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In Siemens' recent video on tips and tricks for Designcenter™ NX™ software, it was explored how Synchronous Modeling allows for quick and efficient modifications to 3D geometry. This set of commands makes it extremely easy to implement rapid design changes without needing to understand how a model was originally built, significantly increasing productivity, even in large assemblies. Check out the video below or scroll down to learn more about how to leverage Synchronous Modeling commands to make fast, flexible design changes across your models and assemblies. Move Face – Reposition Geometry with Ease One of the main features of Designcenter NX Synchronous Modeling is the ability to directly manipulate your geometry and the Move Face command is a perfect example of this. Rather than revisiting your modeling history or rebuilding features, Move Face allows you to simply select any face and reposition it instantly. You can click and drag using the directional arrows to adjust the distance or angle interactively, or manually type in precise values directly in the command window. Figure 1: Move Face command feature showing ability to offset a selected face Figure 2: Move Face command feature showing ability to adjust angle of a selected face The Move Face command gives you direct, intuitive control over your geometry no history required. This flexibility makes Move Face an ideal tool for rapid design iterations. Whether you need to shift a surface by a few millimeters or adjust the angle of a feature, the command adapts to your workflow, putting you in full control of your design without unnecessary complexity. Resize Hole Command When working with components that contain multiple holes, maintaining consistency is critical. The Resize Hole command within Synchronous Modeling allows users to quickly edit and resize hole features directly on the geometry. Once a hole face is selected, the command window presents the Face Finder tab, where the Find Clone option can be found. This powerful feature combined with the Resize Hole command allows you to select and edit multiple holes simultaneously saving time and ensuring consistency across your model. Figure 3: Resize Hole command with find clone selection filtering being demonstrated Figure 4: Resize Hole command demonstrating editing of four separate holes simultaneously This feature acts as a smart selection filter, automatically identifying and grouping identical holes so that you can edit all of them simultaneously in a single operation. Instead of modifying each hole individually, you can make a single change that propagates across all matching instances, ensuring uniformity and dramatically reducing the time spent on repetitive edits. Replace Face – Achieving Symmetry and Clean Geometry Designs often require a level of symmetry or uniformity that can be difficult to achieve through traditional modeling methods. The Synchronous Modeling Replace Face command addresses this by allowing users to replace selected faces with other selected faces, effectively combining or reshaping geometry to produce cleaner, more symmetrical models. The Replace Face command simplifies the process of achieving symmetrical and uniform geometry without the need to rebuild features from scratch. This command is particularly valuable when working with imported geometry or models without feature history, where traditional symmetry tools may not be available. By directly replacing faces, you can quickly bring your model into alignment with your design intent, regardless of how it was originally constructed. Delete Face Sometimes the most effective design change is the removal of unnecessary geometry. The Synchronous Modeling Delete Face command allows users to remove selected faces from a component, resulting in a cleaner, smoother part. Rather than suppressing features or editing a history tree, you can simply select the faces you want to remove and delete them directly. The Delete Face command streamlines your components by removing unwanted faces, producing smooth, clean geometry in just a few clicks. Figure 5: Delete Face command in action Figure 6: Results of Delete Face command removing unnecessary geometry This straightforward approach makes it easy to clean up imported models, remove outdated features, or simplify geometry for downstream processes all without the need to understand or navigate the original modeling history. Resize Pattern – Adjust Patterns On the Fly When working with patterned features such as bolt holes, the Resize Pattern command within Synchronous Modeling provides a fast and intuitive way to modify both the layout and count of a pattern. Accessible through the More drop-down in the Synchronous Modeling group, this command allows you to simply select one of the faces within the pattern, and Designcenter NX will automatically recognize and load the full pattern for editing. The Resize Pattern command automatically detects and loads existing patterns, allowing you to adjust count and pitch distance quickly and efficiently. Figure 7: Resize Pattern feature command window identifying pervious patterns Figure 8: New pattern generated for Resize Pattern feature From there, you can adjust key parameters such as the count and pitch distance, preview your changes using the Show Results option, and confirm or discard the update as needed. This level of flexibility ensures that pattern modifications can be made rapidly and accurately. Radiate Face — Offset Geometry Along the Axis of Revolution For components with rotational geometry, the Radiate Face command in Synchronous Modeling offers a specialized and highly effective way to offset faces. Unlike a standard offset that moves faces normal to their surface, Radiate Face offsets geometry normal to the axis of revolution making it the ideal tool for maintaining the integrity of cylindrical and revolved features during design changes. This feature allows for proper modeling of shrinkage and machining stock and allows for the press or interference fit geometries. The Radiate Face command ensures that offsets on revolved geometry remain accurate and consistent by referencing the axis of revolution rather than the face normal. Figure 9: Radiate Face command showing offset of a geometry normal to the axis of revolution By simply selecting the faces you want to adjust and specifying the desired offset distance, Designcenter NX handles the complexity of the offset calculation, ensuring that your geometry remains accurate and well-defined throughout the modification. Summary of Synchronous Modeling Synchronous Modeling in Designcenter NX represents a fundamentally different and more flexible approach to design modification. By working directly with geometry rather than feature history, these commands Move Face, Resize Hole, Replace Face, Delete Face, Resize Pattern, and Radiate Face collectively empower users to make fast, precise, and confident design changes across any model or assembly, regardless of its origin. Whether you are refining an imported model, making rapid iterations on an existing design, or managing changes across a large assembly, Synchronous Modeling ensures that your workflow remains efficient, flexible, and fully in your control. Designcenter NX Synchronous Modeling transforms how engineering teams make design changes, bringing more agility, flexibility, and productivity even to complex assemblies. Want to discover how to apply these features to your workflow and accelerate your development processes? Schedule a meeting with CAEXPERTS and see how we can help your company get the most out of Designcenter NX. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Real-Time Collaborative Workflow Editing for Engineering Teams Engineering teams today are under constant pressure to explore more design alternatives, faster—all while managing increasingly complex, multidisciplinary systems. Yet collaboration often becomes the bottleneck. Disconnected workflows, lack of real-time visibility, and fragmented compute resource management make it difficult for distributed teams to work together efficiently and confidently. Simcenter HEEDS Connect 2604 tackles these challenges head-on. This release introduces breakthrough capabilities for real-time collaborative editing, AI‑accelerated design exploration, and centralized compute resource management, enabling teams to explore, iterate, and decide together—online and in real time. Packed with powerful enhancements, Simcenter HEEDS Connect 2604 transforms how engineering teams collaborate. Whether you’re building multidisciplinary system models, running AI-driven exploration studies, or coordinating complex workflows across organizations, this release removes friction from collaboration and accelerates results. Teams can now edit workflows together in real time, align instantly on decisions, and move from exploration to insight faster than ever before. Real‑Time Collaborative Workflow Authoring—Beyond Parameter Editing Simcenter HEEDS Connect 2604 delivers comprehensive workflow authoring capabilities that go far beyond simple parameter editing: Structural workflow editing: Modify groups, analyses, and connections directly in the web interface. Portal-specific settings: Configure analysis-specific settings for your workflows without leaving the collaborative environment. Loop configuration: Edit loop execution and output settings with immediate persistence across all users. Real-time collaboration: Instant synchronization to ensure consistency across your entire team. Seamless Simcenter HEEDS MDO integration: Changes made in Simcenter HEEDS Connect are immediately reflected in Simcenter HEEDS MDO, maintaining perfect alignment between environments. Engineering teams can now collaboratively shape workflow structures, respond instantly to evolving requirements, and continuously refine their design‑exploration processes — all directly in the web environment. This marks a significant leap toward fully cloud‑native workflow management, delivering the agility of the cloud without sacrificing the power and flexibility engineers rely on. Evaluate multidisciplinary systems with a single click Designing complex systems with interconnected disciplines has always been a challenge. How do you evaluate the response of a multidisciplinary system where outputs from one discipline feed into another? How do you propagate variables and files through a hierarchy of interconnected analyses? Simcenter HEEDS Connect 2604 introduces the Evaluation Matrix, a powerful new capability that transforms multidisciplinary system analysis. Previously, engineers had to manually coordinate multiple studies and transfer data between them—a time-consuming and error-prone process. The Evaluation Matrix eliminates this friction by enabling system-level analyses with automatic variable propagation. Key capabilities include: Efficiency built in: Feed-forward variable propagation automatically propagates outputs from higher-level studies to inputs of dependent studies. Maintain consistency: Shared variable management links inputs across multiple studies throughout your system. One click execution: Run your entire multidisciplinary system analysis with one click — no iterative loops, just clean hierarchical evaluation. Gain deeper insights: Streamlined system analysis enables you to evaluate the response of interconnected disciplines efficiently, understanding how your system behaves as a whole. Evaluate complex multidisciplinary systems with confidence, eliminating manual data transfer and enabling true system-level design exploration. Visualize and manage AI-accelerated studies Simcenter HEEDS Connect 2604 introduces comprehensive AI simulation predictor integration that makes AI-accelerated studies fully visible and manageable in the collaborative web environment. Explore boosted designs and configurations Clear design distinction: Filter and visualize predicted vs. simulated designs in database tables and scatter plots with custom coloring. Comprehensive model information: View prediction model parameters including initial designs, training models, prediction levels, and constraint confidence settings. Grouped parameter editing: Review boost parameters across multiple analyses simultaneously. Input/output visualization: Explore available and selected inputs and outputs for each boosted analysis. Multi-selection comparison: Select multiple boosted items and compare their properties concurrently, with common values clearly displayed. Engineers can now fully leverage AI Simulation Predictor capabilities with Simcenter HEEDS Connect, accelerating design exploration cycles while maintaining complete visibility into model configurations and prediction accuracy. Centralized compute resource management Simcenter HEEDS Connect 2604 introduces the Managed Resource Catalog, transforming how organizations manage and deploy compute resources across engineering teams: Administrator-controlled definitions: Simcenter HEEDS Connect administrators can define and maintain compute resources in a central catalog accessible to all users. Eliminate setup errors: Managed resources come with pre-defined analysis of portal configurations, eliminating setup errors. Shared Across All HEEDS MDO Users: Resources defined in Simcenter HEEDS Connect can be leveraged by all Simcenter HEEDS MDO users in your organization. Define once, apply everywhere: Eliminate duplicate definitions and maintain resource configurations across different installations. Reduced configuration errors: Standardized resource configurations eliminate inconsistencies from misaligned copies. Maintain consistent compute resource configurations across entire engineering organizations, reducing errors and simplifying resource management for everyone. Cloud-based licensing with named user licenses Simcenter HEEDS Connect 2604 joins the Simcenter Named User License portfolio, bringing cloud-based licensing capabilities to the collaborative design exploration platform: Centralized user support: Unique centralized session for each user operation to enable multiple users to work simultaneously without conflicts. Session management: View secure identifier session expiry dates and revoke sessions directly from Simcenter HEEDS Connect. Seamless cloud integration: Leverage the ease of use of Named User Licensing with automatic session management. Simcenter cloud licensing: Simcenter HEEDS Connect now aligns with the broader Simcenter cloud licensing infrastructure. Teams can now access Simcenter HEEDS Connect with the same cloud-based licensing convenience they enjoy across the Simcenter portfolio, with robust and centralized support for concurrent users. The bottom line Simcenter HEEDS Connect 2604 represents a significant step forward for engineering teams committed to advancing their collaborative design exploration capabilities: Comprehensive workflow editing with real-time collaboration Multidisciplinary system evaluation with the new Evaluation Matrix AI-accelerated design exploration with full AI simulation predictor integration Centralized resource management with the Managed Resource Catalog Cloud-based licensing with Named User License support As a leader in simulation and test solutions, Siemens continues to advance collaborative engineering capabilities that help teams across aerospace, automotive, and industrial equipment industries accelerate innovation. Whether you’re collaborating on complex workflows, evaluating multidisciplinary systems, leveraging AI predictions, or managing compute resources across your organization, Simcenter HEEDS Connect 2604 empowers you to explore possibilities faster, work together seamlessly, and deliver better designs — all while collaborating smarter in the cloud. Transform your engineering team's collaboration with Simcenter HEEDS Connect 2604. With real-time workflow editing, AI integration, and centralized resource management, your company can accelerate decisions and improve multidisciplinary projects with much greater efficiency. Schedule a meeting with CAEXPERTS and discover how to apply these innovations in practice to contribute to your results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Modernized design optimization with AI surrogates, streamlined resource management and enhanced simulation integration This blog outlines how the latest release from Simcenter HEEDS 2604 accelerates design optimization workflows for engineers working with high frequency electromagnetic simulations, CFD, and multidisciplinary optimization – delivering faster compute resource management, AI-powered surrogates, and enhanced simulation integrations. Compute resource management reimagined Optimization projects often demand significant computational resources. Managing remote devices, schedulers, cloud services, and local execution a tedious effort – until now. The new resource catalog Simcenter HEEDS 2604 reworks the user experience for distributed execution by revamping remote execution capabilities into a new Resource Catalog that transforms how you manage and deploy compute resources: One-click resource creation: Set up any resource type with a just a few clicks. Managed catalogs: Download pre-configured managed resources from Simcenter HEEDS Connect for immediate use, or customize them by copying to your local catalog and modifying locked configurations. Multiple submission items: Create multiple submission items on the same device using direct submission, PBS, LSF, SLURM, MSHPC, or custom schedulers – all with automatically configured default parameters. Pre-configured job templates for Rescale: Leverage Rescale’s cloud computing power with pre-configured job template IDs that Simcenter HEEDS clones automatically. Easy resource access: Select pre-defined resources directly from the Process tab, with smart filtering to show only resources with portal-specific properties. Persistent configuration: Mapped local and remote drives mean you don’t have to redefine paths again – your resource configuration carries across all your Simcenter HEEDS projects. The payoff? No more tedious resource reconfiguration. Deploy analyses across diverse computing environments with confidence and consistency. Resource usage optimization for non-optimization studies Fast running analyses in design of experiments studies can fill up resources and consume significant disk space. Simcenter HEEDS 2604 allows for the option of enhanced control over resource allocation during the execution of DOE, Robustness and Reliability, and Evaluation Only studies. The new resource allocation option helps you balance computational efficiency with disk space management based on your study’s specific needs: Opt-in resource control: Prevent non-optimization studies from unexpectedly consuming excessive storage and execution resources. A modernized Neural Network builder with enhanced capabilities Simcenter HEEDS 2604 introduces a modernized Neural Network builder within Simcenter HEEDS POST that makes AI-powered surrogates faster to train and seamless to integrate into optimization workflows. Explore the enhanced Neural Network Builder Simcenter HEEDS 2604 delivers a modernized Neural Network builder. Here’s what’s new: GPU-accelerated training: Significantly reduce model training time with GPU acceleration – enabling faster surrogate model development for optimization workflows Automatic early stopping: Prevent overfitting as the system automatically stops training as soon as your model converges. Seamless workflow integration: Export your trained neural networks to your optimization workflow with a single click. ONNX format compatibility: Models are automatically saved in ONNX format for maximum compatibility and portability. Easy comparison and extension: Save, evaluate, and compare your trained models with other surrogates in Simcenter HEEDS POST effortlessly. Need to refine your model? Simply extend or continue training without starting from scratch. Engineers can now easily replace computationally expensive analyses with accurate neural network surrogates, speeding up optimization cycles while maintaining the fidelity their designs demand. Monitor the convergence of your multi-objective study Designing products with competing objectives is always a challenge. How do you know if your multi-objective Pareto optimization is actually converging? How do you know when feasible solutions have emerged? And what if you need to justify additional computational budget to your stakeholders? Introducing the Pareto convergence plot Simcenter HEEDS 2604 introduces the Pareto convergence plot, a powerful new run-time analytics feature that transforms how you monitor multi-objective studies. Track convergence behavior during the optimization run and make informed decisions on the fly. Key capabilities include: Convergence monitoring: Track the evolution of Pareto solutions across optimization cycles to assess convergence effectiveness and determine if additional computational budget is justified – all while the study is running. Feasibility detection: See exactly when feasible solutions emerge. Monitor the convergence of your multi-objective studies, eliminating guesswork, and enabling smarter optimization budget allocation decisions. End-to-end automated Altair Feko analysis and optimization Simcenter HEEDS 2604 introduces a set of brand-new Altair Feko portals which deliver comprehensive optimization capabilities for high-frequency electromagnetic simulations: Integrated workflow: Set up complete CADFEKO, Feko Solver, and POSTFEKO processes directly within Simcenter HEEDS, with the capability to run each portal on a separate compute resource thereby streamlining turnaround time. Rich visualization: Get deeper insights with comprehensive visualization files for detailed results analysis. Interactive data exploration: Leverage Simcenter HEEDS-specific plot formats with hover-over data points for detailed, interactive insights into your optimization results. Now Altair Feko users can optimize antennas, RF components, and other high-frequency electromagnetic devices easily. Enhanced Ansys Fluent and Fluent Meshing Portals For engineers working with Ansys Fluent, Simcenter HEEDS 2604 brings significant enhancements powered by the Fluent’s Python API. The new and improved Fluent integration includes: Streamlined design process: Conduct geometry modifications, remeshing, and mesh/zone replacement all within a robust, parametric framework. Integrated geometry and meshing: Move from geometry to CFD simulation in one cohesive workflow. Use the new Fluent portals to execute complete parametric design-to-CFD workflows. Faster parsing for Siemens Designcenter (NX) and Simcenter 3D Working with large, complex designs from Siemens Designcenter (formerly NX) or Simcenter 3D? Simcenter HEEDS 2604 eases the integration of your models. The new release provides a solution to large, complex design challenges in Siemens Designcenter (NX) and Simcenter 3D with significant parsing improvements that transform the experience of working with enterprise-scale models: Faster parsing: Using a new group-based parsing approach, Simcenter HEEDS 2604 can process models with thousands of expressions significantly faster. The bottom line Simcenter HEEDS 2604 represents a significant step forward for engineers and organizations committed to advancing their design optimization capabilities: Centralized Resource Catalog for streamlined resource setup Modernized Neural Network with improved integration Run‑time Pareto convergence tracking with new plotting tool Expanded simulation support with Feko integration and enhanced Fluent portal As a leader in simulation and test solutions, Siemens continues to advance collaborative engineering capabilities that help teams across aerospace, automotive, and industrial equipment industries accelerate innovation. Whether you’re optimizing antennas, fluid flow, structural designs, or complex multi-physics systems, Simcenter HEEDS 2604 enables you to explore possibilities faster, make more confident decisions, and deliver better designs – all while working smarter, not harder. Ready to accelerate your results with AI-driven optimization and integrated simulations? Schedule a meeting with CAEXPERTS and discover how to apply Simcenter HEEDS 2604 to your engineering workflow, reducing computational costs, increasing analysis speed, and improving the quality of your design decisions. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Rotating systems such as Gas Turbines are used for power generation in the energy industry, or propulsion in the aerospace industry and are subjected to harsh conditions with high temperatures and loads. Under such stress, defects induced during the manufacturing process can have catastrophic effects on the performance of a part. When there is an imbalance in the external loading or when internal imperfections affect the symmetry of the system, vibrations can occur that, depending on the rotational speed and intrinsic characteristics of the system, damage part or the whole assembly. The thermal deformation (thermal bow) of a turbomachine appears in the presence of a vertical temperature gradient, induced by the cooling process after a shutdown of the engine. When the engine restarts, the rotor bow can be responsible for high vibrations in the engine and must be studied carefully. Bow can also be due to static forces or pressure. In such a system we can expect vibrations when the structure deforms due to temperature and static load. but vibrations of each rotating part can also be due to its imperfections, such as an eccentricity of the centre of mass of a bladed disk. Gas turbine where the high-pressure rotor (outer part at the centre) rotates three times faster than the compressor and turbine (inner part). Blades are modelled by lumped inertia on the rotor axis Moreover, many rotating systems are made of different components which do not rotate at the same speed. For example, in a gas turbine, the high-pressure turbine and compressor stages can rotate three times faster than the low-pressure turbine and compressor stages. It is therefore necessary to understand how rotation-dependent defects will be considered in the harmonic response. This post addresses two important scenarios in harmonic response: the dynamic unbalance induced by a deformed shape (thermal bow), and multiple unbalances on different rotating parts. When thermal and static deformation induce vibrations of a rotating structure Rotating systems often experience harsh conditions of temperature, pressure, or loads that might create vibrations of the system. Furthermore, the dynamics can be affected by defects in the rotor due to manufacturing, like the non-uniform distribution of mass or a misalignment of the shaft. In this blog, we see how the external conditions causing rotor deformation will influence the vibrational behaviour of the system. The bow shape induced by the thermal bow deforms the structure making it asymmetric about the rotor axis. This deformation moves the mass so that it is no longer uniformly distributed around the rotor axis. The eccentricities with respect to the rotor axis induce unbalanced loads that are proportional to the square of the rotation speed. In the above example (1), a rotating system is made of two rotors: the High-Pressure rotor rotates 3 times faster than the Low-Pressure rotor. The system is modelled by 2D Fourier elements (axisymmetric model with Fourier harmonics), which is the best alternative to 3D models, in terms of accuracy and CPU time. External conditions are such that a static force deforms the rotors in the form of a bow. (2) The bow shape breaks the symmetry of the rotor around the rotation axis. Consequently, it naturally induces an imbalance on both rotors that is equal to: Massa x ecc x Ω 2 Where Ω is the rotor speed of each rotor and ecc is the eccentricity deduced from the deformed shape. Those unbalances on both rotors cause vibrations in the whole system that can be studied in the operating frequency ranges, to ensure that levels of vibrations are acceptable. Vibrations are represented for a selected frequency and selected harmonic in (3), and orbit plot in (4) can reproduce the total vibration of the system for a selected location and frequency. Later in this blog, we will discuss how software can manage unbalanced loads that correspond to different rotational speeds. Imperfections in the rotating parts induce undesirable loads in the rotating system Initial defects coming from the manufacturing process are independent of the external loading on the structure. However, they induce loads in the structure that result in vibrations of the structure. Among the typical scenarios in rotor dynamics that require testing, the simulation of unbalanced defects is the top priority for engineers. An unbalanced defect occurs when the mass and geometric centres do not coincide. Unbalance creates a load that is amplified with the rotor’s rotation speed Ω, proportionally to Ω2. In the clip below, the unbalanced rotating system mounted on flexible bearings can show very high vibration amplitudes when the system rotates, especially for certain rotation speeds. The peaks in the response correspond to the unwanted critical speeds of the system. Multiple harmonics in frequency response In an assembly made of many rotors rotating at different speeds, multiple unbalanced defects can exist where each induces forces that are linked to different rotational speeds, and subsequently different frequencies. When studying the harmonic response, engineers are interested in the behaviour of the system for one full cycle, in a range of frequencies (or rotation speeds). With a single defect, the equation of motion is solved for a single frequency ω: or by defining the dynamic stiffness matrix Z(Ωω)q(ω)=g(ω) Now, for multiple defects corresponding to different rotation speeds, Simcenter 3D Rotor Dynamics uses multiple harmonics simultaneously to solve the simulation. Each harmonic corresponds to the individual frequency (rotation speed) of each rotor. Equation (5.1) becomes a system of equations to be solved, for different frequencies ω. Equations of the system are not coupled when the rotors are axisymmetric so the whole assembly can be solved in a fixed reference frame, with the bearings’ behavior represented by linear functions. In a simulation using multiple harmonics, results are output for each individual harmonics ω1, ω2,… , making postprocessing less intuitive compared to the mono-harmonic case. Fortunately, it is possible to recombine results of all harmonics in the time domain through orbit plots for one or several cycles to display the final result. In the example of the dual rotors, where the unbalances were deduced from the deformed shape, two harmonics (ω1 and ω2) were used according to the two rotor speeds Ω1 for the low-pressure rotor, and Ω2 =3Ω1 for the high-pressure rotor. For each reference frequency, the result for the displacement in the Y direction for harmonics ω1 and ω2 are shown in the picture below. Having identified the peak of displacements at ω1=55 Hz, we can check the total vibration by combining the two harmonics for the Displacement in the X and Y directions (perpendicular to the rotor axis): X (t) = X ω1 e i (w1 t + φ 1) + X ω2 e i (w2 t + φ 2) Y (t) = Y ω1 e i (w1 t + φ 1) + Y ω2 e i (w2 t + φ 2) Where (Xω1, Yω1) and (Xω2, Yω2) are the vibrations computed in the harmonic 1 and 2 respectively. A point on a rotor axis that is linked to a high-pressure rotor can be represented with an orbit plot (X(t), Y(t)). Where, one period at the reference frequency of the first (low pressure) rotor corresponds to 3 cycles of the second (high pressure) rotor: With such an orbit plot, the engineer can determine if the vibration in the operating frequency range is acceptable. A demo of how this can be done in Simcenter 3D is shown below. With harmonic response, the engineer has the possibility to study the behavior of their rotating system in a range of frequencies and rotation speeds. With such analysis, defects like unbalance or misalignment can be studied in a frequency range, and external loads can be applied as a function of frequency. In this blog post, we presented how Simcenter 3D Rotor Dynamics can study simultaneous unbalance defects on different rotors rotating at different speeds, but also the natural unbalance that can be deduced from a deformed geometry (thermal bow or static bow) due to external factors such as applied loads and temperature. Simcenter 3D streamlines the complete workflow from the definition of the geometry to the post-processing tools and includes dedicated tools to complete all the steps in the process. Ensure the reliability and performance of your rotating systems before vibrations and imbalances compromise your projects — talk to CAEXPERTS. Our experts can help you apply advanced simulations with Simcenter 3D to identify faults, optimize dynamic behavior, and reduce operational risks. Schedule a meeting and discover how to bring more safety and efficiency to your systems. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Introduction In the rapidly evolving realm of e-mobility and electrical stationary storage systems, it becomes crucial to have precise battery models for effective system design. Battery models can be broadly categorized into two types: the equivalent circuit model and the electrochemical model: The equivalent circuit model simplifies the complex electrochemical processes occurring within a battery by representing it as an electrical circuit composed of resistors, capacitors, and voltage sources. This model provides a practical and straightforward approach to simulate battery behavior and is widely used in system-level simulations (e.g., battery pack simulating) and real time application (e.g., battery management system in the vehicle). On the other hand, the electrochemical model or P2D (Pseudo-Two-Dimensional) model delves deeper into the intricate electrochemical processes that take place within a battery. It considers the various physical and chemical phenomena, such as lithium-ion diffusion, migration, and chemical reactions, to provide a more accurate representation of battery performance. The electrochemical model considers factors like electrode kinetics, concentration gradients, and temperature effects, making it suitable for detailed analysis and research-oriented studies. A brief comparison of both models is given in the table below. While the equivalent circuit model offers simplicity and ease of implementation, it may not capture all the nuances of battery behavior, such as lithium plating. Conversely, the electrochemical model provides a more comprehensive understanding but requires more computational resources and detailed input parameters. A brief comparison between the equivalent circuit model and the electrochemical model In this article, a focus is given to the electrochemical model in Simcenter Amesim. You will find out the main features of this model which allow you to get insight into different critical battery behaviors, such as: Electrochemical process inside the battery cell Lithium plating Aging Simulating the electrochemical process inside the battery Figure 1 gives a schematic representation of the battery P2D electrochemical model. The active materials are depicted as spherical particles for each electrode. Each electrode is discretized into multiple layers, with each layer containing one particle in contact with the electrolyte. Each particle is also discretized into multiple layers. This approach allows for a detailed understanding of the behavior and interactions of different elements (e.g., active materials, Li-ion, electrolyte) within the battery system. By representing the battery in this manner, the model can capture the intricacies of particle-level phenomena, contributing to a comprehensive analysis of battery internal behaviors, such as the Li-ion concentration at the surface of different particle surfaces, the voltage drops on different elements of the battery, the average anode potential, etc. Simcenter Amesim also offers a simplified version of the P2D model, known as the single particle model with electrolyte (SPMe), which reduces computational complexity by representing each electrode with a single particle. This version is well-suited for scenarios requiring quicker analysis while maintaining effective simulation accuracy. Figure 1: Schematic representation of the battery P2D electrochemical model Figure 2 shows the simulation results of a constant discharge with the P2D model in Simcenter Amesim for a 5Ah NMC/SiC battery cell. The parameter values of the P2D model are taken from the work of Chen et al. [2]. Besides the cell voltage, the model can simulate various internal indicators, including the ohmic and kinetic overpotential within each electrode, electrolyte lithium concentrations, and mean diffusion overvoltage in the electrolyte. Figure 2: Simulation results of a constant discharge with the battery P2D electrochemical model It is also possible to interconnect multiple Simcenter Amesim P2D models to have a first level discretization of the cell. Figure 3 shows such an example with a 4×3 discretization for a prismatic cell. Each discretization node has a P2D model coupled with a thermal mass to calculate the local temperature. The current collectors are also discretized by using resistances. Compared to a detailed CFD simulation with thousands of meshes of the cell, this approach helps to get fast simulation results and limit the scope of the detail simulations to be carried out in a CFD software such as the 3D cell design capabilities of Simcenter STAR-CCM+. Figure 3: Example of battery cell discretization for local thermal study Lithium-plating Lithium plating occurs when metallic lithium deposits on a battery’s negative electrode during improper charging (e.g., fast charging at low temperature), leading to reduced efficiency and safety hazards. As fast charging is one of the important usage scenarios for electric vehicles and other battery-based systems (e.g., eVTOL, battery stationary storage systems), employing techniques such as model estimation is crucial for understanding and preventing lithium plating. With the Simcenter Amesim electrochemical model, you can easily get access to an internal variable of the model to detect the risk of lithium plating occurrence. This variable is the negative electrode liquid to solid overpotential. During the charge, lithium plating can happen if the negative electrode liquid to solid overpotential drops below 0 V. Figure 4 shows an example of the CCCV charge simulations results for a 45 Ah NMC/C battery cell at two different temperatures. The results indicate that towards the end of the charge at 10 °C, there is a risk of lithium plating occurrence as the negative electrode liquid to solid overpotential drops below 0 V. Figure 4: Example of simulation results for CCCV charges for a 45Ah NMC/C cell Aging With the Simcenter Amesim battery electrochemical model, the battery aging behavior can also be simulated via the modeling of different aging mechanisms causing capacity loss such as the growth of the SEI layer and the lithium plating. Figure 5 presents an example to simulate the loss of capacity due to lithium plating for a high energy NMC/C cell at two different temperatures. Figure 5: Example of simulation results for capacity loss due to lithium plating at two different temperatures References 1. Astaneh, M.; Andric, J.; Löfdahl, L.; Maggiolo, D.; Stopp, P.; Moghaddam, M.; Chapuis, M.; Ström, H. Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications. Energies 2020, 13, 3532. 2. C.-H. Chen, F. B. Planella, K. O’Regan, D. Gastol, W. D. Widanage, and E. Kendrick, “Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models,” J. Electrochem. Soc., vol. 167, no. 8, p. 080534, Jan. 2020 3. Demo “Electrochemical NMC-SiC Pseudo-Two-Dimensional (P2D) battery model comparison”, Simcenter Amesim Help, V2410, 2024 4. Demo “Charging strategies based on negative electrode overpotential – Lithium plating detection”, Simcenter Amesim Help, V2410, 2024 5. Demo “Lithium plating modeling”, Simcenter Amesim Help, V2410, 2024 Ensure more accurate decisions in battery development with expert support. Schedule a meeting with CAEXPERTS and discover how to apply advanced electrochemical models in Simcenter Amesim to improve the performance, safety, and lifespan of your systems. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Turbomachinery represents one of the most challenging and sophisticated fields of modern engineering. Its development demands operation under extreme conditions of temperature, pressure, and speed, imposing stringent requirements on materials, mechanical design, and operational reliability. Currently, jet engines and other turbines operate at temperatures exceeding 1,500 °C and under severe pressure levels, in regimes that surpass the conventional limits of most materials. Even so, these systems must maintain high efficiency, structural integrity, and low weight, especially in aeronautical applications. In this context, the advancement of turbomachinery depends directly on the ability to understand, predict, and control the behavior of its components and operating phenomena through experimental testing, rigorous simulations, and the continuous building of knowledge over time. Several engineering disciplines need to work together to create a gas turbine In other words, artificial intelligence needs good input data. The difference is that modern AI can process more data, learn from it faster, and apply those lessons on an unprecedented scale, shaping the future of turbomachinery in ways we are only beginning to understand. The hurdles OEMs face in a fast-paced world The future of turbomachinery depends on balancing multiple engineering challenges; all must be optimized simultaneously Today's turbomachinery manufacturers and suppliers face an ambitious challenge: designing and producing engines that are more flexible, more powerful, larger, with faster launch times, more sustainable, quieter, lighter, and with optimized cooling. Optimizing one attribute often means making concessions in another, requiring sophisticated tools and specialized knowledge to achieve the perfect balance. This balance will define the future of turbomachinery development. Furthermore, disconnected workflows between design and manufacturing create a problem. They disrupt the knowledge chain that should flow from design to production. When a design team in one location uses different tools and data standards than the manufacturing team in another, performance information is lost in translation. This can lead to inefficiencies, rework, and costly delays, including project launch delays, due to a lack of continuous information flow. Unplanned downtime can result in high costs for everyone involved. Accurately estimating the detailed thermal performance of subsystems, especially critical components such as cooled turbine blades and vanes, requires managing a complex convergence of CAD, aerodynamics, mechanical integrity, and aeromechanics. Each of these disciplines brings its own challenges, best practices, and the need to push boundaries through research to create the best possible engine. Siemens' Plan for Accelerated Innovation and the Future of Turbomachinery At Siemens, overcoming these challenges is considered to lie in a holistic approach that integrates cutting-edge technology with intelligent workflows. The proposed answer is integrated, AI-driven performance engineering, a robust convergence capable of dramatically accelerating innovation cycles while preserving the accumulated experience throughout engineering history. The so-called digital thread is often cited as the cornerstone of the future of turbomachinery engineering. The future of turbomachinery depends on seamless digital integration, from design to manufacturing Integrated information throughout the entire product lifecycle, from design to manufacturing, means connecting the CAE-CAD-CAM chain, drastically reducing production cycles and fostering a truly collaborative environment. But fundamentally, it means creating a single source of truth for all relevant data: design intent, simulation results, manufacturing tolerances, production variations, and ultimately, real-world performance. A comprehensive approach is adopted in which various engineering disciplines converge—aerodynamics, structures, thermodynamics, acoustics, and materials science—enabling holistic improvements. It ensures that every design decision is made with a complete understanding of its impact across all domains. When adjusting the geometry of a blade, it is necessary to immediately understand not only the aerodynamic benefits but also the structural implications, thermal consequences, and manufacturing feasibility. This requires that all relevant data be up-to-date, accurate, and readily available. Simulations of Combustion Turbine Interaction The era of isolated tools and fragmented workflows is over. This harmonized environment connects the tools, enabling automation and the exploration of large-scale analyses. When tools are disconnected, data is translated and re-entered multiple times, introducing errors and reducing the fidelity of the information flowing through the system. Unified tools preserve data integrity. Efficiency of Film Cooling Using Large Vortex Simulations Engineers are empowered with fast and accurate simulations, democratizing advanced simulation capabilities that lead to accelerated engineering insights and continuous development of parts and assemblies. Speed ​​is important, but accuracy is what truly matters. A fast simulation based on low-quality input data can lead to misinterpretations. Therefore, the goal is to ensure that the simulations performed are fast and reliable, based on validated models and high-quality input data. By combining virtual and physical tests, robust evidence of conformity is built. Simulations complement tests, but do not replace them. Data obtained from physical tests validate simulations, while validated simulations allow for the exploration of designs that would otherwise require physical testing. In this way, a virtuous cycle of progressively more accurate models and increasingly reliable decisions is established. AI-trained bird strike simulations and a testing platform for engine certification and verification Through the Siemens Xcelerator, manufacturing is transformed with an AI-based digital thread that creates a complete end-to-end connection between domains, from design to manufacturing. This encompasses CAD design and multiphysics optimization (noise, vibration, force, fluid, pressure, temperature) through CAM programming, data management, planning, CNC machining, and inspection. Gear pumps with optimized topology, 3D printing, and CNC simulations exemplify the future of turbomachinery: 80% faster when trained by AI The transformative power of AI and machine learning, based on solid data A machine learning model capable of predicting the fatigue life of turbine blades does so based on training performed using historical data on blade materials, operating conditions, failure modes, and observed results. The better the quality of this data, the more accurate the predictions tend to be, and the greater the reliability in defining the future direction of turbomachinery. The power of AI is combined with simulation to deliver better performance faster. AI-powered design exploration enables automated and intelligent optimization, helping engineers discover the best designs at each stage. Artificial Intelligence (AI) from Physics Trained on Common Manufacturing Deviations and Operational Defect Simulations Surrogate models are used for complex analyses, such as 3D finite element creep analysis, providing high accuracy in predicting critical locations and values ​​and significantly accelerating these time-consuming processes. These surrogate models are trained with high-fidelity simulation data; essentially, they learn the patterns that detailed physical simulations would capture. But, again, their accuracy depends entirely on the quality of the training data. Industry leaders are seizing every opportunity for data reuse, adopting AI at an accelerated pace through simulation with configuration management. Siemens Energy, for example, uses the HEEDS AI predictive simulator to streamline the integration of CAD and CAE processes across various engineering disciplines. Optimization of multidisciplinary projects accelerated by AI predictions Ready to tackle the most complex challenges in turbomachinery development with greater efficiency, integration, and innovation? Schedule a meeting with CAEXPERTS and discover how our solutions in simulation, integrated engineering, and digital transformation, such as Simcenter 3D and Simcenter STAR-CCM+ , can accelerate your projects, reduce risks, and take your results to a new level. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The latest version of Designcenter NX is not an incremental update; it's a strategic investment in digital transformation. We are providing the tools you need to compete in an AI-driven, cloud-connected, and model-based future. This new version features a broad set of enhancements in assembly, design, drawings, and user experience. This update focuses on expanding core modeling and construction capabilities, improving drawing creation and control, and continuously enhancing the overall usability of the browser-based experience. All new capabilities are underpinned by 4 fundamental pillars. These four pillars are: The 4 Pillars Designcenter X NX – Offering a Complete Collaboration Solution We are living in an era of transformation, in which global companies are migrating to Software as a Service (SaaS) models. There is a real desire in the industry to reduce time to market, manage increasing complexity, and ensure a simplified IT strategy to enable instant connectivity. Siemens research indicates that SaaS models result in a 19% increase in productivity and an almost 100% increase in uptime and reliability. Therefore, investment continues in the Designcenter X NX strategy to create a truly collaborative environment for users. Designcenter allows you to design for the future, whether through a browser or on a computer, offering unparalleled flexibility. It is the most complete and uniquely scalable environment for product development, with best-in-class product engineering software. Introducing Live Share Let's take a closer look. Cloud collaboration for globally distributed teams is fundamental as companies continue to migrate to a hybrid work model. There is a huge demand for companies to be able to work on product development in real time. Live Share enables real-time collaborative creation while preserving industry-proven security and business logic. With it, multiple engineers can work simultaneously on the same assembly or part in real time. Furthermore, it works with data managed both in the cloud and by Teamcenter® software. Live Share enables true concurrent engineering, reducing project cycle time by 30-50% and eliminating costly delays. Automation with Artificial Intelligence The power of Artificial Intelligence continues to transform the competitive landscape for engineers worldwide. AI is the primary area of ​​technology investment for manufacturers. New AI capabilities are reducing the need for repetitive tasks, accelerating the work of design engineers and freeing them to focus on innovation instead of manual operations. Early adopters of AI technologies report time savings exceeding 40% on common tasks, which is why it's at the heart of the continuous rollout strategy. Here are some of the new AI-based workflows in the latest version of Designcenter NX . Designcenter X NX Copilot Designcenter X NX Copilot is the engineering-focused generative AI assistant that brings innovation to life by transforming natural language into action. The new tool leverages industry best practices and Siemens' expertise to guide users through complex tasks with knowledgeable and informative responses. With Copilot, you can ask questions in plain language and everything will be translated into area-specific commands, assisting with everything from best design approaches to troubleshooting errors. The benefits are real; Copilot reduces the time and effort spent on complex tasks, streamlining workflows and making Designcenter X NX more accessible and user-friendly, especially for new users looking to unlock more features. CAD interface with mechanical model and Copilot displaying suggestions Digital Threads Let's take a look at digital threads and where this version focuses. New features for the Design for Manufacturing (DFM) Advisor DFM Advisor is a design-to-manufacturing application that provides critical information about part production during the design phase. It's a new application developed to identify problematic and high-cost manufacturing areas in any design model. For the new version of Designcenter NX , additional checkers have been added to support various manufacturing methods, including milling checkers, assembly checkers, and sheet metal checkers. New Ship Structure Capabilities Ship Structures is a highly productive and collaborative environment for modeling naval structures. With complete coverage of naval design workflows at all design stages, you can reuse design data for subsequent production information and drawing generation. New tools to enhance workflow support from production information to design, along with significant performance improvements and AI tools, have resulted in a 20-30% increase in performance for section views and updates. Ship sructure in the Ship Structures environment Design for Sustainability in Designcenter NX Finally, let's talk about the productivity gains associated with sustainability. Design for Sustainability is an application developed specifically for this purpose, which integrates sustainability into product design in a transparent way. It allows for data-driven decision-making through advanced visualization and reporting, enabling real-time environmental impact assessment. Furthermore, Design for Sustainability can be integrated with coatings and manufacturing to ensure that all sustainability goals are aligned with production efficiency. The Importance of Immersive Engineering Next, we will highlight the new features added in conjunction with immersive engineering. Immersive Engineering continues to transform the way engineers create products. It's a product for the future, allowing everyone to experience digital twins naturally in an immersive environment. A product not limited to a single sector, it's a tool that offers the only fully integrated XR CAD environment, meaning you can stay in your usual engineering application and ensure the security of your data. It's an award-winning solution, designed by engineers, for engineers. Live Updates in Immersive Collaborative Meetings The Immersive Engineering offering is being enhanced through Immersive Collaborator. Immersive Collaborator adds real value and facilitates decision-making by enabling live updates and design changes during meetings. Changes made by the session host are communicated to other users so they can observe and interact. Real-time updates are enabled for Animation Designer, Mechatronics Concept Designer (MCD), and other compatible design operations. This represents a true revolution for simulations and design changes, particularly in identifying collisions and the need to make rapid changes. Cyclist analysis with aerodynamic simulation in a virtual environment Expanded assembly capabilities in NX Centering Constraint. Position components in an assembly by centering them between two faces. Assembly workflows receive several important additions in this version, primarily regarding positioning, movement, and visualization. New constraint types, including center constraint, cylindrical joint, and sliding joint, expand the possibilities for defining and defining component behavior in an assembly. This allows for more realistic movements and positioning, such as allowing rotation and translation along a common axis or restricting movement to a single direction. To facilitate the visualization and communication of assemblies, a manual explode function has been introduced, allowing users to move and rotate components using the Intelligent Triad. This function can also be used to automatically refine exploded views. Additional improvements include the ability to copy components directly from the Assembly Browser using drag-and-drop workflows, expanded access to standard parts in various global standards, and improved performance when working with large assemblies. Faster and more flexible design workflows Delete Body. Removes bodies from the model associatively in the history tree. This feature can be suppressed to resurrect bodies. Design improvements in this version focus on making it easier to create, modify, and manage geometry, reducing difficulties. New features, such as the sketch pattern, allow users to quickly replicate geometry in linear or circular arrays, while the sketch chamfer adds more control when defining details at the sketch level. Model editing has also become more flexible with the addition of "delete body" and "delete face" options, which allow users to remove geometry while maintaining associativity in the history tree. These features can be suppressed to restore geometry if necessary. The main modeling commands have also been expanded, with extrude, revolve, and face replacement now supporting draft and thickness parameters. Updates to the shell command provide greater control over the direction of displacement, and enhancements to the query command facilitate access to detailed face information, such as surface area and centroid. More complete and customizable drawings Center mark, centerline, and primitive circle diameter. Use these annotations in drawings to communicate design intent, locate elements, and manufacture parts with precision. Drawing functionality has been significantly expanded to support more comprehensive documentation workflows. Users can now edit embedded drawings directly within the same NX part file, simplifying transitions between modeling and detailing. New layer controls allow for better management of drawing visibility and selection, helping to reduce clutter. Several new annotation and detailing features have been introduced, including center marks, centerlines, and primitive circle diameter annotations, along with expanded control over dimension and annotation settings. For sheet metal workflows, the addition of a bend table allows for the inclusion of specific manufacturing information, such as bend radius, angle, and direction, directly in the drawing. Additional updates to drawing view settings, preferences, parts lists, and hole tables provide more flexibility and control, helping teams align results with internal standards. Continuous improvements to usability and user experience Version Update Notification. Receive automatic notifications whenever the product is updated, with a link to information about the new features. This version also introduces several updates designed to improve usability and make the application more intuitive. Users can now select user profiles to align mouse and keyboard behavior with NX or other conventional CAD systems, reducing the learning curve. Dependencies between features are now easier to understand thanks to visual highlighting in the history tree, and direct sketch editing allows users to quickly modify geometry without having to navigate through features. Additional improvements include version update notifications, product version visibility, improved file path access, and simplified workflows for opening files, contributing to a smoother daily experience. The new 2026 version of Designcenter X NX brings significant advancements that make your engineering processes more agile, precise, and intuitive—from assembly to final documentation. Want to understand how to apply these improvements to your daily work and extract maximum value from the tool? Schedule a meeting with CAEXPERTS and discover, in practice, how to optimize your workflows and elevate the level of your projects with NX . WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

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

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

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

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

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