What goes into a well-executed computational fluid dynamics simulation?
What you get out of a CFD study will only be as good as what you put into it. Poor or incomplete data will generate a poor or incomplete model, which will impact the quality of your results and limit your ability to apply those results to the real world.
This second “must have” element of a strong CFD project piggybacks on the first one. Your CFD team needs to understand the meaning and significance of the physical laws and equations that govern the conservation of mass, momentum, and energy, and they need experience in deploying that understanding to create an appropriately detailed computational domain—one that’s supported by the correct numerical models with consistent boundary conditions.
Teams that are new to CFD analysis may assume that sophisticated software will compensate for their lack of understanding or experience, but software alone isn’t capable of making the decisions that are required as part of a CFD study. The engineer must know how to select the correct boundary conditions and physical models, make the right limiting assumptions, generate a high-quality mesh that accurately resolves important flow characteristics, choose appropriate numerical algorithms, and on and on.
To get each of these decisions right, teams must rely on a deep well of knowledge and experience. This is the only way to understand what is behind the “black box” of the CFD solver.
More than just CFD modeling
CFD is not a tool to use in isolation. Its success depends on its synergistic relationship with the other two philosophical approaches of fluid mechanics, namely, theory and experimentation. A skillful CFD engineer looks for opportunities to use these three tools in combination, creating the best possible conditions for finding, analyzing, and applying accurate solutions to problems.
- Based in the laws of physics and emerging from a long history of assumptions and postulations, theorems provide a macro perspective that complements the granularity of a computational simulation.
- Theoretical solutions alone are inadequate in the face of complex engineering problems.
Experimentation and validation
- Experimental analysis provides a method for validating the results of a computational simulation and allows us to quantify possible errors in the model.
- Solving a complex problem through experimentation alone can be an expensive and time-consuming endeavor. Depending on the scenario, experimentation can also tie up valuable space and equipment, which limits manufacturing throughput. To address this challenge:
- The team can conduct a thorough literature review to uncover information in the public domain, which they can then leverage to validate the CFD model.
- Experiments need not be conducted on the actual or full-scale problem that will be simulated via CFD. A scaled-down version conducted in a lab will typically generate enough information to validate the CFD model.
Modeling and simulation
- As one technique within the larger category of modeling and simulation, CFD allows project teams to telescope in from the theoretical “macro” view in order to thoroughly analyze a granular, three-dimensional view of the problem from any point in time or space.
- When they use CFD in isolation, project teams have no way to evaluate its “truthfulness,” leaving them vulnerable to hidden errors. Teams can avoid this mistake by grounding their approach in theory and by validating their model against experimental data, where possible.
What are the typical steps involved in the computational fluid dynamics process?
CFD step 1: pre-processing
In this step, the CFD team defines the engineering problem and creates the computational model in which it will interrogate and solve that problem.
Define the problem, establish the modeling objectives, and gather data.
In this initial phase, CFD team members work in collaboration with the project owner to understand the problem or the analytical opportunity that will be the focus of their study. At this early stage, the team confirms that CFD is the appropriate technique, and they determine whether a steady-state or transient analysis is most suitable (see explanation above).
Team members then establish their modeling approach and its objectives. This is a period of important due diligence; the team conducts research, gathers necessary files, and collects the data they’ll need to build and refine their model.
In some cases, such as a renovation or a process optimization exercise, the project owner may have that data available. Occasionally, the CFD team conducts a site visit to gather physical measurements in the absence of a CAD model.
In the case of a greenfield project, the CFD team works closely with the project architects and engineers to develop assumptions and generate the necessary data based on available drawings and designs.
Build the model.
The CFD team translates all of this project data into a computational domain, called a CAD (computer-aided design) model. This is the digital environment in which team members will eventually carry out their solution. Sometimes this entails cleaning and refining an existing CAD model; in more complex or custom scenarios, the team may have to build a model from the ground up.
Defining the model’s boundary conditions is an important component of this work. The CFD team must identify any physical boundaries in the domain, such as a wall, a roof, a person, or an inlet or outlet, and then formulate those boundaries into data that’s propagated throughout the model’s geometry. This helps the team build a picture of velocity, pressure, and/or temperature gradients across the computational domain.