Multiscale Heat and Mass Transfer and Artificial Intelligence
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Thermal Engineering".
Deadline for manuscript submissions: 10 November 2024 | Viewed by 122
Special Issue Editors
Interests: heat transfer; complex fluid; AI for thermal management; active cooling techniques
Interests: smart cities; built environment; low-carbon energy systems; sustainable mobility; intelligent computing
Special Issue Information
Dear Colleagues,
Heat and mass transfer is an important research area that is relevant to a variety of emerging technologies in the fields of energy, low-carbon energy utilization, chemical engineering and aerospace engineering, etc. Multiscale simulation techniques and complement experimental studies from atomic to macroscopic scale brought about numerous breakthroughs. Recently, cutting-edge artificial intelligence (AI) technologies have emerged as powerful tools to accelerate fundamental physics-based understanding and applications in heat and mass transfer research. In view of these achievements, this Special Issue is devoted to showcasing cutting-edge research and developments in the field of multiscale heat and mass transfer and AI technologies. This Special Issue will cover a broad spectrum of topics, including but not limited to:
- Flow and temperature field reconstruction technology;
- Machine learning and AI in thermal management;
- Multiscale simulation techniques in heat and mass transfer characteristics;
- Physics-informed neural networks (PINNs) for heat-transfer problems;
- Advanced measurement and experimental techniques;
- Micro/nano-scale thermos-fluidics;
- Heat and mass transfer under extreme conditions;
- Multidisciplinary research on thermal energy conversion and heat transfer.
Dr. Guice Yao
Dr. Chuang Wen
Dr. Jin Zhao
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- heat transfer
- multiscale modelling
- multidisciplinary energy conversion
- temperature reconstruction
- flow dynamics
- physics-informed neural network
