CFD basics & useful information – Understanding flow simulation

What is CFD?

CFD stands for “computational fluid dynamics”. This involves mathematically modeling physical processes such as flows, temperature distributions, and pressure conditions. The calculations are based on the Navier-Stokes equations and enable the virtual analysis of complex fluid processes in gases and liquids. CFD is used wherever classic testing technology reaches its limits or where digital development processes are intended to save time and costs.

A brief look at the history of CFD

The origins of computational fluid dynamics (CFD) date back to the 1960s, when the first methods for calculating flows in aviation were developed, with a focus on aerodynamics. Since then, CFD has evolved in all directions: through increasing computing power, advanced turbulence models, multiphase calculations, couplings with heat transfer and chemistry, and automated optimization methods. Today, CFD is an integral part of modern product development – from medical devices to power plants.

How does a CFD simulation work?

A CFD analysis follows a structured process:

  • Definition of objectives based on the underlying task
  • Geometry preparation (simplification, error checking, CAD cleanup)
  • Mesh generation (discretization of the fluid volume)
  • Definition of boundary conditions, material data, and physical models
  • Iterative solution of equations, including convergence control
  • Validation and quality assurance
  • Evaluation and visualization (e.g., streamlines, temperature fields, forces)
  • Creation of meaningful documentation

What can be simulated with CFD?

Accuracy across many physics domains

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  • Internal flows (e.g., in housings, ducts, pipes)
  • External flows (e.g., flow around vehicles, components, or buildings)
  • Temperature distribution, cooling, thermal management
  • Pressure profiles, resistance, cavitation
  • Multiphase flows (e.g., gas-liquid systems)
  • Gas mixture flows (e.g., diffusion and reactions)
  • Transient processes (e.g., changing boundary conditions, pulsations)
  • Aeroacoustic phenomena (noise generation and propagation)
  • Particle simulations (solids and droplets in flows)
  • Combination with structural or chemical simulation (co-simulation)

Technical questions about CFD

Different turbulence models are used depending on the application:

  • k-ω SST model: The industry standard for technical flows. Particularly suitable for wall-near flows, separations, and flow deflections. Combines the advantages of k-ε and k-ω models and delivers robust results even for complex geometries.
  • LES (Large Eddy Simulation): High-resolution turbulence model for direct calculation of large vortex structures. Particularly used for unsteady processes requiring a high level of detail – e.g., pulsating flows, vortex formation, or acoustic calculations.

The choice of turbulence model influences both the accuracy and the computational effort of the simulation.

The computational mesh is crucial for the quality of CFD results. Discretization that is too coarse can obscure or completely overlook fine flow details. The following are particularly important:

  • Local refinements in critical areas such as bottlenecks or vortex zones
  • y+ adjustment for correct modeling of wall-near flows depending on the selected turbulence model
  • Ratio of cell count to computing time in order to find a practical balance between accuracy and effort
  • Achieving satisfactory mesh quality in order to obtain a precise, stable, and convergent solution

An accompanying mesh dependency study ensures that the results are not overly dependent on the selected mesh.

Boundary conditions define how the model behaves at its outer edges. Typical examples:

  • Inlet velocity or volume flow
  • Pressure specifications at the outlet
  • Temperature or heat flux values
  • Symmetry, wall friction, or free outflows

Ideally, the selection should be based on real operating data, empirical values, or standard conditions. An inappropriate boundary condition can significantly distort the simulation results or even make them unstable.

Transient CFD simulations capture time-dependent flow processes, e.g.:

  • Start-up processes
  • Periodic oscillations
  • Pressure surges or moving components

The solution is obtained using defined time steps, often with a start transient to map start-up processes. Although the computational effort is significantly higher compared to steady-state simulation, transient analyses provide realistic results for dynamic systems.

Careful validation increases the significance and credibility of CFD results. Common methods include:

  • Comparison with measurement data from experiments or real-world operation
  • Comparison with literature values or validated benchmarks
  • Sensitivity analyses to check the influence of individual input parameters or model settings

Reliable validation is essential, especially for safety-critical or standard-relevant projects.

Glossary: CFD basics explained simply

CFD basics

Computer-aided method for simulating flow and heat processes. CFD is used to numerically solve complex physical processes such as air, gas, or liquid flows — e.g., to optimize components, systems, or processes.

Fundamental equations of fluid mechanics. They describe the conservation of momentum, mass, and energy in a flowing medium. In CFD, they are solved using numerical methods on a computational grid.

A law of physics stating that mass cannot be lost in a closed system. In CFD, it is part of the continuity equation, which ensures that the calculated flow remains physically consistent.

Thermodynamic principle according to which energy is neither created nor destroyed, but only converted. In CFD, the energy equation is used to record the transport of heat, internal energy, and work — e.g., in heating or cooling processes.

Dimensionless number used to characterize the flow. It describes the ratio of inertial to viscous forces. Low values indicate laminar flow, high values indicate turbulent flow – decisive for the choice of modeling.

Chaotic, unsteady flow behavior with vortices, momentum transfer, and strong gradients. Turbulence typically occurs at high Reynolds numbers and significantly influences energy distribution, resistance, and heat transfer.

Mathematical approximation for describing turbulent effects. Models such as the k-ω-SST model enable the simulation of technical, turbulent flows without the extremely high computational load that would be required for a direct calculation of all turbulence scales.

CFD modeling

Numerical division of the calculation area into small cells (volume elements). The quality, density, and structure of the mesh significantly influence the accuracy and stability of the CFD simulation.

Definition of physical conditions at the boundaries of the simulation model, e.g., inlet velocity, pressure, temperature, or wall properties. They are essential for the reproducibility of real conditions in the computational model.

Method for quantifying and reducing the numerical error caused by the discretization of the computational domain. The influence of the computational mesh on the solution is systematically analyzed and reduced to an acceptable level depending on the application. In practice, Richardson extrapolation is often used to evaluate grid convergence and estimate the discretization error.

Dimensionless parameter for evaluating the mesh resolution near the wall. It determines whether wall functions can be used or whether a fine grid resolution is necessary for direct modeling of the boundary layer.

Flow in which several phases such as gas, liquid, or solid are present at the same time – e.g., air bubbles in water, liquid droplets in gases, or particle flows. In CFD, special models such as VOF, Euler-Euler, or Lagrange are used for this purpose.

Modeling of the distribution and interaction of several chemical components in a gas mixture. So-called “species transport” models are used to take into account the transport, diffusion, and reactions of individual gases – e.g., in air quality, combustion, or contaminated flows.

Numerical method for modeling discrete particles (e.g., solid particles, droplets, powders, aerosols) within a continuous flow. The movements of individual particles are usually calculated using Lagrangian models, e.g., in combination with an Eulerian fluid model. CFD software offers special approaches such as DPM (Discrete Phase Model) to analyze forces, heat transfer, or chemical reactions between particles and fluid.

Solution of the CFD model

Numerical calculation module that solves the discretized equations. Depending on the application, different solver types are used – e.g., for steady or unsteady, incompressible or compressible flows.

Residual error that indicates how much an equation is still violated in an iteration step. The magnitude of the residuals serves as an indicator of the accuracy of the solution and is an important criterion for monitoring the quality of the results.

A state in which the solution no longer changes significantly during the course of the iterations. A convergent solution is a prerequisite for reliable and reproducible simulation results. Ideally, both the residuals and the relevant result variables are used to evaluate convergence.

Simplified model for describing natural convection. It assumes that density changes only play a role in buoyancy, which reduces the computational effort without significantly compromising accuracy.

Result analysis & visualization

Amount of fluid flowing through a defined cross-section per unit of time – e.g., in m³/h or l/min. A key parameter for the design and analysis of technical systems such as ventilation, cooling circuits, or pipe networks.

Reduction in static pressure along a flow path – caused by friction, deflections, cross-sectional changes, or fixtures. In CFD, pressure loss is often calculated between two levels or across a component and is a key parameter for the design and efficiency evaluation of flow systems.

A line that is tangential to the local flow direction at every point. Streamlines are a useful tool for visualizing flow patterns, deflections, and dead zones in the calculation domain.

Measurable values derived from the CFD simulation – e.g., temperature distributions, flow velocities, forces, heat fluxes, or mass flows. They are crucial for technical evaluation, validation, and comparison with measurement data or specifications.

Moving visualizations of time-dependent or transient flow processes – e.g., vortex developments, temperature fronts, or particle trajectories. Animations are an effective means of representing complex dynamic processes and communicating CFD results to customers, partners, or decision-makers.

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Dr.-Ing. Yannick Lattner
CFD Team Lead

“I am fascinated by the fact that the highly complex CFD simulations are so ensure that the results provide our customers with a real – enabling you to create better, better develop safer and more efficient products.”