The project creates an open-source curated dataset for data driven turbulence modelling for heat exchangers. It is part of Open Digital Solutions for Net Zero Energy, a project funded by UK Research and Innovation (UKRI) under the Small Business Research Initiative (SBRI) competition that aims to develop open software, hardware and data solutions that address the challenges off transforming to a Net Zero energy system.
The dataset is 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 features a variety of direct numerical simulation (DNS) and large-eddy simulation (LES) data. The dataset can be used machine learning augmented corrective turbulence closure modelling. This repository presents an approach to develop a model for turbulence modelling.
The datasets are available by contacting TOffeeAM (https://www.toffeeam.co.uk/contact/). Please include "SBRI Dataset - Airfoil" access in the subject line and the dataset you are interested in, or "SBRI Dataset - All" for all datasets. The following datasets are available (licensed by TOFFEEAM LIMITED under CC BY-NC-SA 4.0):
- Airfoil (LES/DNS)
- Gyroid (LES/DNS)
- Schwarz (LES/DNS)
The LES and DNS simulations were generated using OpenFOAM v8 (https://openfoam.org/version/8/).