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Christmas is a time marked by gatherings, celebrations, and, of course, the preparation of traditional dishes. Among them, roast turkey occupies a prominent place. But what happens inside the oven while the roast is being prepared? To answer this question in a technical and accessible way, a simulation was performed using Simcenter FLOEFD , a Computational Fluid Dynamics (CFD) software, with the aim of analyzing air circulation and heat distribution in a convection oven during the preparation of a turkey. Model of a turkey in a convection oven in Simcenter FLOEFD The simulation scenario The turkey did not have exactly the correct dimensions, being just a solid block. There was no cavity for stuffing or for the neck, therefore the measurements were estimated visually and some cuts were made. As one of the most relevant issues was the amount of airflow through different spaces (in the cavities and under the turkey), some objects were created to collect this data. The oven was configured in convection mode, with a fan located at the rear, responsible for propelling air horizontally over the roasting area. For this model, the height of the rack is set at just over 1mm above the bottom of the roasting pan. Turkey cavity This model was run as a snapshot, meaning the turkey temperature was set at a point where it was not yet fully cooked (120°F). The oven temperature was set to 375°F, with the heating elements positioned at the bottom of the oven operating at a slightly higher temperature of 400°F. Boundary Conditions for Turkey Roaster Analysis Initially, the flow lines are observed, which are analogous to the smoke lines shown in wind tunnel tests used in car commercials. These lines indicate the direction of airflow. The fan was defined as the starting point of the flow lines. Although the flow lines exhibit quite chaotic behavior, it is possible to extract relevant information from them. By sectioning the model, the interior of the roaster and the turkey cavity become visible. Compared to the flow lines outside the turkey, it is observed that little air enters the roaster and passes under the turkey, and an even smaller amount passes through the interior of the turkey. This result was expected, especially regarding the airflow in the cavity. The fan propels the air transversely to the width of the turkey, not along its length. For the air to enter the cavity, it would be necessary to go around the turkey and then make a 180-degree turn, which is not physically plausible. Furthermore, due to the large size of the turkeys, it is not possible to orient them in the same direction as the airflow generated by the fan. The airflow from the oven fan surrounds the turkey, whose color varies according to the temperature The oven fan's airflow is directed towards the roasting pan and the turkey cavity Observing a contour plot of air velocity passing through the central plane of the turkey and the oven, it can be seen that the air velocity through and under the turkey is very low, while higher values ​​are observed above the turkey and below the roasting pan. Low-velocity air is considered to be air whose magnitude is comparable to that of a natural convection oven, where the typical air velocity is on the order of 0.2 m/s. Therefore, there is no significant gain in heat transfer provided by the fan, since most of the turkey's surface is subjected to air velocities below 0.2 m/s. Air Velocity Contour Graph For a more precise understanding of this behavior, a graph of the air velocity near the surface of the turkey is observed. The image has been divided into two parts: one representing the surface of the turkey facing the fan and the other showing the opposite side. The difference between the two regions is evident. As a consequence, one side of the turkey tends to cook or dry out more quickly than the other if the food is not rotated periodically. Higher air velocities result in more intense convection, a principle illustrated by the act of blowing on soup to accelerate its cooling. Speed ​​close to the surface of the turkey, near the fan Air velocity near the surface of the turkey, opposite to that of the fan. Returning to the contour plot, when analyzing the temperature distribution, it is clearly observed that the air inside the turkey has significantly lower temperatures. This occurs due to air stagnation, since there is no effective circulation of hot air inside the turkey. It is also observed that, below the turkey, in the space of approximately 1 cm (0.4 inches) provided by the grill, the air temperature is lower than that of the rest of the oven. Again, this behavior is explained by the limited circulation of renewed hot air in this region. Air temperature contour graph along the centerline of the turkey The question arises as to why air has difficulty penetrating the space between the turkey and the bottom of the roasting pan. Observation of the flow lines indicates that the cause is essentially the same as that which prevents air from entering the inside of the turkey. The air from the fan tends to follow the path of least resistance. To flow under the turkey, the air would need to go around the wall of the roasting pan, descend through the space between the turkey and that wall, and then make a 90-degree turn to flow under the turkey. Along this path, there is a reduction in speed and a loss of temperature. Both factors are relevant, since colder air tends to descend. Furthermore, since the flow velocity is lower than that of a natural convection current, the warm, renewed air cannot displace the air already present in that region. For this reason, it is observed that the air reaches the roasting pan, but cannot advance under the turkey, instead recirculating near the wall of the roasting pan. Contour graph of air velocity and streamlines along the width of the turkey The images provide a good qualitative understanding of the phenomenon; however, in many cases, a quantitative analysis is necessary. Data evaluation indicates that the oven fan moves approximately 22.8 CFM of air. The airflow that effectively enters and exits the roaster is about 0.35 CFM, which corresponds to approximately 1.5% of the total fan flow. Regarding the air entering the turkey cavities, the inflow and outflow were analyzed in both the neck cavity and the larger rear cavity. The measured flow rates were 0.08 CFM and 0.146 CFM, respectively. From these results, it is concluded that the stuffing is not responsible for preventing air circulation inside the turkey, since this circulation is already intrinsically very limited. This does not exclude the effect of the additional thermal mass of the stuffing, which can result in longer cooking times and drier meat—a topic that deserves specific analysis. Nor should one expect significant air circulation under the turkey capable of producing a completely crispy skin. A higher rack or a roasting pan with lower sides might offer some improvement, although this effect is questionable. In practice, using a rack or vegetables like carrots, celery, or potatoes serves a similar function, elevating the turkey and keeping it away from the accumulated fat. Want to understand how simulation can bring that same level of technical analysis to the real-world challenges of your engineering? Schedule a meeting with CAEXPERTS and discover how CFD and advanced simulation solutions can optimize your projects and processes. We also take this opportunity to wish you a Merry Christmas and a Happy New Year! 🎄✨ WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The new release of Simcenter Culgi 2511 brings significant advancements to empower your computational chemistry simulation workflows. With enhanced viscosity prediction, you can now achieve reliable results more quickly, with greater accuracy and even the most complex fluids, thereby reducing the need for extensive laboratory testing. The support for realistic crystalline structure modeling unlocks new possibilities for simulating complex materials, while auto-completion for Python scripting and a built-in library of physical constants streamline your workflow and minimize errors. These features collectively enable you to explore more complex systems, accelerate your material development process, and ensure higher precision in your results. Achieve dependable viscosity predictions validated against industry standards Measuring viscosity is a complex and time-consuming task, yet it is a critical property across many industries. Traditionally, obtaining accurate viscosity values for different formulations has required extensive lab work and sophisticated experimental setups, often leading to project delays. With the new release of Simcenter Culgi 2511 , you can now leverage state-of-the-art viscosity prediction methodologies, including the impact of shear, at both atomistic and coarse-grained levels. Simcenter Culgi 2511 integrates the SLLOD equation methodology, enabling you to predict viscosity under various conditions with ease. Additionally, the Stop-on-Met precision feature automatically halts measurements once a specified accuracy (such as 0.5%) is achieved, further optimizing your formulation development process. This comprehensive toolkit enables rapid candidate screening and significantly reduces the need for laboratory tests. As a result, you benefit from reliable, industry-standard, validated results, ensuring your predictions are both accurate and dependable. Ultimately, this empowers you to make informed decisions faster and with greater confidence, driving innovation in your projects. Model realistic crystalline structures with atomic precision As simulation techniques advance, the demand for modeling increasingly complex systems, such as crystalline structures, continues to grow. The limitations of traditional simulation geometries, often restricted to cubic or rectangular boxes, have made it challenging to accurately represent real-world crystalline forms. With Simcenter Culgi 2511 , you can now import and simulate non-rectangular simulation boxes, overcoming the previous constraints. This new capability allows you to import your unit cell and expand your crystalline structure for simulation, both at the atomistic and coarse-grained levels. By enabling realistic modeling of crystalline structures, you can reduce the need for physical testing and identify potential limitations before moving to experimental phases. This enhancement not only streamlines your workflow but also ensures that your simulations are more representative of actual materials, ultimately leading to better-informed decisions and more successful outcomes. Accelerate your scripting with 90% reduced command lookup time Engineers often appreciate the flexibility of exporting and integrating Simcenter Culgi scripts into advanced Python workflows. Remembering the multitude of Simcenter Culgi-specific commands and their functionalities can be a significant hurdle, especially when developing or modifying scripts from scratch. With the latest Simcenter Culgi 2511 release, you now have access to auto-completion and command help directly within your preferred Python IDE. As you write scripts, you can quickly look up native commands, access help by hovering, and autocomplete commands as needed. This accelerates the development of complex multiscale workflows, reduces the time spent searching for the right commands, and lowers the barrier to entry for new users. The result is a 90% reduction in command lookup time, enabling you to focus on innovation rather than routine tasks. Faster onboarding and improved user experience mean your team can deliver results more efficiently. Ensure improved calculation precision Coarse-grained simulations, such as Dissipative Particle Dynamics (DPD), are a hallmark of Simcenter Culgi, but they traditionally lack real units, requiring manual conversion to physical units. This process often involves manually entering physical constants, such as the Boltzmann constant or Avogadro number, which is both tedious and prone to error. With Simcenter Culgi 2511 , you benefit from a built-in library of the most common physical constants used in computational chemistry. Now, you can simply select the required constant and continue building your equations without worrying about transcription errors or loss of precision. This enhancement not only streamlines your workflow but also ensures that your results maintain the highest level of accuracy. By removing a common source of error and saving valuable time, you can develop multiscale workflows with greater confidence and efficiency. If you want to accelerate your projects, reduce lab testing, and increase the accuracy of your chemical simulations with the latest advancements in Simcenter Culgi 2511 , schedule a meeting with CAEXPERTS now and discover how we can support your team in getting the most out of these new features. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Believe it or not, the Smoothed-Particle Hydrodynamics (SPH) technology, which has significant applications today, was actually developed for astrophysical purposes. It was originally used to simulate the dynamics of galaxies and the behavior of stars and planets: Smoothed Particle Hydrodynamics | Annual Reviews Monaghan, J. J. 1992. “Smoothed Particle Hydrodynamics.”, Annual Review of Astronomy and Astrophysics 30:543-74. doi: 10.1146/annurev.aa.30.090192.002551. Just like cosmic formation, where countless particles coalesce into refined structures to form stars and planets, the SPH solver in Simcenter STAR-CCM+ 2510 now offers local particle refinement. But you don’t have to go to outer space to make use of this capability: it can actually be used for any down-to-earth application, like, for example, to better capture the oil around planetary gears. And no, planetary gears are not an astrophysical application, even though this thing is pretty close to one: Enhance simulation precision with SPH particle refinement technique In previous versions of SPH in Simcenter STAR-CCM+ , achieving higher fidelity required refining the particle size, which inevitably increased simulation time. Conversely, opting for faster simulations meant coarsening particle size, sacrificing accuracy. This is the classic CFD dilemma with no easy solution. Now, with version 2510, Simcenter STAR-CCM+ introduces local particle refinement for the SPH solver, allowing you to enhance flow accuracy precisely where it’s needed without necessitating a fine particle size across the entire fluid domain. This new capability enhances precision in critical areas while maintaining efficient simulation time, offering a balance between local high-fidelity results and computational performance. The performance improvement largely depends on the application and the size of the refinement area. In the SPH solver’s adaptive time-stepping, the chosen time step is still determined by the finest particle size. Consequently, the performance boost is not driven by the time step but achieved by reducing the total number of particles compared to a fully refined particle simulation. As a result, the more localized and specific the refinement areas are, the greater the performance gains you will experience. As illustrated in the animation, you now have the ability to locally create geometry particle refinement criteria using block, cylinder or/ and sphere shapes. In the example, cylinder refinement criteria have been defined around each gear to accurately capture the oil distribution close to the teeth. Your simulation can incorporate one or multiple refinement shapes, and they can even overlap as needed, especially when dealing with complex geometries such as gear teeth. You can define up to 10 levels of refinement, allowing particle size specifications to go below one micrometer, starting from a base particle size of 1 mm. Also noteworthy is the ability to assign a coordinate system to the refinement shapes. This is particularly useful if you need the refinement to follow a moving solid, ensuring it maintains an accurate resolution while moving along a trajectory in space. To demonstrate the higher fidelity benefit for this planetary gear application, this chart depicts the average wetted surface over time. As shown, the simulation using particle refinement (particle base size of 1 mm using two levels of refinement) achieves accuracy that closely matches the finest simulation (0.25 mm). In contrast, it outperforms the coarse simulation (1 mm), highlighting the effectiveness of particle refinement in balancing precision and computational efficiency. Another key advantage of particle refinement is its significant reduction in memory consumption. As illustrated above, using particle refinement results in a fourfold decrease in memory usage compared to the finest simulation, enabling you to efficiently handle more complex cases. Simplify planetary gearbox simulation with just a few clicks Just as effortlessly as planets revolve around the sun in our solar system, setting up a planetary gearbox in Simcenter STAR-CCM+ has never been easier. Starting with version 2506, the new kinematics solver allows for the use of Planetary Gear and Revolute Joint Body Couplings, enabling you to configure motions with just a few clicks. Additionally, version 2506 introduced enhanced data analysis capabilities. You can now measure mass flow or various other quantities across section planes, thanks to the compatibility of the SPH solver with constrained plane and arbitrary section-derived parts. Furthermore, visualization of the free surface is now possible using the SPH solver’s compatibility with the iso-surface derived part of the liquid volume fraction. Those enhancements in the motion and data analysis contribute to a faster setup and getting more insights into the quantitative solution. Accelerate SPH simulations with lightning-fast GPU workflows In Simcenter STAR-CCM+ 2510 , SPH simulation feels akin to traveling at the speed of light, thanks to seamless GPU acceleration compatibility throughout the entire workflow. The solver supports native GPU acceleration since version 2410 for single GPU and expanded to multiple GPUs in version 2502. With the latest version 2510, data analysis capabilities are now also ported to GPU hardware. As a result, you can now utilize and visualize point probes, free surfaces, constrained plane sections, and arbitrary sections derived parts up to five times faster compared to before. This allows for rapid solution analysis, maintaining your workflow at a lightning-fast pace. In this specific example, running the planetary gear lubrication simulation on an NVIDIA RTX6000 GPU achieves a speedup of nearly five times faster compared to using 56 CPU cores. This demonstrates that the entire workflow for this application, including particle refinement, is fully optimized and compatible with GPU acceleration. Explore new simulation frontiers with enhanced SPH capabilities Simcenter STAR-CCM+ continues to advance its SPH solver with significant enhancements, including local particle refinement, a streamlined workflow for planetary gears, and additional data analysis capabilities, as well as robust GPU acceleration. With more accuracy, faster setup, and runtime, the SPH solver may help you reconnect with your inner child by looking at all new types of planets and stars in planetary and sun gears. And ultimately, we want to enable you “to boldly go where no (wo)man has gone before.” Perhaps even for your SPH simulation, one day space will become the final frontier. Schedule a meeting with CAEXPERTS and discover how to leverage the full potential of SPH in Simcenter STAR-CCM+ to increase the accuracy of your simulations, reduce computational costs, and accelerate your GPU workflows—all with the expert technical support of those who deeply understand these technologies. Let's take your analyses to the next level? WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

In today’s competitive engineering landscape—defined by tight development timelines, limited budgets, and no tolerance for design failures—teams must quickly explore more design alternatives while optimizing increasingly complex systems to balance objectives such as performance and cost. Modern engineering solutions must bridge web and desktop environments to support distributed teams working from anywhere, while intelligently leveraging AI to automate routine tasks and accelerate decision-making. This combination of flexible access and intelligent automation enables engineers to focus on high-value creative work. This release delivers just that - Simcenter HEEDS 2510 for simulation-based design optimization and Simcenter HEEDS Connect 2510 for workflow integration address these needs with enhancements that make advanced optimization accessible and productive for teams of all sizes, accelerating innovation through simulation-driven design. SHERPA’s enhanced multi-objective capabilities: better trade-offs, faster results At the heart of this release is a major update to SHERPA's multi-objective search strategy, the HEEDS optimization strategy used to efficiently explore project spaces and find optimal engineering solutions. For teams tackling multi-objective trade-off studies with constraints, you’ll benefit from improved search strategies that help you discover more robust Pareto frontiers faster. Regardless of your use case or industry, the improved multi-objective SHERPA enables you to make informed decisions more quickly and with greater confidence. Benchmarking SHERPA’s enhanced multi-objective capabilities with an XLR UAV (analysis tool: Simcenter STAR-CCM+). Performance and speed comparisons are based on median values, represented by dotted lines, while the shaded areas indicate the 95% confidence intervals. AI-powered acceleration with more iterations, less waiting Simcenter HEEDS 2510 features an updated AI Simulation Predictor, leveraging artificial intelligence to accelerate optimization studies. By intelligently predicting simulation outcomes, this capability reduces optimization time by up to 30% without compromising solution quality. The intuitive interface democratizes AI-powered optimization, eliminating the need for machine learning expertise. This allows more design iterations, faster turnaround, and improved productivity, enabling teams to focus on innovation rather than waiting for results. Native HyperMesh integration: from mesh to results in one workflow Simcenter HEEDS 2510 enables seamless tool integration with specialized connectors for Altair HyperMesh and HyperView/HyperGraph . These connectors automate the entire optimization workflow: parameterized mesh morphing in HyperMesh , automated simulation execution, and consolidated results visualization in HyperView/HyperGraph . By eliminating manual file transfers and standardizing post-processing procedures, engineering teams can concentrate on design insights rather than data management, thereby reducing the time spent on each design iteration. Smarter optimization setup with intelligent guidance Setting up optimization studies can be challenging, particularly when deciding the appropriate number of evaluations. The new Optimization Intelligence feature offers automated settings for minimum evaluation counts, tailored to specific problem characteristics, including the number of design variables, response objectives, variable types, and workflow complexity. Optimization Intelligence analyzes your setup and recommends the minimum number of evaluations required for meaningful results. Visual alerts guide users toward best practices, reducing guesswork and helping both novice and experienced engineers in developing robust studies that deliver reliable results. This helps set realistic expectations and encourages the selection of evaluation budgets that reflect the available engineering budget. SHERPA’s adaptive algorithms continue to enhance solution quality with additional evaluations, allowing the discovery of superior design alternatives while requiring careful resource management. Simcenter HEEDS Connect: Seamless web-to-desktop workflow transitions Simcenter HEEDS Connect 2510 enables teams to collaborate and iterate efficiently, regardless of location. The new “Open in Desktop” feature bridges HEEDS Connect’s web environment with the full power of HEEDS MDO on the desktop. With project locking and automated data sync, users transition workflows between environments without losing context or data integrity. Teams can use the cloud for fast collaboration and the desktop for in-depth editing and analysis. Seamlessly move between Simcenter HEEDS Connect and Simcenter HEEDS desktop Workflow editing with real-time adjustments Building on previous workflow visualization capabilities, Simcenter HEEDS Connect 2510 now allows direct editing of key analysis parameters for Simcenter STAR-CCM+ , NX CAD , and Microsoft® Excel® integrations— directly within the browser. Engineers can make real-time adjustments to simulation setups, validate changes instantly, and collaborate on parameter modifications without needing to switch environments. This results in a more agile, accessible, and collaborative design exploration process. Edit analysis properties directly in Simcenter HEEDS Connect web interface Immersive 3D visualization for collaborative reviews With the addition of VCollab 3D Visualization, HEEDS Connect 2510 delivers an immersive, browser-based experience for reviewing CAD and CAE results. Teams can interactively explore complex geometries, annotate models, and measure features in real time to accelerate decision-making and foster more engaging design reviews. This capability enhances cross-team communication and streamlines the review cycle, helping organizations bring better products to market faster. Interactive 3D navigation, annotation, and measurement tools for detailed model exploration Designed for the engineering community Simcenter HEEDS 2510 and HEEDS Connect 2510 demonstrate our commitment to supporting the engineering community with integrated, intelligent, user-friendly solutions. Whether optimizing complex systems, collaborating across teams, or accelerating innovation, these releases provide the tools needed to succeed. If you want to leverage the full potential of Simcenter HEEDS' new features to accelerate your optimizations and integrate teams more intelligently, schedule a meeting with CAEXPERTS . We can show you how to apply these solutions directly to your workflow and transform your engineering efficiency. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

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

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

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

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

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

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

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

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