- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
The rapid growth of machine learning in recent years has made it a popular tool for data analysis, modeling, and prediction. 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.
Contents
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
