of the overall volume is filled with thin prisms, with improved treatment of stacked thin sections. The enhanced algorithm provides improved transition between thin layer and unstructured meshes, with curve segments now fully compatible. These improvements result in higher-quality meshes for thin structures, leading to faster convergence and more accurate results for applications involving heat exchangers, electronic components, sheet metal parts, and other geometries with thin features. The enhanced meshing capabilities reduce the need for manual mesh adjustments, streamlining the overall simulation workflow. Accelerate design optimization for discretized geometries Engineers often struggle with the tedious setup process of surface morphing for shape optimization based on field functions. The latest Simcenter STAR-CCM+ 2506 release addresses this challenge by extending the Morph Surface Mesh Operation with a new Parametric Morphing option. You can now leverage a seamless workflow between the parametric surface morpher and Design Manager, enabling reliable morphing of any tessellated input geometry through user-defined vector parameters in cartesian directions. This enhancement significantly accelerates the design optimization process for discretized geometries. Create photorealistic imagery quickly with powerful GPU-accelerated rendering Creating photorealistic digital twin representations with simulation results has traditionally been very time-consuming, requiring specialized expertise. The new Studio Scene feature in Simcenter STAR-CCM+ 2506 enables effortless photorealism with an intuitive workflow and GPU-accelerated ray tracing. You can now experience the democratization of photorealism with real-time view interaction, making high-quality visualization accessible to all users. The feature provides automatic and workflow-aware defaults that reduce overall setup time, along with an interactive workflow to create, assign, and change photorealistic rendering materials. Supported for any NVIDIA RTX line GPU, this capability delivers up to 11 times faster rendering compared to CPU-based methods. Photorealistic hardcopies can be created while the simulation is running, enabling you to produce compelling visualizations for presentations and reports with minimal effort. Run faster Conjugate Heat Transfer (CHT) simulations The exchange of information at contact interfaces between fluid and solid domains has traditionally impacted simulation performance due to suboptimal partitioning strategies. The latest version of Simcenter STAR-CCM+ introduces a new partitioning method called Inter-Continuum partitioning, specifically optimized for interactions between fluid and solid continua. You can now experience faster performance for simulations with multiple continua such as conjugate heat transfer, with benefits for both contact and mapped contact interfaces on both CPUs and GPUs. Performance improvements are substantial, with speed-ups of 21% for a cooled turbine blade case on 4 GPUs, 52% for a sports car vehicle thermal management simulation on 12 GPUs, and 12% for a transient battery thermal simulation on 320 CPU cores. These enhancements make Simcenter STAR-CCM+ 2506 significantly more efficient for thermal management applications across industries. Accelerate mesh generation The concurrent meshing of geometries with substantial size differences can result in suboptimal scaling, creating a bottleneck in preprocessing workflows. With Simcenter STAR-CCM+ 2506 , you can now leverage the new Concurrent-parallel per-part meshing mode that intelligently accounts for part sizes in process assignment. This enhancement delivers up to 2.15x speedup between concurrent and concurrent-parallel modes, making more efficient use of available computational resources. You’ll experience faster mesh generation while maintaining consistent mesh quality with serial execution. This improvement is particularly beneficial for complex assemblies with components of varying sizes and complexity, allowing for more efficient preprocessing and faster overall simulation turnaround times. Speed up surface meshing To speed up end-to-end simulation pipelines, every step including surface remeshing must contribute to overall efficiency gains. Simcenter STAR-CCM+ 2506 delivers enhanced Surface Remesher capabilities that provide faster surface remeshing on a single processor. You can now experience up to 40% reduction in surface remeshing time depending on case complexity, without requiring any additional input to enable the feature. This improvement is particularly valuable for workflows involving multiple remeshing operations or large, complex geometries. With Simcenter STAR-CCM+ 2506’s reduced surface preparation time, engineers can shift their focus from preprocessing tasks to valuable analysis and design improvements. Enhance LES combustion efficiency with 20% faster processing Large Eddy Simulation (LES) approaches for unsteady combustion simulations demand substantial computational resources and lengthy calculation periods. Simcenter STAR-CCM+ 2506 introduces the PISO-Consistent (PISOC) implicit scheme for the Segregated Flow solver, designed to speed up these demanding simulations. Similar to SIMPLEC, no relaxation is applied to the pressure equation, allowing for deeper convergence of the PISO residual. You can now achieve faster solve times per time step as the scheme requires fewer correctors. Testing on an industrial gas combustor with 51 million cells using LES (WALE) and FGM approaches shows up to 20% faster simulation times. This enhancement significantly improves productivity for engineers working on combustion applications, allowing for more design iterations within project timelines. Run DEM analysis with GPU-native solver Particle simulations with Meshfree DEM have traditionally been limited to CPU-based solvers, restricting simulation size and speed. Simcenter STAR-CCM+ 2506 introduces a GPU-native Meshfree DEM solver that delivers faster turnaround times and more energy and cost-efficient simulations for a wide range of applications involving particle flow. You can now run these simulations on GPUs while maintaining equivalent solutions between CPU and GPU implementations. The GPU-native DEM is fully compatible with rigid body motions and DFBI (Dynamic Fluid Body Interaction), ensuring versatility across application areas. This release marks the first step in porting all DEM features to GPU, beginning with support for spherical particle types. With less costly simulation, you can tackle larger particle systems or run more design iterations within the same timeframe, leading to optimized designs for applications like bulk materials handling and solids mixing. Extend simulation insight to the enterprise digital thread Managing simulation data within PLM systems has traditionally been challenging due to the massive file sizes involved. With the new release of Simcenter STAR-CCM+ , you can now directly visualize simulation files (.sce) in Teamcenter without checking in large datasets. The solution enables quick results upload to Teamcenter and includes comprehensive visualization options such as histograms, time history plots, bubble charts, and heat maps within your CAE 3D models. This seamless integration maintains the digital thread between CAE experts and the PLM system while significantly reducing data management overhead. Streamline gear systems setup with automated kinematics Setting up gear systems simulations has traditionally required manual calculation and application of individual component kinematics, leading to labor-intensive setup processes and increased risk of errors. Simcenter STAR-CCM+ 2506 introduces an efficient configuration approach for gear systems through the new DFBI motion Kinematics Solver and specialized joints. You can now achieve quick and easy motion coupling for gears, eliminating the need for complex manual calculations and setup. This new capability is compatible with SPH and Finite Volume methodologies, making it versatile across different simulation approaches. Applications include gearbox and differential lubrication, ball-bearing simulation, rotorcraft transmissions, and many other mechanical systems. The result is significantly easier and more efficient setup of gear systems simulations, allowing you to focus on analysis rather than model configuration. Maximize simulation performance with enhanced SPH capabilities In today’s demanding engineering environment, computational fluid dynamics (CFD) simulations often face challenges with accuracy, processing time, and complex setup procedures. With the release of Simcenter STAR-CCM+ 2506 , you can now leverage significantly enhanced Smoothed Particle Hydrodynamics (SPH) capabilities that address these critical challenges head-on. You’ll experience increased accuracy in run-off applications and torque predictions, along with improved free surface rendering that delivers more realistic results. The new release streamlines your workflow with enhanced motion setup features, including reference frame capabilities for rotating inlet boundary conditions and efficient gear system simulation setup. When it comes to performance, the results are significant – you can achieve up to 25% faster turnaround time and benefit from up to 3 times faster initialization through optimized ghost particle generation. These improvements are demonstrated in benchmarks using NVIDIA A100 GPU, where Simcenter STAR-CCM+ 2506 release shows significant performance gains over its predecessor. With these comprehensive enhancements, you can now focus more on innovation and less on computational overhead, ultimately delivering better designs in less time. Do you want to get the most out of the new version of Simcenter STAR-CCM+ 2506 to speed up your simulations, improve your visualization and optimize projects more efficiently? Schedule a meeting with CAEXPERTS and find out how to apply all these advances directly to your engineering challenges. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Engine fails at 40,000 feet In 2017, an Air Asia flight from Australia to Kula Lumpur had to turn around, and ground crews prepared for an emergency landing after an engine failure resulted in the entire plane vibrating, with seats shaking like a rattlesnake’s tail. Fortunately, due to the quality of engineering, the engine survived this, but how can engineers make engines that can survive such instances? See in this post. An unbalanced engine is undesirable; fortunately, it is possible to simulate the phenomenon and reduce the chance of its occurrence. However, it is a complex challenge that the software only recently has managed to overcome, so it is necessary to first analyze the root of the engineering problem. An engineer’s most basic set of tools are the equations of motion. However, for these to be easily applied to a rotor mechanics problem, the object under investigation must have symmetry around its axis of rotation. To be more precise, it needs to have axisymmetry, not cyclic symmetry. Axisymmetry vs cyclic symmetry For an object to have axisymmetry, it must consist of a continuous solid around an axis, such as a spinning top. However, an object with cyclic symmetry is symmetrical around the axis but this may consist of sectors, such as the blades of a helicopter. Generally, rotor dynamics theories rely on the axisymmetry of rotors because any data recorded by the observer of a cyclically symmetrical rotating object will show a periodic frequency as each rotor blade passes its observation point. A cyclically symmetrical object is therefore generally referred to as unsymmetrical in the field of rotor mechanics. Therefore, to understand how an unbalanced jet engine with cyclically symmetrical turbines can make the whole airplane shake, it's necessary to consider ways of evaluating the rotor that are not the traditional external observer recording the object under observation. Rotating vs fixed frame When positioning a measuring instrument on the axis of rotation, the forces can be measured in the shovel without the measurement suffering from the frequency problem. Although this provides the values ​​required for the load on the turbine shovel (s), such as centrifugal loads and Coriolis effects, this creates a new problem. Since the entire plane is not revolving around this axis, in fact, it is not spinning at all, it is not possible to evaluate the plane with this rotation plan, and it is necessary to study it in the inertial plane. To compute accurate simulations of such a system that is correct with respect to theory, the simulation will need to adopt a mixed frame approach: the rotor must be computed in a rotating frame, with effects of rotations added in the equations of motion, and the stator and bearings (and the rest of the airplane) computed in a fixed frame. The developers of Simcenter 3D Rotor Dynamics and Simcenter Nastran propose that you couple such reference frames on the axis of rotation, and then offer a solution to the analysis of unsymmetric models. With this, it is possible to model the engine and the plane together and transfer loads between them, analyzing more closely how to establish critical problems originated in the engine. Forward and backward modes in rotor dynamics There may be numerous forces being applied to a rotating system perhaps from another rotor set within the engine or perhaps due to gyroscopic effects or structural damping. In such cases, the eigenfrequencies will get an imaginary part, and the corresponding mode shapes will split into two so-called whirling modes: one rotating in the same direction as the rotor (called a forward mode), another opposite (called a backward mode). Why use multiple harmonics and why combine a fixed and rotating frame These whirling modes play a crucial role in rotor dynamics engineering. They are the dominant deformation patterns that will appear when phenomena occur that could lead to instability, especially at critical speeds. Therefore, for an asymmetric rotor, it raises the necessity to use multiple harmonics in a simulation. All harmonics are then superimposed linearly and recombined during postprocessing to form Orbit Plots. Now with the most recent versions of Simcenter 3D and Simcenter Nastran it is possible to apply loads corresponding to different harmonics, in fixed or rotating reference frame, and recombine the final signal in the postprocessing. So now it's possible to consider how forces generated by the unbalanced engine can propagate to the rest of the plane. The airplane’s rotor was damaged, and this instability created a vibration that was then propagated through the plane. Although the vibration was disturbing, the plane was able to land without a catastrophe, so it would seem that the vibrations were not exactly the same as the natural frequency, let’s take a closer look at this considering the rotor (rotating frame) with its stator and bearings (fixed frame). During the design phase, engineers determine the natural frequency to ensure the design avoids them as much as possible. The first step in doing this is to review the Campbell diagrams. Critical speeds and stability of unsymmetrical models How initial and rotational reference points are used it's necessary to understand how these appear differently in the Campbell diagrams. Modes’ eigenfrequencies are represented as function of the rotation speed, their variation being a direct consequence of the gyroscopic effect. In an inertial frame, resonance occurs when the rotation speed corresponds to an eigenfrequency. These rotational speeds are called the critical speeds of the system. In a rotating reference frame, the physics is less intuitive. On a Campbell diagram, eigenfrequencies of forward modes first decrease with the rotation speed, reach the X-axis at the critical speed (when the critical speed corresponds to a mode eigenfrequency of 0 Hz), and then increase with the rotation speed. On the same Campbell diagram, eigenfrequencies of backward modes increase with the rotation speed. Critical speeds of backward modes are found at the intersection of the line ω=2Ω, then when the rotation speed equals to half of the mode eigenfrequency (ω=2Ω). The different interpretation of the Campbell diagram in fixed and rotating reference frames also helps to understand why a forward rotating force in the fixed frame is equivalent to a static force at 0Hz in the rotating frame, and why a backward rotating force in the rotating reference frame is equivalent to a backward rotating force at 2 Ω in the rotating reference frame. Figure 1: Campbell diagram in the FIXED and ROTATING reference frames are represented; computed on the same axisymmetric model. In green: forward modes. One critical speed in the range of rotation speed range, at 1100 rpm. It corresponds to the intersection between mode 2 and the 1P line (order 1) in fixed frame, and the X axis in rotating frame (order 0). In blue: backward modes. The two critical speeds in the rotation speed range are found at the intersection of modes 1 and 3 with the 1P line (order 1) in the fixed frame, and the 2P line (order 2) in the rotating frame. In a rotating frame approach, the rotor can be unsymmetric (remember, cyclic symmetry does not count as symmetrical in these applications), but the stator and bearings must be isotropic. Moreover, the interpretation of Campbell diagram in a rotating frame is not straightforward. To remove these limitations, when the assembly is unsymmetric, there is an approach that is valid when the rotor has cyclic symmetry. This allows you to compute a Campbell diagram for the unsymmetric assembly, and output results in a fixed reference frame. When the rotor is cyclic symmetrical, an unsymmetric assembly can be solved with Simcenter 3D Rotor Dynamics or Simcenter Nastran to compute the Campbell diagram, critical speeds, and stability. This capacity allows a cyclically symmetrical rotor to be calculated in a rotary framework using time invariant matrices through coleman transformation, and then converted to a fixed framework. In the above picture, on the top right: assembly of the cyclic symmetric rotor, coupled to an unsymmetric stator by anisotropic bearings solved thanks to the Coleman transformation. On the bottom right, mode shape at 1500 Hz for rotation speed 6000 rpm. On the left: Campbell diagram in fixed reference frame. The critical speed of this assembly in the range [0;42,000 rpm] is found at 25,200 rpm, which corresponds to a frequency of 420 Hz. Harmonic response of unsymmetric models Hopefully it is now clear that a mixed frame approach can be used in the simulation of unsymmetric models, in which the rotor will be computed in the rotating reference frame, and the stator will be computed in the fixed reference frame. The coupling of both reference frames is done on the axis of rotation. It was also seen that loads are applied differently in the rotating and fixed frames: for a same load, different harmonics are used. As an extension, for vibrations computed by a harmonic response analysis, harmonic ω₁=Ω will be necessary to compute the simulation in the fixed reference frame on the bearings and stator, and this harmonic will be coupled to harmonic ω₀ at 0Hz and ω₂ at 2Ω for the rotor computed in rotating reference frame. In cases with strong bearing anisotropy, higher harmonics are necessary. Then, when considering the rotor speed Ω as the sweeping frequency of the simulation, different coefficients [0, 1, 2, 3, …] of the Hill series will be used to describe the different harmonics. For the equivalent model of the previous section, modeled in 3D using superelements to speed up the calculations, an unbalance has been applied at the center of the unsymmetric disk. As shown in the picture below, on the right, displacement at the center of the disk is output for each harmonic separately: harmonic at 0Hz and harmonic at 2Ω. The results of the different harmonics can be recombined in the form of an orbit plot at a chosen node and at a selected frequency. In the left picture, the orbit plot is represented for the reference frequency of 350 Hz. Harmonic ω₀ at 0Hz highlights the effect of the unbalance (unbalance is a static force in the rotating reference frame). It also provides the (X,Y) coordinates of the center of the orbit. Its peak is found at about 425 Hz, which can be related to the previous simulation of the critical speeds calculation. A finer mesh would have allowed to find closer values. Harmonic ω₂ at 2Ω highlights the effects of the bearing anisotropy and corresponds to the expansion of the orbit seen on the left. Indeed, for a system under unbalance, isotropic bearings would have provided an orbit plot reduced to a single point. Figure 4: Left: orbit plot at the unbalance node for harmonic response, at 350 Hz. Upper right: displacements at the unbalance node for harmonic ω₀ at 0Hz. Lower right: displacements at the unbalance node for harmonic ω₂ at 2Ω. When the rotating system is totally unsymmetric, vibrations in the frequency domain can be studied in Simcenter 3D Rotor Dynamics or Simcenter Nastran for different types of rotor defects or loads, thanks to the use of multiple harmonics and a mixed frame approach. Considering the investigation of the plane, it is possible to model both the rotary parts of the engine and its fixed components and the stator and the bearings. The complex frequencies can also be determined, both in the fixed and rotating plane, and then combine the results. Now, with the use of multiple harmonics along with the mixed structure approach, it is feasible to consider abnormal defects or loads. As engines with engines are not uncommon, it is possible to simulate the effect of a rotor shovel being damaged in such an incident. However, the engine does not always rotate at the same speed and as the speed of rotation changes, the load also changes. This also needs to be considered to ensure that the engine is safe. Transient analysis of unsymmetric models The same model can also be solved in a transient analysis. To be able to compare the results, let’s consider the same unbalance load, and reproduce the steady state behavior by defining a run up with an increasing rotation speed from 0 to 350 Hz, and then a constant rotation speed of 350 Hz. For time-varying rotating speed or loads, the response of the rotating system is calculated by a transient analysis. A combination of superelements for the rotor in the rotating frame, stator in fixed frame, and the assembled by bearings accelerates the simulation and provides accurate results. Vibrations at the center of the disk for a run-up followed by a stabilized rotation speed at 350 Hz are represented in the picture below. At that speed, the (transient) vibrations can be compared to the orbit computed in harmonic response for a steady-state simulation at a frequency of 350 Hz. You can observe that the orbit in transient for the stabilized rotation speed can be superimposed to the orbit in harmonic response when all harmonics are recombined, for that frequency of 350 Hz. The vibration oscillates around a mean position. This mean position corresponds to the center of the orbit and corresponds to the results of harmonic at 0Hz in for the harmonic response. Further transient analysis, for a similar steady-state scenario at a constant rotation speed, provides comparable results in the frequency response using multiple harmonics. resultados comparáveis ​​na resposta de frequência usando múltiplos harmônicos. Figure 5: left: orbit plot at the unbalance node for the run-up analysis. Upper right: displacements at the unbalance node in the simulation and evolution of the rotation speed of the rotor. Lower right: zoomed in to a few cycles of the displacements at 350 Hz. Conclusion Being on an airplane when there is a mechanical malfunction is scary. However, you can take comfort in knowing the engine manufacturer and their engineers have considered many scenarios and simulated the consequences. While this was difficult in the past, new tools such as those in Simcenter 3D and Simcenter Nastran are making it easier to build models and simulate more cases, reducing the risk of possible oversights and continuing to work towards safer aircraft. With the new capability of Simcenter 3D Rotor Dynamics and Simcenter Nastran to compute vibrations on unsymmetric rotating assemblies, the types of applications that can be solved have been expanded. Indeed, as casing and bearings are rarely isotropic, flexible rotors that are definitely not axisymmetric were left aside by rotor dynamics solutions. Now, the Campbell diagram, modified stiffened structure behavior at high rotation speeds, rotor defects like unbalance or misalignment, or any type of loads, can be studied in the time and frequency domains. In this blog post, an unsymmetric rotating assembly has been solved in complex modal analysis (rotor is cyclic symmetrical), in harmonic response, and in transient response. A complete study of an unbalance analysis was performed, and the results of the different simulations were compared together. Want to ensure your engine project survives critical vibrations and avoid catastrophic failures? Schedule a meeting with CAEXPERTS and find out how to apply advanced simulations with multiple harmonics and mixed structures to accurately evaluate the stability and integrity of your rotating system. Our team is ready to help you turn complexity into safety and reliability. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br