Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer
This work addresses the problem of slow numerical simulations in optimizing heat transfer for fluid flow systems, particularly in chemical engineering, though it is incremental as it applies existing ML methods to a known bottleneck.
The researchers tackled the bottleneck of optimizing complex channel wall geometries for heat transfer by combining numerical simulations with convolutional neural networks (CNNs) to predict drag coefficient and Stanton number, achieving accurate predictions at a fraction of the simulation time and enabling virtual high-throughput screening of wall architectures.
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.