Mohamed El-Amin – författare

This book serves as a practical guide to using numerical methods with MATLAB®/Python implementation for simulating fluid flow and transport in porous media, covering the fundamentals and their application in a way that is easy to understand for those without a technical background. In addition, it covers the governing equations of fluid mechanics and heat transfer, boundary conditions and initial conditions, dimensional analysis and scaling laws, and analytical and numerical methods for solving fluid flow and heat transfer problems.Provides a comprehensive introduction to numerical methods for simulating fluid flow and transport in porous media.Examines numerical methods and their implementation in porous media simulations using MATLAB® and Python.Explains the applications for numerical methods in porous media, including transport, heat transfer, and fluid dynamics.

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.