Applying machine learning to reveal the microscopic heat transfer mechanism of nanofluids as coolants

Nanofluids are considered as excellent coolants to optimize thermal management of electronic devices, where the nanoparticle morphology and the addition of surfactants can affect the thermal transport performance of nanofluids. Due to the limitations of high economic and computational cost in previous experimental and numerical simulation methods, the design of nanofluids urges for more efficient approaches. In this work, a novel machine learning framework coupled with molecular dynamics methods was proposed to model the multi-component mixing nanofluidic systems and explore the deep heat transfer mechanisms. Multi-input attribute point cloud dataset, dual channel sampling network and multi-nanoscale optimization scheme were used to improve the prediction performance of machine learning. The computational cost of the machine learning method is shortened by 36000 times compared with simulation methods. Moreover, our work can achieve up to 90 % prediction accuracy for surfactant adsorption properties. Furthermore, algorithm optimization strategy can improve the prediction accuracy of nanofluidic heat transfer performance by 40 %. The proposed framework has the potential to shorten the development cycle of nanofluidic design.