Gaoyang Li , Haiyi Sun , Dan Han , Shukai Cheng , Guoqi Zhao , Yuting Guo
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引用次数: 0
Abstract
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.
期刊介绍:
Thermochimica Acta publishes original research contributions covering all aspects of thermoanalytical and calorimetric methods and their application to experimental chemistry, physics, biology and engineering. The journal aims to span the whole range from fundamental research to practical application.
The journal focuses on the research that advances physical and analytical science of thermal phenomena. Therefore, the manuscripts are expected to provide important insights into the thermal phenomena studied or to propose significant improvements of analytical or computational techniques employed in thermal studies. Manuscripts that report the results of routine thermal measurements are not suitable for publication in Thermochimica Acta.
The journal particularly welcomes papers from newly emerging areas as well as from the traditional strength areas:
- New and improved instrumentation and methods
- Thermal properties and behavior of materials
- Kinetics of thermally stimulated processes