Dr. Mohamed Moustafa is a dedicated researcher in nuclear engineering and thermal hydraulics, with a strong focus on multiphase flow behavior, water film dynamics, and advanced diagnostic techniques essential for improving heat transfer and safety in nuclear systems. His work spans experimental analysis, numerical simulation, and machine learning applications, offering a comprehensive approach to understanding gas–liquid interactions and thin-film behaviors under various operating conditions. Dr. Mohamed Moustafa has made notable contributions through the use of Planar Laser-Induced Fluorescence (PLIF) to study wavy water films on horizontal plates and within horizontal rectangular ducts, generating high-resolution insights into film instabilities, flow distribution, and interfacial behavior. He has also pioneered the integration of artificial intelligence into thermal-hydraulic research by employing artificial neural networks and deep convolutional neural networks for predicting wavy film characteristics and classifying critical water-film images. His publications, presented in reputable journals and international conferences, include work on numerical simulation of spent fuel pools using shell-and-tube heat exchangers, PLIF-based characterization of gas–liquid films, and state-of-the-art reviews on water-film properties. Dr. Mohamed Moustafa research aims to enhance cooling performance, improve thermal-hydraulic predictive capabilities, and strengthen safety analyses within nuclear engineering. By combining experimental rigor, computational modeling, and data-driven techniques, he continues to advance scientific understanding of complex thermal-hydraulic phenomena and contribute meaningful solutions for modern nuclear reactor design and operation.
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Moustafa, M. (2015). Numerical simulation of spent fuel pool using shell and tube heat exchanger. International Journal of Engineering and Technology Research, 869(12), 96–104.
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Mohamed, M., Ruifeng, T., Bo, W., Wen, J., Ullah, A., & Mohamad, H. A. E. (2023). A detailed experimental evaluation of gas–liquid film attributes in a horizontal rectangular duct by Planar Laser-Induced Fluorescence (PLIF) approach. Nuclear Engineering and Design, 408, 112331.
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Mohamed, M., Ruifeng, T., Wen, J., Bo, W., Ullah, A., Alm ElDin Mohamad, H., & Cheng, H. (2024). Modeling of wavy water film by application of artificial neural network: A state of art study. Nuclear Engineering and Design, 417, 112731.
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Mohamed, M., Ruifeng, T., & Renteria, F. (2019). Classification of water film critical images using deep convolutional neural networks. In Proceedings of the 2nd International Conference on Algorithms, Computing and Artificial Intelligence (pp. 225–229). Association for Computing Machinery.
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Mohamed, M., & Ruifeng, T. (2019). Review paper on water film characteristics. In Proceedings of the 18th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH 2019) (pp. 5616–5630).