CFD simulation

Whether it’s gases or liquids, Simcenter offers a comprehensive set of computational fluid dynamics models for single‑phase flows. From compressible to incompressible, and from laminar to turbulent regimes, Simcenter supports CFD analysis across a wide range of flow conditions and applications. For turbulent flows, engineers can choose from a state‑of‑the‑art portfolio of turbulence models, including Reynolds‑Averaged Navier–Stokes (RANS), Detached Eddy Simulation (DES), and Large Eddy Simulation (LES), enabling the right balance between accuracy and computational cost.

In addition to finite‑volume CFD approaches, Simcenter also includes panel method capabilities to support rapid aerodynamic and hydrodynamic analysis. Panel methods use surface‑based, vorticity‑driven formulations to efficiently capture key flow features around complex geometries without the need for volumetric meshing. This approach is especially well suited for early design and concept evaluation, enabling fast prediction of loads, forces, and performance characteristics, and complementing high‑fidelity CFD for later‑stage validation.

Hypersonic and supersonic flows are at the forefront of both research and next‑generation aerospace applications. In these high‑speed regimes, strong shock waves, high temperatures, and extreme pressure gradients dominate the flow behavior. As gas dissociation and ionization effects become significant, ideal‑gas assumptions may no longer be valid, requiring real‑gas and thermochemical modeling approaches to accurately capture flow physics and performance.

Simcenter CFD simulation provides advanced capabilities for modeling compressible, supersonic, and hypersonic flows, supporting the analysis of shock interactions, aerodynamic heating, and high‑Mach‑number flow phenomena with appropriate physical models and numerical methods. For rapid assessment of external supersonic and hypersonic aerodynamics at early development stages, Simcenter also includes a panel‑method. This surface‑based method efficiently captures key aerodynamic characteristics such as pressure distribution, forces, and moments on complex configurations without volumetric meshing, complementing high‑fidelity CFD simulations used later for detailed flow physics and validation.

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Real-world engineering problems cover multiple flow regimes across stratified, dispersed, discrete, films, etc. To date, there is no single multiphase model that can cover all regimes; however, Simcenter CFD provides a comprehensive range of models for multiphase CFD simulations which can be used together to cover many flow regimes, including the transitions between them. Predict and understand the real-world behavior of your products by simulating multi-regime, multi-scale multiphase flows.

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Particle dynamics play a critical role in many engineering applications, including fluidized beds, cyclone separators, coating processes, conveyors, roasters, and bulk material handling systems. Whether dealing with dilute or dense particle flows, Simcenter CFD simulation helps to optimize particle distribution, reduce energy consumption, minimize wear and abrasion, and maximize overall system performance. For applications where particle–particle interactions and contact mechanics dominate the physics, Simcenter provides advanced Discrete Element Method (DEM) capabilities. These particle‑resolved approaches enable realistic modeling of particle shape, size distribution, contact behavior, and material properties, supporting accurate prediction of bulk material flow, segregation, breakage, agglomeration, and wear. This is particularly important for dense granular systems where conventional CFD‑based particle models are not sufficient.

By combining DEM and CFD within a unified simulation framework, Simcenter enables detailed simulation of complex particle–fluid systems. Engineers can flexibly choose the most appropriate modeling approach, from CFD‑based particle methods for dilute flows to full DEM for dense particulate systems, ensuring accuracy and efficiency across a wide range of industrial applications.

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Simcenter delivers a comprehensive suite of reacting flow and emission models, covering a wide range of applications. It enables tight coupling between reacting flow models and heat transfer, radiation, multiphase reactions and surface chemistry. Our reacting flow models help understand and optimize flame shape and location, minimize solid component temperatures, reduce emissions and maximize performance efficiency. You can predict and understand flame dynamics, heat transfer, thermal wear, emissions, yield, conversion, selectivity, and undesirable conditions to accurately capture real-world physics of your designs.

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Smoothed-particle hydrodynamics (SPH) is a rapid meshless computational fluid dynamics method. As no meshing is required it enables engineers to easily handle complex geometries and motions with a rapid and robust method. Using SPH one can resolve complex highly dynamic fluid/fluid interface with no extra effort. As such the SPH method is part of a new generation of numerical methods, developed to overcome meshing-related constraints with traditional approaches while still being based on the Navier-Stokes equations.

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Innovative products of today, from electronic devices to mobility solutions need innovative cooling and thermal management solutions. Simcenter CFD suite allows engineers to simulate heat transfer in a medium or between media accurately and efficiently. With conduction, convection, radiation, conjugate heat transfer, and boiling, Simcenter CFD simulation offers a full suite of heat transfer physics to accurately predict temperature distributions in fluids and solids. The Simcenter portfolio includes leading CFD analysis with specific electronics cooling simulation capabilities for chip package level, printed circuit board (PCB), rack, and enclosures to large datacenters.

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Flow‑induced noise is a significant component of the acoustic signature of vehicles, buildings, fans, and industrial equipment. Simcenter offers a comprehensive set of modeling approaches for predicting aeroacoustic noise sources, covering steady‑state methods, transient Lattice Boltzmann method (LBM), high‑fidelity direct simulations based on DES and LES, sound propagation models and advanced acoustic perturbation equation (APE) solvers.

Simcenter supports hybrid aeroacoustic workflows in which CFD simulations first capture turbulent flow structures that act as noise sources. These sources can then be transferred to dedicated acoustic simulations, including finite‑element‑based approaches, to predict sound propagation while accounting for reflections, absorption, and environmental effects. Typical applications include HVAC noise, side‑mirror noise, and other flow‑induced sound phenomena.

In addition, Simcenter extends its aeroacoustics capabilities with fast simulation approaches based on LBM and GPU acceleration. This method is an attractive alternative for accurate predictions of external aeroacoustic noise by directly capturing unsteady flow behavior at high temporal resolution.

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Almost all real-world engineering problems ultimately depend on the interaction between fluids and solid structures. Simcenter CFD simulation offers both finite volume (FV)-based computational fluid dynamics and finite element (FE)-based computational solid mechanics (CSM). This enables engineers to simulate fluid-structure interaction FSI in an easy-to-use single integrated user interface. Using this approach, you can solve static, quasi-static, and dynamic problems including those with nonlinear geometry and multiple parts using bonded and small sliding contacts.

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Extending mechanical fluid-structure interaction, Simcenter CFD simulation offers efficient workflows for thermal stress analyses. Thermal engineers can solve heat transfer problems between a fluid and a structure, and based on that assess thermal stresses in the solid regions.

With computational rheology in Simcenter CFD analysis, engineers can simulate fluids dominated by diffusion and viscous or viscoelastic behavior. Computational fluid dynamics of non-Newtonian fluids is useful to engineers in consumer products, waste processing, and food and beverage industries who work with mixers, flow containers, slurries, extrusions, and material processing. Simulating rheology accurately is the key to reducing power consumption, emissions, and raw material usage while improving product reliability, user experience, and liability costs.

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Simcenter CFD suite comes with a wide range of electromagnetic models to address a range of electromagnetics simulations from magnetic valves, solenoids, actuators and transformers to electric machines. As a multiphysics CFD suite, it offers integrated electromagnetics capabilities that allow you to tightly couple to coolant flow, heat transfer and electromagnetics in one single simulation. It also enables engineers to simulate magnetohydrodynamics (MHD) applications, including plasma arc simulation, gas-blast circuit breakers and welding.

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For the realization of a holistic digital twin, Simcenter CFD simulation is embedded into the wider Simcenter portfolio. This allows engineers to stay integrated while running extended multidisciplinary projects when analysis needs to go beyond multiphysics CFD. Simcenter enables you to couple to other simulation tools through dedicated interfaces and common data formats. This allows engineers to create holistic multidisciplinary simulations using time scales ranging from microseconds to thousands of seconds, and physics ranging from sophisticated solid mechanics to rapid system simulations while leveraging the best-suited simulation technology for each discipline.

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As engineering systems grow more complex and design cycles shorten, engineers need faster ways to explore large design spaces while maintaining confidence in performance predictions. AI‑based Geometric Deep Learning (GDL) enables rapid predictions directly from geometry and simulation data, accelerating early decision‑making and unlocking broader design exploration.

Simcenter integrates Geometric Deep Learning into its CFD workflow to create high‑fidelity surrogate models that complement traditional simulation. By learning directly from simulation results, whether newly generated or reused from existing datasets, GDL models deliver fast performance predictions in minutes instead of hours. This approach allows engineers to focus computationally intensive simulations on the most promising concepts while using AI to efficiently screen, compare, and refine design alternatives. The result is faster iteration, better use of historical simulation investments, and scalable, AI‑driven insight seamlessly connected to physics‑based simulation workflows.

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