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      <title>SIMULIA</title>
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      <description>SIMULIA</description>
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      <title>
      <![CDATA[ From Particles on a Grid to the Wind Tunnel&#8217;s Digital Rival: The Rise of LBM in Defense Aviation ]]>
      </title>
      <link>https://blog.3ds.com/brands/simulia/from-particles-grid-wind-tunnel-digital-rival-rise-lbm-defense-aviation/</link>
      <guid>https://blog.3ds.com/guid/302269</guid>
      <pubDate>Tue, 12 May 2026 09:00:00 GMT</pubDate>
      <description>
      <![CDATA[ How the Lattice-Boltzmann method became a CFD powerhouse — and how pushing it beyond its original limits is changing the way military aircraft are developed.
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The Lattice-Boltzmann method (LBM) — well established over the past 20 years as the leading CFD tool for automotive aerodynamics — is now becoming the engine behind cutting-edge simulations of aircraft aerodynamics and acoustics with particular benefits for applications in the defense industry. Here is how we got here – from &nbsp;a theoretical curiosity in the 1980s to the wind tunnel’s digital rival today.



A Brief History: From Lattice Gas to Boltzmann



In the 1980s, researchers studying so-called lattice gas automata (LGA) showed that fluid-like behavior could emerge from simple rules governing how fictitious particles move and collide on a discrete lattice [1]. The approach was elegant but impractical: because particles were represented as simple on/off values, simulations were noisy and needed to be averaged over many runs to extract useful results. The breakthrough came when the crude particle counts were replaced with smooth probability distributions, connecting the method to the classical Boltzmann equation from kinetic theory. The noise vanished, the physics improved, and the lattice-Boltzmann method was born [2]. Subsequent refinements in the early 1990s made it fast and stable enough to consider for real engineering problems — though it remained limited, for now, to low-speed flows.



Exa Corporation and the Automotive Proving Ground



Founded in Burlington, Massachusetts in the early 1990s, Exa Corporation made a bold bet: that LBM could be turned into a practical industrial tool, starting with automotive aerodynamics. Their product, PowerFLOW, offered something traditional CFD solvers struggled to do —simulating complex, turbulent, unsteady flows around realistic vehicle geometries without spending weeks building computational meshes. Because LBM works on regular Cartesian grids that are generated automatically, engineers could import a full vehicle model and begin a simulation in hours rather than weeks.



The automotive industry took notice. Predicting aerodynamic drag, wind noise, and cooling airflow around a production car model involves extremely detailed geometry — door mirrors, underbody components, wheel arches, engine bay openings — and highly turbulent, separated flow that conventional steady-state solvers handle poorly. PowerFLOW&#8217;s inherently unsteady simulation approach, based on a Very Large Eddy Simulation (VLES) turbulence treatment [3], resolved most turbulent flow structures directly rather than modeling them away, producing results that correlated well with wind tunnel measurements. Major automakers adopted it as part of their development process, and PowerFLOW established itself as one of the leading commercial CFD tool through the late 1990s and 2000s.



Early Aerospace Inroads: Landing Gear and High-lift Noise



The same strengths translated naturally to aerospace applications where low Mach numbers prevail — in particular, the flow environment during approach and landing. Landing gear assemblies and multi-element high-lift wings generate intense broadband and tonal noise through unsteady turbulent interactions that PowerFLOW proved to be uniquely able to predict. Early validation campaigns on these configurations showed strong agreement with wind tunnel acoustic measurements [4], establishing PowerFLOW&#8217;s credibility in the aerospace community and laying the foundation for the far more ambitious NASA airframe noise partnership that would follow.



Unsteady airflow around a nose landing gear (Courtesy of NASA).







The Speed Ceiling — and the Decision to Break It



Automotive success, however, only took Exa so far. Cars travel at low Mach numbers where the standard LBM formulation is perfectly valid. Commercial transports cruise at around Mach 0.85; military aircraft operate well into supersonic territory. At these speeds, compressibility effects become dominant: the air&#8217;s density, temperature, and pressure change significantly as it flows over the aircraft, shock waves form where the flow locally exceeds the speed of sound, and the equations governing momentum and energy become tightly coupled. The standard LBM formulation, built on assumptions of small velocity perturbations and uniform temperature, breaks down under these conditions.



Exa&#8217;s leadership recognized that cracking the aerospace market meant solving this problem. The technical challenge was extending the speed range while preserving LBM&#8217;s core advantages: automatic meshing, inherent parallelism, and direct resolution of unsteady turbulent flow. A hybrid approach was developed in which the standard low-speed LBM kinetic solver was coupled to a high-order scheme capable of accurately representing the steep pressure and density gradients that appear across shock waves [5]. Stability at high Mach numbers — a persistent nemesis of compressible LBM formulations — required careful numerical design that took years of iteration to get right.



Initial demonstration of PowerFLOW capability to simulate transonic flows: Collision of a planar shock with a finite wedge. Left: Experiment, Right: PowerFLOW Simulation.







The payoff was a version of PowerFLOW that could simulate flows from low Mach numbers all the way to approximately Mach 2, covering the full subsonic, transonic, and low-supersonic flight envelope [6]. The same technology that had proven itself on car aerodynamics could now be pointed at an airliner wing at cruise, a fighter inlet at supersonic conditions, or a weapons bay generating intense acoustic loads.



In our next post in this series, we will describe the decade-long process of gaining credibility for this breakthrough in the aerospace and defense community through participation in AIAA workshops, countless validations against industry standard test cases, and an ongoing partnership with NASA.







References



[1] Frisch, U., Hasslacher, B. &amp; Pomeau, Y. (1986). Lattice-gas automata for the Navier-Stokes equation.&nbsp;Physical Review Letters, 56(14), 1505–1508.



[2] Chen, H., Chen, S. &amp; Matthaeus, W.H. (1992). Recovery of the Navier-Stokes equations using a lattice-gas Boltzmann method.&nbsp;Physical Review A, 45(8), R5339–R5342.



[3] Teixeira, C.M. (1998). Incorporating turbulence models into the lattice-Boltzmann method.&nbsp;International Journal of Modern Physics C, 9(8), 1159–1175.



[4] Casalino, D., Noelting, S., Fares, E., Van de Ven, T., Perot, F., Bres, G. Towards numerical aircraft noise certification: analysis of a full-scale landing gear in fly-over configuration, AIAA paper 2012-2235



[5] Lattice-Boltzmann/ Finite-Difference Hybrid Simulation of Transonic Flow, Nie, Shan, Chen, RIRR 2009-139.



[6] Noelting, S., Fares, E. et al. (2016). Validation of PowerFLOW for transonic and supersonic flow regimes. AIAA Paper 2016-0585.











Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.
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      <![CDATA[ Simulating Precision: How Medtronic Engineers Advance Heart Valve Innovation ]]>
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      <link>https://blog.3ds.com/brands/simulia/simulating-precision-medtronic-engineers-advance-heart-valve-innovation/</link>
      <guid>https://blog.3ds.com/guid/302228</guid>
      <pubDate>Fri, 08 May 2026 12:27:02 GMT</pubDate>
      <description>
      <![CDATA[ We had the opportunity to interview David Martin, Senior Principal Engineer at Medtronic, at the 2025 SIMULIA EuroNorth Regional User Meeting (RUM) about his presentation: Role of Modelling & Simulation Tools in the Development of Transcatheter Heart Valve Devices at Medtronic | EURONORTH RUM 2025. Hear how Medtronic is using SIMULIA Abaqus’ structural simulation to aid in the development of live-saving transcatheter heart valves. 
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Challenge:



Accurately simulate the full crimping of delicate bioprosthetic heart valves under extreme deformation to ensure safety and real-world reliability before physical testing.



Solution:



Medtronic engineers worked with simulation experts from the SIMULIA Services Hub and their structural simulation technology, Abaqus, to run high-fidelity, iterative crimp simulations that captured complex nonlinear tissue and material behavior.



Benefit:



Full-crimp modeling accelerates design confidence, cuts the number of costly physical builds, strengthens regulatory evidence, and supports safer, faster development of next-generation transcatheter heart valves.







Simulating Precision: How Medtronic Engineers Advance Heart Valve Innovation



SIMULIA Abaqus enables Medtronic engineers to design and test next-generation heart valves with speed, accuracy, and patient-first precision.



Medtronic has always skillfully combined engineering excellence and medical innovation. From the first battery-powered pacemaker to today’s miniaturized, leadless devices (implanted via a vein), the company’s mission has remained constant: Harness technology to improve lives. That same commitment drives its pioneering work in transcatheter heart valve replacement, a procedure that spares patients the need for open-heart surgery by delivering a bioprosthetic valve through a catheter system.











Over 5 million people in the U.S. alone are diagnosed with heart valve disease each year. Left untreated, the disease can lead to heart failure and death. The condition develops when native valve leaflets stiffen from calcification, forcing the heart to work harder to pump blood. For decades, the only treatment was open-heart surgery, often a high-risk option, especially for older adult patients.



The Hidden Challenge Inside Every Valve



Over the past 20 years, transcatheter heart valve replacement has transformed cardiac care. Instead of opening the chest, surgeons now guide a bioprosthetic valve through a small incision in the groin and deploy it inside the failing native valve. “The bioprosthetic valve is then deployed using the delivery system … and the patient is typically discharged the same day,” explains David Martin, Senior Principal Engineer at Medtronic.



Developing these life-saving devices presents formidable design challenges. Each valve must be crimped to a fraction of its standard size to fit inside a catheter and navigate the body’s vascular system. “The valves could be 20 to 40 mm in diameter,” Martin notes. “To be delivered into the heart, they must be crimped to 6-7mm diameter and loaded into a catheter delivery system. The catheter is then inserted into the femoral artery in the groin and tracked through the aorta into the chambers of the heart”.











The crimping process induces extreme stress and deformation across multiple materials—metal frames, bioprosthetic soft tissue, and sutures—all of which must function flawlessly once deployed. “The  focus of our work is ensuring the bioprosthetic valve can be delivered accurately to the target anatomy, where it should then function reliably for decades after deployment” emphasizes Martin. Patient safety is job number one at Medtronic, which is why Medtronic engineers utilize only the highest-end simulation tools in their product development process.



Cracking the Crimping Code



To overcome these complex challenges, Medtronic turned to Abaqus, SIMULIA’s Finite Element Analysis (FEA) software. Using simulation, engineers can virtually model the entire life cycle of a heart valve, from crimping to deployment, before a single prototype is built. Medtronic engineers use modeling tools widely in all stages of the design and development of transcatheter heart valves, including to accelerate concept assessment, for detailed design optimization, and in design verification activities prior to regulatory submission.











In a recent collaboration, Medtronic engineers teamed up with simulation experts from the SIMULIA Services HUB to tackle one of the toughest steps in heart-valve design: simulating the full crimping process including all the tissue components. “This is a very challenging problem due to the extreme deformations and contact,” Martin says. The challenge wasn’t just compressing the device to fit inside a delivery catheter; it was doing so with the bioprosthetic tissue, treated biological material sourced from animals, attached. Unlike metal or plastic, bioprosthetic tissue stretches, wrinkles, tears, and buckles easily, making the physics particularly unforgiving. “The tissue is incredibly delicate, and the deformations and contact are extreme. There’s a lot of nonlinear materials all coming in contact with one another,” he explains. The work pushes the limits of biomechanical simulation, which ultimately helps engineers build safer valves for patients who rely on them.



SIMULIA Services HUB experts in the UK, worked closely with the Galway Medtronic team over several weeks. “We gave them everything they needed. We contributed where we could, but they carried out the [simulation] work … until they achieved the goals that we agreed in the problem statement,” Martin said. This project not only demonstrated that full-scale crimp simulation could be performed efficiently, but it also provided a deeper understanding of how the tissue components may behave during the crimping process.



Martin credits SIMULIA Abaqus for its proven reliability in such demanding applications: “I’ve personally been using Abaqus because of the long history of good general performance with the solvers.”



Building Confidence Through Simulation



Modeling and simulation have revolutionized the way Medtronic designs, tests, and verifies its devices. Martin clarifies, “Manufacturing and testing the valves that we work on is incredibly costly. Anything we can do upfront to make sure we’re only building and testing valve designs that we are confident in helps us. And that’s where modeling and simulation is really having a big impact in Medtronic.”











Simulation now supports every stage of development, from early concept exploration to regulatory verification. “We’re going to squeeze every ounce of performance out of it that we can,” Martin adds. The virtual testing data also supports FDA submissions by demonstrating safety and efficacy, which ensures that the device will perform as intended and poses no risk to patients.



In addition, there are substantial efficiency gains. In one project, Medtronic optimized frame designs across 100 analysis runs, work that would have taken months or even years to reproduce physically. (Frame designs are lattice-like components ofmetal stent structures that hold the bioprosthetic tissue in place once the valve is crimped, delivered, and deployed.) “If you consider the time to build and test a hundred frames, that would be months, if not years,” attests Martin. “That’s work that we did … in less than a quarter [three months].” Demonstrating that simulation-driven virtual testing can be done up front and presenting the actual data to the management team makes a strong case for continuing a simulation-driven approach to product development.











Beyond efficiency, simulation reinforces Medtronic’s uncompromising standards for accuracy and patient safety. “We do a huge amount of work at Medtronic to make sure our models are accurate to real life. Verification and validation establish accuracy,” Martin concludes.



From Validation to Vision



Simulation has become deeply embedded in Medtronic&#8217;s engineering culture. “When I joined the company in 2014, there were four dedicated simulation engineers working in the Structural Heart business unit,” Martin recalls. “Now there’s nearly 40 or more.” That tenfold growth underscores the company’s confidence in simulation and its belief that virtual testing is now central to medical device development.



By partnering with SIMULIA and leveraging their structural simulation technology, Abaqus, Medtronic continues to lead the evolution of transcatheter heart valve design while improving reliability, reducing cost and development time, and ultimately delivering safer outcomes for patients worldwide. Once a supporting tool, simulation is now a key cornerstone of how Medtronic turns ideas into life-saving innovations.











Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.








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      <title>
      <![CDATA[ Why Quantum Hardware Will Be Designed in Simulation First ]]>
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      <link>https://blog.3ds.com/brands/simulia/why-quantum-hardware-will-be-designed-simulation-first/</link>
      <guid>https://blog.3ds.com/guid/302037</guid>
      <pubDate>Thu, 07 May 2026 09:00:00 GMT</pubDate>
      <description>
      <![CDATA[ Learn about the critical role of simulation-led engineering in advancing quantum computing beyond laboratory prototypes and how Dassault Systèmes’ MODSIM approach integrates modeling and simulation into the design process, enabling engineers to tackle complex, multiphysics challenges virtually. By adopting this methodology, the industry can accelerate innovation, reduce costs, and pave the way for scalable, practical quantum systems.
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Quantum computing is often described as a race for better qubits, but as the industry begins to scale beyond laboratory prototypes, different constraints are becoming clear. The real challenge is not just quantum physics, it is engineering at the limits of physics.



Modern quantum systems operate under extreme conditions. Data rates are in the gigabit range, materials behave in superconducting regimes, and everything must function reliably at cryogenic temperatures approaching absolute zero. At this level, even seemingly simple components such as interconnects, cables, or printed circuit boards become highly complex, tightly coupled multiphysics problems.



A 3D model of a multi-channel flex-to-coax interface used in a quantum computer.







A zoom into one of the sections of a flex-to-coax module.







This creates a fundamental bottleneck. Physical experimentation alone is no longer sufficient. Iterating hardware in a cryogenic environment is slow, expensive and often impractical. As a result, the industry is beginning to undergo a familiar transition, one already seen in aerospace, automotive, and semiconductor design, toward simulation-led engineering.



This is where MODSIM, as delivered by Dassault Systèmes, becomes critical.



Rather than treating simulation as a downstream validation step, MODSIM integrates modelling and simulation into the design process from the outset. Engineers can explore design options, understand coupled physical effects, and validate performance virtually—long before hardware is built. In domains where physical testing is inherently constrained, this shift is not just beneficial; it is necessary.



Characteristic impedance view of a bias/drive/signal line. Calculated with a full-wave solver.







A clear example of this transition can be seen in recent work with a company developing high-performance interconnect solutions for quantum computers. Their challenge was to predict the behavior of superconducting transmission structures operating at high frequency under cryogenic conditions. This required accurate modelling of inductance, loss mechanisms, electromagnetic coupling, and shielding, phenomena that are difficult to isolate experimentally, particularly in early-stage design.



Using SIMULIA’s electromagnetic simulation capabilities, it became possible to capture these effects in a unified environment. Superconducting materials were modeled using surface impedance approaches, while full-wave simulations provided insight into signal propagation and coupling across complex geometries. Subtle physical effects, such as the role of London penetration depth in determining electromagnetic behavior, could be analyzed directly within the simulation. In one case, the results showed that when conductor thickness exceeds the penetration depth, coupling between structures effectively vanishes—an insight that would be challenging to derive through testing alone.



The S-parameters and TDR results of a connector segment inside a flex-to-coax module. A 3D model has been created to assist designers during the impedance and Xtalk optimization stage. 







What makes this particularly significant is not just the accuracy of the simulation, but the way it changes the design process. Instead of building and testing multiple physical variants, engineers can explore a wide design space virtually, comparing configurations, materials, and geometries in a fraction of the time. The result is faster iteration, reduced cost, and a deeper understanding of system behavior.



Importantly, this is not limited to electromagnetics. Quantum hardware is inherently multiphysics. Thermal effects influence performance and stability, mechanical constraints emerge from extreme temperature gradients, and transient behaviors impact signal integrity. These domains are tightly coupled, and solving them in isolation is no longer viable. A MODSIM approach enables these interactions to be captured holistically, providing a more complete and predictive view of system performance.



Today, most simulation efforts in quantum computing remain focused at the component level. Interconnects, amplifiers, and cryogenic electronics are analyzed individually, often in disconnected workflows. However, as systems scale, the need for integration becomes unavoidable. The next step is to connect these elements into a coherent system model, capturing signal paths, thermal flows, and electromagnetic interactions across the entire architecture.



Example of a low pass filter (LPF) to optimize the level of HF noise suppression in a bias/drive line. Model created with CST Studio Suite: 3D Filter Designer.







This is where the concept of the virtual twin begins to emerge. By creating a virtual representation of the quantum system, engineers can validate performance, identify bottlenecks, and optimize designs before committing to physical implementation. In an environment where experimentation is costly and constrained, this capability becomes a powerful enabler of innovation.



The broader implication is clear. Quantum computing is entering a phase where engineering discipline will determine the pace of progress. Just as simulation transformed industries such as semiconductors and aerospace, it is poised to play a foundational role in the development of scalable quantum hardware.



The companies that recognize this shift early will be better positioned to navigate the complexity ahead. By adopting simulation-led design, they can reduce development cycles, explore more ambitious architectures, and ultimately accelerate the path to practical quantum systems.



Quantum computing may be born in the lab. But it will be scaled, optimized and industrialized in simulation.











Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.
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      <![CDATA[ Structures Simulation Enhancements in the R2026x Release ]]>
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      <link>https://blog.3ds.com/brands/simulia/structures-simulation-enhancements-r2026x-release/</link>
      <guid>https://blog.3ds.com/guid/302107</guid>
      <pubDate>Fri, 01 May 2026 09:00:00 GMT</pubDate>
      <description>
      <![CDATA[ Discover an overview of the recent enhancements within the Abaqus solvers new features from the latest 2026x releases.
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Key Takeaways



Automate geometry idealization:&nbsp;Extract middle wires and neutral fibers to speed up mid-surfacing workflows for complex 3D structures.




Improve mesh quality:&nbsp;Apply a new algorithmic approach to quad-dominant meshing, adjust mesh density with interactive edge seeding and use partition hex meshing on polyhedral volumes.



Configure reusable contacts:&nbsp;Set up model-based contact properties once and apply them across multiple simulations so you don&#8217;t recreate definitions for each scenario.



Account for variations in the manufacturing process: Achieve predictive simulation results for specialized manufacturing techniques. 



Analyze model quality efficiently:&nbsp;Spot poor-quality areas using updated review tools that pinpoint failing elements, highlight connected trias and visualize penetration vectors.




Introduction



Engineers working with complex structural problems depend on technology that delivers reliable results and efficient workflows. Our structural simulation capability continues to evolve to address advanced engineering challenges. The latest R2026x release delivers key updates across the 3DEXPERIENCE platform and Abaqus Unified FEA applications, further improving user experience and analytical accuracy.



Supporting Structural Analysis with Advanced Tools 



Simulation demands accuracy and computational efficiency. Balancing detailed models with reasonable analysis time is a constant challenge.



This release introduces features that automate repetitive, time-consuming tasks, enabling you to focus on applying your engineering knowledge to data interpretation and performance improvements.&nbsp;The 3DEXPERIENCE platform brings these capabilities together with a cohesive, unified user experience. When you update your geometry, downstream simulations are automatically updated, supporting robust design to analysis workflows.



R2026x introduces further improvements in geometry handling and meshing.



Geometry Idealization Updates



Simplifying geometry for simulation is critical. With R2026x, you can now extract a middle wire between two wires, adding efficiency with the mid-surfacing workflow of complex 3D structures.











Another enhancement enables the extraction of the neutral fiber from circular rods or pipes, storing the minimum, maximum and average radii for each edge along the fiber. This lays the groundwork for precise geometry to simulation beam section definition in future releases.











With the trim with pieces function, a new partial extrapolation mode provides granularity in how geometry extensions are handled, improving model fidelity and enhancing downstream meshing activities.



Next-Generation Meshing



Mesh quality heavily influences both computational demands and result fidelity. Interactive mesh edge seeding now offers faster adjustments you can fine-tune mesh density without waiting for updates or slow interface refresh rates.











Solid Meshing Updates



The updated Tet-filler tool, with enhancements in domain recognition and group creation, enables the optional filling of internal cavities and a more customizable selection for subsequent property assignments.



The partition hex meshing capability has been extended to support generalized polyhedral volumes, so you can easily apply hex meshes to geometric solids that previously warranted a tetrahedral approach. This development improves mesh consistency and increases the accuracy of results where stress concentrations matter.











Enhancements in Quad-Dominant Meshing



A new algorithmic approach in the quad-dominant mesher automatically reduces mesh irregularities, improving on both quality and flow. In cases where manual mesh manipulation is required, mesh editing commands have been enhanced to create persistent local mesh refinement, preventing edits from being overwritten by subsequent geometry adjustments and saving hours of rework.



Finite Element Modeling and Composites



Group Modeling



You can now create spatial element groups directly from solid bodies, extracting all relevant mesh elements or from extracted 3D mesh domains (is it possible to link “mesh domains” to the “Solid Meshing Updates” above?). This simplifies &nbsp;the management, and strengthens the customization of complex models. Additionally, enhancements for surface-based groups, enable users to control direction with easy switching and alignment functionality, further reducing errors and improving workflows involving shell elements.



Braided Composite Modeling



Composite shell section creation has been enhanced to support composite braided parts.&nbsp; It is now possible to account for variations introduced through the manufacturing processes when those braiding processes are performed on the 3DEXPERIENCE platform, essential for accurate simulation of parts manufactured through this specific composite manufacturing technique.



This new enhancement extends our composite simulation capabilities, resulting in more predictive simulations.











Model Review and Quality Analysis



Right First Time is essential for speeding up project timelines and reducing costs. The updated review tools now highlight connected tria elements in quad dominant meshes, helping analysts spot poor quality areas within their mesh or areas where improvements could be made by eliminating trias. Quality analysis tools introduce new contour plotting options for selected mesh criteria within the review tools and pinpoints failing elements, highlighting the most critical quality issues.











Visualization of penetration vectors, supported by statistical information, further enhances digital de-penetration workflows, allowing you to resolve geometric conflicts before they affect solver convergence.











Interactions and Connections



Contact definition is more efficient than ever. Model based contact lets you configure contact properties, initialization and handling at the model level, these definitions are now reusable across multiple simulations, eliminating recreation for each scenario. R2026x also incorporates support for Butler-Volmer Kinetics properties for lithium battery behavior workflows, as well as support for Eulerian materials as supports enabling Coupled Eulerian-Lagrangian (CEL) applications.











Contact Refinement and Fastener Updates



Following the model-based contact definitions, a new feature streamlines contact refinement directly in the scenario apps, enabling suspension, resumption or adjustment of the model-based contact definitions. While both model-based and scenario-based contact approaches will coexist, ensuring smooth transitions, it is recommended that users adopt model-based general contact. Fastener detection within the automated FEM method gets a significant boost with exact and maximum layer specifications, as well as enabling geometric surface detection for surface fasteners, streamlining model setup and aligning functionality with the standalone tools. Constraints based on element surfaces are now available for couplings and connections, including springs anchored to shell edges, with best practices in mind and a tighter solver integration.



Build Your Expertise Further



Expert Jamie Wheat leads an in-depth webinar about these new features. Jamie’s experience in structural workflow development for the transportation and mobility industry, including work for a premier Formula 1 team, ensures this session is technical and directly applicable.



The session covers solver enhancements and platform refinements in detail, emphasizing practical applications for analysts seeking higher efficiency and model confidence. Don’t miss this opportunity to see the R2026x features in action and learn practical techniques for advanced simulation efficiency.



Watch the Webinar Replay Now











Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.








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      <![CDATA[ BMW Turns to SIMULIA for Managing Complexity in Electric Drivetrains ]]>
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      <link>https://blog.3ds.com/brands/simulia/bmw-simulia-managing-complexity-electric-drivetrains/</link>
      <guid>https://blog.3ds.com/guid/302042</guid>
      <pubDate>Tue, 28 Apr 2026 14:14:37 GMT</pubDate>
      <description>
      <![CDATA[ SIMULIA had the privilege of interviewing Norbert Schroeder of BMW at the EuroCentral 2025 SIMULIA Regional User Meeting about his presentation Lightweight Rollerbearing Seats: Investigation and Optimization with Simpack, Tosca and Abaqus. 
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German automaker BMW uses SIMULIA Abaqus, Simpack and Tosca tools to simulate the performance of lightweight electric drivetrain systems with complex roller bearing interactions



ChallengeBMW faced the challenge of developing lightweight, safe and efficient electric drivetrain systems. &nbsp;It needed tools to capture the complex behavior of roller bearing seats – the part of a component that supports a roller bearing – under real-world conditions.



SolutionBy leveraging SIMULIA technology, including Abaqus, Simpack and Tosca, BMW engineers created detailed system-level simulations of electric drive systems, including highly accurate modeling of roller bearing behavior under realistic operating loads.



Results




Achieved reliable system-level simulations of complex electric drivetrain systems



Prevented system failures such as excessive noise, friction and housing breakage



Eliminated guesswork in early design stages



Provided a solid foundation for transitioning to the 3DEXPERIENCE platform




&#8212;



For over 100 years, German automaker BMW has been committed to advancing the automotive industry by solving complex engineering problems. Now, its engineers face what is perhaps their biggest challenge yet: Reimagining vehicle systems for a fully electric future.



Norbert Schroeder, a simulation specialist at BMW and a SIMULIA 2025 Champion, is working at the heart of the industry’s transformation. “Originally, I worked in combustion engine development,” he said at the recent EuroCentral 2025 SIMULIA Regional User Meeting, which took place in Bamberg, Germany.“We used to simulate traditional engines – mainly isolated components like the crankshaft. But with electric drivetrains, everything is integrated. The rotor, gearbox, housing and electromagnetics must be considered as one system.”











This integration demands a new approach to simulation. “The detail of each part may get smaller, but the scope of what you have to consider becomes much broader,” Norbert said. “That’s where system-level simulation becomes essential.”



Norbert is uncompromising in his choice of simulation tools, having used SIMULIA products since university; first Abaqus and shortly after Simpack. Today, he regularly works with the latest versions of Abaqus for nonlinear and contact problems, Simpack for engine-related multi-body dynamics and Tosca for topology and non-parametric optimization.











In isolation, these solutions are incredibly effective. But it’s their combined capabilities that Norbert believes set them apart. “It’s a fully integrated workflow,” he said. “There’s a huge benefit when you combine tools from the same ecosystem. That synergy really boosts outcomes.”



Using these SIMULIA solutions, Norbert and his team can tackle some of the most demanding areas of electric drive simulation like roller bearing seat design, for example. A roller bearing seat is the part of a component (usually a casing) that supports a roller bearing – a type of bearing that uses rolling elements to reduce friction and support loads.











“In traditional engines, large stiff bearing seats absorbed the loads,” Norbert said. “However, in lightweight electric vehicle structures, we must reduce the housing weight. But that also reduces stiffness. We’re pushing things to their limits, so now we need highly detailed roller bearing models to ensure safety.”



Using simulation tools, BMW engineers can model precisely how the rollers in the bearings apply force to the outer ring, and how that force transfers to the gearbox cover. “These components must be precisely supported within the housing,” Norbert said. “If we get it wrong, the system can get too noisy, produce excess friction, or even fail.”



To ensure the accuracy of their simulations, Norbert and his team rely on a sequential coupled process. “We use Simpack to calculate system-level forces, Abaqus to compute the structural response and Tosca to optimize the design based on those forces,” Norbert said.











Simulation doesn’t just help BMW validate its designs – it delivers insights that physical testing alone can’t uncover. “There are cases where, without simulation, we wouldn’t even know what the problem was,” Norbert said. “Simulation doesn’t just shorten development – it enables it.”



For the team at BMW, simulation has become a trusted stand-in for real-world testing. “We’re very confident in our simulations,” Norbert said. “In some cases, we can apply cleaner boundary conditions virtually than we can in physical testing.”



As the further electrification of the automotive industry brings more challenges, Norbert is confident that SIMULIA simulation solutions will help BMW find more innovative solutions. “The future lies in multiphysics,” he said. “It’s a very challenging – but necessary – field, especially for battery development. Moving to the 3DEXPERIENCE platform is key. We’re using SIMULIA to adapt our well-established workflows to that platform, which allows us to reuse them effectively in future projects.”











Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.
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      <![CDATA[ Mastering Output Filtering in Abaqus/Explicit &amp; Abaqus/Viewer ]]>
      </title>
      <link>https://blog.3ds.com/brands/simulia/mastering-output-filtering-in-abaqus-explicit-abaqus-viewer/</link>
      <guid>https://blog.3ds.com/guid/301895</guid>
      <pubDate>Tue, 21 Apr 2026 09:00:00 GMT</pubDate>
      <description>
      <![CDATA[ This blog explores the critical role of output filtering in Abaqus/Explicit and Abaqus/Viewer, emphasizing its importance in reducing noise and ensuring accurate interpretation of simulation data. It explains the challenges of aliasing, a phenomenon where insufficient sampling rates distort data, and provides practical solutions through runtime and post-processing filters. By mastering these techniques, engineers can achieve clean, reliable data that accurately reflects the physical behavior of their models, enabling sound design decisions.
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Key Takeaways




What is Output Filtering?Output filtering reduces unwanted noise in simulation data, highlighting relevant physical responses for better clarity and reliability.



The Problem of Aliasing:Aliasing occurs when data is sampled at a rate too low to capture high-frequency signals, leading to misleading results. The Nyquist frequency is a critical benchmark to avoid this issue.



Filtering Solutions in Abaqus:

Runtime Filters (Abaqus/Explicit):&nbsp;Prevent aliasing by filtering data before it is saved. The built-in anti-aliasing filter is highly effective.



Post-Processing Filters (Abaqus/Viewer):&nbsp;Allow further refinement and smoothing of data after simulation.





Best Practices for Output Filtering:

Perform a frequency analysis to identify significant dynamic characteristics.



Use the 6x rule of thumb for output sampling rates to ensure data fidelity.



Avoid over-filtering, which can remove meaningful structural responses.



Monitor extreme values efficiently using runtime filter operations like MAX or ABSMAX.





Mitigating Filter Artifacts:Be aware of time shifts and end distortions caused by filtering, and design simulations to minimize their impact.



A Robust Workflow:Combine frequency analysis, runtime filtering, and post-processing to ensure clean, interpretable data for critical engineering decisions.












&nbsp;



What is Output Filtering in Abaqus?



Output filtering is the process of applying mathematical techniques to simulation results to reduce unwanted noise and highlight the relevant physical responses of a system. By filtering simulation outputs, engineers can improve the clarity and reliability of their data, making it easier to interpret true system behavior and draw accurate conclusions.



Why Output Filtering Matters in Abaqus/Explicit



In finite element analysis, particularly with dynamic explicit simulations, the data generated can be vast and complex. A common challenge engineers face is distinguishing the true structural response from high-frequency solution noise. Raw data from Abaqus/Explicit can be filled with oscillations that, while mathematically part of the solution, can obscure the underlying physical behavior of the model. This is where output filtering becomes an indispensable tool. The proper application of filters enables analysts to attenuate this noise, prevent significant data misinterpretation, and gain a clear, actionable understanding of a model&#8217;s dynamic performance.



This blog offers an in-depth exploration of the technical principles and practical applications of output filtering in both Abaqus/Explicit and Abaqus/Viewer. We will explore the fundamental problem of aliasing, detail the extensive filtering options available, and provide robust guidance for configuring simulations to yield clean, reliable, and insightful data.



What is Aliasing in Abaqus/Explicit?









At its core, aliasing is a form of data corruption caused by an inadequate output frequency. Imagine trying to understand a fast-spinning wheel by only looking at it once every second. Depending on when you glance, the wheel might appear stationary, spinning slowly, or even rotating backward. You are sampling reality at a rate too slow to capture the true motion. In Simulation, the same phenomenon occurs.



The Nyquist-Shannon sampling theorem provides the mathematical foundation for this concept.




It states that to accurately reconstruct a signal, the sampling frequency must be at least twice the highest frequency component present in that signal. This threshold, known as the Nyquist frequency, is a critical benchmark in data acquisition.








When you request simulation output at a rate below this, you risk aliasing. The data points you save are individually correct and lie on the true, high-frequency curve, but when plotted, they create a new, misleading waveform that doesn&#8217;t exist in reality.



How Aliasing Affects Abaqus Animations 









A classic, intuitive example of aliasing is the zoetrope. This 19th-century device creates the illusion of motion from a sequence of static images. In a modern simulation context, consider a rapidly rotating rigid body animated with a specific frame rate. Due to the relationship between the spin rate and the output rate, the object can appear to deform and flex as if it were made of a soft material. This &#8220;frame rate effect&#8221; is a direct visual manifestation of aliasing, leading to a complete misinterpretation of the object&#8217;s rigid body motion.



A more direct engineering example involves simulating a turbine fan where a blade detaches and strikes the outer casing. When the animation frames are saved too infrequently, the fan can appear to rotate backward. An analyst unfamiliar with aliasing might waste valuable time investigating a non-existent problem with their boundary conditions, believing the prescribed rotation was not enforced correctly. The issue isn&#8217;t the physics simulation; it&#8217;s the way it&#8217;s observed. To see the true counter-clockwise rotation, more frames must be saved, capturing the fan&#8217;s position at smaller increments of its revolution.



How Aliasing Appears in Abaqus XY Plots









The impact of aliasing is just as profound on time history plots. Consider a simple cantilever beam subjected to a suddenly released pressure load, causing it to vibrate freely.




The &#8220;True&#8221; Signal: If we record the vertical velocity at the beam&#8217;s tip at every single time increment of the Abaqus/Explicit simulation, we obtain a clean, high-resolution curve that accurately depicts the vibrational motion. This represents the ground truth.



The Aliased Signal: Now, if we request the same output variable but at a much lower frequency (e.g., every few hundred increments), the resulting plot is drastically different. The handful of data points we collect fall on the true curve, but they miss all the intervening peaks and troughs. Connecting these points creates a new, lower-frequency signal that completely misrepresents the beam&#8217;s behavior.




In a simple case like this, we have the luxury of the high-resolution data for comparison. In a complex, real-world analysis, we often only have the low-frequency data. Without an understanding of aliasing, an engineer might mistakenly conclude the structure&#8217;s vibrational response is much slower and has a lower amplitude than it actually does, leading to flawed design decisions.



Which Abaqus Outputs Are Most Affected by Aliasing?















Not all output variables are created equal when it comes to aliasing. The susceptibility is directly related to the variable&#8217;s &#8220;frequency content.&#8221; Operations that involve integration tend to smooth out a signal, removing high-frequency components. Conversely, operations involving differentiation tend to amplify them.



This leads to a clear spectrum of susceptibility in structural analysis:




Low Susceptibility (Displacements): Displacement is the result of integrating velocity over time, which itself is the integral of acceleration. This double integration process naturally filters out high-frequency noise. As a result, displacement plots are often smooth and less prone to aliasing issues.



Medium Susceptibility (Velocities): As the first integral of acceleration, velocity contains more frequency content than displacement but is generally less noisy than acceleration. It occupies a middle ground.



High Susceptibility (Accelerations and Reaction Forces): These quantities are often the &#8220;rawest&#8221; signals in an explicit dynamic analysis. They directly reflect the high-frequency waves propagating through the mesh elements and are therefore extremely noisy and highly susceptible to aliasing.




Quasi-static simulations, despite being run in Abaqus/Explicit, typically have very slow loading rates, which minimize dynamic effects and render aliasing a non-issue. However, for any analysis involving impacts, collisions, or high-frequency vibrations, careful consideration of output requests for acceleration and force is paramount.



Should I Filter Abaqus Results During or After the Simulation?



Abaqus provides a comprehensive suite of filtering tools to combat noise and aliasing. These tools can be deployed at two distinct stages: during the simulation run (runtime filtering) and after the simulation is complete (post-processing).



1. How Runtime Filtering Works in Abaqus/Explicit



Runtime filters are the first and most critical line of defense. They operate on the data before it is written to the output database (ODB) file. This approach is fundamentally important because once a signal is undersampled and the aliased data is saved to the ODB, the lost high-frequency information cannot be recovered.



Abaqus/Explicit offers several industry-standard filter types that you can define using the *FILTER keyword:




Butterworth: Known for its maximally flat frequency response in the passband, making it a very popular general-purpose filter.



Chebyshev Type I: Exhibits a steeper roll-off than a Butterworth filter, but at the cost of introducing ripples in the passband.



Chebyshev Type II: Has ripples in the stopband instead of the passband.







Step-1 frame rate 0.5 frames/s. Aliasing avoided.






Using Abaqus&#8217; Built-In Anti-Aliasing Filter



For most applications, defining a custom filter is unnecessary. Abaqus provides a robust, pre-configured anti-aliasing filter that is highly effective. When you request this filter, Abaqus automatically implements a second-order, low-pass Butterworth filter. The intelligence is in how it sets the cutoff frequency.



The cutoff frequency is automatically defined as one-sixth (1/6) of the output sampling rate you specify in your output request.



Let&#8217;s break this down with an example. Suppose you request history output every 0.1667 milliseconds.




Output Sample Rate: 1 / (0.1667 x 10⁻³ s) = 6,000 Hz (6 kHz).



Nyquist Frequency: Per the sampling theorem, the highest frequency you can possibly resolve is half the sample rate, which is 3 kHz. Any frequency content above 3 kHz in your raw signal risks causing aliasing.



Filter Cutoff Frequency: The anti-aliasing filter sets its cutoff at 1/6th of the sample rate: 6,000 Hz / 6 = 1,000 Hz (1 kHz).




By setting a cutoff at 1 kHz, the filter strongly attenuates all frequencies above this, ensuring that any content near the problematic 3 kHz Nyquist frequency is eliminated before the signal is sampled and written to the ODB. This provides a clean, unaliased representation of the signal. A significant benefit of using this filter is that Abaqus will issue a warning if your chosen output rate is too low for the filter to be effective, acting as a built-in safety check against aliasing.



2. How to Filter Results in Abaqus/Viewer



If you have successfully saved a rich, high-frequency signal to the ODB (ideally using a runtime filter), you can perform additional filtering within Abaqus/Viewer. This is done through the Operate on XY data dialog box. Post-processing is useful for:




Comparing different filter types and cutoff frequencies without re-running the analysis.



Applying smoothing for presentation purposes.



Further reducing noise that was not fully eliminated at runtime.




Single-Pass vs. Dual-Pass Filtering in Abaqus



A crucial technical distinction exists between runtime and post-processing filters.








Abaqus/Explicit Runtime Filters: These are single-pass, causal filters. They process data only in the forward direction of time, as the simulation progresses. This is a physical necessity—the solver cannot &#8220;see into the future&#8221; to know what data is coming. A consequence of this single-pass operation is a phase shift, or time delay, in the filtered output. The peaks of the filtered curve will be shifted slightly to the right (later in time) compared to the raw signal.



Abaqus/Viewer Filters: These are dual-pass (or bidirectional) filters. They process the entire data series forward and backward in time. The backward pass effectively corrects the phase shift introduced by the forward pass. As a result, Viewer-filtered signals are perfectly aligned in time with the original unfiltered signal.












While often small, this time shift induced by runtime filters is a real distortion that analysts should be aware of, especially when trying to precisely correlate events in time.



Best Practices for Filtering Abaqus Results



Theory is valuable, but effective filtering requires a practical approach. The goal is to balance data fidelity with manageable file sizes.



·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; How to Choose Your Output Frequency



The ideal output frequency is high enough to capture the structurally significant response but not so high as to generate unmanageably large ODB files. A systematic approach is best:




Perform a Natural Frequency Analysis: Before running your explicit dynamic simulation, run a natural frequency extraction analysis (*FREQUENCY) in Abaqus/Standard on the same model. This will tell you the characteristic frequencies of your structure&#8217;s dominant vibration modes.



Identify the Highest Structurally Significant Frequency: Your structure may have hundreds of modes, but typically only the first several are structurally significant. Identify the highest-frequency aspect of your engineering problem that you genuinely care about. For many structural vibration problems, this range is between 50 Hz and 5,000 Hz. This is usually much, much lower than the highest frequency in the entire model, which governs the stable time increment (often in the hundreds of kilohertz).



Design Your Output Rate: Use the highest significant frequency to determine your output rate. A good rule of thumb is to set a filter cutoff frequency at or slightly above this value. If you use the built-in anti-aliasing filter, you can work backward:





Desired Cutoff Frequency: Let&#8217;s say 5,000 Hz.



Required Output Sample Rate: This must be 6 times the cutoff, so 6 * 5,000 Hz = 30,000 Hz.



Required Output Time Interval: 1 / 30,000 Hz ≈ 3.33 x 10⁻⁵ seconds (0.0333 ms).




Requesting history output at this interval with the anti-aliasing filter provides a robust starting point. It&#8217;s often pragmatic to increase this sampling rate (e.g., by a factor of 2) if disk space allows. It is always better to have too much data than not enough.







·       Can You Over-Filter Abaqus Results?



Just as undersampling is problematic, so is over-filtering. If you set your filter cutoff frequency too low, you can inadvertently remove physically meaningful parts of your structural response.











Revisiting the example of the noisy acceleration signal, if we apply a filter with a very low cutoff, the resulting curve might look smooth and clean, but it will have filtered out the entire structural vibration signature. The over-filtered signal might have a drastically reduced peak amplitude and a significant time shift, making it look more like the response of a heavily damped, single-degree-of-freedom system than a complex structural vibration. This is another form of misinterpretation.



·       How To Track Peak Values without Large ODB Files 



A powerful and often underutilized feature of runtime filters is the ability to monitor for extreme values. By adding the OPERATION parameter to your *FILTER or *OUTPUT definition, you can track peaks without saving the entire time history.



OPERATION = MAX: Reports the maximum value encountered up to that point in time.OPERATION = MIN: Reports the minimum value.OPERATION = ABSMAX: Reports the maximum absolute value.



This is exceptionally useful for field output. Imagine trying to find the maximum principal strain in a large model during a crash event. Instead of saving contour plots for thousands of time increments, you can request field output for the strain variable with OPERATION = MAX at a single time point (the end of the step). The resulting contour plot will show, for each element, the peak value it experienced during the entire simulation. Abaqus also writes the time at which this peak occurred to the results file, allowing you to efficiently identify the most critical moment for further detailed analysis.



·        Can Filtering Affect Abaqus Results?



Filters are powerful but not perfect. Beyond the time shift discussed earlier, another artifact to be aware of is end distortion.



Filtering algorithms require a certain window of data points to perform their calculations. At the very beginning and very end of a signal, this window is incomplete. This can lead to distortions or &#8220;ringing&#8221; in the filtered output near the start and end times. If you have a critical event of interest that happens right at the end of your simulation step, the filtered representation of that event may be compromised by end distortion. A simple solution is to extend the simulation time so the event of interest occurs well within the analysis period, away from the endpoints.



How to Get More Accurate Abaqus Results



Output filtering is not an optional extra; it is a fundamental component of sound engineering practice in explicit dynamics. By mastering these tools, you can move from noisy, ambiguous data to clear, interpretable results.



A robust workflow is key:




Understand Your Structure: Use a frequency analysis to determine the significant dynamic characteristics of your model.



Filter at the Source: Always use runtime filters in Abaqus Explicit to create a rich, unaliased ODB file. The built-in anti-aliasing filter is the recommended starting point for its simplicity and effectiveness.



Choose Output Rates Wisely: Base your output frequency on your desired structural response, using the 6x rule of thumb for the anti-aliasing filter. When in doubt, save more data.



Leverage Post-Processing: Use the bidirectional filters in Abaqus/Viewer for additional smoothing and comparative analysis, confident that your underlying data is sound.



Be Aware of Artifacts: Understand the effects of time shifts and end distortions and design your simulations to minimize their impact on your interpretation.




By adopting this methodical approach, engineers can confidently navigate the complexities of dynamic data, ensuring their simulation results provide a solid, accurate foundation for critical design decisions. To learn more about output filtering, check out our webinar here.



Extending Output Filtering Workflows on the 3DEXPERIENCE Platform



Within the&nbsp;3DEXPERIENCE platform, output filtering workflows in Abaqus can be managed as part of a model-based, data-centric simulation environment. Rather than treating filtering as an isolated solver or Viewer operation, the platform enables it to be included within a traceable simulation lifecycle.



Abaqus analyses executed through SIMULIA roles (such as Physics Simulation or Structural Simulation Engineer) retain filtering definitions, including *FILTER parameters, output requests, and sampling rates, within the simulation object or associated input data. This ensures that:




Filter configurations are persistent and version-controlled: Changes to cutoff frequencies, anti-aliasing settings, and output intervals are tracked alongside model revisions.



Simulation provenance is maintained: Results can be correlated with the input configuration used to generate them.



Simulation data is centrally managed: Large datasets generated with high sampling rates are stored and governed within the platform environment, improving accessibility and reducing duplication.




Post-processing operations typically performed in Abaqus/Viewer, including XY data filtering and signal conditioning, can be reproduced through scripting or process automation workflows where required. This supports:




Standardization of filtering methodologies&nbsp;across teams.



Automation of result extraction, including filtered signals and extreme value monitoring (e.g., MAX/ABSMAX operations), when implemented through user-defined processes.



Reuse of simulation processes, improving consistency across programs.




By incorporating output filtering within a governed data environment, the 3DEXPERIENCE Platform supports more consistent handling of aliasing and signal fidelity, while maintaining traceability of how simulation results are generated and interpreted.











Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.




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      <![CDATA[ Fast and Accurate Vehicle Dynamics Assessments ]]>
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      <link>https://blog.3ds.com/brands/simulia/fast-accurate-vehicle-dynamics-assessments/</link>
      <guid>https://blog.3ds.com/guid/301777</guid>
      <pubDate>Tue, 14 Apr 2026 20:34:41 GMT</pubDate>
      <description>
      <![CDATA[ By integrating design and simulation, SIMULIA empowers automotive OEMs, startups, race teams and suppliers to innovate faster and more efficiently. Our solutions help you deliver the safe, comfortable and high-performance vehicles your customers expect.
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      <![CDATA[ 








Customers expect a safe, reliable and high-quality driving experience. For Original Equipment Manufacturers (OEMs), meeting these demands while reducing costs and increasing durability presents a significant challenge. Predicting vehicle dynamics behavior virtually is essential for accelerating development, achieving cost savings, and delivering the superior driving experience that customers demand.



SIMULIA provides both standalone and design-integrated multibody simulation solutions, including Simpack and the Suspension Analyst Role on the 3DEXPERIENCE platform. These tools address a wide range of challenges in vehicle dynamics applications, enabling engineers to analyze and validate vehicle performance with precision.



Modern Vehicle Development Challenges



Developing modern vehicles involves several complex hurdles. Siloed design and analysis processes often lead to long design iteration loops and significant delays, especially when requirements change. Managing multiple models for various vehicle dynamics applications results in redundant data and tedious model conversion processes.



In addition, extreme load events or large-scale Design of Experiments (DOE) simulations of complex models demand exceptional solver stability and high numerical accuracy. Applications such as Software-in-the-Loop (SiL), Hardware-in-the-Loop (HiL), and Driver-in-the-Loop (DiL) require accurate, reliable real-time simulations with easy interfaces to hardware for both objective and subjective vehicle performance evaluations.



A Unified and Robust Solution



SIMULIA addresses these challenges with an integrated approach to multibody simulation. The Suspension Analyst Role on the 3DEXPERIENCE platform provides a unified Modeling and Simulation (MODSIM) environment. This approach breaks down the barriers between design and analysis, facilitating rapid product development and a quicker path to market.











MODSIM enables fast design iterations by providing a single source of truth for all stakeholders in the product development cycle. &nbsp;The unified environment on the 3DEXPERIENCE platform enables designers to harness simulation power early in the development process, fostering seamless collaboration and ensuring reliable results with minimal manual effort.



Robust and Accurate Multibody Dynamics Simulations



Our enterprise-level solutions feature the industry-proven Simpack solver technology. This allows simulating extreme load events without needing to tune solver settings. The solver&#8217;s accuracy is proven even for higher frequency applications, such as gearbox noise and vibrations. Fast Key Performance Indicator (KPI) assessments ensure quick, projectable results, helping teams make informed decisions.



Real-Time Simulation Capability



The unique Simpack Real-Time solver enables detailed, high-fidelity models to be simulated in real time without simplifications. This is crucial for SiL, HiL and DiL applications. User-friendly interfaces permit on-the-fly model parameter changes on simulators, eliminating time-consuming model reduction and validation steps. This capability reduces time and cost by minimizing the number of prototypes and tests while accelerating KPI assessments.



Streamlined Simulation Data Management



Our enterprise solution offers a single, user-friendly model database suitable for all vehicle dynamics applications. This provides a structured environment for storing model and simulation data. Companies can realize significant cost savings by reducing their tool landscape and eliminating model conversion processes. This also applies to the automation of simulation processes, where large-scale DOEs or multiple variant simulations require the evaluation and comparison of numerous product KPIs.











Why Choose SIMULIA?



Our solutions provide the tools necessary to overcome the challenges of modern vehicle dynamics assessments.




Robust and Accurate Solver: The efficient multibody dynamics solver delivers precision.



Real-Time Simulation: Unmatched capabilities for real-time model simulation without simplification.



Flexible Body Integration: A unique technology for integrating flexible bodies.



Intelligent Automation: Advanced scripting capabilities enable smart automation.



Concurrent Engineering: The 3DEXPERIENCE platform facilitates fast, efficient and concurrent engineering.




By integrating design and simulation, SIMULIA empowers automotive OEMs, startups, race teams and suppliers to innovate faster and more efficiently. Our solutions help you deliver the safe, comfortable and high-performance vehicles your customers expect.



To find out more about how our multibody simulation solutions can support your vehicle development process, explore Multibody System Dynamics &amp; Motion Simulation and get in touch with our sales team.



Keep reading:




https://blog.3ds.com/brands/simulia/understanding-multibody-dynamics-simpack
















Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.
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      <![CDATA[ Submarine Fluid Simulation: Hydrodynamics, Propeller Dynamics and Wake Tracking ]]>
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      <link>https://blog.3ds.com/brands/simulia/submarine-fluid-simulation-hydrodynamics-propeller-dynamics-wake-tracking/</link>
      <guid>https://blog.3ds.com/guid/301632</guid>
      <pubDate>Tue, 07 Apr 2026 18:49:27 GMT</pubDate>
      <description>
      <![CDATA[ In this article, we show how simulation can enhance submarine hydrodynamic performance by optimizing efficiency, reducing drag, and analyzing acoustic effects and wake tracking. We demonstrate workflows in SIMULIA PowerFLOW for analyzing submarine hydrodynamics throughout development on the DARPA SUBOFF benchmark. We will show how hull drag and propeller performance can be analyzed both in isolation and installed, with excellent agreement between benchmark measurements and simulation data.
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Why Simulate Submarines?



Computational Fluid Dynamics (CFD) models fluid flow and fluid-structure interaction. The marine and offshore industry widely uses CFD simulation to design and analyze ships, submarines, and offshore structures.



CFD can be used to model the hydrodynamics and drag of the vessel. The flow of water around the hull includes areas of turbulent flow, especially around sharp corners and edges. Also, the boundary layer, dependent on the Froude number, is typically turbulent. Turbulent flow is chaotic and difficult to calculate. One powerful approach for modeling turbulence is simulation using the Lattice Boltzmann Method (LBM). SIMULIA PowerFLOW uses LBM with a Very Large Eddy Simulation (VLES) approach. This models fluid flow around large geometries efficiently while still capturing turbulent flow details.



Submarine engineering requires special design considerations. Submarines need control surfaces: rudders, fins, and diving planes. These surfaces allow three-dimensional movement, similar to an aircraft. Just as aerodynamics is crucial for ensuring the maneuverability of aircraft, hydrodynamics is needed to ensure safe, efficient submarine control. Simulation allows engineers to optimize control surface design and streamline the hull to reduce drag.



Many submarines are built for naval applications, where stealth is a primary objective. The military value of a submarine comes from its ability to move underwater without detection. Deep underwater, where radar waves do not penetrate, the detectability of a submarine is largely determined by its interaction with the water around it. Propeller cavitation effects can generate considerable noise, as well as its turbulent wake. This noise can propagate for many miles in the water. Acoustic solvers in SIMULIA PowerFLOW can model noise propagation in water without the need to combine different simulation tools.



A submarine also leaves a telltale wake, a trail of disturbed, turbulent water that can be detected behind it and can even cause waves visible from the surface. The powerful, efficient LBM-based solvers in SIMULIA PowerFLOW can model wake propagation for an extended distance behind the submarine, helping engineers find solutions to minimize the residual wake turbulence.



SIMULIA PowerFLOW is part of the wider Dassault Systèmes portfolio, which also includes design tools such as CATIA on the 3DEXPERIENCE platform. Unified modeling and simulation (MODSIM) breaks down the silos between designers and analysts, enabling designers to evaluate how their designs will perform without building and testing physical prototypes. This streamlines development, lowers costs and minimizes risk.



Submarine Hydrodynamics Workflow



Hull resistance



Hull resistance can be analyzed both in terms of the bare hull and the full assembled vessel with appendages such as diving planes and the sail fin. In the SUBOFF benchmark, the bare hull is labeled AFF-1 and the hull with appendages as AFF-8. The first study was to calculate total resistance. As shown below, excellent agreement was found between measured and simulated data for all water velocities.



Comparing simulated hull resistance with PowerFLOW to measured data from “Summary of DARPA Suboff Experimental Program Data” (1998) by Liu and Huang. Left: AFF-1. Right: AFF-8.







With the accuracy verified across all water velocities, we can now analyze the pressure distribution and skin friction along the hull. This will capture localized effects such as drag around the nose and the impact of the diving planes. The simulation shows excellent agreement. We observe this particularly around the submarine&#8217;s nose, where both the peak value and slope are precisely predicted.. This indicates that the simulation effectively captures key boundary layer characteristics, including its size and transition location. These results reinforce the reliability of the simulation in reproducing critical local flow phenomena.



Comparing simulated pressure distribution (left) and skin friction (right) on AFF-1 to measured data from Huang.







Open water propeller performance



In addition to validating hull resistance, this study also examines the performance of the propeller both in isolation and when installed. For a full description of the propeller simulation workflow, using the INSEAN E1619 propeller benchmark, see our blog post “Validating underwater propeller performance with SIMULIA PowerFLOW”.



In general, the propeller performance curves show close agreement between measurement and simulation.



Thrust (KT), torque (10KQ), and efficiency (ŋ), as measured in the INSEAN benchmark (orange) and simulated in PowerFLOW (blue). Measured data from INSEAN testing on March 16th 2006 by Andrea Mancini, owned by CNR-INSEAN.







Wake tracking



Analyzing the hull and propeller in isolation alone is not enough to fully understand the behavior of the submarine as a complete vessel. The wake of the submarine hull and its appendages will cause instabilities in the flow around the propeller. These will affect the total drag experienced by the vessel and the propagation of the wake. A full analysis therefore, must take into account the installed propeller.



Analyzing the near-wake profile provides a more qualitative assessment of installed propeller performance and also validates the accuracy of the simulation. The images below show the comparison of simulation and particle image velocimetry (PIV) measurement.



Wake contour for appended configuration (AFF-8) at x/ LOA=0.978 for simulation (left) and experiment (right). Experimental data from “Simulations of the DARPA Suboff submarine including self-propulsion with the E1619 propeller” (2012) by Chase.







Side view of the wake contour simulation.







The simulation reveals several features of the submarine wake. First and most obviously, there is a very high wake velocity in the immediate vicinity of the propeller. Secondly, there are four clear projections in the wake caused by the four appendages at the tail. Third, there is a faint wake from the sail on top of the hull. Although minor, this can still interact with the propeller wake and cause instabilities. All of these are visible in both the simulation and the measurement. A fourth characteristic is visible in the simulation data – vortices from the tips of the appendages. These are not visible in the measurement data due to the limited resolution of the PIV. In this case, the simulation reveals behavior missed by the measurement.








Animation of the wake of a fully submerged, installed propeller.


We can also analyze the far wake. The efficiency of PowerFLOW for turbulent flow allows us to simulate a length of 10 submarine lengths behind it. By understanding the far wake and its impact both on the hydrodynamics as well as acoustics, engineers can optimize designs to reduce the wake behind the submarine and improve its stealth characteristics.



Simulation of the submarine far wake, in side view and 3D isosurface view.







Conclusion



Simulation enables marine engineers to design more efficient and stealthier submarines. SIMULIA PowerFLOW is well-suited to the challenges of submarine simulation, using the Lattice Boltzmann Simulation to model the flow of fluid around the hull, including all appendages, and the propeller. The accuracy of PowerFLOW has been verified against established benchmarks, showing close agreement with the measurements. Simulation can be implemented as part of the design workflow using a unified modeling and simulation (MODSIM) workflow. SIMULIA PowerFLOW integrates with design tools such as CATIA and other SIMULIA simulation tools on the&nbsp;3DEXPERIENCE platform.



Read more on this topic: https://blog.3ds.com/brands/simulia/validating-underwater-propeller-performance-simulia-powerflow/ 




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      <![CDATA[ Improving e-NVH Test and Simulation Correlation with Manatee ]]>
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      <link>https://blog.3ds.com/brands/simulia/improving-e-nvh-test-simulation-correlation-manatee/</link>
      <guid>https://blog.3ds.com/guid/301449</guid>
      <pubDate>Tue, 31 Mar 2026 20:20:13 GMT</pubDate>
      <description>
      <![CDATA[ In this latest installation of our Manatee series, let’s explore the transformative capabilities of Manatee, a critical tool that enables the management of magnetic noise & vibrations of electric machines and drives.
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Executive Summary



Let us explore the transformative capabilities of Manatee, a critical tool that enables the management of magnetic noise &amp; vibrations of electric machines and drives. By adhering to acoustic standards, engineers can optimize their approaches to enhance noise management and overall system efficiency in electric machine design.



Key Takeaways




Manatee: A vital tool for tackling noise and vibration in electric machines, enhancing engineering outcomes.



Magnetic Noise &amp; Vibrations of Electric Machines and Drives Understanding: It is necessary to have awareness about e-NVH to bridge gaps between testing and simulation results.



Consistent Measurements: Always use the same units (SPL vs. SWL) and accurately input parameters for valid comparisons.



Noise Sources: Exclude mechanical and aerodynamic noises to ensure precise assessments of electromagnetic noise.



Damping Importance: Consider actual modal damping in simulations as it significantly impacts acoustic behavior.



Standards Compliance: Following acoustic measurement standards boosts accuracy and reliability in noise evaluations.








Introduction 



Manatee is transforming how we address noise and vibration challenges in electric machines.







Originally designed to enhance consulting activities, this advanced tool has proven effective in troubleshooting and mitigating the noise associated with magnetic forces. With successful applications across more than 200 electrified systems, Manatee is a vital resource for predicting and managing electric machine noise, helping engineers refine their designs and improve overall system performance.



If you’re new to Manatee, our introductory blog on getting started with Manatee provides helpful background for this discussion.



Manatee can be used at the preliminary design stage to understand the physical phenomena responsible for noise and vibration and to design reduction techniques. An absolute correlation between tests and simulation is neither required nor accessible at those design stages. However, at a later design stage, when more input data are available, a good test of simulation correlation can be reached. In this blog, we will explore key considerations to prevent discrepancies between testing and simulation.



Sound Pressure versus Sound Power Level 









When comparing tests and simulations, it&#8217;s essential to use consistent units. The manatee acoustic noise level can be expressed either as a sound power level (SWL) or a sound pressure level (SPL).



While SPL measurements are straightforward to obtain, they can come with considerable uncertainties, such as background noise, reverberation, and directivity. Therefore, careful attention is necessary when using SPL for comparisons. When employing an analytical SPL model in Manatee, it’s important to specify the actual room constant for the noise, vibration, and harshness tests to properly account for reverberation.



When comparing measured and simulated SPL results, be sure to enter in Manatee:




the correct distance from the center of the e-machine to the microphone



the correct directivity coefficient



the correct room constant for the reverberant field




Electrical machines are typically tested in factories where the directivity coefficient may not be ideal, and the reverberation field can be uncertain. As a result, SPL simulation results may differ from experimental outcomes by up to ±10 dB.



It is advisable to perform sound power level measurements in accordance with acoustic standards (e.g., ISO 3745, ISO 3744, ISO 3746) for valid comparisons.



In loaded cases, the sound power level of the tested machine should exclude background noise and loading machine noise. Therefore, the intensimetry technique is recommended (in accordance with ISO 9614 standards).



Mechanical and Aerodynamic Parasitic Sources



When comparing measured and simulated SWL due to electromagnetic forces, ensure that non-magnetic acoustic noise sources, such as aerodynamic and mechanical noise, are excluded from your experiments. Be aware that aerodynamic noise may occur at the same frequencies as electromagnetic noise in specific applications.



Additionally, Manatee allows for the import of non-magnetic noise, including gear noise.



Noise Radiation Paths



Manatee offers various modeling levels suitable for the concept to the preliminary design phase. During the early design phase, semi-analytical vibroacoustic models primarily account for airborne noise radiated by the outer structure.



However, experiments typically reveal a portion of inner-borne noise, which can help explain discrepancies between test results and simulations. Inner-borne noise, caused by rotor excitation through magnetic forces, can be incorporated by using Manatee with a 3D FEA mechanical model, such as through the Electromagnetic Vibration Synthesis algorithm.



It&#8217;s essential to align the rotor FEA model, particularly the rotor bending modes and Rotor Housing Coupling mode, with experimental data for accurate SBN estimation. Additionally, the presence of the rotor can influence some stator modes (for example, the bending mode of a clamped-free stator), potentially causing differences between calculated and measured airborne noise if the rotor is excluded from the analysis.



When an electric motor is housed in a casing and sound power level calculations are performed using the outer envelope nodes of the casing in Manatee, the results will only consider structure-borne noise (caused by the vibrations transmitted from the motor to the casing), overlooking motor air-borne acoustic radiation through leakages. These leakages can contribute to the overall noise radiation, leading to higher noise levels.



Damping 











An important simulation parameter for achieving accurate absolute sound and vibration power levels due to magnetic excitations is modal damping, which typically ranges from 0.5% to 4% in electrical machines. Since damping cannot be calculated numerically, it depends on various factors such as temperature, resin type, winding technology, and mode/frequency.



Manatee default simulation workflows use a default average damping value of 2%. Simulations may then lead to &nbsp;= -12 dB to ) = +6 dB gaps compared to the test for the SWL at resonance peaks. &nbsp;



A step-by-step Experimental Modal Analysis is highly recommended to quantify the modal damping of your application. When measured damping is used in your simulation, the accuracy of vibration and sound levels can be brought down to +/-3 dB.



Additionally, discrepancies between simulations and tests may arise if the simulated modal basis is not representative.



Structural Modes



Discrepancies between simulation and tests can be obtained if the simulated modal basis is not representative of reality. This can be due to the following issues:




Missing rotor in 3D FEA mechanical model



The 3D FEA mechanical model has not been fitted with experiments



3D FEA mechanical model fitting has been carried out in different boundary conditions than operational ones (e.g., free-free)



Stiffening effect of magnetic pre-stress on structural modes in some specific geometries



Effect of coolant (e.g., oil film or water jacket) on structural modes and damping.




Magnetic and Geometrical Asymmetries



Eccentricities and geometrical or magnetic asymmetries can introduce new resonances due to additional magnetic force harmonics, significantly affecting vibration and noise levels.



Eccentricities, in particular, modulate all pulsating forces with Unbalanced Magnetic Pull (UMP) harmonics, which can easily excite different structural modes. If you have simulated a symmetrical machine in Manatee, you might find that some resonances are missing in comparison to experimental results.



It is recommended to take the following measurements:




Phase current, resistance, and inductance (to evaluate current unbalance)



Assess uneven turn distribution due to manufacturing constraints



Measure stator bore radius (to identify non-uniform airgap)



Balance the rotor and measure static and dynamic eccentricity (both direct mechanical and indirect electrical), including conical eccentricity



Check IPMSM rotor magnetization along axial and circumferential directions (to detect non-uniform magnetization).




Current Waveforms











The current waveform influences magnetic excitation harmonics, which can vary between tests and experiments, particularly when using Manatee with a sine supply. Variations may arise from factors such as:




Unbalanced phase currents



Back EMF phase belt harmonics or Rotor Slot Harmonics (RSH or PSH) in induction machines



Converter-induced low-frequency components, including 5f/7f voltage harmonics



Parasitic harmonics resulting from faults




To address these issues, it&#8217;s advisable to measure the three-phase currents and incorporate them into Manatee simulations to assess their impact on e-NVH.



Data Acquisition Post-processing



Figure 1: Poor frequency resolution.







Figure 2: Poor time resolution.







The signals obtained from a Data Acquisition System are typically post-processed using specific algorithms, such as the Short-Time Fourier Transform (STFT), RPM extraction, and order-tracking analysis. The parameters used in these post-processing techniques can significantly affect the results.



In addition to ensuring the accuracy of the test setup, it is important to consider the accuracy of post-processing. For instance, the STFT used to generate spectrograms involves a trade-off between time and frequency resolution. When comparing order levels, synchronous sampling (rather than fixed sampling) is recommended.



Order extraction should be performed by integrating energy over a specified bandwidth. It is advisable to conduct a sensitivity study on these parameters before comparing dB levels from tests and simulations.



Fluid-Structure Interaction in Electrical Machines



The following phenomena may impact the noise and vibration performance of the electric system:




Temperature: Magnet temperature affects remanent flux and the amplitude of magnetic force harmonics.



B(H) curve: If there is a high dependency on the fundamental frequency, ensure it is included in magnetic calculations.



Axial magnetic forces: These may result from skewing or axial misalignment.



Speed ripple or load fluctuations: These might not be included in numerical simulations.



Strong electromechanical coupling: This includes the combined effects of centrifugal forces and eccentricities due to Unbalanced Magnetic Pull.



Gyroscopic effects: Relevant for high-speed machines or when the magnetic circuit deforms under centrifugal forces.



Strong rotor vibrations: These can modulate magnetic flux and may not be related to magnetic forces.



Strong fluid/structure interaction: This applies to cases such as underwater electric motors or water-cooled electrical machines.




Modeling Accuracy



Manatee offers various modeling levels; when comparing Manatee results with experimental data, it&#8217;s important to progressively increase the modeling detail as discrepancies arise. Specifically:




Electromagnetic loads should be calculated using electromagnetic Finite Element Analysis (FEA).Structural responses should be assessed with a 3D mechanical FEA model that includes both the rotor and stator, tuned to match experimental results.

Acoustic responses should be evaluated with 3D acoustic FEA or Boundary Element Method (BEM) in free-field conditions under the following circumstances:If there are noise issues at low frequencies, the Equivalent Radiated Power model may overestimate sound levels.

If significant acoustic leakage occurs, such as through the casing enclosing the electric motor.








Conclusion



In conclusion, Manatee serves as a critical tool for addressing noise and vibration challenges in electric machines. Its integration into the design process enables engineers to systematically analyze the noise and vibration performance of the electric system. When used for validation, discrepancies between simulation and experimental results must be avoided. By adhering to recognized acoustic standards and implementing consistent measurement protocols, practitioners can enhance the validity of noise evaluations. Essential considerations, such as excluding non-electromagnetic noise sources and incorporating damping effects, significantly contribute to accurate modeling. This methodological approach not only optimizes the design of electric machines, but also enhances their performance metrics, positioning Manatee at the forefront of noise and vibration engineering in electrified systems.



Read More




Understanding Magnetic Noise and Vibration in Electric Vehicles



Getting Started with Manatee Part 2
















Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.
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      <![CDATA[ SIMULIA Welcomes 2026 Champions to Program ]]>
      </title>
      <link>https://blog.3ds.com/brands/simulia/simulia-welcomes-2026-champions/</link>
      <guid>https://blog.3ds.com/guid/301267</guid>
      <pubDate>Fri, 27 Mar 2026 09:00:00 GMT</pubDate>
      <description>
      <![CDATA[ SIMULIA is proud to announce our 2026 Champions! SIMULIA Champions are among our most passionate and talented users who work every day to share their expertise on simulation. We are already in the seventh year of the Champions program and are delighted to have more than 180 active SIMULIA Champions.
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      <![CDATA[ 








These simulation ambassadors are from around the world and represent countries across Asia, Europe, North America, South Africa, and Brazil. They work in industries, including aerospace, automotive, energy, high-tech, manufacturing, life sciences, consumer packaged goods, and academia. Our Champions include scientists, engineers, professors, and researchers who focus on electronics, additive manufacturing, energy, and more.



Our 2026 Champions include companies such as 3M, Airbus, Amazon Lab126, Boeing, Caterpillar, Daimler Truck, Henkel, Kuhmo Tire, Mahindra, Panasonic, Pratt &amp; Whitney, Tata Motors, Thales Alenia Space Italia, and Yangtze Delta Region Institute of Tsinghua University — to name a few.



This year’s Champions include:




Muhammad Ashraf is actively engaged in the research and development of commercial antenna products as an RF Engineer at Poynting Antennas. He applies his strong hands-on expertise in electromagnetic simulation and RF system design using SIMULIA tools, especially CST Studio Suite, to model, analyze, and optimize antennas, RF-EMF exposure, and RF systems.





Pawel Bajerski isan R&amp;D Team Leader on the Simulation Specialists Team at the ABB Corporate Technology Center, with more than 14 years of experiencein engineering, research, and numerical simulations of electrotechnical products. He specializes in analyzing internal arcs, environmental calculations, plastic processing, and complex multiphysics phenomena, with a focus on coupling the electromagnetic field with the overall system’s mechanical response.





Carlo Bergamelli, a Process Engineer in the R&amp;D department in the steel manufacturing sector at Tenaris S.p.A., is responsible for developing industrial numerical simulations across all Tenaris production sites worldwide to enhance product quality and optimize manufacturing processes.





Lorenzo Castiglioni, CAE Manager at Fabbrica D’Armi Pietro Beretta S.p.A., has extensively used Abaqus to improve the design of existing firearms and develop new models in close cooperation with product development platforms. He is currently working on a project to analyze both fluid and solid domains of a firing process, including bullet crimping, powder burning, bullet engraving, bullet exit, and the uncorking of gas into the atmosphere.





Adrien Cheminet, a Principal Physics Expert and R&amp;D Technical Manager at Sodern, with more than 10 years of industry experience, specializes in designing, validating, and qualifying sealed neutron tubes and uses CST Studio Suite to master complex multiphysics challenges, focusing on electrostatic and magnetostatic simulations and charged-particle transport.





Dario Dotolo,as the Head of Virtual Modeling at Prometeon Tyre Group, uses his expertise in Abaqus to focus on dynamic material characterization, aiming to understand elastomer behavior and has developed advanced models to validate experimental curves and an automated methodology using Python scripts to run thermo-mechanical simulation loops.





Kevin Fallon is an Associate Technical Fellow at The Boeing Company with more than 28 years of experience in the aerospace industry and more than 20 years using Abaqus, CATIA, ENOVIA, and the 3DEXPERIENCE platform. He currently serves as the Digital Tools and Process Lead for the Mechanical and Structural Engineering functions supporting existing BCA programs (such as 737, 777, and 787), upcoming product offerings, and specializes in the nonlinear behavior of large assembled airframe structures for both metallic and composite material systems.





Abolhasan Giashi is a simulation engineer at SIG Combibloc System with more than 13 years of experience in finite element method (FEM) simulations, including extensive use of SIMULIA software, develops and implements FEM models for packaging processes, material improvements, product development, filling machine design, and process optimization.





Alessandro Giordani, a Satellite Systems Engineer at Thales Alenia Space Italia, specializes in electromagnetic compatibility and advanced simulation for optimizing Earth observation satellites using CST Studio Suite to design and optimize Earth observation satellites, focusing on antenna modeling, inter-antenna coupling, resonant cavities, magnetostatic analysis, and the propagation of electromagnetic waves in advanced materials.





Ashwin Balaji Govindaraj, a CFD and Durability Lead at Royal Enfield with more than 15 years of simulation and engineering experience, specializing in FEA and motorcycle performance improvement, uses a range of SIMULIA tools to address complex engineering problems in the two-wheeler industry, including Abaqus for nonlinear structural analysis, fe-safe for fatigue and durability, Tosca for topology optimization, and Isight for process automation.





Sami Hienonen, an Antenna Technology Manager at Convergentia Ltd., in addition to working with RF/EMI/SI/PI simulations, millimeter-wave antenna arrays, and their radomes, has designed internal antennas for hundreds of portable devices, including smartwatches, phones, rings, trackers, and various IoT devices.





Ashraf Idkaidek, a Senior Engineering Team Lead at Caterpillar with more than 20 years of experience in structural design, finite-element analysis, and safety-critical system development, manages analytical and design initiatives for ROPS, FOPS, and OPS structures, while driving cross-functional solutions to complex structural issues, managing ISO certification testing, and continuously improving structural analysis methodologies.





Bob Johnson,a Technical Director at Realistic Engineering Analysis, having been extensively using Abaqus since 1993, is a seasoned mechanical engineer and finite element analysis expert with decades of experience and is well-known for his expertise in detailed stress and failure analysis of offshore structures, including wellhead systems, mooring systems, jacket structures, and other similar systems.





Hongbin Ju,as a Senior Principal Engineer at Pratt &amp; Whitney, specializing in unsteady CFD/CAA, aerodynamic noise prediction and mitigation, flow-structure interaction, flutter analysis, and reduced-order modeling for system instability, has contributed to the design of turbofan engines, including the GE9X, GEnx, geared turbofan engines, PW1100G/1500G/1900G, the F135 military engine, and open rotors.





Hoje Kang, as Head of Virtual Modeling at Prometeon Tyre Group, applies his expertise in Abaqus to focus on dynamic material characterization, aiming to understand elastomer behavior and has developed advanced models to validate experimental curves and created an automated methodology using Python scripts to run thermo-mechanical simulation loops.





Sung Ju Kang,an Antenna Technology Manager at Kumho Tire with more than 15 years of experience, specializing in structural analysis, simulation workflows, and the integration of physical testing with virtual engineering for tire innovation, has recently expanded her expertise to tire manufacturing process simulations, working closely with Dassault Systèmes engineers to develop advanced analysis tools on the 3DEXPERIENCE platform.





Saharash Khare,a seasoned simulation expert at Hero MotoCorp with more than 18 years of experience, currently leading advanced simulation methodologies and digital validation platforms by using Abaqus, fe-safe, Verity, Tosca, and Isight to develop numerous simulation techniques to establish a comprehensive digital validation platform covering linear, nonlinear, fatigue, implicit/explicit analyses, and optimization to ensure robust and reliable designs.





Kuljinder Khera specialized in strength, durability, and multibody dynamics as a CAE Engineer in Underbody Systems Engineering at Ford Motor Company. Kuljinder has more than 25 years in the automotive industry and extensive experience with Abaqus, Tosca, CATIA V5, and the 3DEXPERIENCE platform and is currently supporting Ford’s digital transformation by leading the migration of legacy CATIA V5 CAD and CAE templates to the 3DEXPERIENCE platform while fully aligning with Ford’s vision of a unified ecosystem.





Marc Lässing, as a Senior CAE Engineer at Daimler Truck AG with nearly 25 years of experience in Simpack, including modeling, scripting, and developing user elements, is responsible for full vehicle simulations focused on drivetrain-induced vibrations and SiL co-simulations, as well as managing Simpack installation and licensing at Daimler Truck.





Gang Hoon Lim, as a Senior Manager at LG Display, uses Abaqus to evaluate structural performance before physical testing by analyzing design trends and assessing material structures in advance, thereby optimizing performance, reducing physical testing, enhancing development efficiency and reliability, and lowering costs.





Xu Long,a professor at Tsinghua University and a renowned expert in electronic packaging mechanics, holding multiple degrees in mechanics and earning more than 30 international and national awards, has played a leading role in integrating Abaqus into the electronic packaging community, closely combining advanced finite-element simulation with scientific research and real-world engineering applications.





Paul Lucas, a Technical Fellow for Loads and Dynamics at Daimler Trucks North America, with 15 years of experience in advanced CAE, specializing in finite element analysis (FEA) and multibody dynamics (MBS), provides technical leadership in durability, vibration, and dynamic load analysis, supporting the development and validation of commercial vehicle platforms.





Max Mao,a Principal FEA Engineer at Maxxis International with more than 20 years of experience applying SIMULIA solutions to advanced engineering challenges, leads a global cross-functional team to develop high-fidelity virtual tire models, mainly focusing on tire force and moment simulations that facilitate simulation-driven design and product innovation.





Victor Messias, an R&amp;D Engineer at Titan Tire with more than years of experience advancing R&amp;D capabilities in off-road (OTR) tire development and a leader in integrating R&amp;D workflows, implementing new calculation methods with Abaqus and the 3DEXPERIENCE platform, specializes in simulation-driven design, virtual and physical prototyping, and innovation management.





Yasunori Miyamoto,with more than 25 years of experience in advanced acoustic simulations and multiphysics analyses, works as a Senior Expert at Panasonic Connect and uses Wave6 for acoustic simulations, including developing new acoustic devices, active vibration and noise control systems, and conducting large-scale room acoustic analyses, such as Speech Transmission Index (STI) evaluations for public spaces like railway stations.





Giovanni Maria Mongini,an EMC and Electrical System Engineer at Thales Alenia Space Italia in Rome, leads EMC engineering for various satellite programs and specializes in system-level electromagnetic compatibility analysis.





Gwenael Neveu, an M&amp;T Predictive and MBSE Engineer at Airbus Helicopters, has led digital transformation efforts in Simulation Process and Data Management (SPDM), deployed parametric modeling and Model-Based Systems Engineering solutions, and fostered cross-disciplinary collaboration using the 3DEXPERIENCE platform.





Khaled Obeidat, an Antenna Engineer Technologist at AmazonLab126 and a distinguished antenna and RF engineer with a Ph.D. and more than 20 years of experience in wireless technology, has led innovative antenna engineering projects in wearables, mobile, UAV, and ground satellite stations, resulting in more than 20 patents and significant cost savings through design optimizations.





Sanjay Patil,a seasoned automotive R&amp;D expert with more than 20 years at Tata Motors, specializing in virtual validation, digital transformation, and full-vehicle program delivery, extensively using Abaqus, Isight, and Tosca to develop high-confidence simulations for both ICE and EV platforms.





Christoph Rudhart, a Ph.D. theoretical physicist and development engineer at Mercedes-Benz, specializes in multibody and finite element modeling for powertrain, chassis systems, and ride comfort assessment of passenger cars using digital prototypes with Simpack.





Fay Salmon, with more than 25 years of experience with Abaqus and other SIMULIA tools, as a Senior Staff Scientist at 3M, develops advanced physics-based modeling methods to support technology and product development across the company through various simulations—covering adhesives, abrasives, consumer electronics displays, and semiconductor chemical–mechanical planarization—to optimize product performance and deliver solutions for 3M customers.





Hiroshi Sato is a Senior Engineering Manager at Panasonic Systems Networks R&amp;D Lab who introduced CST Studio Suite to the company and has led R&amp;D driven by electromagnetic simulations for antennas and wireless systems. He develops SAR-compliant, low-coupling/low-correlation MIMO antennas for smartphones and IoT devices, designs antennas for space missions and automotive platforms, and engineers high-efficiency wireless power transfer (WPT) coils for IoT terminals, including operation in seawater.





Sanjeet Sharma, an accomplished engineering leader at Mahindra with more than 20 years of experience, possesses expertise across the entire product lifecycle—conceptual design, detailed engineering, validation, structural qualification testing, and advanced FEA, using SIMULIA to optimize designs, improve durability, and accelerate product development.





Harsharoop Singanamalla, as a Manager at Henkel Consumer Brands, oversees predictive modeling for packaging by using simulation-based methods to enhance packaging design and performance, with a current focus on integrating simulation into packaging development by fostering a virtual development mindset and including it in standard engineering workflows.





Julnar Musmar Solis is a communication engineer and signal-processing specialist at AUMOVIO and holds advanced degrees in Communication Engineering and Statistical Signal Processing. Her expertise includes high-speed design, PCB engineering, and signal integrity simulation, with a current focus on high-speed channel analysis and optimizing signal integrity for electronic systems.





Sjoerd Van der Veen, a Senior Expert at Airbus Operations SAS with a background in mechanical and aeronautical engineering, specializes in physics-based simulation of manufacturing processes.





Xiao Xu, a Senior CAE Engineer at Shenzhen Inovance Technology Co., Ltd., uses Dassault Systèmes mechanical simulation software to perform analyses such as Harmonic Response Analysis, Random Vibration Analysis, Shock Analysis, Response Spectrum Analysis, PCB Assembly Stress, and Thermal Stress during product development to identify structural failure risks at the digital prototype stage, facilitate design optimization and risk mitigation, enable forward-looking design, and shorten the product development cycle.





Lawrence Yan is a Strength Simulation Specialist and Technical Leader at Scania and focuses on strength simulation and fatigue-related models. He helps develop strategies and methods to accelerate Scania’s development through CAE-driven predictions.




SIMULIA is pleased to welcome the 2026 Champions and thanks them for their efforts to advance the development and adoption of simulation technology. You can find a more detailed look into our Champions&#8217; backgrounds by visiting our SIMULIA Champions Program web page. We look forward to learning about their work and expertise as they share their stories here in the SIMULIA blog and in the&nbsp;SIMULIA&nbsp;Community.















Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.
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