Deep convolutional surrogates and degrees of freedom in thermal design

TL;DR

This paper develops CNN-based surrogate models to predict heat transfer and pressure drop in complex fin geometries, enabling faster thermal design iterations with acceptable accuracy, especially for single fin configurations.

Contribution

It introduces CNN-based surrogate models for CFD predictions of complex fin geometries, demonstrating high accuracy and computational efficiency, particularly with the Xception network for single fin designs.

Findings

High prediction accuracy for single fin designs using Xception CNN.

Prediction errors increase with multiple fins but stay within 3%.

GPU acceleration enhances the efficiency of the surrogate modeling process.

Abstract

We present surrogate models for heat transfer and pressure drop prediction of complex fin geometries generated using composite Bezier curves. Thermal design process includes iterative high fidelity simulation which is complex, computationally expensive, and time-consuming. With the advancement in machine learning algorithms as well as Graphics Processing Units (GPUs), we can utilize the parallel processing architecture of GPUs rather than solely relying on CPUs to accelerate the thermo-fluid simulation. In this study, Convolutional Neural Networks (CNNs) are used to predict results of Computational Fluid Dynamics (CFD) directly from topologies saved as images. The case with a single fin as well as multiple morphable fins are studied. A comparison of Xception network and regular CNN is presented for the case with a single fin design. Results show that high accuracy in prediction is observed for single fin design particularly using Xception network. Increasing design freedom to multiple fins increases the error in prediction. This error, however, remains within three percent for pressure drop and heat transfer estimation which is valuable for design purpose.