Applications of Artificial Intelligence Model of Heat and Mass Transfer

Dear Colleagues,

An array of data are experimentally measured and presented in various heat and mass transfer-related area to demonstrate the road plan for scholars to develop novel heat transfer engineering applications. These huge data can be made more valuable by means of machine learning models, artificial intelligence techniques, and Big data focusing on different angels of heat transfer engineering such as nanofluid and natural convection, thermal systems, thermophysical properties, convective heat transfer in single-phase and multiphase flow, thermal energy storage, porous media, nanoscale heat transfer, Solar Energy, Fuel Cells and phase change materials. Heat transfer improvement is a requisite for developing cutting-edge engineering applications. Meanwhile, reducing energy consumption and improving energy savings activities can be achieved when heat transfer equipment working efficiently. In addition, improving heat transfer equipment can be led to reducing hazardous emissions such as CO₂ and making them under control. Both industry and research zero in on heat transfer enhancement. However, there are a vast amount of experimental data which are not evaluated and investigated with newly presented evaluation techniques. Hence, to include both experimental and analysis aspects of heat transfer engineering applications in energy systems, we promote a new special collection and cordially invite all submissions which are related to heat and mass transfer investigations.

Prof. Dr. Mohsen Sharifpur
Dr. Mohammad Hossein Ahmadi
Guest Editors

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