Student Posters Repository

Mesh creation and refinement is one of the most time-consuming steps in any CFD simulation; even automated mesh generation requires high levels of expertise and fine-tuning. This project attempts to circumvent some of this complexity by leveraging deep convolutional neural networks to predict mesh densities for arbitrary geometries.

An automated pipeline was created to generate random geometries and run CFD simulations, iteratively performing targetted mesh refinement utilizing adjoint sensitivies. A comprehensive 6TB dataset consisting of 65,000 geometry-mesh pairs was assembled via an extensive post-processing and evaluation setup.

Current literature indicated that the UNet architecture extended by Thuerey et al. was suitable to predict flow-related quantities, but had never been used for mesh prediction. In this work, we present a deep, fully convolutional network that estimates mesh densities based off geometry data. The most recent model, tuned with network depth, channel size and kernel size, had an accuracy of 98% on our testing dataset.

The current pipeline provides a proof-of-concept that convolutional neural networks can, for specific use-cases, generate accurate mesh densities without the need manual fine-tuning. Such a product, if further tuned and extended, can provide significant time savings in future CFD workflows, completely independent of personnel expertise.