Python based API to post-process CFD data
Harpreet Singh Chhabra, Dilip Kalagotla, Paul D. Orkwis · AIAA SCITECH 2023 Forum · 2023
View Video Presentation: https://doi.org/10.2514/6.2023-1225.vid A growing interest in finding new ways to solve Computational Fluid Dynamics (CFD) problems has motivated researchers to employ a data science approach for numerical analysis. This paper discusses an ongoing project in the Gas Turbine Simulation Laboratory (GTSL) at the University of Cincinnati (UC) to support CFD data interaction and manipulation needs of several projects. As most of these projects are based on machine learning (ML) and Python is widely used in data science, the language choice makes the advanced programming interface (API) more user-friendly. An extensive discussion is provided on the design and the simple use of the API. A variety of algorithms for reading, writing, converting file formats, data extraction, splitting blocks, in-line visualization, and computing variables from CFD data were rewritten in Python to be more efficient. The paper discusses current functionalities and provides a roadmap for developing an open-source library for the CFD community that can handle structured meshes to facilitate data manipulation and visualization.