Deep convolutional surrogates and freedom in thermal design

Abstract

A deep learning approach is presented for heat transfer and pressure drop prediction of complex fin geometries generated using composite Bézier 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), parallel processing architecture of GPUs can be used to accelerate 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. A design space 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. Xception network provides 98 percent accuracy in heat transfer and pressure drop prediction of the single fin design. Increasing the design freedom to multiple fins increases the error in prediction. This error, however, remains within three percent of the ground truth values which is valuable for design purpose. The presented predictive model can be used for innovative BREP-based fin design optimization in compact and high efficiency heat exchangers.

Keywords

• Geometric deep learning
• Geometry processing
• Heat exchanger
• Design freedom
• Surrogate modeling