In the fast-paced world of product development, engineers constantly strive for innovation, efficiency, and accuracy. Yet one persistent bottleneck continues to slow the journey from design (CAD) to high-fidelity simulation (CAE): geometry preparation. It is a question familiar to many CAD and simulation engineers: Why do we need a dedicated simulation geometry preprocessor when powerful CAD systems already exist? The answer lies in the unique demands of simulation. Meshing algorithms, the foundation of accurate simulations, require geometry that is not only visually correct but also clean, robust, and watertight. Sliver faces, unnecessary features, and small gaps can compromise mesh quality and ultimately affect simulation accuracy. Equally important is the ability to parameterize imported geometry, enabling rapid design exploration, iteration, and optimization. The qualities of a true geometry preprocessor A simulation geometry preprocessor is far more than a CAD viewer or repair tool. It serves as the critical bridge between design and simulation, transforming engineering data into simulation-ready models while preserving design intent. To achieve this, it must excel in five key areas: 1. Seamless CAD and PLM connectivity A geometry preprocessor should import data from a wide range of CAD and PLM systems while preserving assembly hierarchies, part instances, metadata, and design information. For complex products such as automobiles, aircrafts, ships, or railway systems, maintaining this digital continuity is essential for efficient simulation workflows. 2. Simulation-focused geometry preparation Simulation demands geometry that is clean, robust, and watertight. A dedicated geometry preprocessor must automatically remove unnecessary features such as logos, holes, sliver faces, and small entities, while repairing topology issues and closing gaps to create simulation-ready models for reliable mesh generation. 3. Geometry creation, modification and design exploration Engineers must be able to rapidly evaluate design alternatives without repeatedly returning to the original CAD environment. A comprehensive geometry preprocessor should provide a full suite of modeling tools, including extrude, revolve, loft, sweep, synchronous modeling, and freeform modeling. Combined with parameterization capabilities, these tools enable efficient geometry creation, modification, optimization, and what-if studies directly within the simulation workflow. 4. Change management through seamless re-import Design changes are inevitable throughout the product development cycle. A true geometry preprocessor should support seamless re-import of updated CAD data while automatically preserving existing geometry operations, parameters, and simulation preparation work. This capability eliminates repetitive preprocessing effort and significantly reduces turnaround time between design revisions and simulation. 5. Advanced engineering and automation capabilities Beyond geometry preparation, a powerful preprocessor provides specialized tools for CFD and FEA workflows, supports both B-Rep and convergent modeling, enables intelligent geometry queries, and offers scripting capabilities for workflow automation. These capabilities allow engineers to efficiently handle applications ranging from electronics and batteries to turbines, engines, aircrafts, and ships. Simcenter STAR-CCM+’s geometry preprocessor: The ultimate bridge Among the solutions available today, Simcenter STAR-CCM+ geometry preprocessor stands out by combining these capabilities within a single simulation-focused environment, acting as a seamless bridge between CAD systems and CAE workflows. The seamless CAD-to-CAE integration: A Siemens success story The discussion around the optimized pipeline within the Siemens Digital Industries Software Ecosystem highlights a groundbreaking solution for seamless CAD-to-CAE integration, accelerating digital engineering and enhancing product lifecycle efficiency. The core business bottleneck often arises from the friction experienced by engineering teams when extracting complex models from secure PLM systems into simulation environments. Manual exports lead to a loss of critical data provenance, degraded structural constraints, and stripped-away parametric engineering intelligence, significantly extending verification loops and increasing costs. The enterprise-integrated solution involves an automated, managed data workflow starting from Designcenter NX, dynamically governed through Teamcenter using PLMXML schemas and the Designcenter NX Adaptor. This ensures that structured assets are passed directly into the geometry preprocessor and onto Simcenter STAR-CCM+, creating a fully traceable architecture that preserves design structure, assemblies, metadata, and unique geometry. Key operational differentiators: Secure, trackable PLM data flows: Minimizing raw geometry exports and maintaining data integrity. Fully associative assembly hierarchy Automated geometry cleaning tools: Including defeaturing, logo/irregular hole removal and small entities removal tools. Specialized parametric tools: Covering freeform and turbomachinery, battery, IC engine fields, with capabilities to extract internal/external fluid volumes and track contacts. IC engine assembly demonstrating assembly constraint-based positioning Electronic package model prepared for simulation using instancing and assembly constraints Turbine blade fluid-passage extraction workflow Fluid volume extraction in thin GPU water-cooling geometries Core technical pillars of the geometry preprocessor Geometry preprocessor seamlessly transforms PLM-managed structures into simulation-ready computational domains, built on several core technical pillars: Full assembly support: Extracts and displays the complete hierarchical tree structure directly from Teamcenter via PLMXML metadata, retaining custom names, part strings, and tracking attributes. Allows users to apply assembly constraints. Advanced repair tools: Scans, identifies, and resolves non-manifold structural design anomalies, building watertight computational boundary volumes required for mesh generation. This includes: Defeaturing: Automatic removal of secondary blends, chamfers, and rounds. Logo removal: Rapid stripping of corporate branding text and part stamps. Irregular hole removal: Patching complex unlinked structural cutouts. Bridge volume: Closing clearance gaps between parts. Repair-Cad: Converts invalid geometry into simulation-ready geometry. Advanced preprocessing specializations: Leveraging tailored functional sandboxes to fast-track targeted physics and geometric setups, such as: Freeform modeling tools: Integrating a direct, highly adaptive modeling engine within the simulation preprocessing canvas, empowering analysts to make localized adjustments without reverting to standard CAD workflows. This includes direct surface morphing, aerodynamic optimization loops, and interactive shaping. Dedicated turbine tools: For blade profiles, passages, hub, and shroud trimming. Facet/Mesh to B-REP: Converts convergent model into editable B-rep geometry. Search tools and query mechanism Various query mechanisms to identify topological entities Usage of queries in individual Features Contact browser: Provides a quick way to verify face-to-face contacts before meshing. Case study: Parametric modeling and geometry modification using freeform tools The Freeform tool in geometry preprocessor provides a powerful way to modify and parameterize imported geometry. By controlling edge boundary conditions, engineers can rapidly create and evaluate multiple design variations without returning to the source CAD model. Case study: Preparing a complex engine model for CFD Simulation This workflow demonstrates how a complex engine assembly can be transformed from production CAD data into a simulation-ready computational domain. Using advanced repair, defeaturing, topology cleanup, and fluid volume extraction tools, engineers can rapidly eliminate geometric issues, generate watertight fluid regions, and accelerate the transition from design data to CFD analysis without returning to the source CAD model. The example highlights how a dedicated geometry preprocessor accelerates simulation setup while preserving design intent. Quantifiable business Impact The optimized integration pipeline yields significant business benefits: Reduced geometry preparation time: Automated defeaturing, feature stamp/plug cleaning, and irregular hole cleaning can shrink preprocessing time from days to mere hours. Improved design iteration rate: Parametric variants cycle quickly from Designcenter NX/Teamcenter via the Designcenter NX Adaptor directly into active simulation loops without resetting parameters. 100% data provenance & structure integrity: Automated Teamcenter managed PLMXML + Designcenter NX Adaptor pipeline flow replaces traditional neutral file exports that cause history and hierarchy loss. Lightweight models & fast file load ins: Smart object instancing coupled with metadata transfer addresses challenges of unmanaged component duplications in large assemblies. In an era where simulation-driven engineering is essential for product innovation, the geometry preprocessor has become a strategic enabler rather than just a preprocessing tool. By bridging the gap between CAD and CAE, Simcenter STAR-CCM+ geometry preprocessor helps engineers spend less time preparing geometry and more time generating insights that drive better products. If your company is looking to reduce geometry preparation time and accelerate the transition from CAD to simulation, schedule a meeting with CAEXPERTS to learn how the Simcenter STAR-CCM+ geometry pre-processor can optimize this workflow. We conduct 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

Designing high-performance antennas requires more than accurately predicting electromagnetic behavior. Engineers also need to explore design alternatives efficiently and understand the trade-offs between different performance objectives. By combining the electromagnetic simulation capabilities of Simcenter Feko with the automated design exploration capabilities of Simcenter HEEDS, engineers can move efficiently from simulation to optimized antenna designs. From antenna simulation to design optimization Antenna performance depends on numerous interdependent parameters, such as geometric dimensions, substrate properties, feed position and ground plane size. Simcenter Feko enables engineers to model these effects and evaluate antenna performance across different design variants using numerical electromagnetic simulation. As the number of design variables and performance objectives increases, however, manually exploring these variants becomes increasingly challenging. Connecting Simcenter Feko with Simcenter HEEDS introduces automated optimization capabilities, including single- and multi-objective optimization, robustness analysis and reliability optimization, surrogate model creation, and workflow automation. In this blog, we demonstrate this approach through the shape optimization of a dielectric lens placed on top of a horn antenna. The simulation uses a hybrid method of moments (MoM) and finite element method (FEM) solver in Simcenter Feko at 3.05 GHz. The FEM region represents the waveguide, including an anisotropic circulator, while MoM is used for the metallic horn. The dielectric lens is modeled using the surface equivalence principle (SEP). The objective is to optimize the lens shape to maximize antenna gain while making the gain pattern as rotationally symmetric as possible. Figure 1. Simcenter Feko model of the horn antenna and dielectric lens with the corresponding computational regions Parametrizing the antenna model for design optimization To enable efficient design exploration, the lens geometry is parametrized using the NURBS tool in the CADFEKO module in Simcenter Feko. One octant of the lens is represented by a rational biquadratic Bézier surface defined by nine control points and nine weights. The complete lens is then constructed by cutting the surface along the z=0 plane and mirroring it with respect to the symmetry planes x=0, y=0 and z=0. The control points of the Bézier surfaces have a geometrical meaning, making it straightforward to control G0 – and G1 - continuity along the patch connections. The resulting lens parametrization forms an 11-dimensional design space. Video 1. Parameterized modeling of the dielectric lens in Simcenter Feko CADFEKO Four goal functions are defined to evaluate antenna performance throughout the optimization. Goal 1 represents the antenna gain in the boresight direction, while goals 2 and 3 describe the side lobe levels (SLL) in the E- and H-planes. Goal 4 represents the minimum gain along the θ=15° isocurve. Maximizing goal 4 helps produce an almost rotationally symmetric gain pattern. With the parametrized model and goal functions defined, the electromagnetic simulation is performed in Simcenter Feko to calculate the antenna responses and generate the relevant plots in the POSTFEKO module. Connecting Simcenter Feko with Simcenter HEEDS With the parametrized Feko model and simulation setup in place, the next step is to connect the workflow to Simcenter HEEDS. The Feko portal in HEEDS is configured by pointing it to the Simcenter Feko installation directories. The electromagnetic simulation workflow can then be defined in three main steps: opening the parametrized model in CADFEKO, running the Feko solver to compute the responses, and visualizing the results in POSTFEKO. The resulting data is passed to Simcenter HEEDS, which determines the next set of design variables according to the selected analysis approach and automatically repeats the electromagnetic simulation workflow in Simcenter Feko. Both 3D and 2D visualizations from POSTFEKO are also available in HEEDS, allowing engineers to monitor the design and its corresponding performance throughout the optimization. Video 2. Connecting the Simcenter Feko simulation workflow with Simcenter HEEDS Exploring and optimizing antenna designs with SHERPA Once the Simcenter Feko simulation workflow is connected to Simcenter HEEDS, the design space can be systematically explored and optimized. Simcenter HEEDS offers several approaches for design exploration and optimization, with SHERPA as its main optimization strategy. SHERPA stands for Simultaneous Hybrid Exploration that is Robust, Progressive, and Adaptive. Rather than relying on a single optimization method, SHERPA simultaneously employs multiple search strategies and adaptively allocates computational effort to the methods performing best as the study progresses. This reduces the need for engineers to select a specific optimization algorithm in advance, making SHERPA particularly well suited for high-fidelity, simulation-driven design optimization where every evaluation counts. In this example, a multi-objective optimization study is defined in Simcenter HEEDS to explore more than 150 antenna designs. Rather than producing a single optimal design, the study identifies a set of Pareto-optimal solutions representing different trade-offs between the defined performance goals. Video 3. Exploring the antenna design space with SHERPA in Simcenter HEEDS Evaluating Pareto-optimal antenna designs Once the SHERPA optimization is complete, the Pareto-optimal solutions can be analyzed using the postprocessing capabilities in Simcenter HEEDS. The Trade-offs tool under the Discover ribbon provides a visual representation of these solutions, helping engineers compare performance trade-offs and select an appropriate antenna design. In this example, design 148 provides a good balance between the optimization goals, improving antenna gain by 22% and the roundness KPI by 59%. Video 4. Evaluating Pareto-optimal antenna designs in Simcenter HEEDS Conclusion Combining Simcenter Feko with Simcenter HEEDS enables engineers to systematically explore a broader antenna design space while maintaining high-fidelity electromagnetic simulation at the core of the process. Automating the repeated simulation and evaluation of design variants can help reduce design time and identify promising solutions while supporting informed trade-offs between competing performance goals. While this example focuses on antenna design, the same approach can be extended to other electromagnetic applications, including the optimization of EMC behavior and radar cross section (RCS). Do you want to streamline antenna development and more efficiently explore different design alternatives? Schedule a meeting with CAEXPERTS to discover how Simcenter Feko and Simcenter HEEDS can support your projects through electromagnetic simulation and automated optimization. We conduct initial projects in collaboration with your team to maximize return on investment, helping your company extract peak performance from the tools and achieve faster, more accurate, and reliable results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The rise of legged robotics Just a few years ago, robot dogs seemed like futuristic demonstrations. Today, they are becoming valuable industrial assets. Energy operators use them to inspect substations and offshore facilities. Mining companies evaluate them for operations in hazardous environments. Legged robots are becoming essential technology for surveillance and autonomous operations. At the same time, other types of leg-based robots like insect-inspired robots are opening entirely new possibilities. Thanks to their exceptional stability and ability to navigate confined or irregular environments, hexapod robots are emerging as promising solutions for inspection and exploration applications. Behind every successful robot lies a complex combination of mechanical design, actuation systems, controls, software, sensors, power management and AI. As competition intensifies and development cycles shorten, robotics companies can no longer rely solely on physical prototyping. System Simulation digital twins with multiphysics and controls to get mechatronics robots This is where Simcenter Amesim provides a decisive advantage. Through multi-domain system simulation, engineering teams can evaluate, optimize and validate robot architectures before building hardware. Why System Simulation is key Customers purchasing robotic platforms expect more than a machine that can walk. They expect a robot that can operate reliably, safely and efficiently in real-world environments. To achieve this, developers must answer critical questions early: Will the robot remain stable on uneven terrain? How much payload can it carry? How long will the battery last? Which actuator technology is optimal? How will the control system respond to disturbances? Can autonomy algorithms handle unexpected obstacles? System Simulation enables engineers to answer these questions virtually. The runs are executed in few seconds of CPU-time, not hours. Users can access all variables for the computed results like the forces, torques, velocities, accelerations, temperatures, currents, voltages, pressures or any types of results they want to predict thanks to the digital twins. It’s not just animations with imposed displacements, but real physical quantities computed directly from dynamic equations for all types of physics: mechanics, electrics, hydraulics, pneumatics, thermal, controls, … Simcenter Systems solutions offer real computation from solving dynamic equations in few seconds of CPU-time, this is not just animation Using Simcenter Amesim, teams can combine mechanical dynamics, hydraulic systems, electric powertrains, controls and energy management into a single digital model. Problems can be identified before costly hardware iterations are required. The result is faster innovation, lower development risk and higher-performing robotic platforms. From hydraulic quadrupeds to electric ant robots Let’s see how Simcenter Amesim is accelerating legged robotics development with system simulation. Actually it’s just a matter of learning from Nature’s most successful walkers. Nature has already solved many mobility challenges. Modern robotics increasingly borrows these solutions. The two Simcenter Amesim digital twins that we introduce hereafter show complementary robot architectures: a four-legged articulated robot driven by hydraulic actuators and servo-valves, and a six-legged ant-inspired robot driven by electric motors. Together, they illustrate how system simulation can support the development of advanced mobility systems before physical prototypes are built. Complementary architectures with 4-leg robot with hydraulic actuators and 6-leg robot with electric motors Let’s start first with the robot dogs and quadruped platforms. This Simcenter Amesim quadruped digital twin showcases how four-legged robots achieve a balance between speed, agility, mobility and stability. Its articulated legs, hydraulic actuation system and advanced gait control strategies enable: Stair climbing Obstacle crossing Navigation of rough terrain Stable payload transportation 4-legged robot with hydraulic actuators – Digital twin and performances The model demonstrates both trotting and walking gaits, highlighting the importance of coordination between leg pairs and body stability. For industrial customers, this capability translates into robots capable of reaching locations inaccessible to wheeled systems. Let’s now move to the second example of mechanical ants and hexapod robots. This digital twin of the six-legged mechanical ant demonstrates a different philosophy. Using a tripod gait, three legs always remain in contact with the ground while the other three advance. 6-legged robot with electric motors – Three legs always remain in contact with the ground This approach delivers high static stability, excellent terrain adaptation, reduced risk of overturning and reliable motion in confined spaces. 6-legged robot with electric motors – Digital twin and performances Therefore, there are many possible applications like inspection inside industrial facilities, pipelines, tunnels, underground infrastructure and remote exploration environments. That’s impressive, isn’t it? Gimbal systems for advanced observation Let’s now go a bit further in the analysis by adding some auxiliary systems that are more present in legged robots. Indeed, modern quadruped robots are increasingly becoming mobile sensing platforms rather than simply walking machines. One emerging trend is the integration of stabilized multi-axis gimbal systems mounted on top of the robot. These intelligent payloads can carry optical, thermal, LiDAR or multi-sensor packages while maintaining a stable line of sight even as the robot traverses uneven terrain. Quadruped robot with a gimbal system attached to its head, walking on a predefined trajectory, to continuously observe a moving object This is actually a six-degree-of-freedom (6 DoF) challenge. Theoretically speaking, we could imagine it’s not so easy to solve, while it’s finally quite straight forward to get impressive results in the Simcenter Amesim environment. Indeed, all subsystems and all physics can be combined in few clicks, while the intelligent solver computes all equations instantaneously, from slow dynamics to very fast transients or high-frequency phenomena. Such a successful approach can be conducted while including all detailed physics inside, from mechanical frictions, to hysteresis, gaps, leakages, … The quadruped robot constantly experiences body motion caused by locomotion, terrain irregularities, acceleration and turning maneuvers. At the same time, the onboard sensor payload needs to continuously track and observe a moving point of interest within a dynamic environment. The challenge here is to ensure that the sensor payload remains accurately pointed while the robot itself is in motion. Developing such an integrated system requires understanding the interaction between mobility, sensing and controls. Simulation helps identify whether performance limitations originate from the robot platform, the gimbal mechanics, the control strategy or the sensor architecture. Virtual validation of tracking performance A digital twin enables engineers to assess thousands of tracking scenarios before hardware testing begins. Possible scenarios include: Observation of moving objects at varying speeds Rapid changes in direction or elevation Operations on rough terrain Vibrations induced by trotting or walking gaits Temporary loss of line of sight Hundreds of virtual scenarios are investigated with virtual validation By combining robot dynamics, gimbal mechanics and control algorithms within a single simulation environment, teams can evaluate the overall mission effectiveness rather than optimizing individual components in isolation. The strategic advantage of simulation-driven robotics As industrial companies expand their use of autonomous systems, expectations for reliability, autonomy and efficiency continue to rise. Organizations developing robot dogs, hexapods and future generations of autonomous inspection robots need engineering tools capable of managing this growing complexity. By validating performance virtually before hardware deployment, robotics companies can reduce development risk, accelerate innovation cycles and bring more capable robotic platforms to market faster. We can for example consider developing the advanced controls for two-legged robots standing up (let’s say it’s a “chicken” robot), whose challenge is to ensure the right balancing while the actuation effects and body dynamics impact its motion depending on its speed (slow or fast motion). That’s a matter of finding out the right control logics to ensure its equilibrium at all times. And counter-intuitively, we found that the highest speeds were easier to manage compared to slow motions, since the balance momentum is reduced. Two-legged robot: a bipedal chicken robot running With Simcenter Amesim, engineering teams can evaluate its locomotion, actuation, energy systems, controls, autonomy and AI workflows within a single simulation environment. You can even connect joysticks to pilot it remotely thanks to the dynamic live simulation capabilities of the Dashboard with controller inputs in Simcenter Amesim. Even to the point of designing robot reindeers Let’s go further with some more fun. Let’s pull Santa Claus’ sleigh with some robot reindeers, it’s faster and cheaper than real reindeers. To improve productivity and delivery task planning (it’s key to deliver thousands of gifts on time), Santa Claus converted his moose and reindeers into their robotized versions. It’s finally more efficient, you just program the path to follow and the robot reindeers self-drive the sleigh to the selected GPS location to deliver the gifts to the right locations. Santa Claus celebrates Christmas with robot reindeers in Simcenter Amesim thanks to its 3D Mechanical library for multibody simulation Boston Dynamics celebrates Christmas by getting robot reindeers to pull Santa’s sleigh Takeaways – Experience so many benefits using system simulation It’s now time to conclude and summarize the benefits of using System Simulation when designing legged robots. Simcenter Amesim enables engineers to evaluate complete robotic systems through multi-domain system simulation, helping teams optimize locomotion, actuation, power systems, control software, and AI-enabled autonomy before physical prototypes are built. From robot dogs inspecting industrial facilities to insect-inspired robots exploring confined spaces, legged robotics is transforming how industries operate. However, achieving the right balance between mobility, autonomy, energy efficiency, intelligence, and reliability requires a comprehensive engineering approach. By adopting model-based engineering and digital twin methodologies, organizations can accelerate innovation, reduce development risk and bring more capable robotic platforms to market faster. The future of quadruped and multi-legged robotics will be built not only in hardware laboratories, but first in the virtual world through advanced system simulation. To leverage the potential of system simulation in the development of legged robots—and to accelerate the validation of locomotion, actuation, controls, and energy management—schedule a conversation with CAEXPERTS. Our team can support the implementation and application of Simcenter Amesim, collaborating 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 results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Electromagnetic simulation plays a critical role in the development of electric machines, power electronics and other low-frequency electromagnetic devices. As electromagnetic models become larger and more complex and development cycles continue to shorten, engineers are expected to balance simulation accuracy, workflow efficiency and engineering productivity while supporting an increasingly connected simulation process. Simcenter Flux 2026.1 introduces new capabilities that help engineers accelerate electromagnetic simulations, simplify model creation and extend electromagnetic workflows across optimization, multiphysics and system-level engineering activities Accelerate electromagnetic simulations As electromagnetic models continue to grow in size and complexity, engineers are constantly looking for ways to reduce simulation effort while maintaining accuracy. Simcenter Flux 2026.1 introduces several enhancements that accelerate low-frequency electromagnetic simulations through numerical innovations, while also providing greater flexibility during model preparation and modification. A major addition in this release is the extension of the Integral Method to transient magnetic simulations. By eliminating the need to mesh large surrounding air regions, engineers can significantly reduce model complexity and simulation effort while maintaining accurate representation of eddy-current and capacitive effects. Figure 1. Comparison of finite element and Integral Method approaches for transient magnetic simulations Figure 2. Example of non-conforming meshes for conductor and parasitic extraction analyses using the Integral Method The Integral Method workflow is also enhanced through support for non-conforming meshes in conductor and parasitic extraction analyses. Individual components can now be meshed independently, simplifying geometry preparation and providing greater flexibility during model modifications. Simcenter Flux 2026.1 also introduces access to selected magnetic material databases originating from Simcenter MAGNET. By bringing valuable material knowledge into Simcenter Flux, this enhancement promotes greater consistency across low-frequency electromagnetic simulations and simplifies the reuse of validated material data across the Simcenter electromagnetic portfolio. Enhance modeling productivity and user experience Simcenter Flux 2026.1 introduces more than 30 enhancements across low-frequency electromagnetic modeling workflows. Leveraging Simcenter Simlab, a multiphysics simulation platform, these improvements simplify geometry preparation, machine definition and model setup, helping engineers create electromagnetic models more efficiently. Figure 3. Selected productivity enhancements across low-frequency electromagnetic modeling workflows A major enhancement in this release is the expanded support for axial flux machine development. Engineers can rapidly create and evaluate parameterized axial flux machine models while performing electromagnetic, NVH, thermal and system-level analyses within a unified simulation environment. Video 1. Rapid modeling and multiphysics evaluation of axial flux machines Another major addition is a new component-based workflow for radial machine assembly. Engineers can rapidly construct machine models using predefined and customizable components, reducing repetitive geometry preparation and promoting reuse of validated machine building blocks across projects. Video 2. Rapid modeling of radial electric machines using predefined and customizable components For permanent magnet machines, model preparation is further simplified through automated magnet orientation propagation. Engineers can define a reference orientation and automatically apply it to the remaining magnets, reducing repetitive setup tasks and minimizing the risk of modeling errors. Video 3. Automated propagation of magnet orientations in permanent magnet machine models Together, these enhancements help engineers create low-frequency electromagnetic models more efficiently while improving support for advanced electric machine development workflows. Extend electromagnetic models across engineering workflows Electromagnetic simulation is increasingly part of a broader engineering process. Beyond field analysis, electromagnetic models are often reused for design exploration, optimization and system-level studies. Simcenter Flux 2026.1 further strengthens connectivity across the Simcenter portfolio, enabling electromagnetic models and simulation data to support a wider range of engineering workflows. Engineers can now leverage NX parametric geometry directly within electromagnetic simulation workflows, enabling associative geometry updates while preserving design intent and accelerating iteration cycles between CAD and simulation environments. Video 4. Reuse of NX parametric geometry within electromagnetic simulation workflows Connectivity with Simcenter HEEDS enables automated design exploration and optimization studies, allowing engineers to investigate larger design spaces and identify high-performing designs more efficiently. Video 5. Export of electromagnetic models to Simcenter HEEDS for design exploration and optimization Enhanced reduced motor model workflows further extend the value of electromagnetic simulations. Support is now expanded to additional machine architectures, including axial flux, wound field and multiphase machines, while export capabilities enable efficient integration with PSIM or Simcenter Amesim for system-level investigations. Video 6. Expanded reduced motor model support for advanced electric machine architectures By extending the use of electromagnetic models across the Simcenter portfolio, engineers can reuse simulation data more effectively throughout optimization, multiphysics and system-level engineering workflows. Exploring new approaches to low-frequency electromagnetic simulation In addition to enhancements across simulation performance, modeling and engineering workflows, Simcenter Flux 2026.1 also highlights emerging approaches that combine simulation, optimization and AI technologies for low-frequency electromagnetic applications. One example is the combination of Simcenter Flux, PSIM and Simcenter PhysicsAI to create surrogate-model-based multiphysics workflows. By generating simulation data and training surrogate models, engineers can rapidly evaluate thermal and NVH responses while significantly reducing the computational cost associated with design exploration and optimization studies. Figure 4. Combined Simcenter Flux, PSIM and Simcenter PhysicsAI workflow for surrogate model generation Video 7. Example of electric machine topology optimization combining electromagnetic and structural requirements Another area being explored is magneto-mechanical topology optimization. By combining electromagnetic and structural simulations within a unified optimization process, engineers can simultaneously consider electromagnetic performance and mechanical feasibility at high speed, enabling the exploration of innovative designs that would be difficult to identify using traditional approaches. Looking ahead From numerical innovations and advanced machine modeling to optimization, system simulation and emerging AI-assisted technologies, Simcenter Flux 2026.1 continues to expand the role of low-frequency electromagnetic simulation across the product development process. By improving simulation performance, modeling productivity and engineering connectivity, Simcenter Flux helps engineers move more efficiently from concept exploration to engineering decision-making. If you want to unlock the full potential of Simcenter Flux 2026.1 and accelerate your electromagnetic simulation projects, schedule a consultation with CAEXPERTS. Our team consists of engineers trained and qualified to provide support, implementation, and application services for Altair solutions, helping your company optimize electric machines, integrate multiphysics analyses, and connect simulation to optimization and systems engineering workflows. We collaborate with your team on initial projects to maximize return on investment, helping you extract peak performance from the tools and achieve faster, more accurate, and reliable results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Electromagnetic simulation is essential for developing modern communication, radar, satellite and sensing systems. As designs become more connected and complex, engineers need to simulate larger models, evaluate realistic environments and make design decisions faster. Following the integration of Feko (former Altair Feko) into the Simcenter portfolio, Simcenter has strengthened its high-frequency electromagnetic simulation capabilities. Building on decades of experience, Simcenter Feko 2026.1 introduces enhancements across electromagnetic simulation, antenna engineering, wireless system analysis and connected engineering workflows. Simulate larger and more complex electromagnetic systems with Simcenter Feko As electromagnetic systems continue to increase in size and complexity, engineers must analyze larger antenna arrays, more detailed platform models and increasingly realistic operating environments. Maintaining simulation scalability while controlling computational cost remains a key challenge. Advanced iterative solver (AIS) technology for MLFMM simulations One of the major highlights in Simcenter Feko 2026.1 is the introduction of the advanced iterative solver (AIS) technology for multilevel fast multipole method (MLFMM) simulations. Designed for large-scale electromagnetic analysis, AIS significantly improves memory efficiency and convergence robustness. In benchmark examples, memory requirements can be reduced by up to 87%, enabling substantially larger models to be solved using existing computing resources. By reducing memory consumption while maintaining solution accuracy, AIS helps engineers tackle increasingly demanding electromagnetic simulation challenges without the need for additional hardware resources. While the focus of AIS is improved memory efficiency rather than raw solver speed, it enables simulations that would otherwise be impractical due to memory limitations. This enables larger antenna arrays, more detailed platform models and more realistic electromagnetic environments to be analyzed with greater confidence. Video 1. Demonstration of AIS technology for large-scale MLFMM simulations Efficient modeling of multilayer dielectric structures Scalability improvements continue with enhancements to thin-layer modeling for multilayer dielectric structures. By representing thin dielectric layers on surfaces modeled with the surface equivalence principle (SEP), engineers can dramatically reduce mesh complexity while maintaining simulation accuracy. The result is lower memory requirements, faster simulations and more efficient analysis of complex dielectric structures, including multilayer coatings, radomes and aerospace applications. Figure 1. Example of thin-layer modeling applied to a multilayer coated dielectric sphere Enhanced RL-GO performance for electrically large simulations Figure 2. Example demonstrating improved RL-GO simulation efficiency for cabin sensing applications For electrically large and high-frequency applications, Simcenter Feko 2026.1 introduces enhancements to the ray-launching geometrical optics (RL-GO) solver. A more uniform ray-launching approach improves simulation efficiency and reduces source-location dependencies, enabling faster and more consistent analysis of complex electromagnetic environments, including automotive sensing, cabin radar, wireless coverage and large platform applications. Accelerate antenna and communication system workflows Beyond solver technology, Simcenter Feko 2026.1 introduces several enhancements designed to improve modeling productivity and streamline engineering workflows. The CADFEKO component library has been expanded with nine new fully parameterized antenna models, further broadening the range of ready-to-use antenna building blocks available to engineers. Including lens and conformal patch antennas, these configurable components help accelerate model creation and support rapid design exploration across a wide range of antenna applications. Video 2: Conformal patch antenna creation using the CADFEKO component library A new Optical Coverage tool enables fast line-of-sight and field-of-view assessment directly on CAD geometry without requiring meshing or launching a solver. Engineers can quickly evaluate antenna visibility, compare placement options and identify potential coverage limitations, helping accelerate antenna placement studies and communication system design workflows. Video 3: Optical coverage analysis directly on CAD geometry Advance wireless system analysis and radio channel modeling As wireless networks, satellite communication systems and connected environments continue to evolve, engineers require more efficient tools for radio channel, network planning and radar system evaluation. Video 4: Example of unified multi-time satellite constellation modeling for NTN applications Simcenter Feko Winprop 2026.1 continues to expand wireless system analysis capabilities, supporting advanced propagation modeling, network planning and emerging non-terrestrial network (NTN) applications. A key enhancement in this release is the introduction of unified multi-time satellite constellation modeling, which significantly simplifies the setup of dynamic satellite communication scenarios through automatic satellite position calculations over time. For network planning applications, Simcenter Feko WinProp 2026.1 introduces major performance improvements for trajectory-based analyses. Optimized processing of large trajectory datasets enables engineers to evaluate high-density mobility scenarios without simplifying trajectory descriptions. In examples containing more than 16,000 trajectory samples, runtime was reduced from about one hour to less than twenty seconds, enabling significantly faster evaluation of cell assignment and transmission mode studies. Video 5: Example of accelerated trajectory-based analysis for network planning Additional enhancements further strengthen support for modern wireless communication systems. New MIMO modeling capabilities enable more realistic representation of radio units and antenna configurations across operating bands and polarizations, while support for vertical uniform linear arrays (ULA) improves the modeling of advanced beamforming and multi-element antenna systems used in cellular and wireless infrastructure applications. The release also introduces several usability enhancements across the WRAP tools, including improved handling of satellite constellation data, broader support for external map formats and enhanced visualization on high-resolution displays. Connect simulation and optimization As electromagnetic designs become increasingly complex, engineers are often required to evaluate large design spaces and balance multiple performance objectives. Manual design iterations can quickly become time-consuming and difficult to manage. Simcenter Feko 2026.1 strengthens connectivity with Simcenter HEEDS, enabling automated design exploration and optimization workflows directly from electromagnetic simulation models. Engineers can automatically evaluate design alternatives, identify high-performing solutions and gain deeper insight into design trade-offs, helping accelerate simulation-driven product development. Video 6: Electromagnetic design optimization using Simcenter Feko and Simcenter HEEDS By combining the simulation capabilities of Simcenter Feko with the optimization technologies of Simcenter HEEDS, engineering teams can accelerate decision-making and bring better-performing products to market more efficiently. Looking ahead Simcenter Feko 2026.1 introduces a range of enhancements designed to help engineers simulate larger electromagnetic systems, accelerate antenna and wireless engineering workflows, and to connect simulation with broader design processes. Building on decades of experience in high-frequency electromagnetic simulation, Simcenter Feko continues to support the development of next-generation communication, radar, satellite and sensing technologies. If you want to unlock the full potential of Simcenter Feko 2026.1 and accelerate your electromagnetic simulation projects, schedule a consultation with CAEXPERTS. Our team consists of engineers trained and qualified to provide support, implementation, and application services for Altair solutions, helping your company simulate more complex systems, optimize designs, and achieve results with greater efficiency. We collaborate with your team on initial projects to maximize return on investment, helping you extract peak performance from the tools and achieve faster, more accurate, and reliable results. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

If you’ve spent any time working with CFD or FEA results, you already know that simulation data is not like most data. It doesn’t sit neatly in rows and columns. It lives on meshes – complex, irregular, three-dimensional structures where every node has neighbors, every face carries flux, and every element is shaped by its surrounding geometry. Yet for years, the dominant strategy for applying machine learning to engineering simulation shared a common goal: accelerate or replace expensive numerical solvers by learning the mapping from simulation inputs to outputs. To do this, practitioners flattened rich geometric information into vectors, grids, or point clouds, and hoped that a neural network would figure out the rest. Sometimes it worked – but largely on simple, regular geometries where the flattening didn’t destroy too much structural information, or where the domain was small enough that brute-force approximation was sufficient. In real engineering workflows, with tetrahedral meshes, unstructured CFD grids, and complex boundary conditions, it didn’t work well enough to trust. This approach wasn’t just a computational compromise – it was a fundamental mismatch between the data structure and the model architecture. The reason is straightforward: standard neural networks are blind to geometry. A fully connected network treats each input as an independent feature, unaware of spatial relationships. Neither architecture was designed with a tetrahedral mesh or an unstructured CFD grid in mind. Convolutional Neural Networks (CNNs) improve on this for image data – but only because images have a very specific structure: a regular, uniform grid where the notion of “neighborhood” is mostly consistent. That assumption breaks entirely on an engineering mesh. This is precisely the gap that Geometric Deep Learning (GDL) was built to close. What exactly is Geometric Deep Learning? Let’s be honest – “Geometric Deep Learning” sounds like something a mathematician invented for his PhD thesis. But the core idea is surprisingly elegant. Traditional deep learning – the kind powering image recognition, language models, and recommendation engines operates on regular, structured data. Images are grids of pixels. Audio is a sequence of samples. These structures are Euclidean, uniform, and predictable. CNNs thrive here because the notion of “neighborhood” is consistent everywhere on the grid. Engineering geometry is a different beast entirely. A finite element mesh is an irregular graph. A point cloud from a 3D scan is an unordered set. A CAD surface is a manifold. None of these live comfortably on a regular grid. If you try to flatten them into one, as early AI-for-engineering approaches did, you lose the very geometric relationships that make the physics meaningful. You’re essentially crumpling a map to fit it into a square box, then wondering why the roads don’t connect. GDL extends deep learning to these non-Euclidean domains – graphs, meshes, point clouds, and manifolds – by building neural networks that respect the underlying geometric structure of the data. The theoretical backbone comes from the concept of symmetry: a well-designed geometric neural network should produce consistent predictions regardless of how you rotate, translate, or renumber the input. This property – known as equivariance – is not just mathematically beautiful but also physically essential. The engine underneath: Geometric Deep Learning architectures for CAE The elegant insight at the heart of Geometric Deep Learning, formalized by Michael Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst, and later expanded in the now-famous “Grids, Groups, Graphs, Geodesics, and Gauges” framework, is – the right neural network architecture for any domain is the one that respects the symmetries of that domain. For engineering meshes, the relevant symmetries are clear: Permutation invariance: the physics doesn’t change if you renumber your mesh nodes Rotational and translational equivariance: a stress field on a bracket rotated 45° should rotate accordingly, not produce a different answer Local geometric structure: the behavior at a node depends on its neighbors, their distances, and their relative orientations – not on some global coordinate Graph Neural Networks (GNNs) were among the first architectures to naturally encode these properties for engineering simulation. A GNN treats a mesh as a graph – nodes represent mesh vertices or cell centers, edges represent connectivity, and features at each node are passed along edges, aggregated, and updated through successive layers. This message-passing mechanism mirrors, in a learned and differentiable way, the same local information exchange that numerical solvers perform when propagating boundary conditions or iterating residuals through a domain. The result is a model that is deeply aware of mesh geometry – learning, for instance, that the physics near a sharp trailing edge behaves differently from a smooth freestream region, without ever being explicitly told so. As the field has matured, more powerful architectures have emerged. Transformer-based models – originally developed for natural language processing and later adapted for scientific computing – have proven to be remarkably effective for surrogate modeling in CAE. At their core, Transformers use an attention mechanism that allows every node in the mesh to dynamically weigh its relationship with every other node – learning which parts of the geometry matter most for predicting the physics at any given location. This ability to capture long-range dependencies across the entire mesh in a single operation makes Transformers particularly well-suited for complex flow fields and geometries where global context matters as much as local structure, positioning them as the state of the art for high-accuracy surrogate modeling. For use cases where the objective shifts from full-field prediction to scalar quantities of engineering interest – such as drag coefficient, maximum stress, or total pressure drop – the Shape Encoders offer a highly efficient alternative. Rather than resolving the entire solution field, they compress a design’s geometry into a compact, learned latent representation – a geometric fingerprint – and directly map it to the target output, making them exceptionally fast and well-suited for early-stage design screening and broad design space exploration. In Simcenter PhysicsAI, these three architectural paradigms – message-passing graph networks, attention-based Transformers, and geometry-encoding regressors – are realized through the Graph Context Neural Simulator (GCNS), the Transformer Neural Simulator (TNS), and the Shape Encoding Regressor (SER) – each suited to different simulation needs and complexity levels. It is worth noting that this remains a rapidly evolving field; new architectures are continuously being developed, benchmarked, and integrated into production workflows. The underlying principle, however, stays constant: the right architecture is the one that respects the geometry, symmetry, and physics of the domain it is learning from. Simcenter PhysicsAI is built with this principle at its core – and is designed to evolve as the field does. The industrial impact: From days to seconds Let’s ground this in engineering reality. A mechanical engineer is optimizing a control arm for an electric vehicle. The design space involves dozens of geometric parameters – wall thickness, fillet radii, rib configurations. Each design variant requires a full nonlinear FEA run. Even on a powerful HPC cluster, a single run takes 2 – 4 hours. Exploring 500 design variants? That’s months of compute time and significant cost. With a GDL surrogate trained on a representative set of simulations: New geometry variants are evaluated in seconds, not hours Full-field stress and displacement predictions are available across the entire mesh – not just scalar outputs Design optimization loops that once took weeks now complete overnight Engineers spend time on insight and decision-making, not waiting for solvers The same story plays out across disciplines: aerodynamic shape optimization in CFD demands thousands of solver evaluations, and the same is true in thermal management, electromagnetics, and acoustics. This is where the concept of field prediction becomes critical: rather than returning a single drag coefficient or a peak temperature, a GDL model returns a full spatial distribution of pressure, velocity, or temperature across every node of an unstructured mesh. It’s solver-quality output, delivered at surrogate speed. The generalization question (because someone always asks) The most common skepticism about AI surrogates in engineering is generalization: “Sure, it works on the training geometries. What about something genuinely new?” It’s a fair challenge. Early surrogate approaches, particularly those based on fixed-input-size neural networks or regression on geometry parameters, generalized poorly. A model trained on one design family would often fail badly on even modestly different shapes. GDL architectures handle this more gracefully, for a structural reason. Because they operate on graphs and meshes rather than fixed-size vectors, they can be designed to be geometry-aware, encoding spatial relationships such as distances, angles, and surface normals directly into the message-passing process. This means the same model weights can be applied to meshes with different node counts, connectivity patterns, and, to a significant degree, different topologies – provided the test cases fall within the physical regime, geometry range, and boundary condition space represented in the training data. That generalization has limits, of course. A GDL model trained on external aerodynamics won’t spontaneously understand internal combustion. But within the physics domain, transfer learning capability is meaningfully stronger than before, and with targeted fine-tuning on a modest number of new simulations, these models can adapt to new geometry families at a fraction of the cost of retraining from scratch. In industrial deployment, where generating training data is always the bottleneck, that efficiency is decisive. GDL doesn’t replace the physics solver. It learns from it, distills its knowledge, and deploys that knowledge at unprecedented speed. So where does Simcenter PhysicsAI come in? What makes this moment genuinely exciting is that GDL is no longer confined to academic papers and conference proceedings. It is arriving now, in production-grade engineering software, ready to be used by simulation engineers, not just AI researchers. The transition from “interesting research” to “tool I can use on Monday morning” is the hardest leap in any technology’s lifecycle. It requires not just algorithmic innovation but deep integration with existing CAE workflows, robust training pipelines, and the kind of validation rigor that engineering demands. That transition is happening and faster than most people in the industry realize. Simcenter PhysicsAI is purpose-built to bring the full power of GDL into the hands of practicing engineers. A few things worth highlighting, because they reflect genuine engineering decisions, not just marketing checkboxes: Native mesh compatibility: Simcenter PhysicsAI operates directly on the FEA and CFD meshes your solvers already work on – no voxelization, no geometric approximation, no fidelity loss. The simulation assets you have already built become your training data. Solver-agnostic by design: Whether your simulation history comes from Simcenter Optistruct, Simcenter Radioss, Simcenter Star-CCM+, Simcenter Feko, Simcenter Flux, Simcenter Nastran, or other industry-standard solvers, Simcenter PhysicsAI learns from it. There is no requirement to standardize on a single solver stack or rebuild existing workflows. Seamless integration with CAE ecosystem: Simcenter PhysicsAI lives inside Simcenter Hypermesh, Simcenter Star-CCM+, Simcenter Simlab and Simcenter Inspire – the environments your engineers already use and trust. Model training, validation, and deployment happen without a separate ML platform to procure, a data export pipeline to build, or any context switching between tools. Full-field output: The output of a Simcenter PhysicsAI surrogate is a spatially distributed field – stress, pressure, temperature, velocity at every node, not just scalars. Same format as a solver output, fitting naturally into post-processing workflows and giving engineers the spatial insight they need to make real design decisions. Design exploration at scale: At seconds per evaluation, Simcenter PhysicsAI surrogates integrate directly into parametric studies, optimization loops, and digital twin workflows, enabling systematic design space exploration that transforms product development from iterative refinement into genuine optimization. The focus shifts from compute to engineering judgment, which is exactly where it should be. None of this requires a profound machine learning background to use. The underlying GDL architecture handles the geometric complexity, empowering the engineer to stay in control of the decisions that truly matter. Shape your future with Simcenter PhysicsAI The organizations that will lead their industries in the next decade are not necessarily those with the most powerful solvers or the largest HPC estates. They are the ones that learn to extract maximum insight from their simulation investments – compressing design cycles, expanding the solution space they can realistically explore, and deploying engineering talent where it creates the most value. If you are curious about how GDL applies to your specific simulation domain – structural, CFD, thermal, electromagnetic, or manufacturing, the best starting point is your own legacy simulation data. Simcenter PhysicsAI gives you the intelligence – faster, at greater scale, and with a depth of insight that traditional simulation alone could never deliver. If you want to harness the full potential of Simcenter PhysicsAI and accelerate your development processes with artificial intelligence and Geometric Deep Learning, schedule a conversation with CAEXPERTS. Our team has trained and modified engineers to support, implement and apply Altair solutions, helping your company transform simulation data into faster and more reliable specifications. We drive first projects in collaboration with your team to maximize return on investment, helping your company extract maximum performance from tools and achieve faster, more accurate and more reliable projects. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

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