Special Issues

Machine Learning and Data-Driven Techniques for Mass and Thermal Transport

Submission Deadline: 30 September 2024 View: 128 Submit to Special Issue

Guest Editors

Weitao Wu, Professor, School of Mechanical Engineering, Nanjing University of Science and Technology, China
Prof. Wu obtained his PhD degree from the Department of Mechanical Engineering at Carnegie Mellon University in 2015. He has extensive experience in the research on heat and mass transfer, computational fluid dynamics, and machine learning driven by physical information, etc. He has published more than 90 journal papers, of which 3 papers were selected as the cover papers of Journal of Fluid Mechanics and Physics of Fluids. He was invited to deliver international keynote speeches on four occasions.

Mei Mei, Associate Professor, Sino-French Engineer School, Nanjing University of Science and Technology, China
Assoc. Prof. Mei obtained her PhD degree from the Toulouse Biotechnology Institute at INSA-Toulouse University in 2021. She has extensive experience in research on the enhancement of heat and mass transfer, experimental fluid dynamics, and bio-inspired multifunctional surface fabrication technologies, etc. She has published 14 journal papers in the journal of Physics of Fluids, and Chemical Engineering Science, etc.


Heat and mass transfer play a vital role in a bunch of scientific and engineering applications, including the thermal management of electronic devices, energy conversion for sustainable heating and cooling technologies, chemical reaction engineering, etc. In recent decades, significant advancements have been made in investigating and manipulating the mass and thermal transport for different scenarios and scales. However, complex mass and heat transfer problems continue to pose numerous challenges, e.g., persistent difficulties that traditional analytical, computational, or experimental approaches struggle to address. Machine learning and data-driven modeling have emerged as promising and powerful tools for overcoming these challenges, offering new opportunities for tackling complex heat and mass transfer phenomena. This special issue aims to present and discuss the latest advances in the development and application of machine learning and data-driven modeling techniques in the field of mass and thermal transport.

Topics covered include, but are not limited to:

1. Artificial intelligence techniques for heat and mass transfer applications;

2. Development of machine learning algorithms for modeling multi-phase heat and mass transfer processes;

3. Machine learning for diffusion mass transfer and heat conduction;

4. Machine learning for convective mass and heat transfer;

5. Acquisition and standardization of data related to thermal and mass transfer processes;

6. Construction of Data-driven models for solving mass and thermal transport problems;

7. Novel and efficient surrogate models for heat transfer and multi-phase mass transfer;

8. Machine learning data analysis for complex fluid dynamics with thermal or mass transport measurements.


Machine learning, data-driven techniques, heat and mass transport

Published Papers

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