Deep convolutional surrogates and degrees of freedom in thermal design
This work addresses the computational expense of iterative high-fidelity simulations in thermal design, though it is incremental as it applies existing CNN methods to a new domain-specific dataset.
The authors tackled the problem of accelerating thermal design by using convolutional neural networks as surrogate models to predict heat transfer and pressure drop from fin geometry images, achieving errors within three percent for pressure drop and heat transfer estimation.
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.