3D CFD

1D simulation describes flow and system behaviour along simplified pipes, volumes and components. It is highly efficient and well suited for complete systems, maps, engine processes, gas exchange, turbo matching or cooling circuits. 3D CFD, by contrast, calculates the spatial flow field in a real geometry with local velocities, pressures, temperatures, vortices and separation. 1D simulation usually answers the system-level question: which operating points, mass flow rates and pressures occur in the overall system? 3D CFD answers the local detail question: where do losses, hot spots, maldistribution or critical flow structures occur? In development, the two methods are not competitors but complementary tools, for example when 1D simulation provides boundary conditions for 3D CFD or 3D CFD generates characteristic data for 1D models.

Adaptive mesh refinement is a method in which the computational mesh is selectively refined during or between simulations. Refinement is applied where high gradients, vortices, shocks, interfaces or other important flow details occur. This increases accuracy without unnecessarily refining the entire domain. AMR is especially useful for unsteady structures, multiphase flows, shock waves and locally confined phenomena.

Advection describes the transport of a physical quantity by the mean flow velocity. The term is often used when a scalar such as temperature, concentration or turbulence quantity is carried by the flow. Advection is closely related to convection, but is often used in a more mathematical and narrower sense for transport by the velocity field. In CFD, the numerical treatment of advection is crucial for stability and accuracy.

Ansys CFX is a commercial CFD software package often used for turbomachinery, pumps, compressors, turbines and rotating flow machines. It is known for robust steady-state and transient simulations of rotating machinery. Compared with Ansys Fluent, CFX is more strongly associated with classical turbomachinery workflows. The choice between CFX, Fluent and other CFD tools depends strongly on application, available models, meshing and postprocessing workflow.

Ansys Fluent is a widely used commercial CFD software package from Ansys. It is used for flow simulation, heat transfer, multiphase flows, combustion, reacting flows and many industrial applications. Fluent is especially known for its broad range of models and long history of industrial use. In practice, it is often compared with STAR-CCM+, Ansys CFX or OpenFOAM, depending on workflow, licensing model, automation and application.

An automated CFD simulation uses standardized workflows for geometry preparation, meshing, boundary conditions, solving and postprocessing. This allows variants to be calculated faster, more consistently and with less manual effort. Automation is especially valuable for parameter studies, design studies, optimization and surrogate models. It reduces user errors and makes CFD easier to integrate into modern development processes.

Backflow means that fluid flows back into a region or across a boundary opposite to the expected main flow direction. It can be physically real, for example in recirculation zones, or numerically problematic, for example at poorly placed outlets. In CFD, backflow should always be checked because it can strongly influence boundary conditions and convergence. Appropriate backflow treatment is especially important at outlet boundaries.

A boundary condition defines how the computational domain interacts with its surroundings. It specifies, for example, how fluid enters, leaves or behaves at solid walls. Typical boundary conditions include inlet, outlet, wall, symmetry plane or periodic boundary condition. Incorrect boundary conditions can lead to physically wrong results even with a high-quality mesh.

Boundary layer resolution describes how finely the mesh represents the flow close to walls. Within the boundary layer, velocity, turbulence quantities and temperature often change strongly over a very small distance. Good boundary layer resolution is crucial for wall friction, pressure loss, heat transfer, flow separation and aerodynamic forces. Depending on the modelling approach, the boundary layer is either resolved near the wall or approximated using wall functions.

Cell size describes the spatial resolution of the computational mesh in a CFD simulation. Small cells can capture local gradients, boundary layers, vortices, narrow gaps or strong temperature changes more accurately. At the same time, smaller cells increase the cell count, runtime and memory demand. A good cell size distribution is therefore always a compromise between accuracy and computational efficiency.

CFD postprocessing is the evaluation and visualization of calculated simulation results. It includes section planes, streamlines, pressure and temperature fields, force evaluations, mass balances, diagrams and comparison tables. Good postprocessing turns raw data into engineering insight. The key is not just an attractive flow image, but a reliable interpretation of the relevant result quantities.

CFD preprocessing includes all steps before the actual calculation. These include geometry preparation, definition of the computational domain, meshing, material selection, boundary conditions, physical models and solver setup. The quality of preprocessing has a major influence on whether a simulation runs stably and produces technically meaningful results. Many CFD problems do not originate in the solver itself, but in incorrect assumptions during preprocessing.

A CFD simulation calculates the behaviour of fluids such as air, water, oil, coolant or gases using numerical methods. Typical results include velocity, pressure, temperature, pressure loss, mass flow rate, forces and heat transfer. The simulation is based on physical conservation equations that are solved on a computational mesh. In industrial development, CFD is used to evaluate and optimize components and systems before they are built or tested.

CFD and FEA are different numerical methods or application fields within CAE. CFD is used for fluid flow, heat transfer, pressure losses, fluid forces, mixing or multiphase phenomena. FEA, often called FEM in German-speaking engineering, is mainly used for structural mechanics, stresses, deformations, natural frequencies, strength and thermo-mechanical loads. In many development projects, both methods interact, for example when a CFD pressure field is used as a load for a structural FEA analysis. CFD primarily focuses on the fluid, while FEA primarily focuses on the solid structure. For coupled tasks such as fluid-structure interaction, thermal deformation or component cooling, both domains must be linked correctly.

CFD and wind tunnel testing are two different ways of investigating aerodynamic behaviour. CFD provides detailed insight into pressure fields, velocity fields, vortices, wakes and the local causes of forces. Wind tunnel testing provides real measurement data under defined test conditions and is especially valuable for validation and final confirmation. CFD is flexible, suitable for variant studies and can be used early in development, but it strongly depends on modelling assumptions, mesh, boundary conditions and validation. The wind tunnel is physically real, but more expensive, slower and dependent on model setup, measurement access and scaling effects. In a good development process, CFD does not simply replace the wind tunnel; it reduces test effort and makes measurement campaigns more targeted.

The CFL number, also called Courant number, describes how far flow information travels within one time step relative to the cell size. It links time-step size, local velocity and mesh resolution. In transient CFD simulations, it is an important criterion for stability and temporal accuracy. High CFL values may be acceptable depending on the solver and numerical scheme, but they must be assessed deliberately.

A compressible flow is a flow in which density changes are significant for the behaviour of the fluid. It typically occurs in gas flows with higher Mach numbers, strong pressure changes, compression, expansion or shock waves. Compressible CFD requires suitable density models, the energy equation and often specialized solvers. Typical applications include turbochargers, nozzles, exhaust flows, gas turbines and high-speed aerodynamics.

An incompressible flow is calculated under the assumption that density changes are negligible. This is often reasonable for liquids and for gases at low Mach numbers. A compressible flow accounts for relevant density changes caused by pressure, temperature, compression, expansion or high velocity. It is important for nozzles, turbochargers, exhaust flows, gas turbines, high-speed aerodynamics and pressure waves. The choice between compressible and incompressible affects solver, boundary conditions, energy equation and computational cost. An incorrect simplification can significantly distort pressure losses, mass flow rates and temperatures.

The computational domain is the spatial region that is calculated in a CFD simulation. It can be the inside of a pipe, a vehicle engine bay, a cooling air duct or the surroundings of a vehicle. The choice of domain influences boundary conditions, runtime and result quality. A domain that is too small can artificially affect the flow, while an unnecessarily large domain increases computational effort.

Computational fluid dynamics is the field that investigates fluid flows using mathematical models and computer-based numerical methods. It combines fluid mechanics, thermodynamics, mathematics, numerical methods and software engineering. The goal is to make flow fields and thermal processes in technical systems predictable by simulation. In practice, the term is often used synonymously with CFD.

The computational mesh divides the domain of a CFD simulation into many small cells or elements. Within these cells, quantities such as pressure, velocity, temperature or turbulence variables are calculated numerically. The mesh resolution determines how well local gradients, boundary layers, vortices or narrow gaps are captured. A mesh that is too coarse may miss important flow effects, while an overly fine mesh greatly increases runtime and memory demand.

Conjugate heat transfer describes the coupled calculation of fluid flow and heat conduction in solid components. It makes it possible to simulate how a fluid heats or cools a component and how heat spreads through the material. CHT is important when wall temperatures cannot simply be prescribed, but result from the interaction between the fluid and the solid. Typical examples include cooling channels, radiators, battery cold plates, engine components and turbomachinery.

Conservation equations state that physical quantities such as mass, momentum and energy must be balanced within a system. In CFD, these equations are applied to many small cells of the computational mesh. This creates a large system of equations that is solved numerically. The quality of a CFD simulation strongly depends on whether these conservation equations are modelled physically and solved in a numerically stable way.

Conservation of energy describes the balance of internal energy, kinetic energy, heat and work in a system. In CFD, it is required when temperature changes, heat transfer, compressibility, viscous heating or chemical reactions are relevant. For purely isothermal flows, the energy equation may sometimes be omitted. In cooling, combustion, heat exchangers and high-speed flows, however, it is essential.

Conservation of mass means that mass is neither created nor destroyed in a closed system. For fluid flows, this means that mass flow rates at inlets, outlets and within the domain must be balanced consistently. In CFD, conservation of mass is especially important for pressure loss analyses, flow distribution, leakage paths and cooling circuits. Errors in the mass balance are a strong warning sign of numerical or modelling problems.

Conservation of momentum describes how the motion of a fluid changes due to forces. These include pressure forces, viscous forces, inertial forces and, if relevant, external forces such as gravity or rotation. In CFD, momentum conservation is the basis for velocity fields, pressure distributions, shear stresses and flow forces. It explains, for example, why pressure losses occur or why a flow separates from a wall.

The continuity equation is the mathematical form of conservation of mass. It ensures that no non-physical mass is created or lost in the simulation. For incompressible flows, it simplifies to the requirement that the velocity field must be divergence-free. In CFD solvers, the continuity equation is closely linked to pressure-velocity coupling.

Continuum mechanics describes matter as a continuously distributed medium rather than modelling individual molecules. This assumption makes it possible to describe flows using fields such as pressure, temperature, density and velocity. For most technical CFD applications, this approach is highly suitable. Only at very small scales, in highly rarefied gases or in molecular effects does the continuum assumption reach its limits.

Convection describes the transport of heat, momentum or species by the motion of a fluid. In technical flows, convection is often the dominant mechanism for cooling, mixing and energy transfer. A distinction is made between forced convection, for example by fans or pumps, and natural convection caused by density differences due to temperature gradients. In CFD, convection must be represented numerically in a stable way with as little artificial diffusion as possible.

Convergence means that the numerical solution changes only slightly with further iterations. In CFD, convergence is often assessed using residuals, stable mass flow rates, constant forces, pressure losses or temperatures. A formally converged simulation is not automatically physically correct. Therefore, numerical convergence, plausibility and, where possible, comparison with test data must always be evaluated together.

Correlation with test data compares simulation results with real measurements. The goal is to check and, if necessary, adjust modelling assumptions, boundary conditions, material data, turbulence models or heat transfer approaches. Good test data correlation increases confidence in the simulation and improves its predictive capability. It is a central step in development, especially for engines, cooling systems, aerodynamics and thermal applications.

A coupled solver solves several equations, such as pressure and velocity, simultaneously within one solution procedure. This can capture the coupling between physical quantities more robustly and quickly. Coupled solvers are often advantageous for strongly coupled flows, difficult convergence behaviour or compressible effects. However, they usually require more memory and can be more computationally expensive per iteration.

A deforming mesh adapts its cell positions to a changing geometry. It is used when components move or deform without completely rebuilding the topology of the computational domain. Typical applications include valve motion, membranes, flaps, small structural deformations or coupled fluid-structure problems. Mesh quality must be monitored carefully because strong deformation can create poor cells and numerical problems.

DDES stands for Delayed Detached Eddy Simulation and is an extension of DES. The approach is designed to prevent the simulation from switching too early from RANS to LES-like modelling in near-wall boundary layers. This reduces undesirable effects caused by insufficient wall resolution. DDES is often used to analyse separated flows with large vortex structures when a fully wall-resolved LES would be too expensive.

Density describes the mass of a fluid per unit volume. It is a central material property for mass flow rate, buoyancy, pressure forces, inertia and compressibility. In liquids, density is often approximately constant, while in gases it depends strongly on pressure and temperature. In CFD simulations, correct density modelling is especially important for compressible flows, natural convection, multiphase flows and heat transfer.

A density-based solver uses density or conservative variables as central solution quantities. It is especially suitable for compressible flows, high Mach numbers, strong pressure waves and shock phenomena. Such solvers are often used in high-speed aerodynamics, nozzle flows, turbomachinery or compressible gas flows. For slow incompressible flows, a pressure-based solver is often more efficient.

A design study investigates different design variants using simulations. The goal is to evaluate geometric or functional changes with respect to flow, pressure loss, heat transfer, forces or packaging space. Unlike a simple parameter study, the focus is usually on comparing concrete design alternatives. Design studies support early development phases by revealing optimization potential before prototypes are built.

DES is a hybrid turbulence modelling approach between RANS and LES. Near walls, DES usually behaves in a RANS-like way, while separated flow regions are computed in a more LES-like manner. LES, by contrast, attempts to resolve large turbulent structures throughout the relevant flow field. The advantage of DES is reduced computational cost compared with full LES, especially at high Reynolds numbers and in wall-dominated external flows. The disadvantage is that DES depends more strongly on mesh, switching criteria and modelling assumptions. LES is more physically direct, but often much more demanding from a computational perspective.

DES stands for Detached Eddy Simulation and is a hybrid approach between RANS and LES. Near-wall regions are usually treated with RANS, while separated flow regions are computed in a more LES-like manner. This aims to reduce computational cost compared with full LES without losing major unsteady vortex structures. DES is especially suitable for separated external flows, wakes and aerodynamic applications with relevant unsteady effects.

Diffusion is the equalization of concentration, temperature or momentum differences through molecular or turbulent transport processes. In flows, diffusion usually acts across gradients and smooths local differences. In CFD, diffusion is important for species transport, heat transfer, viscosity and turbulent mixing. It should be distinguished from convection, where a quantity is mainly transported by the mean flow motion.

Direct Numerical Simulation and resolves all relevant turbulent scales directly without a turbulence model. This makes DNS physically very accurate, but extremely computationally expensive. For industrial flows with high Reynolds numbers, DNS is usually not practical. The approach is mainly used in fundamental research to better understand turbulence, modelling assumptions and reference data.

Discretization influences accuracy, stability, computational time and possible numerical errors in a simulation.

The dissipation rate describes how quickly turbulent kinetic energy is converted into heat by viscous effects. It is commonly denoted by epsilon and is a central variable in the k-epsilon model. High dissipation rates often occur in regions with small turbulent scales, strong shear or near walls. In CFD, the dissipation rate influences the calculated turbulent viscosity and therefore mixing, pressure loss and heat transfer.

The drag coefficient is a dimensionless quantity describing the flow resistance of a body or component. In aerodynamics, it is commonly referred to as drag coefficient or Cd. It allows different geometries to be compared independently of velocity, density and reference area. In CFD, the drag coefficient is used to evaluate aerodynamic variants, housings, vehicles or flow bodies objectively.

Dynamic viscosity describes the internal friction resistance of a fluid against shear deformation. The higher the dynamic viscosity, the more strongly the fluid resists relative motion between neighbouring layers. It influences boundary layers, pressure loss, Reynolds number and heat transfer. In CFD, dynamic viscosity must be defined correctly as a material property or temperature-dependent function.

Eddy viscosity is a modelling concept used in turbulence modelling. It describes the additional apparent viscosity caused by turbulent eddies and their momentum exchange. In many RANS models, turbulence is represented through eddy viscosity instead of resolving all turbulent fluctuations directly. The concept is computationally efficient, but can reach limitations for strongly anisotropic turbulence, rotation or complex separation.

The energy equation describes how energy is transported, stored and converted in a flow. It is used when temperatures, heat transfer, compression, expansion or thermal sources must be considered. In cooling and thermal management simulations, it is indispensable. It couples the flow field with the temperature field and enables the assessment of thermal loads.

The Eulerian-Eulerian model describes multiple phases as interpenetrating continuous fields. Separate conservation equations are solved for each phase, for example for volume fraction, momentum and energy. This approach is particularly suitable for flows with high phase fractions, many bubbles, droplets or particles where tracking individual entities would be inefficient. The quality of the model strongly depends on the interaction models used between the phases.

The Eulerian-Lagrangian model describes the continuous phase, such as gas or liquid, on the Eulerian mesh and tracks particles, droplets or bubbles along their trajectories. It is especially suitable for dilute multiphase flows where the dispersed phase can be tracked individually or in parcels. Typical applications include sprays, particle separation, droplet transport or fuel injection. At high particle or droplet concentrations, the computational cost can increase significantly or an Eulerian-Eulerian approach may be more appropriate.

The finite difference method replaces derivatives in differential equations with differences between neighbouring grid points. It is mathematically intuitive and historically an important basis of numerical flow simulation. For simple and structured grids, it can be very efficient. For complex industrial geometries, however, the finite volume method is often preferred because it handles unstructured meshes more flexibly.

The finite element method divides a domain into small elements and approximates the unknown quantities using shape functions. It is especially well known from structural mechanics and strength analysis. Flow and heat transfer problems can also be solved using the finite element method. In industrial CFD, however, it is often less dominant than the finite volume method, depending on the software and application.

The finite volume method is a numerical method in which the computational domain is divided into small control volumes. The conservation equations are balanced over each of these volumes. This makes the method particularly suitable for flow problems where conservation of mass, momentum and energy must be maintained accurately. Many industrial CFD codes use the finite volume method as their central discretization approach.

The finite volume method and the finite element method are two important numerical discretization methods. The finite volume method balances conserved quantities such as mass, momentum and energy over small control volumes. For this reason, it is particularly common in industrial CFD. The finite element method uses elements and shape functions and is especially strong in structural mechanics, strength analysis and thermo-mechanical problems. In principle, both methods can be used for flow and heat transfer problems. In practice, the choice depends strongly on software, physics, mesh type, accuracy requirements and workflow

The flow field describes the spatial distribution of flow quantities within a computational domain. It includes velocity, pressure, temperature, turbulence quantities and, where relevant, species concentrations. In CFD, the flow field is calculated for every cell of the computational mesh. Its analysis reveals where acceleration, separation, recirculation, losses or non-uniform flow distributions occur.

Flow resistance describes how strongly a component, duct or system restricts the motion of a fluid. It is caused by friction, shape, deflections, internal obstacles or flow separation. High flow resistance usually leads to higher pressure loss and greater energy demand. In product development, flow resistance is reduced to improve efficiency, throughput, cooling performance or aerodynamic behaviour.

Flow separation occurs when the boundary layer can no longer follow the wall geometry and detaches from the surface. Common causes are adverse pressure gradients, strong flow deflections, abrupt area changes or insufficient near-wall momentum. Separation can cause pressure loss, noise, unsteady forces, reduced heat transfer or aerodynamic losses. In CFD, its prediction strongly depends on mesh resolution, turbulence model and wall treatment.

A flow simulation describes how a fluid moves through a geometry or around a component. It can reveal acceleration, dead-water regions, recirculation, flow separation and pressure losses. Depending on the engineering question, the simulation can be steady-state, transient, isothermal or coupled with heat transfer. Flow simulations are used in automotive engineering, motorsport, energy technology, mechanical engineering and plant engineering.

Flow velocity describes how fast and in which direction a fluid moves locally. In CFD, it is calculated as a velocity field throughout the computational domain. It influences pressure loss, heat transfer, mixing behaviour, turbulence and forces on components. Very high or highly non-uniform velocities can indicate restrictions, maldistribution, separation or unfavourable geometries.

Fluid dynamics is the branch of fluid mechanics that deals with fluids in motion. It focuses on flow velocities, accelerations, pressure fields, vortices, turbulence and energy transfer. In CFD, fluid dynamics provides the central physical background for calculating technical flows. The term is mainly used when the motion of the fluid is the primary focus.

Fluid mechanics is the science of the behaviour of liquids and gases. It describes how pressure, velocity, density, viscosity and forces are related in fluids at rest or in motion. It forms the physical basis for CFD simulation, aerodynamics, hydraulics, cooling and many mechanical engineering applications. Without an understanding of fluid mechanics, CFD results are difficult to assess correctly.

A grid convergence study systematically investigates how the simulation result changes as the mesh is refined. It is used to estimate the discretization error and assess the numerical quality of a simulation. Unlike a simple plausibility check, it specifically evaluates the influence of the grid on the solution. For critical development decisions, a grid convergence study is an important proof of simulation robustness.

Heat flow rate describes how much thermal energy is transferred per unit time. It is typically given in watts and therefore represents a power. In thermal simulations, heat flow rate is important for quantifying cooling performance, heat removal and thermal loads. It should not be confused with heat flux, which is related to an area and is usually given in watts per square metre.

The heat transfer coefficient describes how effectively heat is transferred between a wall and a flowing fluid. It depends strongly on flow velocity, turbulence, fluid properties, geometry and near-wall behaviour. In CFD, it is often used to evaluate cooling performance, component temperatures and heat exchanger behaviour. A high heat transfer coefficient does not automatically mean a good overall system, because pressure loss and temperature difference must also be considered.

A heat transfer simulation investigates how heat is transported by conduction, convection or thermal radiation. In technical systems, these mechanisms often occur simultaneously and strongly influence the temperature distribution. Heat transfer simulation is especially important for cooling concepts, heat exchangers, thermally loaded components and fluid-flow components. It helps evaluate cooling performance, component temperatures and thermal safety margins.

IDDES stands for Improved Delayed Detached Eddy Simulation and is an advanced hybrid turbulence model. It combines RANS-like wall treatment with LES-like resolution of larger vortex structures in suitable flow regions. The goal is a better balance between computational cost, wall modelling and unsteady detail resolution. IDDES is mainly used for complex separated flows, vehicle aerodynamics, wakes and detailed aerodynamic analyses.

An incompressible flow is a flow in which density changes are negligibly small. This is often valid for liquids and for gases at low Mach numbers. Incompressible models simplify the calculation and are suitable for many cooling, pipe, internal flow and low-speed aerodynamic cases. It is important not to use this assumption without checking it when strong pressure, temperature or velocity changes occur.

An initial condition describes the starting state of a simulation. It defines initial values such as pressure, velocity, temperature, turbulence quantities or concentrations in the computational domain. In steady-state simulations, it mainly affects stability and convergence speed. In transient simulations, it can also significantly influence the calculated time history and transition behaviour.

An inlet boundary condition describes how fluid enters the computational domain. Depending on the engineering question, inputs such as mass flow rate, volume flow rate, velocity, total pressure, static pressure, temperature or turbulence quantities may be specified. The inlet condition should represent the real operating condition as accurately as possible. Inaccurate inlet data can strongly affect pressure losses, flow distribution, cooling performance and flow separation in the results.

An isothermal flow is calculated under the assumption that temperature remains constant or that temperature changes are irrelevant to the engineering question. In this case, the energy equation can often be omitted, which simplifies and speeds up the simulation. Isothermal assumptions are reasonable when heat transfer, compression or thermal buoyancy are not relevant. For cooling, combustion, heat exchangers or temperature-dependent material properties, this simplification is usually unsuitable.

An iteration is a single numerical solution step within a CFD calculation. In steady-state simulations, the solution approaches a stable state over many iterations. In transient simulations, additional inner iterations may be required within each time step. The number of iterations affects runtime, convergence and the accuracy of the coupled equation solution.

The k-epsilon model is a classical RANS turbulence model with two transport equations for turbulent kinetic energy k and dissipation rate epsilon. It is robust, computationally efficient and well established in many industrial applications. It can provide good results especially for free shear flows, internal flows and fully turbulent flows. However, for strong separation, adverse pressure gradients or near-wall effects, it is often less accurate than, for example, SST k-omega.

Kinematic viscosity is the ratio of dynamic viscosity to density. It describes how strongly viscous momentum effects spread within the fluid. Kinematic viscosity is especially important for the Reynolds number and therefore for classifying laminar, turbulent or transitional flow. In gases and liquids, it can differ significantly due to changes in temperature and density.

The k-omega model is a RANS turbulence model with transport equations for turbulent kinetic energy k and specific dissipation rate omega. It is particularly well suited for near-wall flows and boundary layers. In the free-stream region, however, it can be sensitive to boundary conditions for omega. For this reason, the SST k-omega model is often used in industrial practice as a more robust combination.

A Lagrangian particle model tracks individual particles, droplets or particle parcels through the calculated flow field. Forces such as drag, inertia, buoyancy, gravity and, where relevant, evaporation and heat transfer are considered. The model is useful when the trajectory of the dispersed phase is technically important, for example in sprays, dust, droplet separation or oil mist. A key question is whether the particles passively follow the flow or also influence the flow field through two-way coupling.

Laminar flow is an ordered flow in layers without strong turbulent mixing. Velocity and temperature profiles are comparatively smooth and predictable. Laminar flow often occurs at low Reynolds numbers, high viscosity or very small geometries. In technical applications, it is relevant for microchannels, highly viscous fluids or certain heat transfer cases.

Laminar flow is ordered and moves in layers with relatively smooth velocity profiles. Turbulent flow contains chaotic, three-dimensional and time-dependent motion. Turbulence increases mixing, momentum transfer and heat transfer, but often also causes higher pressure losses. Whether a flow is laminar or turbulent depends on Reynolds number, geometry, roughness, disturbances and operating condition. Many technical flows in vehicles, engines, radiators and pipe systems are turbulent. In CFD, the distinction is important because turbulent flows usually require a turbulence model or a highly resolved simulation.

LES stands for Large Eddy Simulation and directly resolves large turbulent eddies while modelling only the smaller scales. As a result, LES can often represent unsteady vortex structures, mixing and separation more realistically than RANS. However, the computational cost is significantly higher, especially near walls and at high Reynolds numbers. LES is often used for detailed analysis, research, aeroacoustics, complex mixing processes or validation cases.

The Mach number is the ratio of local flow velocity to local speed of sound. It describes whether compressibility effects are relevant in a gas flow. At low Mach numbers, a flow can often be treated as approximately incompressible, while at higher Mach numbers density changes, pressure waves and shock phenomena become important. In CFD, the Mach number influences the choice of solver, boundary conditions and physical models.

The mass flow rate describes how much mass of a fluid passes through a surface, channel or component per unit time. It is typically given in kilograms per second. In CFD, mass flow rate is a central quantity for inlet and outlet boundary conditions, mass balances, cooling circuits and turbomachinery. A consistent mass flow balance is an important plausibility check for the simulation.

Mesh generation is the process of creating the surface and volume mesh for a CFD simulation. The CAD geometry is prepared so that a numerically computable mesh can be generated from it. The goal is a mesh that resolves the relevant flow regions sufficiently well while remaining computationally efficient. Good mesh generation is often more important for result quality than later adjustments of individual solver settings.

A mesh independence study checks whether a CFD result still depends strongly on the chosen mesh resolution. Simulations with different mesh refinements are compared. If key results such as pressure loss, force, mass flow rate or heat transfer change only slightly with further refinement, the result is considered largely mesh-independent. Such studies improve the reliability and traceability of CFD results.

Mesh morphing describes the controlled modification of an existing computational mesh without fully remeshing the geometry. It is often used for design variants, optimization, shape changes or parametric studies. The advantage is that similar variants can be calculated quickly and consistently. The method works particularly well for moderate geometry changes, but reaches limitations for large deformations or poor mesh quality.

Mesh quality describes how suitable a computational mesh is for a stable and accurate numerical calculation. Important criteria include cell distortion, aspect ratio, orthogonality, smoothness of cell size transitions and boundary layer resolution. Poor mesh quality can lead to convergence problems, non-physical results or increased numerical diffusion. Therefore, checking mesh quality is a fixed part of a reliable CFD workflow.

Mixing describes how different fluids, temperatures, concentrations or momentum streams distribute within a flow. It is influenced by turbulence, shear, vortices, diffusion and geometric effects. In CFD, mixing is important for combustion, exhaust gas recirculation, hydrogen-air mixtures, coolant distribution and chemical processes. A good mixing assessment considers not only average values but also local maldistribution and dead zones.

The momentum equation describes the motion of a fluid under the influence of forces. It accounts for pressure gradients, viscous effects, acceleration and external forces. In its viscous form, it is closely related to the Navier-Stokes equations. In industrial CFD simulations, it is one of the central equations for calculating pressure fields, velocities, forces and losses.

A moving mesh describes a CFD method in which the computational mesh moves with a geometry or component. This allows real motions such as rotation, translation, opening, closing or stroke movement to be represented. Moving meshes are important when the geometry motion directly influences the flow. Depending on deformation and relative motion, sliding mesh, overset mesh or deforming mesh approaches may be used.

Moving Reference Frame, or MRF, is a steady-state modelling approach for rotating or moving regions. A rotating region is calculated in a moving reference frame, while adjacent regions can remain stationary. MRF is well suited for initial design, map points and cases where time-dependent interactions are not the main focus. For true transient rotor-stator effects, a sliding mesh method is usually more appropriate.

Moving Reference Frame, or MRF, is a steady substitute approach for rotating regions. Rotation is represented through a rotating reference frame with additional terms, without resolving the real motion over time. Sliding mesh, by contrast, calculates the actual relative motion between rotating and stationary mesh regions transiently. MRF is faster and well suited for initial design, average map points or cases with weak rotor-stator interaction. Sliding mesh is more accurate when the relative position of blades, openings or components causes time-dependent effects. The choice depends on whether averaged values are sufficient or the time-dependent interaction itself is relevant.

A multiphase flow contains multiple phases, such as gas and liquid, liquid and particles, or different immiscible fluids. It is significantly more complex than single-phase flow because phase interfaces, slip velocities, droplets, bubbles, evaporation or condensation may occur. In CFD, different modelling approaches are used, such as Euler-Euler, Euler-Lagrange or Volume of Fluid. Typical applications include sprays, fuel injection, coolant with gas content, oil-air flows and condensation.

The Navier-Stokes equations describe conservation of momentum in viscous fluids. Together with the continuity equation and, where required, the energy equation, they form the main mathematical basis of CFD. They balance pressure forces, inertial forces, viscous forces and external forces. Since these equations usually cannot be solved analytically for real technical flows, CFD solves them numerically.

A non-isothermal flow contains relevant temperature differences within the fluid or at the walls. As a result, heat transfer, density changes, viscosity changes or thermal buoyancy influence the flow. In CFD, the energy equation must be solved and the thermal boundary conditions must be defined carefully. Non-isothermal flows are common in cooling, exhaust systems, batteries, heat exchangers and thermally loaded machine components.

Numerical accuracy describes how close the computed solution is to the mathematically and physically expected solution. It is influenced by mesh resolution, time-step size, discretization scheme, convergence and modelling assumptions. High numerical accuracy does not automatically mean high physical accuracy if boundary conditions or models are chosen incorrectly. Therefore, numerical accuracy must always be considered together with validation and plausibility checks.

Numerical diffusion is an artificial smoothing of the solution caused by discretization and numerical schemes. It can weaken gradients, vortices, shear layers or temperature differences. In practice, this may make a flow appear more stable, but physically too smeared. Numerical diffusion must be assessed carefully, especially for convection-dominated flows, unsteady structures and coarse meshes.

Numerical dissipation describes the artificial damping of fluctuations or structures by the numerical method. It can help keep a simulation stable, but it can also damp real unsteady structures too strongly. The term is especially relevant in transient simulations, turbulence, acoustics and vortex structures. A good CFD setup avoids unnecessary numerical dissipation without compromising solution stability.

Numerical stability describes whether a simulation can be computed without uncontrolled growth of errors. It depends on mesh quality, time-step size, solver, discretization, boundary conditions and relaxation parameters. An unstable simulation often shows increasing residuals, non-physical values or solver failure. Numerical stability is a prerequisite for reliable results, but it does not guarantee physical correctness.

The Nusselt number is a dimensionless quantity for convective heat transfer at a surface. It compares actual heat transfer by flow with pure heat conduction. High Nusselt numbers indicate strong convective heat transfer. In CFD, the Nusselt number is used to evaluate cooling concepts, heat exchangers, wall heat transfer and similarity between different operating conditions.

OpenFOAM is an open-source CFD software library with many solvers, models and extension options. It is used in research, development and industry, especially when high adaptability, automation or custom model implementation is important. OpenFOAM usually requires more methodological and software expertise than many commercial integrated packages. In return, it offers great flexibility for special cases, scripting, HPC usage and customized CFD workflows.

OpenFOAM and Ansys Fluent are both powerful CFD tools, but they follow different philosophies. OpenFOAM is open source and offers great freedom in solvers, models, scripting and custom development. This makes it attractive for research, special applications, HPC environments and highly customized workflows. Ansys Fluent is commercial software with a mature user interface, extensive documentation, support structure and many established industrial models. Fluent is often easier to introduce into standardized industrial processes, while OpenFOAM requires more internal responsibility and CFD expertise. From an engineering point of view, the software name is less important than whether modelling, boundary conditions, meshing, verification and validation fit the task.

OpenFOAM and STAR-CCM+ differ mainly in openness, user concept and industrial process integration. OpenFOAM is open source, highly flexible and well suited when custom models, scripts or special numerical modifications are required. However, it usually requires more CFD methodology knowledge, Linux experience, programming understanding and internal process discipline. STAR-CCM+ is commercial and strongly integrates geometry preparation, meshing, solver, postprocessing and automation within one user interface. This supports robust industrial workflows, but comes with licence costs and less openness for deep code-level customization. The decision depends on whether maximum adaptability or an integrated productive engineering workflow is more important.

An outlet boundary condition describes how fluid leaves the computational domain. Common specifications include static pressure, mass flow rate or a dedicated backflow treatment. The outlet location should be chosen so that it does not artificially influence the flow region of interest. Especially in recirculating, unsteady or multi-outlet flows, the outlet condition is an important factor for stability and result quality.

Overset mesh is a meshing method in which multiple computational meshes overlap and are coupled through interfaces. This allows moving or rotating geometries to be simulated without remeshing the entire domain at every motion step. Typical applications include valves, piston motion, rotating wheels, flaps, rotors or relative motion between components. A clean overlap region, compatible cell sizes and stable interpolation between meshes are essential.

Overset mesh and sliding mesh are methods for moving or rotating geometries in CFD. In overset mesh, multiple meshes overlap and are coupled through interpolation. This is highly flexible for complex motion, large relative movement or components moving through a background mesh. In sliding mesh, mesh regions slide past each other at defined interfaces. This is particularly suitable for rotating machinery with a clear rotor-stator separation. Overset mesh offers more geometric freedom of motion, while sliding mesh is often more efficient and direct for periodic rotation.

A parameter study compares multiple simulations in which selected parameters are varied systematically. Examples include different mass flow rates, opening areas, wall temperatures, geometry variants or operating points. It shows how engineering targets such as pressure loss, cooling performance, force or temperature change. Parameter studies are an important tool for understanding relationships and making data-driven development decisions.

A periodic boundary condition couples two corresponding surfaces so that the flow repeats periodically. It is used when a geometry consists of repeating segments, such as blade passages, pipe sections, heat exchanger structures or rotating machinery. This allows only a representative sector to be calculated. The periodic assumption is only meaningful if the geometry and operating conditions are truly repetitive.

A plausibility check assesses whether CFD results are technically reasonable and physically understandable. It includes checking mass balances, pressure levels, flow directions, temperatures, forces and orders of magnitude. It is necessary even if residuals are low and the simulation is formally converged. In industrial practice, plausibility checks are an important safeguard against visually convincing but incorrect simulation results.

A tetrahedral mesh consists of tetrahedral cells and can be generated relatively easily for complex geometries. However, it may require more cells for the same accuracy and can be less favourable in terms of numerical diffusion or convergence. A polyhedral mesh consists of cells with many faces and can often exchange flow quantities more robustly with more neighbouring cells. In many industrial CFD applications, polyhedral meshes lead to more robust convergence and lower cell counts than purely tetrahedral meshes. Tetrahedral meshes remain useful for fast meshing, complex CAD geometries or as an intermediate step. The better choice depends on geometry, solver, boundary layer mesh and quality requirements.

The Prandtl number is a dimensionless quantity that compares momentum transport and heat transport in a fluid. It depends on viscosity, thermal conductivity, density and heat capacity. A low Prandtl number means that heat diffuses relatively quickly, while a high Prandtl number indicates stronger momentum transport relative to heat diffusion. In CFD, it is important for heat transfer, boundary layers and thermal similarity considerations.

A pressure-based solver uses pressure as the central coupling variable between the continuity and momentum equations. It is commonly used for incompressible and weakly compressible flows. Typical applications include cooling flows, internal flows, low-Mach aerodynamics and many industrial flow problems. For strongly compressible flows or shock waves, a density-based solver may be more suitable.

Pressure drop describes the pressure difference between two points or surfaces in a flow. In practice, the term is often used similarly to pressure loss. Technically, it is useful to distinguish the two: pressure drop is first of all a measurable difference, while pressure loss refers to the irreversible loss of flow energy. In CFD evaluations, it should be clearly defined whether static pressure, total pressure or an averaged pressure is being compared.

The pressure field describes the spatial distribution of static or total pressure in a flow. It is important for evaluating pressure losses, forces, lift, downforce, flow distribution and component loads. Pressure gradients drive flows and can also cause separation or unfavourable backflow. In technical CFD projects, the pressure field is often a central basis for optimization and design.

Pressure loss describes the irreversible loss of mechanical energy in a flow. It is caused by friction, flow deflection, area changes, internal components, turbulence or separation. In CFD projects, pressure loss is one of the most important target quantities for ducts, radiators, filters, valves, exhaust systems and intake systems. Low pressure loss reduces the power demand of pumps, fans or compressors.

Pressure loss and flow resistance are closely related, but not identical. Pressure loss describes the pressure or energy reduction of a flow between two points or across a component. Flow resistance describes more generally how strongly a component, duct or system restricts the motion of the fluid. High flow resistance often leads to high pressure loss, but the exact relationship depends on volume flow rate, density, geometry and operating condition. In CFD, pressure loss is usually evaluated as a concrete result quantity. Flow resistance is more the design-related or physical reason why this loss occurs.

A prism layer is an ordered cell layer directly adjacent to solid walls. It is used to resolve the near-wall flow region more accurately, especially boundary layers, wall friction and heat transfer. Prism layers are important in industrial CFD because many relevant losses and thermal effects occur directly at surfaces. Their height, number and growth rate must match the turbulence model and the intended y+ range.

RANS and LES are two important approaches for modelling turbulent flows in CFD. RANS averages turbulent fluctuations in time or statistically and models their effect using turbulence models. This makes RANS comparatively robust, fast and suitable for many industrial design studies. LES directly resolves large turbulent eddies and models only the smaller scales. As a result, LES can often represent unsteady vortex structures, separation, mixing and aeroacoustics more realistically, but it requires much more computational time and mesh resolution. For many development tasks, RANS is the economic standard, while LES is more often used for detailed analysis, validation, research or strongly unsteady phenomena.

The realizable k-epsilon model is an extension of the standard k-epsilon model. It includes improved modelling assumptions for turbulent viscosity and certain flow states, making it more robust for rotation, curvature or more complex shear flows. It remains comparatively efficient and is often used in industrial CFD applications. Nevertheless, for near-wall separation and strongly anisotropic turbulence, it should be critically compared with alternatives.

Recirculation describes a region where the flow locally moves backwards or circulates in a vortex zone. Such regions often occur behind edges, in dead zones, after sudden expansions or as a result of flow separation. Recirculation can promote mixing, but can also cause pressure loss, deposits, hot spots or poor flow distribution. In CFD, recirculation zones are important indicators of geometric weaknesses or unfavourable boundary conditions.

A dead-water region is an area with very low flow velocity or pronounced recirculation. Fluid exchange is poor in such regions, so heat, particles, gas bubbles or pollutants can accumulate. In cooling and flow distribution tasks, dead-water regions are often undesirable because they can cause hot spots or poor flushing. In CFD, they can be identified using velocity fields, streamlines and residence times.

A residual is a measure of how well the discretized equations are satisfied by the current solution. Decreasing residuals indicate that the numerical error within the equation solution is becoming smaller. However, residuals alone are not sufficient to judge the quality of a CFD simulation. Important engineering quantities such as pressure loss, force, mass flow rate or temperature must also be stable and physically plausible.

RANS stands for Reynolds-averaged Navier-Stokes and refers to an approach in which turbulent fluctuations are averaged in time or statistically. This means that not all eddies are calculated directly; instead, their mean effect is captured by turbulence models. RANS is widely used in industrial CFD because it is comparatively robust and computationally efficient. Typical applications include steady aerodynamics, pressure loss analysis, cooling and many design comparisons.

The Reynolds number is a dimensionless quantity describing the ratio of inertial forces to viscous forces. It helps assess whether a flow is more likely to be laminar, turbulent or transitional. The Reynolds number depends on velocity, characteristic length, density and viscosity. In CFD, it is important for turbulence modelling, similarity considerations, boundary layers and transferring test results to real systems.

The Reynolds stress model is a RANS turbulence model that solves the individual Reynolds stresses more directly than simple eddy viscosity models. This allows it to capture anisotropic turbulence, rotation, strong curvature and complex secondary flows more accurately. However, its computational cost and numerical sensitivity are higher than for k-epsilon or SST k-omega models. It is used when simpler turbulence models do not describe important flow phenomena sufficiently well.

A rotating reference frame describes the flow from the perspective of a rotating coordinate system. This allows rotating components such as fans, pump impellers or compressor wheels to often be approximated in a steady-state manner. Rotation is accounted for through additional apparent forces such as Coriolis and centrifugal forces. The method is efficient, but it does not capture true time-dependent rotor-stator interactions.

A segregated solver solves the equations sequentially rather than simultaneously. This often reduces memory demand and can be very efficient for many industrial applications. Coupling between the equations is established iteratively, for example between pressure and velocity. For strongly coupled or numerically difficult cases, however, a segregated solver may require more iterations.

A sensitivity analysis investigates how strongly a simulation result reacts to changes in individual input quantities. Typical parameters include boundary conditions, material data, mesh resolution, turbulence model, geometry variants or operating points. This makes it possible to identify which inputs are truly decisive for the result. Sensitivity analyses help develop robust designs and avoid unnecessary optimization of unimportant parameters.

Shear stress is a tangential stress caused by friction and velocity gradients in the fluid. It describes how strongly neighbouring fluid layers or the fluid and a wall act on each other. In fluid mechanics, it is important for friction losses, wall loading, boundary layers and viscous effects. In CFD, shear stress can be evaluated within the fluid or directly at walls.

Simcenter STAR-CCM+ is commercial CAE software from Siemens for CFD, multiphysics and automated simulation processes. It is widely used in industry for flow simulation, heat transfer, multiphase flow, moving meshes, aerodynamics and thermal management. A key advantage is the integrated workflow from geometry preparation and meshing to solving, postprocessing and automation. For complex development tasks, STAR-CCM+ is particularly useful when many variants need to be calculated robustly and reproducibly.

SimScale is a cloud-based simulation platform for CFD, structural mechanics and thermal analysis. It is accessed through a browser, reducing local hardware requirements and installation effort. SimScale is particularly suitable for standardized simulation tasks, quick feasibility studies, education and small to medium-sized development projects. For highly specialized models, deep automation or strongly customized workflows, traditional desktop or HPC-based CFD environments may be more suitable.

A single-phase flow consists of only one fluid phase, such as gas only or liquid only. Compared with multiphase flows, it is usually easier to model and numerically more robust. Many typical CFD applications, such as airflow around vehicles, water flow in channels or oil flow in pipes, can be treated as single-phase flow. The assumption is no longer sufficient when droplets, bubbles, particles, evaporation or condensation are relevant.

A sliding mesh is used when one part of the computational mesh moves relative to another, for example in rotating components. Coupling is performed through an interface where flow quantities are transferred between moving mesh regions. Unlike steady substitute models, a sliding mesh can capture time-dependent rotor-stator interactions. It is therefore important for fans, pumps, turbines, compressors and other rotating machinery.

The sliding mesh method calculates the real relative motion between moving and stationary mesh regions in a time-dependent way. It is more accurate than steady MRF approaches when the position of rotor and stator, blades, flaps or openings affects the flow over time. However, it requires transient calculations and more computational time. Typical applications include fans, pumps, turbomachinery, rotating wheels and periodically moving components.

The solver is the numerical algorithm that computes the equation systems of a CFD simulation. It determines how pressure, velocity, temperature, turbulence quantities and other variables are coupled and solved. The choice of solver strongly depends on flow type, compressibility, Mach number, mesh quality and stability requirements. An unsuitable solver can lead to poor convergence, excessive runtime or unreliable results.

Species transport describes the transport of different chemical components or species within a flow. Examples include air components, fuel vapour, hydrogen, exhaust gas components, pollutants or concentrations in mixing processes. In CFD, species transport is governed by convection, diffusion and, where relevant, chemical reactions. It is important for combustion, gas mixing, emissions, leakage analysis and many thermo-fluid processes.

The specific dissipation rate describes the dissipation of turbulent energy relative to turbulent kinetic energy. It is usually denoted by omega and is a central variable in the k-omega and SST k-omega models. This quantity is particularly useful for modelling near-wall flows and boundary layers. In CFD, it influences how turbulence is modelled near walls, in shear layers and in separated regions.

The SST k-omega model combines the advantages of the k-omega model near walls with k-epsilon-like behaviour in the free-stream region. SST stands for Shear Stress Transport and improves the prediction of separation under adverse pressure gradients. It is one of the most widely used RANS models in industrial CFD. Typical applications include aerodynamics, turbomachinery, internal flows, cooling systems and pressure loss analysis.

STAR-CCM+ is the commonly used short name for Simcenter STAR-CCM+. The software is used for industrial 3D CFD simulations, for example in automotive engineering, motorsport, energy, mechanical engineering and product development. It supports RANS, LES, multiphase models, CHT, overset mesh, sliding mesh and automated design studies. For Felsaris, STAR-CCM+ is a central tool for efficiently handling complex flow and thermal management tasks.

STAR-CCM+ and Ansys Fluent are two of the most important commercial CFD software packages in industry. Both can model flow, heat transfer, turbulence, multiphase flow and many specialized physics. STAR-CCM+ is particularly strong in integrated workflows, automated meshing, variant studies and end-to-end process control within one environment. Ansys Fluent is very widely established, offers a broad range of models and is strong in many classical CFD application areas. The better choice does not depend on a generic software ranking, but on application, licensing environment, team expertise, automation needs and validation history. For professional CFD, the key is whether the workflow is reproducible, validated and suitable for the specific engineering question.

A steady-state simulation calculates a time-independent or time-averaged state. It is suitable when the relevant result quantities are not dominated by time-dependent effects. Typical applications include many pressure loss analyses, average flow distribution, steady cooling cases or initial design comparisons. In strongly pulsating, separating or moving flows, however, a steady-state simulation may miss important effects.

A steady-state simulation calculates a time-independent or time-averaged state. It is efficient and well suited for many pressure loss analyses, average flow distributions, steady cooling cases and initial design comparisons. A transient simulation calculates the time-dependent evolution of a flow, temperature distribution or system response. It is required when vortex shedding, pulsations, moving components, pressure waves, load steps or warm-up processes are relevant. Steady-state simulation is not automatically inferior; it is often the correct simplification when the target quantity is not significantly time-dependent. Transient simulation is more informative for dynamic processes, but requires much more computational time and appropriate time steps.

A stopping criterion defines when a simulation or iterative process is terminated. Typical criteria include sufficiently low residuals, stable monitored quantities, reached physical time or a maximum number of iterations. In industrial CFD, stopping criteria should be justified not only mathematically but also from an engineering perspective. The key question is whether the relevant result quantities are stable and reliable enough for the task.

The Strouhal number is a dimensionless quantity for periodic unsteady flow phenomena. It relates frequency, characteristic length and flow velocity. Typical applications include vortex shedding, vibration excitation, pulsating flows and aeroacoustic effects. In CFD and testing, the Strouhal number helps compare unsteady phenomena across different scales or velocities.

A surface mesh represents the geometry of a component or computational domain using small surface cells. It captures edges, curvatures, openings, wall surfaces and CAD details for the CFD model. A clean surface mesh is the basis for a stable and high-quality volume mesh. Errors in the surface mesh, such as gaps, intersections or highly distorted faces, can compromise the entire simulation.

A symmetry plane uses geometric and physical symmetry so that only part of the system needs to be simulated. This can significantly reduce cell count, runtime and memory demand. A symmetry plane is only valid if the geometry, boundary conditions and expected flow behaviour are truly symmetric. If used incorrectly, it can artificially suppress asymmetric effects such as vortices, separation or cross-flow.

The temperature field describes the spatial distribution of temperature within a computational domain. In CFD simulations, it shows where components, fluids or interfaces are hot or cold. It is especially important for cooling, heat exchangers, batteries, engines, power electronics and thermally loaded components. The temperature field is used to assess hot spots, temperature gradients and the effectiveness of a cooling concept.

A thermal simulation calculates temperatures, heat flows and heat distribution in components or complete systems. It is used to identify hot spots, thermal overload, warm-up behaviour or cooling demand. When coupled with CFD, it can also assess the influence of air or liquid flow on the temperature field. Typical applications include battery cooling, power electronics, engine cooling, heat exchangers and vehicle thermal management.

The time step is the time interval between two calculated states in a transient CFD simulation. It determines how finely time-dependent phenomena such as pressure waves, vortex shedding, pulsations or warm-up processes are resolved. A time step that is too large can smooth out important dynamics or cause numerical instability. A time step that is too small increases runtime without necessarily improving the engineering value of the results.

The time-step size describes the size of a single time step in a time-dependent simulation. It must match the flow velocity, cell size and the relevant physical time scale. Especially for moving geometries, acoustic effects, rapid load changes or unsteady vortices, the choice of time-step size is critical. It is often assessed using the CFL number, convergence within each time step and comparison with test data.

A transient simulation calculates the time-dependent evolution of a flow, temperature distribution or system response. In practice, the term is often used similarly to unsteady simulation, but it particularly emphasizes transitions between states. Examples include warm-up processes, load steps, pressure build-up, start-up behaviour or time-dependent cooling. The time-step size and initial condition have a strong influence on stability and the significance of the results.

Transitional flow describes the regime between laminar and turbulent flow. In this state, local disturbances, roughness, pressure gradients or geometric effects can determine when and where turbulence develops. Transitional flows are numerically challenging because simple assumptions of fully laminar or fully turbulent flow are often insufficient. They are important in aerodynamics, boundary layers, turbomachinery and heat transfer.

The turbulence field describes the spatial distribution of turbulent quantities in a flow. These include turbulent kinetic energy, turbulent viscosity, dissipation rate or specific dissipation rate. In industrial CFD, turbulence is usually not fully resolved but modelled using turbulence models. The turbulence field influences mixing, momentum exchange, heat transfer, pressure loss and flow separation.

A turbulence model represents the effect of turbulent fluctuations without directly resolving all turbulent scales. It is necessary because real technical flows are often turbulent and resolving all eddies directly would be extremely expensive. Typical turbulence model classes include RANS, LES, DES and hybrid approaches. The choice of turbulence model often affects pressure loss, separation, heat transfer, mixing and forces more strongly than many other simulation parameters.

Turbulent flow is characterized by chaotic, three-dimensional and time-dependent motion. It leads to stronger mixing and higher momentum and heat transfer, but often also to higher pressure loss. Most technical flows in vehicles, pipes, radiators, engines and industrial systems are turbulent. In CFD, turbulence is usually described using RANS, LES or hybrid models.

Turbulent kinetic energy describes the kinetic energy contained in turbulent velocity fluctuations. It is usually denoted by k and is a central variable in many turbulence models, such as k-epsilon and k-omega. High values indicate regions of intense turbulence, strong shear, separation or mixing. In CFD, it is important for evaluating turbulence level, near-wall behaviour, wakes and mixing processes.

Turbulent viscosity describes the modelled additional viscosity caused by turbulent mixing. It is closely related to eddy viscosity and is often used almost synonymously in CFD software. A high turbulent viscosity means that momentum, heat or species are strongly exchanged by turbulence. In postprocessing, it helps identify regions of intense turbulent mixing or potential modelling issues.

A two-phase flow is a flow containing exactly two phases, such as gas and liquid or liquid and solid particles. Typical examples include air bubbles in coolant, oil-air mixtures, droplets in gas flows or water-steam flows. In CFD, modelling is challenging because phase fractions, interfaces, slip velocities and interactions between the phases must be considered. Depending on the application, Eulerian-Eulerian, Eulerian-Lagrangian or volume-of-fluid approaches may be used.

Under-relaxation is a numerical damping technique in which changes in the solution per iteration are deliberately limited. It is used to stabilize unstable or strongly oscillating solution behaviour. Especially in steady-state simulations with difficult coupling, under-relaxation can improve convergence. Too much under-relaxation makes the simulation slow, while too little can lead to divergence.

A steady flow does not change with time at a fixed location, or changes only negligibly. An unsteady flow shows time-dependent changes in velocity, pressure, temperature or turbulent structures. In practice, real flows may be unsteady even though a steady-state simulation can still be useful for certain averaged quantities. The key question is whether the time fluctuations influence the engineering target. Examples of unsteady flows include vortex shedding, pulsating exhaust flow, pump flow, rotor-stator interaction and load changes. The difference therefore describes the physics of the flow, not only the chosen simulation setup.

URANS stands for unsteady RANS and extends the RANS approach to time-dependent flows. Turbulent fluctuations are still modelled, but large unsteady changes in the mean flow field are calculated in time. URANS is used when steady RANS simulations do not sufficiently capture important time-dependent effects. For strongly unsteady, broadband turbulence or complex vortex structures, LES or a hybrid approach may be more suitable.

An unsteady simulation describes a flow whose quantities change over time. It is required when vortex shedding, pulsations, pressure waves, moving components, load changes or periodic phenomena influence the result. Compared with a steady-state simulation, it is usually much more computationally expensive. In return, it provides time-dependent information that is essential for many real operating conditions.

URANS is a time-dependent extension of the RANS approach. Large changes in the averaged flow field are calculated transiently, while turbulent fluctuations are still modelled. LES, on the other hand, directly resolves large turbulent eddies and therefore provides higher temporal and spatial detail. URANS is significantly cheaper and can be useful when periodic or dominant unsteady effects are of interest, such as pulsations, coarse vortex shedding or rotor-stator effects. LES is more suitable when broadband turbulence, complex mixing, wake structures or aeroacoustics are the main focus. The difference is therefore not just steady versus transient, but which turbulent structures are actually resolved.

Validation checks whether a simulation model represents the real physics with sufficient accuracy. CFD results are compared with measurement data, test bench data, wind tunnel data or known reference cases. Validation answers the question of whether the right model is being used for the real engineering task. It is especially important when simulation results are used for development decisions, design or verification evidence.

The velocity field shows the direction and magnitude of the local flow velocity throughout the computational domain. It reveals how the fluid moves through channels, radiators, openings, engine bay regions or around external surfaces. The velocity field can be used to identify acceleration, dead zones, recirculation and non-uniform flow distribution. It is one of the most important result quantities in any CFD simulation.

Verification checks whether the simulation model has been solved correctly from a numerical point of view. It concerns aspects such as mesh quality, convergence, time step, discretization and possible user or implementation errors. Verification answers the question of whether the equations were solved correctly. It differs from validation, because a numerically correct model may still be physically unsuitable.

Verification and validation are often confused, but they answer different questions. Verification checks whether the simulation model has been solved correctly from a numerical perspective. This includes mesh quality, convergence, time step, discretization, boundary conditions and user errors. Validation checks whether the model represents the real physics with sufficient accuracy. This is done by comparing simulation results with measurement data, test bench data, wind tunnel values or reference cases. In short: verification asks whether the calculation was performed correctly, while validation asks whether the right physical model was used for reality.

The volume flow rate describes how much fluid volume passes through a surface or pipe per unit time. It is commonly given in cubic metres per second or litres per minute. Unlike mass flow rate, volume flow rate in gases depends strongly on pressure, temperature and density. In practice, it is often used for fans, pumps, cooling air paths, pipe systems and test bench conditions.

A volume mesh fills the computational domain with three-dimensional cells. The flow equations are solved numerically within these cells, and the result quantities are computed there. Cell shape and cell distribution must match the geometry and expected flow behaviour. Targeted mesh refinement is especially important in boundary layers, narrow gaps, flow deflections or regions with strong gradients.

A vortex is a rotating flow structure in which fluid circulates around a local axis. Vortices arise from shear, separation, edges, rotating components or flow deflections. They can improve mixing and heat transfer, but can also cause pressure loss, vibrations, noise or unsteady loads. In CFD, vortices are analysed using velocity fields, vorticity, the Q-criterion or streamlines.

Vortex shedding describes the periodic or irregular release of vortices behind a body or at an edge. It can cause fluctuating forces, pressure pulsations, noise and vibration excitation. Typical examples include flow around cylinders, mirrors, guide vanes, suspension components, radiator structures or exhaust components. In CFD, analysing vortex shedding usually requires a transient simulation with sufficient mesh resolution and an appropriate time-step size.

A vortex structure describes the spatial shape and organization of vortices in a flow. Examples include streamwise vortices, spanwise vortices, tip vortices, separation vortices or complex vortex systems. Vortex structures are important because they transport momentum, heat and species and shape many unsteady effects. In CFD, they help identify the causes of pressure losses, noise, mixing behaviour or aerodynamic forces.

The wake is the disturbed flow region behind a body, component or obstacle. It often contains reduced velocities, vortices, turbulence and pressure losses. In aerodynamics, the wake influences drag, downforce, side forces and downstream components. In CFD, correctly resolving the wake is important for vehicles, fans, turbomachinery, radiators, guide vanes and all externally or internally exposed components.

A wall boundary condition describes the behaviour of the fluid at solid surfaces. It can define no-slip behaviour, wall motion, roughness, wall temperature, heat flux or thermal coupling. In CFD, the wall boundary condition is especially important because wall friction, pressure loss and heat transfer occur directly at surfaces. For moving walls, rotating components or thermally active walls, it must be defined carefully.

The wall distance y+ is a dimensionless quantity describing the location of the first cell centre relative to the wall. It relates the physical wall distance to local flow velocity, density and viscosity. In CFD, y+ indicates whether the near-wall flow is resolved directly or should be modelled using a wall function. An unsuitable y+ range can significantly distort wall friction, heat transfer and flow separation behaviour.

A wall function is a model for the near-wall region of a turbulent flow. It replaces the very fine direct resolution of the viscous sublayer with an empirical or semi-empirical wall treatment. This can significantly reduce the cell count and make industrial CFD simulations more efficient. Wall functions are only reliable when the mesh, y+ range, turbulence model and flow situation are consistent.

A wall function models the near-wall region of a turbulent flow without fully resolving the viscous sublayer with very fine cells. This reduces mesh effort and is economically reasonable for many industrial RANS simulations. A wall-resolved simulation represents the near-wall region much more finely and can calculate wall friction, heat transfer and separation more directly. However, it requires much smaller first-cell heights, more prism layers and significantly more computational time. Wall functions are efficient, but more model-dependent. Wall-resolved simulations are more accurate for critical wall phenomena, but require a consistently suitable mesh and model setup.

Wall shear stress is the shear stress directly at a solid surface. It is caused by the no-slip condition and the velocity gradient in the near-wall boundary layer. In CFD, it is important for friction drag, pressure loss, heat transfer, erosion, deposits and flow separation. Accurate calculation of wall shear stress requires appropriate boundary layer resolution, y+ values and wall treatment.