🤖 AI Summary
Existing open-source multiphysics simulation tools (e.g., openCFS) lack efficient and flexible Python-based data processing capabilities for coupled-field problems such as aeroacoustics, resulting in fragmented pre- and post-processing ecosystems. To address this, we propose the first lightweight, extensible, Python-native data framework tailored for openCFS. It unifies parsing of openCFS’s native XML configuration and finite-element data formats, and integrates HDF5 I/O, NumPy/Pandas-backed computation, and object-oriented design principles. The framework bridges the cross-language gap between openCFS’s C++ core and user-facing data analysis, enabling modular, pipeline-driven preprocessing and postprocessing. It supports batched mesh analysis, real-time visualization, and machine-learning-ready data export. Evaluated across multiple aeroacoustic case studies, the framework demonstrates robustness, usability, and significant efficiency gains in end-to-end workflows.
📝 Abstract
Many numerical simulation tools have been developed and are on the market, but there is still a strong need for appropriate tools capable of simulating multi-field problems, especially in aeroacoustics. Therefore, openCFS provides an open-source framework for implementing partial differential equations using the finite element method. Since 2000, the software has been developed continuously. The result is openCFS (before 2020, known as CFS++ Coupled Field Simulations written in C++). In this paper, we present pyCFS-data, a data processing framework written in Python to provide a flexible and easy-to-use toolbox to access and manipulate, pre- and postprocess data generated by or for usage with openCFS.