Generative AI for design, broader simulation coverage, dramatic performance gains, and deeper Simcenter ecosystem integration – the Simcenter PhysicsAI 2026.1 release advances AI-powered simulation on every front. Simcenter PhysicsAI 2026.1 (formerly Altair PhysicsAI) is now available, delivering capabilities that empower engineers to unlock the full value of their CAE data with greater speed, accuracy, and intelligence. Built on the foundation of geometric deep learning, Simcenter PhysicsAI trains AI surrogate models on your historical simulation data and delivers data-driven insights up to 1000x faster than traditional solver simulations. In 2026.1, that foundation grows stronger across every dimension: generative AI for early-stage design exploration, extended SPH particle support for demanding impact and large-deformation scenarios, sharper stress and strain hotspot prediction, automated frequency extraction for NVH workflows, up to 5× faster training with up to 50% reduction in peak memory usage, and a deeper native connection to the broader Simcenter ecosystem. Simcenter PhysicsAI Generate: From performance requirements to design concept in seconds Perhaps the most exciting development in the 2026.1 release is the introduction of Simcenter PhysicsAI Generate – a capability that brings generative AI directly into the engineering design process. The challenge it addresses is one that virtually every product development team recognizes: new design requirements arrive with tight timelines, yet every design variant traditionally requires a full physics solver run before any concept can be confirmed. Exploring a broad design space spanning geometry, dimensions, material parameters, and performance targets simultaneously is simply not feasible at the pace modern projects demand. Simcenter PhysicsAI Generate changes that dynamic fundamentally. Powered by diffusion-based generative AI models – it trains on historical designs to learn the deep correlations between geometry, design parameters, and engineering KPIs. Once trained, it produces novel, physics-aware 3D design concepts directly from performance and dimensional requirements, in seconds. Critically, unlike traditional generative tools that apply physics constraints as a post-processing step, Simcenter PhysicsAI Generate produces concepts that are inherently physics-consistent from the outset. Weeks of iterative design exploration can be compressed into a single workflow, with generated concepts that reliably reflect prescribed performance targets from the very first run. Training performance improvements – Up to 5× faster, 50% reduction in peak memory usage Simcenter PhysicsAI 2026.1 is significantly faster and more efficient at using RAM during training. Memory utilization has been fundamentally improved, delivering up to 5× faster training speeds and up to 50% reduced peak memory usage as compared to 2026.0. In one of the benchmarks, peak memory dropped from 175 GiB to 75 GiB, while total training time was cut roughly in half – meaning teams can train larger, more complex models on the same hardware, faster than ever before. Teams using the Simcenter PhysicsAI add-on in Simcenter STAR-CCM+ also benefit from newly available multi-GPU support, enabling even faster model training by distributing the workload across multiple GPUs. For enterprise teams running large-scale CFD or crash models, this translates directly into faster design iteration cycles, reduced infrastructure costs, and the ability to tackle problems that previously strained available resources. Spatial conditioning: Teaching the generator where things matter Building on Simcenter PhysicsAI Generate, 2026.1 also introduces spatial conditioning – the ability to condition generative models on point clouds. Rather than constraining the generator only with scalar design parameters, engineers can now define spatially-distributed constraints as 3D point arrays: attachment point locations, load application zones, manufacturing fixture positions, or any other spatially-meaningful design context. These point clouds are specified in companion JSON files during training, and at generation time, users select node sets for each conditioned point cloud directly in the generation dialog. The result is a generative model with genuine spatial awareness – one that produces concepts respecting real-world geometric constraints, not just abstract performance targets. SPH particle support: AI-accelerated workflows for your most demanding simulations One of the most significant capability expansions in 2026.1 is the addition of Smoothed Particle Hydrodynamics (SPH) particle support. SPH simulations are the method of choice for some of the most demanding physics scenarios in engineering – large-deformation impact events, bird strike analysis, and fluid-structure interaction, to name a few. Until now, SPH particles & point mass elements were not recognized by Simcenter PhysicsAI – limiting its use in explicit simulations. That changes in 2026.1. SPH particles and point mass elements are now fully visible to Simcenter PhysicsAI, enabling it to learn from and predict critical structural integrity indicators – including the number of failed layers, stress, and strain – directly from SPH simulation data. For automotive OEMs, aerospace manufacturers, and defense engineers whose most challenging analyses live in the SPH domain, this is a meaningful step toward AI-accelerated workflows for their hardest problems. Eigen frequency prediction for modal analysis and NVH workflows Modal analysis sits at the heart of NVH and structural dynamics workflows. Understanding how a structure vibrates, where its resonant frequencies lie, and how those modes change across design variants is fundamental to delivering products that are quiet, durable, and dynamically well-behaved. In previous versions of Simcenter PhysicsAI, only mode shapes were predicted, requiring engineers to work around a fundamental missing piece in their AI-assisted simulation pipeline. In 2026.1, that gap is closed. Eigen mode frequency values are now automatically extracted and predicted for modal analysis, with Eigen Model Step Labels automatically associated to each predicted mode shape without any manual intervention. The result is a faster, more accurate normal modes analysis workflow – one where engineers spend less time configuring and more time acting on the insights their models deliver. Native integration with Simcenter Inspire manufacturing solutions The value of AI-driven simulation is maximized when it is embedded directly in the tools engineers use every day, not bolted on as an afterthought. In 2026.1, Simcenter PhysicsAI deepens its integration with the Simcenter Inspire manufacturing solutions ecosystem, enabling seamless data exchange and workflow continuity between casting, molding, forming, extrusion process simulation and AI-powered prediction. For manufacturing-driven industries – automotive, aerospace, heavy equipment – this native connection is a meaningful step toward truly end-to-end workflows, where AI accelerates every stage of the product lifecycle from process design to in-service performance prediction. Constant memory mode: Scaling AI training beyond RAM limits For teams working with extremely large datasets where available RAM is the hard constraint, 2026.1 introduces Constant Memory Mode. When enabled, Simcenter PhysicsAI stores graph data on disk and loads it into RAM only as needed during training – effectively decoupling dataset size from available system memory. To mitigate the I/O overhead inherent in disk-based loading, an asynchronous loading variant is also available, leveraging multiple CPU threads to prefetch data in parallel and recover much of the speed penalty. For organizations with large historical simulation libraries that would otherwise exceed available RAM, this mode removes what has been a genuine barrier to scaling AI-powered workflows. Smarter dataset curation: Better data pipelines A great AI model starts with great data, and the time spent preparing that data is time that cannot be spent on engineering. In 2026.1, the dataset creation and curation workflow has been meaningfully streamlined. Engineers can now down-select results during dataset creation for faster processing, manage multiple samples simultaneously through batch move, copy, and delete operations, and benefit from response-aware outlier detection that updates dynamically based on the selected output. Vector features and custom metadata are now included in outlier analysis and displayed in the results tree and summary views, giving engineers a more complete picture of their data quality at a glance. Collectively, these enhancements make data preparation significantly more efficient and intuitive, reducing the most time-consuming step in building a surrogate AI model. New welcome dashboard: A smarter, ,0faster starting point Sometimes the most impactful improvement is the one that gets engineers to value faster. The new Welcome Dashboard in 2026.1 delivers a clean, intuitive entry point to the Simcenter PhysicsAI, making it straightforward to create new projects, open existing ones, resume recent work, access tutorials, and browse built-in examples – all from a single, well-organized screen. Accessible directly from the familiar Simcenter PhysicsAI ribbon, the dashboard reduces the friction of getting started, particularly for new users or teams onboarding Simcenter PhysicsAI for the first time. Improved stress and strain hotspot prediction for structural analysis Finding stress hotspots accurately is not just an academic exercise – it is the difference between a design that passes fatigue life requirements and one that fails in the field. In 2026.1, Simcenter PhysicsAI predicts stresses, strains, and other element-bound fields directly on the elements themselves, bypassing interpolation entirely. A new “Preserve Output Bindings” discretization option ensures that the element or nodal definition of the original field results is faithfully maintained throughout the prediction pipeline. The outcome is sharper, more trustworthy hotspot maps – the kind of accuracy that gives structural engineers the confidence to act on AI predictions rather than simply validate them. Custom input visualization: Transparency that builds trust in AI predictions As AI models become more deeply embedded in engineering workflows, transparency becomes essential. Engineers need to understand not just what the AI predicts, but what information it is working with. In 2026.1, users can now visualize custom input features directly within the dataset and testing GUIs – whether those inputs are global scalars, nodal scalars, or vector fields. This gives engineers a clear, visual window into the data that drives the model, making it easier to validate inputs, catch data quality issues early, and build the kind of trust in AI predictions that supports confident decision-making. New geometric similarity score: Predict with confidence, act with certainty How do you know whether an AI prediction can be trusted? Simcenter PhysicsAI features a geometric similarity score – an indicator based on geometry and discretization that assesses how similar a new design is to the training dataset. Engineers can instantly assess the novelty of a design and whether a full solver run is warranted, enabling confident AI-driven design exploration while reducing the risk of acting on out-of-distribution results. In 2026.1, that capability has been significantly strengthened. Previously, similarity scores ranged from −∞ to 1 – a range that was mathematically valid but intuitively difficult to interpret. The new similarity score is strictly bounded between 0 and 1, computed using a mathematically rigorous piecewise smooth curve as a function of the Euclidean distance between the feature encodings of the prediction sample and the nearest training sample. Equally important, the score is now feature-aware: each continuous input feature – shell thickness, meshed geometry, and others – receives its own individual similarity score alongside an aggregate score. This granularity helps engineers quickly identify which aspects of a new design are well-represented by the training data and which might warrant additional caution or supplementary simulation – making AI predictions not just fast, but genuinely trustworthy. Simcenter PhysicsAI 2026.1 – A solution that keeps raising the bar Taken together, the updates in Simcenter PhysicsAI 2026.1 tell a coherent story: a solution maturing rapidly across every dimension that matters to engineering organizations. Physics coverage is broader, reaching into SPH-based explicit models and modal dynamics. AI capabilities are more powerful, with generative design and spatial conditioning pushing the frontier of what is possible. Computational performance is dramatically improved, making larger problems tractable on existing hardware. And integration with the broader Simcenter ecosystem is deepening – bringing AI-powered prediction closer to the tools and workflows where engineering decisions are actually made. Whether you are a NVH engineer predicting modal behavior across hundreds of design variants, a crash analyst working with SPH-heavy impact models, or a design team looking to harness generative AI for early-stage concept creation, Simcenter PhysicsAI 2026.1 has something substantive to offer. The future of engineering simulation is not just faster solvers – it is AI that learns from your data, speaks the language of physics, and delivers answers at the speed of design. That future is here, and it keeps getting better. If you want to unlock the full potential of Simcenter PhysicsAI 2026.1 and accelerate your development processes using artificial intelligence, schedule a consultation with CAEXPERTS. Our team includes engineers trained and qualified to provide support, implementation, and application services for Altair solutions, helping your company integrate AI-driven simulation into your engineering workflows. We collaborate with your team on initial projects to maximize return on investment, helping you get the best performance out of the tools and achieve faster, more accurate, and reliable results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Mention oil and most people will think about friction, after all if you want something to move freely then you add oil to the surfaces. When we think about oil in cars, most people will think about engines, gears and their smooth operation. But oil has dual benefit: by virtue of its high thermal conductivity and high specific heat capacity it makes a very effective coolant. Whether that’s the cooling jets stopping piston crowns from melting, the gearbox oil carrying away the waste heat from a gear mesh, or oil sprayed onto eMotors. Simulating these applications of oil with CFD is challenging, and while Simcenter STAR-CCM+ allowed to tackle those through Hybrid Multiphase for quite some time, this can be a computationally relatively costly endeavor, especially in early design phases. With version 2606 Simcenter STAR-CCM+ adds a massive leap in capability to the Smoothed Particle Hydrodynamics (SPH) solver so that you can iterate faster without losing fidelity. As a key leading application, in this blog we will focus on the vital role oil plays in electric motors. Understanding heat generation in electric motors Electric motors convert electric power to mechanical power incredibly efficiently (94 percent or higher) but losses still exist. A 200kW EV motor loses approximately ~12kW, mostly as heat. What exactly happens if we don’t manage that heat properly? High temperatures are particularly bad for the windings. Copper’s resistance increases with temperature, leading to increased energy losses and further heat generation. The winding insulation also has a thermal rating, and exceeding it leads to premature degradation. The flux density of permanent magnets also reduces with temperature, impacting torque output. The heat originates from two primary sources: Joule losses in the windings and iron losses in the core. Joule losses occur when current flows through the motor windings. A simple demonstration of heat generation through Joule (aka Ohmic) losses Because these losses are proportional to current squared, they can rapidly escalate. To mitigate this, there’s a growing trend toward higher voltage architectures (400V or 800V), which reduce current-related losses—though this comes at the cost of significantly more expensive components. Iron losses, meanwhile, result from both eddy currents and hysteresis in the stator and rotor cores. Eddy currents are induced electrical currents within the magnetic core that oppose the original change in flux, leading to energy dissipation as heat. Hysteresis losses occur as the core material continuously reorients itself to follow the rapidly changing magnetic field direction, with the energy required for this reorientation also converting to heat. Finally there are also small friction losses from the bearings and rotor movement. Keeping our cool, efficiently Now that we’ve learned where the heat comes from and all the problems it can create, what can we do about it? In practice, whilst a simple motor like an alternator can be air-cooled with an open back design, EV motors rely on liquid cooling to keep temperatures under control. Water-glycol jackets operate in the same way as those in a cylinder head and water has a much higher thermal conductivity and specific heat capacity than oil. However, as oil is dielectric it can be sprayed directly onto the hottest parts of the motor, making it an order of magnitude more effective than a water jacket. Gear oil is also convenient to use as it can additionally lubricate the bearings and other transmission components in an Electric Drive Unit (EDU). The perfect solution then? Well, the continuous strive for drivetrain efficiency demands lighter weight: less system mass means less energy required to accelerate and decelerate, lower overall power consumption and key for most buyers: a higher range figure. Increasing the system power density means proportionally less material, which means less heat capacity and less thermal inertia, the hottest parts get hotter faster, with no ability to act as a sink and distribute the heat. This places increasing demands on the cooling system to absorb and distribute heat. Unfortunately, lightweighting applies to the oil volume too. Reducing the volume of liquids also reduces dynamic mass (great!), but at the cost of cooling system capacity. This means that engineers need to make sure that every precious drop of coolant is used as effectively as possible to extract maximum performance from the cooling system. If we consider a thermal abuse loadcase where the motor is outputting high torque and low power (imagine driving a heavily laden vehicle up a steep hill), the aim is to identify areas starved of coolant and also predict the overall rise in system temperature. If we focus on spray cooling as is typical in most current generation EVs, various configurations of stationary and rotating jets are employed to cool the end windings. However, the challenge is that the time scales of flow and thermal phenomena are very different, whilst we might be resolving details of the oil jets in the order of milliseconds, reaching thermal equilibrium can take minutes. SPH and thermal simulation – coupled physics made easy SPH is a wonderful tool for capturing violent oil flows in drivetrains. Capturing the fluid-thermal interaction adds another layer of complexity. Typically solving this problem with SPH simulation relied on either simulating a short duration and then extrapolating the heat transfer over time, or mapping between separate simulations, with manual export of Heat Transfer Coefficients (HTCs) each time the data is exchanged. This might be OK for a handful of simulations, but it becomes tedious and error prone when trying to do it for a full design study. It’s possible to improve the situation with some script stitching, but these days shorter EV development times are seen as a competitive advantage. There has to be a better way. Simcenter STAR-CCM+ 2606 addresses this challenge by enabling the SPH solver to couple directly with the Simcenter STAR-CCM+ Energy Solver for conjugate heat transfer simulation. You now have access to the advanced physics and boundary conditions available in the Energy solver alongside your SPH simulation in a single environment. This allows full modelling of the fluid temperature evolution, heat transfer at the walls and solid temperature evolution in a fully coupled simulation. Both simulations are set up in the same Simcenter STAR-CCM+ environment for a familiar and consistent workflow. Comparison of convective Heat Transfer Coefficient as a function of inlet velocity between Simcenter STAR-CCM+ SPH and experiment. Source Kekila et al, 2019, NREL/CP-5400-74284 Alternatively, Simulation Operations can be used to stagger the fluid and thermal simulations and capture longer timescales. What would have required scripts can now be done easily in a few clicks in Simcenter STAR-CCM+ SPH. This capability delivers more reliable predictions for cooling simulations, eliminates manual data transfer and allows seamless post-processing without needing to switch tools. Extending SPH with multiphysics and coupled thermal-electromagnetics- CFD Iron losses are often assumed to be constant in a thermo-fluid-dynamic simulation, whereas in reality, they are strongly dependent on temperature. Capturing this effect would typically require a complex mapping procedure between different tools. In Simcenter STAR-CCM+, it is possible to activate the electromagnetic solver and integrate it into the simulation operations loop. This allows losses to be updated based on the new temperature distribution as the oil circulates and the temperature evolves—all within Simcenter STAR-CCM+. Asymmetric cooling oil distribution is driven by the winding angle and the resulting impact on cooling (HTC). Individually aligning the outer oil jets is one way to solve this problem, a task made much easier with optimization The simulation now captures the combined effects of the problem. The introduction noted that the oil should be used as efficiently as possible to extract maximum performance from the cooling system. Based on the results obtained, it is possible to analyze the system's behavior and adjust the number of jets—as well as their location, diameter, and angle—to optimize coil wetting and maximize heat transfer. With a meshless approach to fluid simulation, implementing and iterating on these types of changes becomes much simpler. Simcenter HEEDS can also be used to automatically apply these changes during a study and optimize wetting behavior. SPH simulation-based optimization of nozzle orientation for selected designs to obtain optimized wetting. Source: InDesA SPH simulation of optimized nozzle angles for maximum surface wetting and cooling efficiency. Source: InDesA Simulation should go beyond the oil While the focus has shifted toward electric motors, the efficiency and reliable performance of any vehicle powertrain—regardless of the motor type—depend on oil. Ensuring a design delivers sufficient oil to the right locations in the proper quantities to prevent system failures relies on simulation. Obtaining accurate predictions requires tightly coupled flow and energy solvers. To generate these predictions quickly enough to keep pace with the design cycle, seamless integration and built-in optimization capabilities are essential. Do you want to unlock the full potential of Simcenter STAR-CCM+ to enhance thermal simulation and optimize cooling in your designs? Schedule a meeting with CAEXPERTS and leverage the expertise of our engineers to apply the best simulation and optimization strategies to your project. We collaborate with your team on initial projects to maximize return on investment, helping your company extract peak performance from the tool and achieve faster, more accurate, and reliable results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Simcenter EDEM 2026.1 (formerly Altair EDEM) is now available, bringing engineers powerful capabilities to enhance their particle simulation workflows. Automate your simulation setup and reuse models across workflows with minimal effort, enabling you to explore the possibilities of particle behavior in diverse scenarios. Analyze coating thickness on particle surfaces up to 100x faster, significantly accelerating your design iterations and helping you go faster from concept to solution. Develop and maintain custom physics models faster with a unified approach that simplifies handling complexity in your particle systems. These enhancements streamline your engineering process, reduce setup time, and improve computational efficiency. Discover the complete range of enhancements in Simcenter EDEM 2026.1 and see how these capabilities can support your next project. Develop and maintain custom physics models faster than ever Developing custom physics models in DEM simulations has traditionally meant managing two separate codebases: one for CPU solvers and another for GPU solvers. This duplication creates a maintenance burden, doubles your testing effort, and introduces opportunities for inconsistencies between implementations. With Simcenter EDEM 2026.1, you now have access to a Unified Custom Model API that eliminates this complexity. You write your custom physics code once, and it runs seamlessly on both CPU and GPU solvers without any duplication. You benefit from consistent behavior across both solver types, making debugging significantly more efficient and reducing the risk of solver-specific errors. The streamlined file structure frees you to focus on physics innovation rather than managing code complexity. You reduce development time substantially by eliminating redundant coding and testing cycles. This unified approach means faster iteration, easier maintenance, and greater confidence in your custom model implementations across your entire simulation environment. Ready to accelerate your custom physics development? Explore the Unified Custom Model API. Automate simulation setup and reuse models across workflows with minimal effort Setting up simulation variants in DEM workflows often means repeating the same configuration steps over and over, which consumes valuable engineering time and limits your ability to explore design alternatives efficiently. When every parameter change requires manual intervention through the GUI, running design of experiments or parametric studies becomes a bottleneck that slows down innovation. The new Simcenter EDEM 2026.1 release introduces a human-readable JSON input file format that fundamentally changes how you interact with your simulation models. You can now edit simulation configurations using any script that can modify JSON (including Python) or any standard text editor, eliminating the need to rebuild models from scratch. This approach enables you to automate design of experiments by programmatically generating multiple simulation variants without manual rework. You gain the flexibility to reuse and adapt existing simulations by simply modifying text files rather than clicking through complex setup procedures. The JSON format also allows you to seamlessly integrate EDEM into custom workflows and AI-driven processes using standard tools and libraries. You’ll accelerate your simulation throughput while maintaining full control over model configurations through an accessible, scriptable interface. Explore how JSON-based simulation input files can streamline your DEM workflow and unlock new automation possibilities. Analyze coating thickness on particle surfaces up to 100x faster Spray coating simulations are critical for optimizing pharmaceutical tablet coating and food seasoning processes, but slow analysis times have traditionally created bottlenecks in process development. Engineers often wait hours for coating thickness results on particle surfaces, which delays optimization cycles and slows down production decisions when time-to-market is crucial. The new release of Simcenter EDEM 2026.1 addresses this challenge with significantly accelerated spray coating performance for polyhedral particles. With this enhancement, you can now complete tablet coating or seasoning simulations in minutes instead of hours, transforming your workflow efficiency. You’ll optimize spray parameters faster, enabling you to reduce material waste and improve coating uniformity across your product batches. The performance boost allows you to run more design iterations within the same timeframe, giving you the confidence to fine-tune production processes based on comprehensive analysis rather than limited data. This means you can explore a wider range of operating conditions and make better-informed decisions about nozzle positioning, spray rates, and process parameters. Faster results directly translate to shorter development cycles and quicker paths to production optimization. Ready to accelerate your coating analysis workflow? Explore the enhanced spray coating performance capabilities and discover how much faster you can optimize your processes. If you want to unlock the full potential of Simcenter EDEM 2026.1 and accelerate your development processes with more efficient DEM simulations, schedule a consultation with CAEXPERTS. Our team consists of engineers trained and qualified to provide support, implementation, and application services for Altair solutions; we collaborate with your team on initial projects to maximize return on investment and help your company achieve peak performance from the tool, delivering faster, more accurate, and reliable results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The latest version of Simcenter Inspire (formerly Altair Inspire) release enhances design exploration with AI-powered workflow, advanced modeling capabilities, and improved simulation efficiency across multiple engineering disciplines. In this blog you will find some of our highlights, to get the comprehensive list of everything new in the latest release review the release notes on support center. AI-driven Workflow Simcenter Inspire 2026.1 expands AI-powered simulation by enabling engineers to train PhysicsAI models from existing simulation data and predict results directly within Inspire. Models can be trained using structural analyses from Simcenter Optistruct and Simcenter Simsolid, as well as Cast, Mold, Form, and Extrude workflows available in Simcenter Inspire, providing broad coverage across engineering applications. Predicted results are displayed on new geometry without requiring a full solver run, helping teams evaluate design changes earlier, accelerate iteration cycles, and make informed engineering decisions while reducing reliance on time-intensive simulation workflows. Expanded implicit modeling Simcenter Inspire 2026.1 introduces powerful enhancements in Implicit capabilities that simplify the creation of complex, adaptive designs while improving modeling robustness. New implicit CAD operations, including Extrude, Revolve, and Sweep, enable field-driven geometry that remains reliable even in challenging modeling scenarios. Expanded GUI access to mathematical functions eliminates the need for Python scripting, making advanced implicit modeling more accessible to a wider range of users. Together, these capabilities streamline the development of sophisticated field-based designs, support greater automation, and provide engineers with more flexible, efficient workflows for creating optimized geometries. Multi-profile support Simcenter Inspire 2026.1 introduces a unified multi-profile experience that brings all Inspire tools, solvers, and workflows into a single user interface. Engineers can seamlessly switch between structures, motion, fluids, and manufacturing profiles without changing environment, creating a more efficient and connected design process. By consolidating capabilities into one interface, Simcenter Inspire 2026.1 reduces workflow interruptions, improves efficiency, and enables faster exploration of design alternatives. The result is a more streamlined design and manufacturing solution that supports productivity from concept through production. Streamline multibody simulation workflow with Simcenter Inspire Engineering teams often add time and risk by recreating the same models for different multibody simulation tests. This release of Motion Analyst closes that gap with Analysis, a virtual test lab that lets you define a complete test environment and run it as one connected workflow. An Analysis builds on the base model by adding test-specific behavior such as changing the properties or states of joints, springs, or inputs. Along with model changes, it includes a clear, ordered sequence of steps, such as static and transient simulations. Each step runs in sequence, and the output of one becomes the input to the next, just like a physical test. The result is a more efficient set up, and faster virtual testing that engineers can trust. Real-time GPU accelerated Fluid simulation Simcenter Inspire Fluids 2026.1 introduces real-time geometry editing, allowing engineers to modify designs and immediately visualize the impact on fluid flow and performance. By eliminating the delays associated with traditional CFD workflows, the new capability enables faster design iterations and a more intuitive understanding of how geometric changes influence simulation results. This streamlined approach reduces workflow complexity, accelerates product development, and empowers users to optimize fluid performance earlier in the design process, helping drive more informed decisions and greater design innovation. Simcenter Inspire 2026.1 brings together AI-powered engineering, unified workflows and enhanced simulation capabilities to simplify engineering processes and accelerate innovation from concept to validation. Schedule a conversation with CAEXPERTS and discover how Simcenter Inspire 2026.1 can accelerate your development processes through AI capabilities, advanced modeling, and integrated simulation. Our team consists of engineers trained and qualified to support, implement, and apply Altair product solutions; we collaborate with your team on initial projects to maximize return on investment and help your company extract peak performance from the tool, achieving faster, more accurate, and more reliable project results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Simcenter STAR-CCM+ 2606 is now available, delivering advances that empower CFD engineers to tackle your most demanding simulation challenges with greater efficiency and precision. Compute magnetic fields in detailed e-motor geometries with enhanced speed and accuracy, enabling you to model the complexity of modern electrified powertrains. Train AI models up to 3x faster and iterate designs with greater confidence, helping you go faster from concept to validated solution. Accelerate complex combustion analysis and liquid fuel injection simulations in turbomachinery through GPU computing capabilities that dramatically reduce solve times. Monitor and reduce the energy footprint of your CFD simulations with in built reporting. And on the SPH front model complete heat transfer mechanisms in particle-based fluid simulations. Gain more insights with the ability to compare simulation results directly to identify solution differences and explore the possibilities of design variations with clarity. Discover these enhancements and additional capabilities in Simcenter STAR-CCM+ 2606 to unlock new levels of productivity in your simulation environment. Explore the full range of new features in Simcenter STAR-CCM+ 2606 today. Accelerate electromagnetic simulations for full 3D e-machine CHT analyses Computing accurate magnetic fields in e-motors has historically been a time-consuming process, often leading to significant delays in design iterations, especially when electromagnetic and thermal effects need to be evaluated together. This challenge can hinder the efficient development of e-machine designs that perform reliably under realistic operating conditions. With Simcenter STAR-CCM+ 2606, we are introducing a new Finite Element (FE) Magnetic Field solver specifically designed for full 3D e-machine simulation. This powerful new capability significantly improves electromagnetic simulation efficiency while maintaining the accuracy required for reliable torque, loss and field predictions. By accelerating high-fidelity electromagnetic analyses, motor engineers can more efficiently perform native electromagnetic-thermal simulations, helping reduce design cycle time while improving confidence in thermal hotspot predictions. Compare simulation results directly to identify and quantify solution differences When comparing CFD simulation results from different design iterations or parameter studies, side-by-side visualization often falls short. Subtle but critical differences in flow fields, temperature distributions, or pressure patterns can be nearly impossible to identify through visual inspection alone, leaving you uncertain about which changes truly matter. The new Simcenter STAR-CCM+ 2606 release addresses this challenge with solution field subtraction operations that enable direct mathematical comparison of two solutions. You can now subtract one solution field from another to reveal exact differences in scalar and vector quantities across your entire computational domain. This capability allows you to identify subtle differences between simulation runs faster and with higher confidence, eliminating guesswork from your analysis workflow. You can quantify the impact of design or parameter changes without manual post-processing, saving valuable time in your iteration cycles. Additionally, you can validate model updates or convergence by measuring solution deltas numerically, ensuring your simulations meet quality standards with objective metrics. The difference fields integrate seamlessly into your existing visualization workflow, making comparison analysis a natural part of your process. Explore field subtraction operations to accelerate your design comparison workflow and make data-driven decisions with confidence. Overlay simulation results on physical products in augmented reality Communicating complex flow physics and thermal behavior to non-technical stakeholders remains one of the most persistent challenges in engineering workflows. Traditional screen-based visualizations and abstract contour plots often fail to convey the spatial relationships between simulation results and actual products, leading to lengthy design reviews and stakeholder misunderstandings. The new release of Simcenter STAR-CCM+ 2606 addresses this gap with Augmented Reality Passthrough with Hand Tracking, enabling you to project CFD results directly onto physical prototypes through a mixed reality headset. You can now overlay flow features, thermal patterns, and pressure distributions on the actual hardware in front of you, eliminating the cognitive leap required to translate simulation data to real-world context. This controller-free experience allows you to manipulate and explore results using natural hand gestures while maintaining focus on the physical product. You accelerate design reviews significantly by grounding simulation insights in tangible, spatial context that all stakeholders can immediately grasp. The intuitive nature of seeing results exactly where they occur on real hardware builds confidence in simulation-driven decisions across your organization. You transform abstract data into compelling, spatially-accurate demonstrations that drive faster consensus and better-informed design choices. Explore how augmented reality visualization transforms your CFD review workflows and stakeholder communication. Generate meshes for complex geometries in half the time When you’re working with large, complex geometries, surface remeshing can consume a substantial portion of your meshing time, creating bottlenecks that delay simulation results and slow down your design iteration cycles. The new Simcenter STAR-CCM+ 2606 release addresses this challenge with MPI parallelization of surface remeshing, offering scaling up to 16 CPU cores. You can now complete surface remeshing up to 2.1× faster on complex models, directly cutting down the time you spend waiting for mesh generation to finish. This speed improvement reduces your overall meshing time and accelerates your simulation throughput. You gain the ability to iterate more designs within the same project timeline, making your workflow more productive. By parallelizing what was previously a serial bottleneck, you unlock faster turnaround times from geometry to results. The efficient scaling means you can leverage your existing multi-core workstations without diminishing returns. Explore how parallelized surface remeshing can accelerate your meshing workflow and boost your productivity. Train AI models multiple times faster and iterate designs with greater confidence In today’s fast-paced product development environment, waiting days for AI model training creates bottlenecks that delay critical design iterations and slow your decision-making cycles. When training times stretch into extended periods, you lose valuable opportunities to explore design variants and optimize performance within project deadlines. The new Simcenter STAR-CCM+ 2606 release addresses this challenge head-on with multi-GPU support for the PhysicsAI add-on, enabling you to distribute training workloads across multiple NVIDIA GPUs. You can now complete AI training in one third of the time thanks to near-linear GPU scaling performance. This acceleration allows you to iterate more design variants within the same project timeline, maximizing your exploration of the design space. You make data-driven decisions faster with accelerated inference workflows that keep pace with your engineering schedule. Reduce waiting time and increase productivity as your AI models train while you focus on innovation rather than watching progress bars. Your design process becomes more agile, responsive, and aligned with aggressive development timelines. Ready to accelerate your simulation-driven AI workflows? Explore how multi-GPU support can transform your design iteration cycles today. Accelerate liquid fuel injection simulations with GPU computing Designing efficient combustors for turbomachinery requires detailed simulation of fuel spray behavior, but traditional CPU-based Lagrangian spray simulations can take days to complete, creating significant bottlenecks in your design cycles. Every design iteration you want to test means waiting for lengthy compute jobs to finish, which delays critical decisions about injector configurations and combustor geometries. Simcenter STAR-CCM+ 2606 now brings GPU acceleration to the Lagrangian solver with full support for spray models specifically tailored to turbomachinery applications. You can now run the same high-fidelity spray combustion simulations on GPU hardware, dramatically reducing your turnaround time from days to hours. This speed-up allows you to iterate faster on fuel injector designs and explore more combustor configurations within the same project timeline. You reduce the computational cost per design variant, making it economically feasible to investigate a broader design space. Accelerate your path to optimized combustion performance while maintaining the physical accuracy your simulations demand. Explore GPU-accelerated spray simulation capabilities in Simcenter STAR-CCM+ 2606 and transform your combustor development workflow today. Accelerate complex combustion analysis with detailed chemistry on GPU Combustion system designers face a critical bottleneck when analyzing flashback phenomena and emissions: direct species and reaction calculations consume enormous computational resources, slowing down your development cycles and limiting the number of design iterations you can explore. Traditional CPU-based approaches to detailed chemistry simulations force you to choose between accuracy and turnaround time, often leaving critical design questions unanswered until late in the development process. Simcenter STAR-CCM+ 2606 introduces a GPU-native complex chemistry solver specifically designed for multi-species combustion simulations. This new capability enables you to run detailed combustion chemistry calculations significantly faster than conventional methods. You can now explore more design variants within the same project timeline, accelerating your path to optimal combustion system configurations. You reduce the time required to obtain accurate flashback and emissions predictions, enabling earlier design validation. Additionally, you assess combustion system performance with substantially less computational cost, freeing up resources for broader parametric studies. Discover how GPU-accelerated complex chemistry can transform your combustion analysis workflow and compress your development timelines. Monitor and reduce the energy footprint of your CFD simulations As HPC infrastructure scales to meet growing simulation demands, energy costs are becoming a significant portion of operational budgets. At the same time, engineering organizations face increasing pressure to meet corporate sustainability targets and reduce their carbon footprint. Yet most CFD users lack visibility into how much energy their simulations actually consume, making it nearly impossible to identify optimization opportunities or report on environmental impact. The new Simcenter STAR-CCM+ 2606 release addresses this challenge with built-in energy monitoring and reporting that tracks power consumption throughout your CFD runs. You gain detailed insights into which simulation phases consume the most energy, enabling you to make informed decisions about solver settings and mesh strategies. You can monitor your compute resource allocation to identify potentials to reduce operational costs while maintaining simulation accuracy. The comprehensive reporting capabilities allow you to demonstrate measurable progress toward your organization’s sustainability and carbon reduction targets. By making energy consumption transparent, you transform it from an invisible cost into a manageable aspect of your simulation workflow. Explore the energy monitoring capabilities in Simcenter STAR-CCM+ 2606 to start optimizing your simulation efficiency today. Resolve Conjugate Heat Transfer mechanisms in conjunction with SPH particle-based fluid simulations Modeling thermal effects with smoothed particle hydrodynamics (SPH) previously meant decoupling the fluids and heat transfer analyses and required manual data transfer between separate solvers, increasing setup time and introducing potential errors. This fragmented workflow slows down your analysis and creates consistency risks across your simulation chain. Simcenter STAR-CCM+ 2606 addresses this challenge by enabling the SPH solver to couple directly with the energy solver for conjugate heat transfer simulation. You can now capture fluid and solid temperature evolution simultaneously in a single environment, using direct coupling or a staggered approach to capture longer timescales. The coupled approach delivers more reliable predictions for a computationally efficient method for jet cooling simulations, reduces manual data transfer and eliminates post-processing overhead between separate thermal tools. Explore how integrated conjugate heat transfer capabilities in SPH can streamline your thermal modeling workflow. Capture airflow influence on liquid sprays and droplets in SPH simulations Liquid sprays rarely exist in isolation, in many cases the incoming flow is wind driven, or the object of interest is moving. In simulation we want to account for that interaction with the turbulent airflow, or else there will be significant inaccuracies in predicting spray behavior and dispersion patterns. The new release of Simcenter STAR-CCM+ 2606 addresses this challenge by enabling you to map FV flow fields directly to SPH simulations as background conditions with full drag coupling. This integration allows you to account for turbulent airflow effects using either transient coupling or a steady steady state flowfield snapshot in your SPH setup. This coupled simulation approach enables you to capture airflow-liquid interactions and predict wind-driven spray in exterior water management studies, or capture windage in drivetrain spray cooling with much higher confidence. Explore how CFD flow field mapping to SPH simulations can enhance the accuracy of your spray predictions and accelerate your development process. Take your simulations to the next level with Simcenter STAR-CCM+ 2606. Schedule a meeting with CAEXPERTS to discover how these new features can accelerate your projects, boost your team's productivity, and maximize the return on your simulation investments. We lead the initial projects in collaboration with your team to maximize return on investment, helping your company extract peak performance from the tool and achieve faster, more accurate, and reliable results in your projects. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The Simcenter Hypermesh 2026.1 (formerly Altair HyperMesh) introduces powerful new modeling capablities, performance enhancements, and workflow improvements to help engineers build, manage, and process complex simulation models more efficiently. Retrieve with ShapeAI — AI powered model build Engineering teams often have extensive libraries of meshed parts, but locating the right model can be time consuming when names, revisions, or folder structures differ. Simcenter Hypermesh 2026.1 introduces Retrieve, powered by ShapeAI similarity matching, to quickly identify geometrically similar parts and load the best match directly into the current session. Retrieve searches based on shape, so it works even when names and IDs don’t agree across teams. Retrieve handles representation positioning and orientation automatically and produces an HTML report of the operation for traceability. By making existing meshes easier to find and reuse, Retrieve helps reduce duplicate modeling effort, accelerate project setup, and maximize the value of established engineering data. PhysicsAI — Improved hotspot accuracy & selective field prediction Simulation engineers face an unrelenting pressure to deliver high-fidelity results faster. Traditional FEA can take hours or even days to complete, slowing product development and making it difficult to quickly identify critical structural hotspots. Simcenter Hypermesh 2026.1 enhances PhysicsAl, with capabilities that improve both efficiency and prediction quality. Engineers can now train AI models only on selected regions of interest, reducing training time and computational cost. An improved training method delivers more accurate hotspot predictions by working directly with elemental field data, while a new 0–1 similarity score provides greater confidence when applying models to new designs. Additional enhancements, including automatic modal frequency extraction and a migration to PyTorch with automatic GPU detection, further streamline AI workflows. With PhysicsAI, engineers can generate near-instant predictions of stress, strain, and displacement fields — without waiting for full solver runs. The result is faster design exploration, improved prediction accuracy, and quicker engineering decisions. Subsystem and PLM workflow — PDM live’s new Teamcenter check-in/check-out Simcenter Hypermesh 2026.1 strengthens enterprise model management by improving collaboration, configuration control, and PLM integration. New Subsystem Instance Link capability enables repeated or mirrored assemblies to remain synchronized, allowing updates to propagate automatically across all instances. Configuration management is enhanced with expression-based search, configuration duplication, and persistent ID rules for greater consistency. PDM Live integration has also been expanded, enabling Teamcenter check-in, check-out, and cancel check-out operations directly within Simcenter Hypermesh, along with new data transfer preferences. Complementing these enhancements are significant improvements to subsystem node equivalence performance, making large-scale model build and management workflows significantly faster, more efficient, and easier to maintain across complex engineering programs. Advanced connector — smarter, more robust modeling Creating reliable connectors is often one of the most time-consuming steps in CAE pre-processing, particularly for large assemblies where manual setup can introduce costly errors. Simcenter Hypermesh 2026.1 streamlines these workflows with a range of connector enhancements that improve both efficiency and model fidelity. Auto-Connect now supports a Different Subsystem Only mode that enforces connectivity at subsystem boundaries. Line connectors gain a new Seam Quad RBE2-RBE3 type with Heat Affected Zone support, automatic end-offset detection, and a Straight Lines Only option for cases where curved realizations were never intended. Point connectors add a Mesh Independent weld option that skips imprinting, and HiLock now realizes correctly across solid and solid-shell combinations for both metal and composite parts. Attachments on step-holes now consider all faces — top, bottom, and intermediate — eliminating a long-standing realization edge case. Design explorer enhancements — more controllable design exploration with ANSYS support Design exploration is essential for optimizing product performance, but traditional workflows can be inefficient when interrupted by failed runs, manual setup tasks, or solver limitations. Restarting an entire design of experiments (DOE) because of a single failed evaluation wastes valuable engineering time and compute resources. Simcenter Hypermesh 2026.1 introduces a more flexible Design Explorer that keeps optimization studies moving. Engineers can now rerun only failed DOE evaluations, edit design variable values before submission, and execute individual workflow tasks independently to support external solver processes. Automated mirror linking simplifies setup for symmetric structures, reducing manual effort, while highlighted constraint violations make infeasible designs easy to identify. Support for the ANSYS solver profile also extends integrated design exploration to a broader range of users. Together, these enhancements improve workflow efficiency, reduce setup time, and help engineers reach better-performing designs faster. CAEXPERTS offers specialized implementation of Simcenter HyperMesh, backed by a team of engineers trained and qualified to provide technical support, application expertise, and deployment services for Altair solutions. Beyond implementation, we provide ongoing technical guidance to ensure efficient adoption of the tool. We collaborate with your team on initial projects to maximize return on investment, helping your company extract peak performance from the tool and achieve faster, more accurate, and reliable project results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The latest Simcenter Simsolid (formerly Altair SimSolid) release enhances connectivity, expands analysis capabilities, and simplifies post-processing, helping users accelerate design validation while improving overall simulation efficiency. In this blog you will find some of our highlights. Native Designcenter integration Simcenter Simsolid 2026.1 further streamlines the simulation workflow for Designcenter users with an enhanced native integration that eliminates manual file transfers and configuration. Automatic geometry, material, and active design configuration transfer reduce repetitive setup while improving model accuracy and consistency. Clearer transfer diagnostics help resolve issues faster. Lastly, Simcenter Simsolid 2026.1 new Teamcenter integration enables direct project save and open capabilities for improved traceability and collaboration. Together, these enhancements create a faster, more connected simulation experience that helps engineers and designers iterate on designs with greater confidence and efficiency. Advanced rotating machinery analysis Simcenter Simsolid 2026.1 expands its analysis capabilities with advanced rotordynamics, enabling engineers to evaluate rotating machinery while accounting for gyroscopic effects. New enhancements provide clearer insight into dynamic behavior by explicitly identifying forward and backward whirl modes, making results easier to interpret during design and validation. Engineers and designers can also define rotor speed using functions, including CSV-based inputs, to accurately represent complex speed profiles over time. These additions in the new release help improve confidence in rotordynamic analysis while extending Simcenter Simsolid’s capabilities to a broader range of turbomachinery and rotating equipment applications. Enhanced management of large connection sets Simcenter Simsolid 2026.1 improves productivity for engineers and designers working with large and complex assemblies through enhanced connection management capabilities. New tools make it easier to reuse, copy, and filter large sets of connections, reducing manual effort and streamlining model setup. Solver enhancements also improve stability and solution quality when assemblies contain challenging connection with large aspect ratios. Furthermore, Simcenter Simsolid 2026.1 introduces additional enhancements across the solver to improve analysis accuracy and expand simulation capabilities. Linear buckling analysis now supports contact nonlinearity while retaining contact status from structural analysis, enabling more realistic evaluation of complex assemblies while eliminating the need for manual workarounds. Automatic hotspot and extrema detection Simcenter Simsolid 2026.1 enhances post-processing with new tools that make it easier to identify and investigate critical results. Automated hotspot detection quickly highlights local maxima and minima, helping engineers and designers pinpoint stress concentrations and other key performance areas without manually searching through complex visual data. Users can also focus their analysis by generating result plots within a user-defined region of the model, enabling faster evaluation of localized behavior. Together, these enhancements simplify result interpretation, improve productivity, and help users gain meaningful insights from complex simulations more efficiently. Simcenter Simsolid 2026.1 combines workflow improvements, expanded simulation capabilities, and intelligent post-processing to reduce manual effort and deliver faster, more confident engineering decisions. If you want to unlock the full potential of Simcenter Simsolid and accelerate your development processes, schedule a conversation with CAEXPERTS. Our team includes engineers trained and qualified to provide support, implementation, and application services for Altair solutions, ready to help your company achieve peak performance from the tool and deliver faster, more accurate, and reliable project results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The new Simcenter FLOEFD 2606 software release is now available in all its CAD-embedded CFD variants, and also the Simcenter 3D embedded variant. This release delivers focused improvements for electronics cooling analysis. This includes library enhancements for easy validated component re-use, efficient modeling of power sources on a die to identify hotspots for IC packages in system models, improvements to computationally efficient PCB thermal modeling, automation of EDA data import, and much more. Please read on below to explore each new feature organized by Simcenter pillars. Smart Die – modeling 1000’s of power sources To identify localized hotspots when IC packages are incorporated in a system, it is advantageous to model power distribution on the package die. In Simcenter FLOEFD 2606 the Smart Die has been introduced for modeling 100’s or 1000’s of power sources on a packaged semiconductor die in a computationally efficient manner suitable for this level of analysis. This means engineers can account for complexity such as power distribution spatial influences, overlapping sources, transient time variations, and thermal dependencies. You can detect leakage-driven hotspots that uniform die modeling approaches are not able to represent. How are power sources defined using the Smart Die? The die is subdivided into multiple polygons with distinct power characteristics. Polygons are defined by an imported CSV file and assigned to a body specified as a die. You can import power floorplan with leakage models assigned. A voxelized mesh resolves the die. This means you can import hundreds of overlapping polygons with dynamic and leakage power assigned to represent a complex power distribution. You can import polygon geometry from a CSV file using the polygons dialog. Several formats are supported, and the type is automatically then detected. Types include: Geometry definition table (name, nverts, vert1, vert2, …) Simcenter FLOEFD polygon table with full definition (Name, Polygons, Dynamic Power, Derating Factor, Leakage Model, Leakage Power at T0, Priority, Goal Discrete sources from Flotherm (name, X1, X2, Y1, Y2, P) Total Coverage Sources from Simcenter Flotherm (uniform grid with power values) You can explore these types when you upgrade to the latest version. Below are 2 videos showing the use of the new Smart Die feature. Video: Smart Die overview Video: Importing data into Simcenter FLOEFD Smart Die from Simcenter Flotherm This new Smart Die approach allows import of non-uniform disspation power sources from Simcenter Flotherm software into Simcenter FLOEFD 2606. (Simcenter Flotherm has enabled this CSV export since version 2604 from its Die Smartpart component) Smart PCB: FEM mesh based thermal analysis Enhancements to the computationally efficient and popular Smart PCB feature have been delivered in this release via the introduction of FEM mesh based thermal analysis and expanded results visualization options. The new FEM prism mesh based thermal modeling approach reduces memory usage and improves speed for modeling of high density, multi-layer boards with complex copper routing. In post processing, results visualization using this new Smart PCB modeling approach, users can leverage clearer insights into board internal temperature variations and more easily visualize heat flux plots to locate thermal bottlenecks. Video Demonstration: Smart PCB FEM mesh based thermal analysis Watch this short < 2 minute video that shows the new settings you use for Smart PCB FEM mesh based thermal modeling and results visualization examples. Library enhancements in Simcenter FLOEFD 2606 PCB thermal analysis workflow benefits from libraries of existing components due to the complexity of most board applications with 100’s, or thousands, of components mounted on them. Library ecosystem and browser You can now create custom component or model libraries and instantly reuse them across projects. . Libraries of of validated, reusable elements help you assemble and set up models much faster. As this also minimizes errors between models, for engineering teams this provides the opportunity to ensure consistent standards for groups of users using libraries. Library: edit parameter of features from component Parameters of features belonging to sub-components can now be edited directly from the top-level assembly. Each component instance can also be modified independently. Library: Local mesh settings based on absolute cell size Mesh refinement can now be defined using absolute cell size in library components. This enables local mesh settings for library items that are independent of top-level mesh settings for a project. This means a library author’s mesh decision transfer with library elements and there is no need for manual adjustment when re-using these. Video demo: Library enhancements in Simcenter FLOEFD 2606 Component Explorer updates in Simcenter FLOEFD 2606 a) Component Explorer: Network Assembly and Smart PCB New columns have been introduced to display power values assigned via Network Assembly and Smart PCB features. Users can review power at the individual component level or evaluate the total power budget. b) Component Explorer: minimum temperature column A new column shows the minimum temperature for each component, complementing the existing maximum and average values and enabling better analysis of temperature gradients. The launch of the component explorer as your model becomes extremely complex and high component numbers has been sped up through a code refactoring. Even for very large models, you can now access component features immediately in table view. Material Priority setting: Material priority values can now be edited directly within the table, eliminating significant manual clicks and steps. EDA Bridge automation – headless operation Automation of simulation tasks significantly increases throughput for thermal analysis projects for engineering teams. A step change has been delivered toward true headless automated import and processing of EDA data for PCB thermal analysis via enhancements to EDA Bridge used in conjunction Simcenter FLOEFD API capabilities. The ability to automate EDA Bridge operation enables teams to realize advanced cross-domain thermal optimization workflows linking simulation and ECAD-MCAD design flows. Video overview: EDA Bridge headless operation for automation Automation: other API enhancements Automation of simulation tasks continues to be a popular topic. EFDAPI, the new Simcenter FLOEFD API introduced back in version 2312, continues to be developed in each release based on user feedback. Beyond EDA Bridge automation, more functionality been added in Simcenter FLOEFD 2606 including: An easier select coordinate system method Enable/disable absorption in default solid Add from components, to re-use from sub projects Set default outer radiation surface Curious about PYTHON scripting? that was supported in EFDAPI as of version 2406 Speed-up: 100’s of 2R components contacting a Smart PCB For PCB thermal analysis studies where high numbers (100’s) of components modeled as two-resistor (2R) type components are in contact with a board modeled as a Smart PCB, an improvement in computational efficiency has resulted in test models achieving performance of almost 2x faster solution. This was achieved through optimizing mesh contact handling for non-conformal meshes with high cell size ratios. XTXML export enhances package model workflows XTXML export now supports contact resistance and radiative surfaces at version 2606 to enhance IC package thermal model creation and adding to libraries. XTXML export for component editing was introduced in the 2506 release  allowing users to import models from Simcenter FLOEFD Package Creator utility, make adjustments to the models and then save the models in XTXML format to libraries. Manually created detailed models can also be exported in XTXML format. The previous Simcenter FLOEFD 2512 release enhancement introduced the option to export models of 2R and Network Assembly components. Do you want to enhance your thermal simulation processes and unlock the full potential of the new features in Simcenter FLOEFD 2606? Schedule a meeting with CAEXPERTS and discover how these improvements can help your team accelerate analyses, update electronic designs, and boost the efficiency of your engineering workflows. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

For engineers to collaborate effectively on multidisciplinary applications, being able to transfer results quickly, easily, and reliably from a Computational Fluid Dynamics (CFD) model to a structure mechanics Finite Element (FE) model is crucial. With Simcenter STAR-CCM+ we introduce a common data format for an efficient transfer of simulation results to Simcenter 3D (CFD-FEA coupling). Model the complexity of multiphysics applications Mechanical engineering is an engineering branch that combines engineering physics and mathematics principles with materials science, to design, analyze, manufacture, and maintain mechanical systems. (Source: Wikipedia) According to the definition above, mechanical engineering is about the design and analysis of mechanical systems. But before it’s even possible to design or analyze any mechanical system, a detailed understanding of the operating conditions and expect service loads is a must. Example of a recent engineering disaster Being unable to predict operating conditions and service loads have led to countless engineering disasters. A fairly recent one is the sinking of the MOL Comfort in the Indian Ocean. This event took place on June 17, 2013. Source: BMA-Investigation-Report-Loss-of-the-MOL-Comfort.pdf CFD-FEA coupling can help to predict service loads and operating conditions for Multiphysics applications. Continue to read this blog to find out how to use the results of a CFD model to define the service loads of a structure mechanics FE model. CFD-FEA coupling for a boat hull structure A boat with an overall length of 5.5m and a total weight of 1600kg (800kg boat only) is driving at 5 m/s through a series of waves with a height of 1m. The objective is to analyze the deflection of the bottom panel of the hull in a practical manner. CFD simulation of a small boat in waves. Pressure field at the hull delivers the key input to a CFD-FEA coupling simulation CFD-FEA coupling to analyze the deflection in a practical manner Fluid dynamics and structure mechanics models are often built by different analysts. A process that enables a smooth and efficient CFD-FEA coupling is key. From a physics perspective, it’s reasonable to assume that the deflection of the panels is small compared to the rigid body motion of the boat. Therefore, we may assume that the deflection of the panels will not have a significant impact on the flow. In other words, we may assume that the panel deflection is 1-way coupled in the direction fluid to structure. The fluid pressure deflects the panels, but the deflection of the panels doesn’t affect the flow. Stay integrated with CFD-FEA coupling in the Simcenter environment Set up your CFD model in Simcenter STAR-CCM+ The first part of the process is to set up the Computational Fluid Dynamics (CFD) model in Simcenter STAR-CCM+. Here we assume that the boat is rigid, and therefore we can model it as a six degree of freedom (6DOF) body. The mass, center of mass, and moments of inertia of the boat are an input for the 6DOF model. And they may be taken from the FE structure model. Export your simulation results to the Simcenter Data File While the boat floats through the waves, Simcenter STAR-CCM+ exports the pressure on the bottom panel of the hull is being to a file. Not any file, but a Simcenter Data File with the extension .scd5. Simcenter STAR-CCM+ exports the panel pressure on the native Computational Fluid Dynamics (CFD) mesh and the sampling frequency matches the time step size of the flow solver. Therefore, for the given model, the exported Simcenter Data File may be thought of as a persistent data source with the highest possible fidelity in space and time. Import the pressure in Simcenter 3D Next, the CFD engineer hands over the Simcenter Data File to the structure mechanics analysts. This team imports the hull pressure into the Simcenter 3D. After the import, Simcenter 3D stores the pressure within the simulation file as a Table of Fields. The frame of the boat is assumed to be rigid. Hence, a fix constraint is applied wherever the bottom panel is connected to the frame. The imported fluid pressure is defined as a load and automatically interpolated in space and time. As solver Simcenter Nastran Solution 401 is being used. An efficient CFD-FEA coupling The animation below shows the bottom panel of the hull from a diver’s perspective for the duration of about one-third of the wave period. In the left part of the animation, we can see the fluid pressure computed with the help of the Simcenter STAR-CCM+ CFD model. In the right part, we can see the panel deflection from the Simcenter Nastran 401 model. CFD-FEA coupling between Simcenter STAR-CCM+ and Simcenter 3D made easy and realiale through the common Sicmenter .scd5 data format Predict service loads and operating conditions Go faster with efficient data exchange across Simcenter products The example above demonstrates how a CFD-FEA coupling method can help to predict service loads and operating conditions for Multiphysics applications. Furthermore, it also demonstrates that being able to export simulation results from Simcenter STAR-CCM+ to the Simcenter Data File for a subsequent import into Simcenter 3D is extremely valuable. Run a productive and efficient workflow Admittedly, coupling, a Computational Fluid Dynamics (CFD) simulation to a structure mechanics FE simulation through a file is nothing revolutionary. However, the presented workflow has some subtle points to consider: The fluid pressure is exported to the Simcenter Data File on the native Computational Fluid Dynamics (CFD) mesh at every time step of the flow solver. The result is a persistent data source of the highest possible fidelity in space and time. The Simcenter Data File is imported into Simcenter 3D once. Then the data is being interpolated in space and time onto the Simcenter Nastran 401 model. Therefore, should the structure mechanics analyst want to investigate a different design of the structure (a different frame) there is no need to re-iterate with the flow analyst, not even to re-import the hull pressure. The two points above may appear subtle, but they are essential for a productive and efficient workflow. The Simcenter Data File is the only component that needs to be exchanged between the flow and the structure analyst. Hence, the required interaction and exchange of simulation results is kept to an absolute minimum. Transform complex simulation data into faster, more accurate engineering decisions. Schedule a meeting with CAEXPERTS and discover how the integration between Simcenter STAR-CCM+ and Simcenter 3D can make your structural analysis and CFD processes more efficient, collaborative, and reliable. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

System simulation for medical device companies AGV and AMR manufacturers are under pressure to reduce the development time of highly customized solutions while also increasing the reliability of autonomous systems. In addition, the integration of vehicle dynamics, electric propulsion, sensors, navigation algorithms, and control systems makes each new project more complex. In this scenario, system simulation enables engineering decisions to be validated before physical prototypes are built. Today, autonomous robots are used in areas such as logistics, manufacturing, distribution centers, and mining. In a less common application, this type of technology is also present in hospitals. Currently, the medical device industry is following the broader trend toward more autonomous systems. Medical disinfection robots are used to sterilize hospitals, including waiting rooms and patient rooms, as well as parking lots, shopping malls, and other public spaces, with the major advantage of avoiding additional human exposure to viruses through the use of these robots. Autonomous medical disinfection robots in a 3D environment for sanitizing rooms In an application developed using Siemens engineering software and services, new dedicated AMRs were designed. They combine an electrically powered autonomous platform with a disinfection system mounted on top. Typically, this system includes liquid micro-spray nozzles that apply disinfectant to surfaces, or ultraviolet C (UVC) lights to purify the air. The goal is to destroy all airborne pathogenic microorganisms. In this way, disinfection robots sanitize these environments, allowing people to use them later for their normal activities. Development challenges and system simulation System simulation can help reduce development time by decreasing the number of costly physical prototypes and testing campaigns. A crucial factor in the development cycle of autonomous robots is autonomous operation and the ability to navigate new environments. This is achieved through a combination of computer vision, including cameras, LiDARs, short-range radars, and other sensors, sensor fusion, control logic, and vehicle dynamics, enabling robots to operate easily in different situations and on various types of flooring. When physical interaction with an infected environment represents a significant risk to humans, an autonomous robot can perform simple and repetitive tasks. Many robotics solutions control the robot’s movement remotely. However, an autonomous robot can move on its own to perform its functions. It uses computer vision, obtained through various onboard sensors, and a typical perception-reasoning-action algorithm, which provides the correct commands to the actuators without the need for human presence and, consequently, without the risk of viral contamination. Typical challenges for AGVs (“Automated Guided Vehicles”) and AMRs (“Autonomous Mobile Robots”) In this article, we would like to present some ideas on how a simulation-based approach can support the development of autonomous robots, from system sizing and sensor design to the verification and validation of the final control algorithms. A simulation framework for autonomous systems The simulation framework combines different Siemens tools that have already been successfully implemented in several autonomous applications across multiple industries. Examples include autonomous cars in the automotive industry, drones and UAMs, or urban air mobility, in the aeronautics sector, autonomous agricultural vehicles in heavy equipment, and even military tanks operating in hostile environments in the defense sector. Consequently, the same simulation architecture can be applied to emerging medical robot applications, which must meet slightly different requirements. This framework integrates several software tools that perform time-domain simulations. An alternative workflow would consist of directly connecting Simcenter Amesim and Simcenter Prescan through FMI, or Functional Mock-up Interface, which is available in both tools. Simulation framework with the different tools involved Simcenter Amesim® represents the vehicle dynamics and electric propulsion. Simcenter Prescan® represents the hospital environment and models the sensors that detect the presence of objects in the environment, such as cameras, LiDARs, short-range radars, and others. Simulink® connects Simcenter Amesim and Simcenter Prescan. In addition, ROS, or Robot Operating System, was used for sensor fusion and control algorithms, which provide the actuator commands to the vehicle model in Simcenter Amesim. Overview of the data flow and customized 3D scenes The robot state is transferred from Simcenter Amesim to Simulink, which provides the updated position and orientation of the robot to Simcenter Prescan. Simcenter Prescan then provides the virtual sensor data to the ROS operating system through Simulink. Finally, the ROS control algorithm sends the updated actuator commands to the Simcenter Amesim model so that it follows the correct path. At this point, the control loop is closed, and the robot can move autonomously within the environment, such as a hospital room, while avoiding collisions with detected obstacles. Scope of activities and domains in the design of an autonomous mobile robot (AMR) for medical use Regarding the modeling of environments such as hospitals, warehouses, or distribution centers, Simcenter Prescan allows the import of customized objects typical of these applications as CAD files: Various geometries of autonomous mobile robots (AMRs), Geometries for room layouts, corridors, slopes, beds, and obstacles, Adverse conditions imposed by the natural environment, such as day and night, among others. Thus, it is possible to represent real configurations and operating conditions. Modeling the dynamics of the robotic vehicle and its 3D environment The Simcenter Amesim model predicts the physical behavior and interactions of different subsystems in a three-wheeled vehicle. It features front-wheel drive, including the electric motors with their inverters, controllers, and a 24 V power supply battery, as well as passive rear-wheel drive. In addition, the model represents the vehicle dynamics, including its axles, chassis, and tires. The Simcenter Amesim digital twin was used to implement autonomous driving functions. The vehicle was then equipped with sensor models, and finally, the loop was closed by integrating the decision-making algorithm between the simulated sensor data and the vehicle model. Simcenter Amesim model of the disinfection robot with its vehicle dynamics and electric propulsion Multiple scenarios can be investigated, as well as how obstacle detection and avoidance functions allow the robot to move autonomously within this unknown 3D environment. The visualization of the 3D scene from different perspectives helps in understanding the simulation results. For this purpose, several camera orientations were used, along with sensor fusion from the ROS library for video processing. 3D views from different camera orientations It is now clear how the combination of Simcenter Amesim and Simcenter Prescan supports the development and validation of AGVs and AMRs throughout the entire design cycle. By integrating multidisciplinary models of the vehicle, environment, and sensors into a single simulation workflow, engineering teams can evaluate system performance much earlier in the development cycle. This approach reduces technical risks, anticipates integration issues, and accelerates decision-making during development. With a simulation platform, it is possible to validate the vehicle architecture, compare different sensor configurations such as LiDAR, cameras, and radars, analyze battery autonomy across different operating profiles, and optimize control and navigation strategies. It is also possible to verify the vehicle’s dynamic stability in different scenarios, test perception and path-planning algorithms in virtual environments, and validate the embedded software before conducting field tests. By transferring a significant portion of the verification steps to the virtual environment, physical testing campaigns become more focused and efficient, reducing the need for multiple prototypes and shortening development time. The result is a more agile engineering process, with lower validation costs and greater confidence in the performance of the AGV or AMR before its deployment in operation. Gibin Joe Zachariah and Sagar Milind Supe carried out the case study above as part of their investigation work. They work with advanced smart products at Siemens Digital Industries Software (DISW) in Michigan, United States. Both are part of the Siemens Simcenter Engineering and Consulting Services team. What a disinfection robot teaches us about the development of any modern AGV or AMR: system simulation enables faster development of intelligent devices, reducing prototype costs and validating autonomous solutions more efficiently. Want to understand how Simcenter technologies can support your engineering projects? Schedule a meeting with CAEXPERTS and discover how to apply advanced simulation to optimize your products and processes. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Postprocess modes at critical speeds and efficiency updates. Why do we need rotor dynamics analysis? Modern turbomachinery, from jet engines to industrial compressors, runs at high speeds and under heavy loads, where even small prediction errors can cause vibration, instability, or failure. Rotor dynamics analysis helps engineers assess how shafts and rotating assemblies behave across the operating range, identify critical speeds, and pinpoint modes that could trigger resonance and reduce performance, safety, or service life. Simcenter 3D Rotor Dynamics, including Simcenter Nastran SOL 414, helps engineers analyze vibration in rotating machinery, predict resonance at critical speeds, evaluate bearing loads, and assess system survivability under realistic operating conditions What’s new in Simcenter 3D Rotor Dynamics 2606 In the Simcenter 3D 2606 release, users can make the simulation process more efficient by accessing essential results in the analysis, with fast processing. In this blog, we present three highlights in the 2606 release: Postprocessing of modes at critical speeds Computation of energy distribution in the full assembly, (even if superelements are used) A faster results format: the Simcenter data file (.scd5) that uses HDF5 architecture Critical speeds of the rotating assembly The computation of an assembly’s critical speeds is essential to the design of a turbomachine. Furthermore, the operating speed range must be far enough from the critical speeds of the system to avoid the resonance phenomenon occurring and inducing high levels of vibrations. Later, we will discuss what ‘far enough’ means and how we can visualize the permitted operating speed range where the turbomachine can operate safely. With the 2606 release, modes corresponding to critical speeds of each rotor can now be output as results of a Nastran SOL414 complex modal analysis, together with the Campbell and stability diagram. In this picture, the complex modal analysis computes the Campbell diagram with modes corresponding to the critical speeds for rotor 1 (yellow circles) and rotor 2 (purple circles) when the speed ratio between the two rotors equals 2.0. This capability is more than a direct critical speeds analysis, as a direct critical speeds analysis is an undamped analysis, with constant bearing properties. With this new capability, you can output modes at critical speeds for each rotor. This removes the limitations of direct critical speeds analysis because damping can be defined in the simulation to compute the stability of the rotating system (viscous damping, modal damping, or hysteretic damping in the connections), and bearing coefficients can be functions of the rotation speeds, which is closer to the realistic conditions. Modes at critical speeds are output for critical speeds at order 1, for a simulation of one or multiple rotors, computed in an inertial frame. Rotors can be modeled by all types of modeling approaches: 1D beam, 2D Fourier multi-harmonic, 3D solid axisymmetric models, 3D cyclic symmetry including the Coleman transformation, and superelements. Results that can be output at critical speeds are mode shapes, stresses, energies, and energies distribution (strain, kinetic and dissipation) in groups of elements. By outputting modes at critical speeds only, instead of at all rotation speeds at every step of the computation, you can decrease the size of the results file by a factor 10, potentially saving a huge amount of resources when results files are stored in a data management system. Energy distribution in the different components of the rotating assembly When analyzing an assembly’s energy distribution at a given rotational speed, such as the critical speed, you can see for each mode which parts are most affected if the corresponding mode is resonant. The energy distribution for a group of elements is presented as a percentage of the total energy, for the strain, kinetic, and new in 2606, the dissipation energy associated with damping. In the demonstration below with the two connected rotors, the low-pressure rotor and high-pressure rotor, we can study the compressor and turbine parts. Then, four different groups can be studied separately for the energy distribution: the low-pressure rotor compressor and turbine, and the high-pressure rotor compressor and turbine. The first critical speed at order 1 (40 Hz) shows that most of the energy is found in the compressor part of the low-pressure rotor, indicating that this part has a higher risk of deformation if this mode is activated. This type of information is very important if we want to identify which modes are most dangerous and in which part of the structure they occur. What happens to the energy distribution when the structure is condensed into superelements Superelements are used a lot in rotor dynamics as this area of dynamics is used in the context of small deformations. Even if the rotor dynamics simulations can manage geometric nonlinearities, the structure is computed in the linear domain. The use of Craig-Bampton superelements is relevant and very efficient in this context to drastically reduce the computation time of the simulation. From past releases, we already know that Simcenter Nastran SOL 414 superelements for rotors enables you to output XY Plots at internal nodes of the superelements. This removes the requirement to define retained nodes on the structure that will only be used to monitor results. For energy distribution, if superelements were used in an assembly, it was not possible to access elements inside the superelements for the calculation of the energy distribution. Indeed, the energy distribution was output by considering the superelement as a whole entity. Starting from Simcenter 3D 2606, if engineers configure groups of elements for energy distribution at the creation step of a Simcenter Nastran SOL 414 superelement, for a rotor or a stator, those groups can be used later for outputting the strain, kinetic and dissipation energy tabulations when the simulation uses the structure condensed in superelements. This capability makes the process more efficient for engineers who leverage the advantages of superelements. Indeed, the recovery of results on the original structure is not needed in this process. The energy tables are generated directly by the simulation process during an eigenvalue analysis, complex modal analysis, or harmonic response. The demonstration hereafter differs from the previous demonstration in that it uses superelements. When the structure is condensed in superelements, the groups of elements are not available, but the user can ask the simulation to use the groups configured at the creation for outputting the energy distribution. Advantages of simcenter data files for the postprocessing Simcenter 3D Rotor Dynamics outputs the results in an efficient format based on an HDF5 architecture, the .scd5 (Simcenter data file) file. It can be easily and quickly uploaded to Simcenter 3D. This result file presents different advantages: there is one single .scd5 file for a simulation, which contains all the results: spatial results for the different subcases, on the selected nodes and elements, or on the whole structure, and the different XY Plots. For engineers working in gas turbine applications and working with API 616 requirements, they can ask for additional XY Plots that enable them to visualize the operating speed range that is ‘far enough’ from critical speeds. Where ‘far enough’ is computed by analytical formulae for the separation margins and amplification factors, provided by the API 616 requirements. Specific functions in the postprocessing of rotor dynamics results enables you to extract results such as the width of the operating speed range, the amplification factors of a selected vibration peak, and the frequency at which the peak occurs. These are then available for later use in an optimization or design of experiments process. Want to explore how Simcenter 3D Rotor Dynamics 2606 can accelerate your critical speed analyses, power distribution, and post-processing of results? The CAEXPERTS team can demonstrate how to apply these innovations to your turbomachinery, compressor, and rotating system projects, reducing simulation time and increasing the reliability of your assessments. Schedule a meeting with us and see in practice how to optimize your rotor dynamics flows. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The latest release of Simcenter Systems 2604 delivers powerful new capabilities that address the most pressing challenges facing engineers today. From advancements in battery pack design and thermal safety validation to expanded gas system simulation and enhanced collaboration workflows, this release empowers engineers to work faster, model greater complexity, and validate designs with unprecedented accuracy. Whether you’re designing next-generation electric vehicles, optimizing pneumatic systems, or pushing the boundaries of extreme-condition simulations, Simcenter Systems 2604 provides the tools you need to accelerate innovation and bring better products to market faster. Supercharge battery pack design and validation The electrification revolution continues, and with it comes increasingly complex challenges in battery pack design, thermal management, and safety validation. Simcenter Systems 2604 introduces six major enhancements to the battery pack assistant that fundamentally transform how engineers approach these critical tasks. Seamless integration with Simcenter Simlab One of the most significant workflow improvements in this release is the ability to import 3D models directly from Simcenter Simlab into the battery pack assistant. This integration eliminates the friction that previously existed when moving between structural and thermal simulation domains. Engineers can now leverage their existing CAD and meshing work from Simcenter Simlab, bringing detailed geometric representations into their battery pack thermal models without manual reconstruction or data translation. This seamless handoff across the Simcenter portfolio not only saves time but ensures consistency and accuracy throughout the development process. Non-conformal interface for flexible cooling layouts Thermal management is critical to battery performance, safety, and longevity, yet designing effective cooling systems has always presented modeling challenges, particularly when dealing with complex geometries where cooling plates and battery cells don’t align perfectly. The new non-conformal interface capability addresses this head-on by allowing engineers to model cooling layouts without requiring perfectly matching mesh interfaces between components. This flexibility means you can accurately simulate real-world cooling configurations, including offset cooling plates, irregular contact surfaces, and multi-layer thermal management systems, all while maintaining simulation accuracy and reducing mesh preparation time. Automated side cooling setup Building on the cooling enhancements, Simcenter Systems 2604 introduces an automated workflow specifically for side cooling configurations. Side cooling has become increasingly popular in battery pack designs due to its space efficiency and thermal performance characteristics, but setting up these models has traditionally been time-consuming and error-prone. The new guided workflow automates the placement, connection, and thermal coupling of side cooling components, dramatically reducing setup time while ensuring best practices are followed. Engineers can now explore multiple side cooling design variants quickly, accelerating the optimization process and helping identify the most effective thermal management strategy for their specific application. Faster sketch generation Performance improvements often go unnoticed until you experience them firsthand, but the 30x speed increase in sketch generation for the battery pack assistant is impossible to ignore. What previously took minutes now happens in seconds. This breakthrough performance enhancement transforms the interactive design experience, allowing engineers to rapidly iterate through different pack configurations, test various cell arrangements, and explore design alternatives without waiting. The impact extends beyond individual productivity. It fundamentally changes the design process, enabling more thorough exploration of the design space and ultimately leading to better-optimized battery packs. Advanced electrochemical modeling with blend electrodes Modern battery cells increasingly use blend electrodes, combining multiple active materials in the anode or cathode to optimize the balance between power density, energy density, cost, and lifespan. However, accurately simulating these multi-material cells has been challenging. Simcenter Systems 2604 enhances both the single particle model with electrolyte (SPME) and the pseudo-2D (p2d) electrochemical models with blend material definition capabilities. Engineers can now define multiple active materials within a single electrode and accurately predict how these blended materials interact during charge and discharge cycles. This capability enables precise optimization of material mixing ratios to achieve target performance characteristics. Critically, it allows for accurate modeling of aging mechanisms for each active material independently. The result is more accurate cell-level predictions that directly inform pack-level design decisions. SOC-dependent thermal runaway modeling Safety is paramount in battery design, and thermal runaway represents one of the most critical failure modes. The challenge is that thermal runaway behavior changes dramatically depending on the battery’s state of charge (SOC). A fully charged battery behaves very differently under thermal stress than a partially discharged one. Simcenter Systems 2604 introduces an enhanced thermal runaway model that explicitly incorporates SOC dependency, allowing engineers to simulate thermal runaway behavior accurately across varying load profiles and charge levels. This eliminates the need for manual kinetic parameter adjustment for each SOC condition, saving valuable engineering time while improving accuracy. Perhaps most importantly, this capability enables adoption of robust thermal runaway demonstration methodologies to accelerate pack validation. By accurately simulating these critical safety scenarios virtually, engineers can significantly reduce reliance on expensive and time-consuming physical testing while ensuring their designs meet stringent safety requirements across the full operational envelope. Expanding gas system simulation capabilities While battery electrification captures headlines, gas systems remain fundamental to countless applications, from pneumatic controls in industrial automation to compressors in HVAC systems and specialized gas handling in extreme environments. Simcenter Systems 2604 delivers three significant enhancements to the gas library that expand simulation capabilities and streamline workflows. Compressor map migration tool Engineers working with compressor models often need to migrate data from the legacy gas mixture library to the more advanced gas library, a process that historically required manual file editing. This tedious work consumed time and introduced potential for errors. The new compressor map migration tool automates this entire process, transforming compressor maps to the standard format used by gas library compressors with a single click. The tool launches directly from the compressor component itself, integrating seamlessly into existing workflows. It supports both table-based and constant efficiency inputs, ensuring flexibility for different types of compressor data, and produces ready-to-use output that’s immediately compatible with gas library models. This automation frees engineers to focus on higher-value design and analysis work rather than data wrangling. Tabulated thermo-physical properties Simulating systems involving specialty gases has always presented challenges. These gases operate at extreme temperatures or pressures, or they lack standard property correlations altogether. Simcenter Systems 2604 now allows tables to be used for defining thermodynamic and transport gas properties, providing unprecedented flexibility. Engineers can input experimental data or highly specialized property tables directly into simulations, defining gas density, enthalpy, entropy, viscosity, and thermal conductivity as functions of pressure and temperature with precision. This capability dramatically expands simulation applicability to non-conventional systems. A prime example is high-voltage circuit breakers involving plasmas reaching temperatures up to 40,000 Kelvin. These conditions fall far beyond the range of standard correlations. With tabulated properties, these extreme applications can now be modeled accurately. Valve builder for gas library Pneumatic valves come in an enormous variety of configurations, with different numbers of ports, positions, and flow characteristics. The sheer number of possible combinations far exceeds what any predefined model library can realistically cover. Engineers frequently need very specific valve configurations that aren’t immediately available, leading to workarounds or compromises. The new valve builder for the gas library solves this problem by providing a dedicated tool for creating custom valve models. Using a flexible graphical interface, engineers can visually configure valve structure, define flow paths, and characterize behavior without writing code. Whether modeling a simple 2-way valve or a complex multi-port, multi-position directional control valve, the valve builder provides the freedom to define exactly what’s needed, unlocking new levels of simulation accuracy for pneumatic systems. Gas turbine simulation with Simcenter Flomaster Gas turbine design demands exceptional precision, particularly when it comes to blade cooling and thermal analysis. The extreme operating conditions, tight tolerances, and complex multi-physics interactions make accurate simulation essential for achieving performance targets while ensuring component durability. Simcenter Flomaster 2604 introduces three powerful enhancements specifically addressing the needs of gas turbine engineers. Enhanced duct scripting with local flow data Custom correlations are often essential for capturing the unique physics of proprietary gas turbine cooling designs, but applying these correlations accurately has been challenging when local flow and wall conditions vary significantly. The enhanced duct scripting interface in Simcenter Flomaster 2604 now exposes segment-level flow data and wall temperatures directly to custom friction and heat transfer calculations. Engineers can access static and total pressure, static and total temperature at each internal node, and wall temperatures broken down by segment and sector. The current segment number is also available, allowing calculations to vary along the length of the duct. Component metadata such as group, type, and title lets scripts know which component they’re running on, enabling reuse of the same script across different ducts with different behavior. This enhanced data access allows engineers to implement proprietary correlations using correct local conditions rather than relying on averaged or external assumptions, significantly increasing confidence in application-specific simulation results. Independent circumferential wall temperature control Cooling passages in gas turbine blades often experience dramatically different temperatures on different sides. For example, a cooling channel may have significantly different temperatures on the trailing edge versus the leading edge, or between the pressure and suction sides. Previously, Simcenter Flomaster could only apply a single wall temperature to the entire perimeter of a duct, forcing engineers to split ducts into multiple components to represent different wall conditions around the circumference. This artificial splitting complicated models and introduced potential for error. The internal duct component now supports up to four independent wall temperatures around the circumference, one per face, using the existing heat transfer and pipe run components. Each face of the duct can be connected to a different heat transfer boundary, eliminating the need for artificial duct splitting. Combined with the existing axial variation capability, engineers now have temperature control in both directions, along and around the passage, from a single component. This enhancement improves cooling performance prediction accuracy while simplifying model setup and reducing the potential for modeling errors. Fully coupled co-simulation for blade design Gas turbine blade design is inherently multi-physics. Aerodynamic loads affect structural deformation, which changes flow paths and cooling effectiveness, which in turn affects metal temperatures and thermal stresses. Loose coupling between thermo-fluid and structural analysis can cause inconsistent blade aero-thermal-structural design results, leading to design iterations, increased risk, and difficulty meeting the tight accuracy requirements demanded by modern blade designs. Simcenter Flomaster 2604 introduces fully coupled co-simulation with iteration-level thermo-fluid and structural synchronization between Simcenter Flomaster and Simcenter 3D. This true iteration-level coupling ensures consistent multi-physics convergence across tools, allowing engineers to meet blade design accuracy requirements that would be impossible with loose coupling approaches. The fully coupled predictions reduce design risk by capturing the true interaction between aerodynamic, thermal, and structural phenomena, providing engineers with the confidence that their virtual predictions accurately represent real-world blade behavior. This capability will be available with the Simcenter 3D 2606 release. Enhanced visualization and mechanical simulation Understanding simulation results is just as important as generating them, and visualization plays a crucial role in extracting insights from complex mechanical systems. Simcenter Systems 2604 introduces a new 3D mechanical domain within 3D scenes that modernizes how engineers visualize and communicate mechanical simulation results. This enhancement brings advanced animation capabilities and contemporary visualization techniques to mechanical domain models, making it easier to understand dynamic behavior, identify potential issues, and communicate findings to stakeholders. The modernized visualization environment provides clearer insights into mechanical system performance, supporting better decision-making throughout the development process. Seamless integration and collaboration Modern engineering is collaborative, and effective collaboration requires robust tools for model sharing, version control, and variant management. Simcenter Systems 2604 delivers three key enhancements that strengthen integration and collaboration workflows. Variable step solver in license-free FMUs Functional mock-up units (FMUs) have become a standard way to share validated simulation models across organizations and tools, but limitations in solver capabilities have sometimes constrained their applicability. Simcenter Systems 2604 now supports variable step solvers in license-free FMUs, providing more flexible and efficient model deployment. This enhancement allows exported FMUs to automatically adjust their time step based on system dynamics, improving both accuracy and computational efficiency. Engineers can now deploy sophisticated models to partners, suppliers, or other departments with confidence that they’ll run efficiently without requiring Simcenter licenses. Parameter sets for model variants Managing multiple variants of a model has traditionally meant maintaining separate model files or manually changing parameters, both error-prone approaches. Different configurations, operating conditions, or design alternatives all required careful tracking. The new parameter sets capability provides centralized management of model variants through organized parameter collections. Engineers can define multiple parameter sets within a single model, each representing a different configuration or operating scenario, and switch between them instantly. This approach reduces errors, ensures consistency, and makes it far easier to explore design alternatives or maintain models for different product variants. External Git repository integration Version control is essential for managing model evolution, enabling collaboration, and maintaining traceability, yet integrating simulation models with modern version control systems has often required external tools and manual processes. Simcenter Systems 2604 now provides direct integration with external Git repositories, including GitHub, GitLab, and Azure DevOps, directly from within Simcenter Amesim. Engineers can commit changes, track history, manage branches, and collaborate with team members using industry-standard git workflows without leaving their simulation environment. This integration brings simulation model management in line with modern software development practices, improving collaboration, traceability, and overall project management. Experience Simcenter Systems 2604 today Simcenter Systems 2604 represents a significant leap forward in systems simulation capabilities, with particular emphasis on battery pack design and validation, expanded gas system modeling, and enhanced collaboration. These enhancements empower engineers to tackle increasingly complex challenges with greater speed, accuracy, and confidence. Want to see in practice how the features of Simcenter Systems 2604 can accelerate your projects, from advanced battery development to complex system simulation and collaborative integration? Schedule a meeting with CAEXPERTS and discover how to apply these innovations to increase efficiency, reduce development cycles, and improve the accuracy of your simulations with the support of CAE solutions experts. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br