Autorentext

Prof. Mohamed Fathy El-Amin Mousa is a distinguished full professor of applied mathematics and computational sciences at Effat University, Saudi Arabia, and Aswan University, Egypt. With a career spanning more than 25 years, Dr. El-Amin has established a global reputation for pioneering contributions in computational modeling, fluid dynamics, reservoir simulation, porous media transport, heat and mass transfer, hydrogen energy, and renewable energy technologies.

He earned his Ph.D. in Applied Mathematics. His postdoctoral journey included prestigious fellowships from the Alexander von Humboldt Foundation in Germany and the Japan Society for the Promotion of Science (JSPS) in Japan, as well as research appointments at renowned institutions such as Stuttgart University, Kyushu University, King Abdullah University of Science and Technology (KAUST), and the University of Texas at Austin.

Dr. El-Amin has published over 200 peer-reviewed articles, book chapters, and conference papers, alongside several edited volumes and special journal issues. His recent authored books, including Numerical Modeling of Nanoparticle Transport in Porous Media (Elsevier, 2023) and Fractional Modeling of Fluid Flow and Transport Phenomena (Elsevier, 2025), reflect his leadership in bridging mathematical theory with practical energy and environmental challenges.

Currently, Dr. El-Amin leads research projects on atmospheric water generation using desiccant materials and underground hydrogen storage, aiming to support sustainable energy and water security initiatives. His research innovations have led to patents and new prototype developments, particularly involving carbon nanotubes and graphene-based technologies.

An active member of several international scientific societies, including INTERPORE and the Society of Petroleum Engineers (SPE), Dr. El-Amin has been consistently recognized among the World's Top 2% Scientists by Stanford University rankings. His contributions have earned him multiple awards for excellence in research, teaching, and civic engagement.

Beyond research, Dr. El-Amin is deeply committed to mentoring graduate students, supervising numerous MSc and Ph.D. theses, and actively participating in university leadership roles, including chairing promotion and research committees. His philosophy emphasizes interdisciplinary collaboration and the real-world application of scientific knowledge to meet the global challenges of energy, water, and sustainability.

Klappentext

The rapid growth of machine learning in recent years has made it a popular tool for data analysis, modeling, and predictions. As more data is generated from fluid flow simulations and experiments, the use of machine learning algorithms has become essential in making sense of it all. Advances and Applications of Machine Learning in Fluid Flow Problems provides insight into the effective use of machine learning in fluid flow and its potential impact on the field. It examines the application of machine learning techniques in various fluid flow problems, including but not limited to turbulent flow, multiphase flow, complex geometries, flow control, turbulence modeling, particle-fluid interactions, numerical simulations, data-driven modeling, flow in porous media, oil/gas reservoir simulation, permeability prediction, and more. It serves as a useful tool for a wide range of readers in the professional, industrial, and academic sectors.

• Covers both the theories and practical applications of machine learning in fluid flow problems, making the book a unique and valuable resource for professionals and researchers in the field.
• Provides a comprehensive examination of the application of machine learning for all aspects of fluid flow problems.

Inhalt

Table of Contents

Biography

List of figures

List of tables

Part I Introduction

Chapter 1Overview of Machine Learning

Chapter 2 Challenges, Limitations, and Recommendations

Part II ML for Turbulent Flows

Chapter 3 PIV, CFD and ML for Turbulent Jet

Chapter 4 Turbulent Jets Using Time Series

Chapter 5 Machine Learning for Permeability

Chapter 6 Hybrid Forecasting for Petroleum Reservoir

Chapter 7 PINN for Second-Order Porous Medium

Part IV ML for Hydrogen Energy

Chapter 8 Hydrogen Migration in Porous Media

Chapter 9 Hydrogen Leakage

Part V ML for Wind Energy

Chapter 10 Wind Farm Optimization and ML