CFD analysis is an indispensable tool that allows engineers to visualize the behavior of fluid or heat flow within their design, helping reduce the need for physical experiments.
For successful CFD analysis, one needs a strong background in fluid dynamics and heat transfer physics as well as knowledge of how to use appropriate software packages.
Computational fluid dynamics (CFD) software enables engineers to analyze the effects of aerodynamics, thermal heat transfer and turbulent flow on 3D models without physical testing – saving companies design cycles and speeding product launch times. For optimal decision making it’s essential that users of CFD understand its functionality before beginning use – only then can you truly make informed choices based on an in-depth knowledge of what’s happening behind the scenes.
Pre-processing is one of the key steps of CFD analysis, as it prepares data for further examination. This includes identifying and sorting missing fields due to human input errors or improper data formats that result in incomplete sets; data scientists then have to decide whether it would be better to discard records with gaps or fill them in using probability values.
An understanding of the physics involved is also vital, enabling you to make accurate predictions regarding how a system will behave. For instance, when modeling wind turbine airflow it is vital to model both its inlet and exhaust systems accurately in order to predict how much power can be produced and for how long.
An essential step of pre-processing is creating an accurate mesh. A quality mesh can help capture the physics of the problem while still being coarse enough that accuracy issues arise, and this can be measured using various parameters like aspect ratio, Jacobian ratio, skewness angle or warping factor – it all adds up to high-fidelity results in CFD simulations.
Prior to beginning any CFD analysis, it is vitally important that you fully grasp the physics of the problem at hand. CFD models use Partial Differential Equations and Navier Stokes equations which govern their behaviour; without understanding these governing equations accurately enough, you won’t be able to obtain accurate predictions.
If you want to expand your understanding of CFD, there are plenty of resources online that can help. Courses, webinars and blogs on the topic provide essential instruction. But be wary not to become overwhelmed with too much information at once: start off slowly by learning the fundamentals and working on small projects, before moving on to larger ones until you are comfortable using software.
To solve the fluid flow equations that will govern your simulation, the physical domain must first be broken up into discrete geometric and topological cells – known as mesh generation – using various techniques depending on its complexity. Patch conforming is often chosen due to its rapid convergence rate; using both heuristics and algorithms it produces an initial mesh which can then be further iterated on.
Delaunay refinement process offers another approach, using an algorithm that gradually increases resolution over a particular area of domain. This technique can reduce error in solution fields but increases computational cost; to maximize performance it is best to keep as few mesh cells as possible in use.
There are various strategies available for accomplishing this, such as limiting the number of mesh layers and cell sizes. This can help mitigate the effects of turbulence that may be more intense in certain regions of your model. Masonry CFD models allow you to do this by restricting inclusion contours; this will cause bulk mesh conform to those contours instead of creating unnecessary loops that create unnecessary disturbances in simulation results.
One third approach involves employing a custom sizing function such as k-Lipschitz to tailor mesh density per zone based on physical properties that need to be captured.
An additional method for controlling mesh generation is through the MeshDomainWithFeatures_3 concept, which adds member functions to an incidence graph allowing query points to determine whether they intersect boundary surfaces and, if so, in which subdomain they lie. This tool ensures that all features of an input 3D complex are accurately represented in its final mesh – including all 1-dimensional features like corners, curves and surface patches.
CFD analysis is an iterative process wherein the solution field evolves continuously in response to applied boundary conditions. Solvers stop when residuals have stabilized, signifying that convergence has occurred, with the number of iterations required determined by a convergence criterion that depends on physical factors, meshing requirements, and any convergence criteria being chosen appropriately – but selecting an incorrect convergence criteria could result in non-physical simulation results or inaccurate simulation simulation results.
At the start of every CFD simulation, selecting an appropriate solver is essential to getting it right. Your choice should depend on whether your flow is laminar or turbulent and what physics needs to be captured; for instance if compressible flow must be captured using pressure-based solvers; otherwise a density-based solver should suffice.
Once a solver has been selected, a preconditioning algorithm should be selected. This mathematical method serves to enhance approximations by eliminating oscillations in an equation system and improve approximation of solution approximations. Preconditioners vary based on problem type, physical component involved and computing capabilities of computer. An appropriate preconditioner choice can significantly decrease iterations required for convergence.
Once preconditioning is complete, a turbulence model must be chosen. Turbulence models aim to capture the physics behind physical systems by applying mathematics-based formulae; such software exists commercially and can be modified accordingly for specific applications; when combined with an adequate grid they can achieve remarkable accuracies.
CFD simulation is an invaluable tool that enables engineers to test and assess their designs for heat transfer, fluid flow, non-Newtonian materials and turbulence – often at lower costs than physical testing alone. Unfortunately, however, conducting CFD simulations requires considerable time and computational power, making this option unavailable in every situation.
Grid and timestep selection
Once your model is meshed and solver selected, the next step in CFD analysis is identifying an appropriate grid resolution and time step. This decision should take into account several factors including desired spatial resolution of flow, geometry of problem, level of accuracy needed and required computational speed. Choosing an optimum grid resolution can be challenging and it is essential that a balance be struck between resolution and computational speed for maximum results.
Richardson extrapolation can be used to estimate the order of convergence of solutions, providing an estimation of their grid size requirements. It works by computing higher-order approximations of continuum values from lower-order discrete values. As an output from this method comes an estimated fractional error E1 which provides an accurate measure of spatial discretization errors.
When choosing the appropriate time step, it is crucial to keep in mind the Courant-Friedrichs-Lewy condition (CFL). This condition states that numerical solutions cannot move faster than the group speed of waves at mesh scale. While theoretically achievable, in practice such a limit can often prove challenging due to boundary conditions, models, or geometry which limit actual convergence orders.
CFL conditions can also help determine an ideal size of time step for diffusion-controlled models, since their product must remain below 0.3 in order to remain stable.
Time steps of 1 cm or greater will not only decrease simulation times but will also ensure accurate results. To maintain high levels of precision for results analysis, time steps should at least equal the size of each cell within your mesh.
CFD analysis is an incredible tool that can be used to simulate many physical problems, from fluid flow and heat transfer, to deformation. CFD analyses can also be utilized for simulating mechanical devices like automobiles, aircraft and wind turbines; CFD can predict forces, velocities and temperature distributions across these structures.