Catfish Drone CFD Simulation

Drone with vertical takeoff and horizontal movement using propulsive propeller simulated with ANSYS CFX. For turbulence scale adaptive simulation (SAS) model is used. Around 17.3 Million grid cells are used in this case. Immersed body solver is used to solve the simulation of this concept drone.

How to improve a CFD Simulation

Improving a CFD simulation for a concept drone like the Catfish with vertical takeoff and horizontal movement using ANSYS CFX, the Scale Adaptive Simulation (SAS) model, and an immersed body solver can be a challenging and iterative process. Here are some potential improvements you can consider for your next CFD simulation:

1. Refine Grid Resolution: While 17.3 million grid cells is already a substantial number, depending on the complexity of the drone’s geometry and the level of detail you need, you might consider further grid refinement in critical regions. Adaptive meshing techniques can be employed to allocate grid cells where they are needed most, improving accuracy without significantly increasing computational requirements.
2. Turbulence Model Selection: Reevaluate the choice of turbulence model. SAS is an adaptive model, but for certain flow conditions, other models like Large Eddy Simulation (LES) or Detached Eddy Simulation (DES) might provide more accurate results. It’s essential to select a turbulence model that matches the physics of your problem.
3. Boundary Condition Sensitivity: Carefully review and validate the boundary conditions. The accuracy of your simulation can be highly sensitive to boundary conditions, so ensure they are representative of the real-world scenario you are simulating.
4. Physics Models: Depending on the specific aspects of the drone’s behavior you want to investigate, consider including additional physics models. For example, if you’re interested in heat transfer effects from the drone’s propulsion system, incorporate conjugate heat transfer modeling.
5. Mesh Independence Study: Perform a mesh independence study to determine if your results are sensitive to grid resolution. This involves running the simulation with different grid sizes and analyzing how the results change. It can help identify the minimum grid resolution needed for reliable results.
6. Validation: If possible, validate your CFD results with experimental data or real-world measurements. This step is critical for ensuring the accuracy of your simulations and gaining confidence in the results.
7. Parallelization: Optimize the parallelization of your simulation to make efficient use of available computational resources. This can significantly reduce simulation runtimes for large-scale problems.
8. Parametric Studies: If you are exploring design variations of the drone, consider conducting parametric studies. This involves systematically varying design parameters (e.g., wing shape, propeller size) to understand their impact on performance.
9. Post-Processing Automation: Develop automated post-processing routines to efficiently extract and analyze the data from your simulation. Visualization tools can help you gain insights into flow patterns and performance metrics.
10. Documentation: Maintain comprehensive documentation of your simulation setup, including grid generation, boundary conditions, and solver settings. This makes it easier to reproduce and refine the simulation in the future.
11. Collaboration: Consider collaborating with experts in CFD, aerospace engineering, and drone design. Interdisciplinary input can lead to more comprehensive and insightful simulations.

Remember that CFD simulations are an iterative process, and improvements often come through multiple iterations and careful analysis of results. Each simulation should build upon the knowledge gained from previous ones, leading to increasingly accurate and valuable insights into the behavior of your concept drone.