Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer

TL;DR

This paper combines numerical simulations and machine learning, specifically CNNs, to rapidly predict heat transfer properties in complex channel geometries, enabling efficient optimization of wall modifications for enhanced heat transfer.

Contribution

It introduces a CNN-based virtual screening method for optimizing complex channel wall geometries to improve heat transfer, reducing reliance on time-consuming simulations.

Findings

CNN accurately predicts drag coefficient and Stanton number

Data augmentation improves model generalization

Method applicable to complex and reactive flow systems

Abstract

Numerical simulation of fluids plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems. The calculation of heat transfer in fluid flow in simple flat channels is a relatively easy task for various simulation methods. However, once the channel geometry becomes more complex, numerical simulations become a bottleneck in optimizing wall geometries. We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number. We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations. We use the CNN models in a virtual high-throughput screening approach to explore a large number of possible, randomly generated wall architectures. Data Augmentation was applied to existing geometries data to add generated new training data which have the same number of parameters of heat transfer to improve the model's generalization. The general approach is not only applicable to simple flow setups as presented here but can be extended to more complex tasks, such as multiphase or even reactive unit operations in chemical engineering.