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Computational fluid dynamics simulation

Computational fluid dynamics (CFD) simulations are based on the Navier-Stokes equation, used to describe the motion of fluids.

What is computational fluid dynamics?

A computational fluid dynamics simulation involves using the fundamental laws of mechanics, governing equations of fluid dynamics and modeling to formulate a physical problem mathematically. Once formulated, computing resources use numerical methods to solve the equations using CFD software to obtain approximate solutions for the physical properties involved.

Computational fluid dynamics simulations are based on the Navier-Stokes equation, used to describe the motion of fluids. The accuracy of CFD simulations depends on the fidelity of the model, approximations and assumptions used, experimental validation and the computing resources available. It is essential to characterize the uncertainties and errors in the computational fluid dynamics simulation to use it as an effective tool in design and analysis.

There are three main methods of predicting the behavior of fluids and their interaction with the surrounding environment – experimental, analytical and numerical. Computational fluid dynamics is the numerical method of simulating steady and unsteady fluid motion using computational methods and hardware.

Computational fluid dynamics is a well-established methodology often used to replace or supplement experimental and analytical methods to aid the engineering design and analysis of everyday products.

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Multiphysics computational fluid dynamics simulation software screenshot.

Uncover the benefits

Computational fluid dynamics is a well-established methodology often used to replace or supplement experimental and analytical methods to aid the engineering design and analysis of everyday products. Compared to prototyping and experiments, CFD simulations offer the following benefits.

Increase performance

Computational fluid dynamics simulation allows you to rapidly analyze and explore the engineering possibilities for increased product performance.

Time reduction

CFD simulation helps you bring optimized product designs to market faster than a build-and-test method.

Cost reduction

Leveraging computational fluid dynamics, you can significantly reduce your product development cost compared to traditional prototype-based design processes.

Steps in the CFD simulation process

No matter the software, all computational fluid dynamics simulations follow these generalized steps.

Meshing graphics from the Simcenter STAR-CCM+ software.


The preprocessing stage involves creating the geometry and preparing it for CFD simulation. In a process called meshing or grid generation, the CFD tool breaks up the domain into small volumes/cells. The user applies flow conditions, fluid properties and initial boundary conditions.

The CFD simulation software begins iteratively solving the discretized equations using the CFD solver.


The CFD simulation software begins iteratively solving the discretized equations using the CFD solver. This step can require significant time or computing resources. For complex simulations, more enterprises are turning to cloud computing as a cost-effective solution to this issue.

Representation of the benefits of running Simcenter STAR-CCM+ on GPUs provides computational fluid dynamics (CFD) capabilities to go faster while modeling the complexity.


Once the solving is complete, the next step is to analyze and visualize the results of the simulation qualitatively and quantitatively using reports, monitors, plots, 2D/3D images and animations. Verification and validation of the results are also included in this stage.

Multiphysics computational fluid dynamics

Modern computational fluid dynamics is more than just the ability to simulate and predict fluid flow and heat transfer behavior. Today, CFD is embedded into a multidisciplinary computer-aided engineering (CAE) environment, enabling engineers to model a wide range of fluid-related physics, from reacting flows to aeroacoustics, from multiphase flows to particle dynamics, from electronics cooling to aerodynamics and tightly couple those to related fluid dynamics. This is of fundamental importance in a world of increasingly complex products that require holistic cross-domain engineering to achieve maximum performance.

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Frequently asked questions

Are computational fluid dynamics simulations accurate?

Finite element analysis (FEA) has been successfully used for product engineering for decades. Along with that, high-fidelity modeling approaches and more pragmatic ones were continuously developed, which let you obtain sufficiently accurate results faster.

Today, engineers can and must choose the level of accuracy that best fits their needs to answer engineering questions with minimum computational effort. The level of accuracy ranges from high-fidelity modeling techniques that enable the prediction of real behavior within a few percent or even less to quick methods that enable quick trend predictions.

Today, certification and verification processes for FEA simulation tools are well established. They will remain a critical ingredient to the progress of FEA, its reliability and trust in digital twins and its establishment in novel areas. While predictive simulation will continuously reduce the need for expensive measurements and prototyping, it will continue to require rigorous FE methods and best practices validation through experiments. 

What is the difference between mesh-based and mesh-free computational fluid dynamics methods?

Meshfree CFD methodologies offer an appealing alternative approach to mesh-based methods for selected applications. When rapidly getting results is a priority over the highest accuracy, smoothed particle hydrodynamics (SPH) is an efficient tool. However, both methods have their place, and depending on the requirements for time to solution vs. required accuracy, it may be beneficial to choose a mesh-based or meshless approach.

How is turbulence handled in computational fluid dynamics? 

Most of the flows around us and relevant to product developments are turbulent in nature. Over decades, science and industry have established close relations to incorporate turbulence descriptions into the Navier-Stokes equation. For example, meshing the most suited turbulence model for a given application and CFD project heavily depends on accuracy versus simulation speed requirements.

Generally, turbulence modeling can be classified into three main categories: statistical modeling, also known as Reynolds Average Navier-Stokes (RANS), scale-resolving simulation (SRS), like large-eddy simulation (LES) or detached-eddy simulations (DES) and ultimately, direct numerical simulation (DNS), which does not make any modeling assumptions on turbulence.

Is it hard to learn CFD simulation?

Learning CFD requires time, dedication, thorough study and practice. It is critical to understand the underlying fundamental physics of fluid dynamics and the Navier-Stokes equation, grasp numerical methods and their limitations and practice the hands-on usage of the actual computational fluid dynamics software tool. Thanks to automation and continuous improvement of User Interfaces in modern computational fluid dynamics software, the barriers to high-fidelity CFD will further decrease on all levels shifting, the scope to exploring results and making simulation-based decisions. It is also critical to understand fundamental fluid dynamics to judge the results and make meaningful engineering decisions based on CFD results.

What are the hardware requirements for CFD simulation?

The choice of hardware for a CFD project really depends on your project, budget, and current priorities. Some recommendations: x86 CPUs have run simulations for ages now. Every solver was initially developed and verified for this platform. Look for CFD hardware with maximum cache – servers, workstations, and laptops. Graphic Processing Units (GPUs) support many solvers nowadays, and software will adapt to this even further. This solution is very energy efficient. Pay close attention that your required solvers are supported and meet the memory requirements of your use case. This lets you get the most out of multi-GPU workstations and GPU clusters. ARM processors support everything but the graphical user interface. This is an approach for cost-efficient computing, especially on cloud services. Provided your computational fluid dynamics simulation tool supports ARM technology. Generally, cloud-based CFD simulation is a straightforward solution. No investment in expensive computer hardware, no idle cost and scalable on demand.

What are the applications of computational fluid dynamics?

Computational fluid dynamics simulation software is used in a wide range of engineering applications whenever there is a need to understand or predict fluid flow and heat transfer and the resulting effect on the design of a product or system. In industrial product design, computational fluid dynamics simulation has progressed to simulating the multiphysics behavior in complex geometries, enabling companies to fully understand and optimize their product design virtually before building a prototype.

Industries where computational fluid dynamics simulation is widely used include:

  • Aerospace
  • Automotive
  • Chemical
  • Consumer products
  • Marine (ship design, propulsion systems and engine design)
  • Electronics
  • Energy (nuclear, oil & gas and power generation)
  • Building services
  • Life sciences
  • Turbomachinery
  • Sports
  • Other general applications involving fluid flow and heat transfer


On-demand webinar | Redefining the art of the possible in computational fluid dynamics simulation.


Podcast | The past, present and future of computational fluid dynamics with Dr Simon Fischer.


Blogs | Discover all Simcenter computational fluid dynamics blogs.