Adaptive Meshing in SOLIDWORKS Flow Simulation

Introduction

Simulation holds a crucial place in engineering and product design, with meshing at its core. Traditional meshing methods are often time-consuming. However, within SOLIDWORKS Flow Simulation has the capability of adaptive meshing. In this blog, we’ll take a more in-depth look at this advantageous feature.

How does it work?

SOLIDWORKS Flow Simulation utilizes the Finite Volume approach, wherein the 3D model and fluid space remain constant while the computational mesh, typically a Cartesian grid, dynamically adjusts and refines during the calculation process, eliminating the need to restart from the beginning. This approach incorporates solution-adaptive meshing, which involves dividing mesh cells in regions with high flow gradients and merging cells in areas characterized by low flow gradients.

Let’s look at adaptive meshing with an example of external analysis of a cylinder, SOLIDWORKS Flow Simulation tutorial B2 (Help > SOLIDWORKS Simulation > Flow Simulation Tutorials). The default mesh settings give decent results, but it’s clear that more refinement would be helpful to capture the pressure and velocity gradient more accurately.

Initial Mesh Generation: The simulation begins with the creation of an initial mesh, typically uniform and covering the entire computational domain. This mesh forms the foundation for the fluid flow analysis. Then, the solver initiates the simulation based on this initial mesh, calculating fluid flow behaviors within the computational domain.

Figure 1: Initial global mesh

Adaptive Meshing Criteria: As the calculations proceed, the software continuously monitors the solution, checking predefined criteria. These criteria encompass factors like velocity gradients, pressure gradients, or user-defined parameters. This dynamic assessment helps identify regions within the domain that require mesh adjustment.

Mesh Adaptation: When the software identifies areas that require greater precision, it automatically refines the mesh by subdividing cells into smaller ones. Conversely, in regions where the predefined criteria are satisfied or exceeded, the software can coarsen the mesh to lessen the computational burden. This adaptation process is important for achieving greater accuracy.

Iterative Process: The simulation iterates and adapts the mesh as required until specific convergence criteria are met, or a predetermined maximum number of iterations is achieved. This iterative refinement process ensures that the mesh is continually optimized for accuracy.

Figure 2: Pressure plot for initial mesh

Refinement Level: Users can define how many times the initial cells can be split to achieve the desired refinement criteria. A higher level = more thorough mesh refinement. Here, I have selected “level = 2”, meaning each cell may be refined up to two times.

Approximate Maximum Cells: To manage computational resources efficiently, users can set a limit on the total number of cells created during the refinement process. This prevents exceeding the physical RAM available during the calculations.

Figure 3: Calculation control settings for mesh refinement

Refinement Strategy: Flow Simulation offers 3 refinement strategies:

• Periodic Refinement: Users can specify when periodic refinements occur during the calculation. The relaxation interval specifies the additional length of calculation required after the last refinement occurs. This approach is useful for scenarios where mesh changes are predictable.
• Tabular Refinement: For improved flexibility, users can define refinements to occur at specific times or iterations. This is valuable when mesh changes are less predictable.
• Manual Only Refinement: Users can manually trigger mesh refinement in the solver window, when they aren’t sure how long it will take for a particular goal to reach a stable value allowing for precise control. Here, refinement doesn’t occur automatically based on predefined criteria. Instead, the user must explicitly trigger the refinement during the simulation when they feel it is necessary. This strategy provides the most amount of control over refinement, allowing users to decide precisely when and where to refine the mesh.

Figure 4: Manual mesh refinement for advanced control

(Hammer icon highlighted in red)

Figure 5: Mesh around the cylinder after 2 levels of manual refinement

(Observe the increase in the number of cells from Figure 4 to Figure 5 Highlighted in the blue box)

Figure 6: Pressure plot after solution adaptive mesh refinement

Visualizing the Impact:
Understanding the impact of adaptive meshing is crucial for gaining insights into simulation accuracy. SOLIDWORKS Flow Simulation provides several tools to facilitate this:

• Goal Plots: Users can create goal plots to visualize how mesh refinement influences simulation results. These goal plots allow for tracking changes in parameters like temperature or velocity over time or iterations.
• Immediate Feedback: As the simulation unfolds, users can observe precisely when and how mesh refinement occurs. This real-time feedback provides a clear understanding of the ongoing accuracy improvements.
• Improved Results: The most apparent visual impact of adaptive meshing is seen in the enhanced simulation results. Comparing the initial results with those obtained after adaptive meshing reveals the significant accuracy improvements, particularly in critical areas of the domain.

Conclusion
Adaptive meshing in SOLIDWORKS Flow Simulation is a significant advancement in fluid and gas simulations. This dynamic process, customizable and efficient, offers accurate results in less time. Whether for electronics cooling, piping systems, or complex automotive components, solution-adaptive meshing transforms your simulation workflow. It ensures accurate refinement where needed, improving designs and product reliability. Understanding its visual impact empowers users to make informed decisions and achieve simulation accuracy.

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