Machine learning driven interpretation of computational fluid dynamics simulations to develop student intuition

Employers need engineers capable of leveraging CFD simulations to make intelligent design decisions, but undergraduate computational fluid dynamics (CFD) courses are not adequately preparing students for this type of work. CFD courses commonly familiarize students with topics, such as method derivation, domain creation, boundary conditions, mesh convergence, turbulence models, numerical convergence, and error analysis. This approach is an effective way to teach novices how CFD software works and how to prepare CFD analyses. However, it neglects development of higher level CFD skills and intuition important to engineering analysis and design, deferring this task to future study and training. This paper introduces the "Machine Learning Driven Interpretation of Fluid Dynamics Simulations to Develop Student Intuition" (MIFoS) software, a program designed to help CFD novices develop the high-level skills and intuition that employers need in their engineers. A data-driven approach was used to create the MIFoS software, which allows the submission of arbitrary geometries, automates an external flow simulation, and returns expert-level graphical interpretation of simulation data. MIFoS's automated CFD simulation and feedback space allows novices to experiment with expert-level suggestions on their own designs, enabling the skill and intuition development typically gained through years of study, practice, and expert guidance.