AI Driven Open Source Framework for Next Generation Heat Exchangers

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The project selected is the creation of an open-source curated dataset for data driven turbulence modelling.The dataset will be built around printed circuit heat exchanger (PCHE) and cold plate cooling systems. PCHEs and cold plates are heavily used by many industries tackling electrification and NetZero. They are found for example in gas turbines, electric cars, and nuclear reactors. Designing more efficient heat exchangers not only reduces energy consumption to run these cooling systems but is also essential to allow installation components in high temperature environments in a safe and cost-effective manner. However, to design those cooling systems and obtain highly performant ones, accurate computational fluid dynamics software (CFD) to model flow behaviour becomes necessary. Turbulence models are used as part of computer aided engineering (CAE) software packages in almost every single scientific or engineering industry. These include energy generation (from fossil, nuclear, and renewable sources), HVAC, aerospace, automotive, industrial processing, and many others.While higher resolution techniques such as large-eddy simulation (LES) and direct numerical simulation (DNS) are becoming more widespread, the computational demands compared to current capabilities make these techniques unaffordable for many industrial simulations. For this reason, Reynolds-averaged Navier-Stokes (RANS) simulations are expected to remain the dominant tool for predicting flows of practical relevance to engineering and industrial problems over the next few decades. However, flows with strong adverse pressure gradients, separation, streamline curvature, and reacting chemistry are often poorly predicted by RANS approaches. Developing methods to improve the accuracy of RANS simulations will help bridge this critical capability gap between RANS and LES. In this project, we aim to do exactly that by training an AI model which can be used to improve the accuracy of RANS simulations at almost no extra computational cost. The dataset will feature a variety of direct numerical simulation (DNS) and large-eddy simulation (LES) data. It will be for immediate use in machine learning augmented corrective turbulence closure modelling.