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.
