Artificial Intelligence for Computational Fluid Dynamics offers a groundbreaking exploration of how high-performance computing and artificial intelligence (AI) revolutionize computational fluid dynamics (CFD). Designed for students and researchers alike, this book consolidates information on machine learning, deep learning, and neural networking, demonstrating their transformative potential in CFD. With an emphasis on both the current state of the field and future possibilities, the text serves as a vital resource for anyone looking to understand and leverage AI in fluid dynamics. This work not only educates but also inspires innovation within a rapidly evolving scientific landscape.
The book delves into future research directions, aligning with the newly amended CFD vision for 2030, and underscores the importance of continued development. It introduces scientific tools and software integral to AI-driven CFD applications, ensuring that readers gain practical knowledge alongside theoretical insights. Authored by renowned experts with extensive teaching and research experience, this work stands at the intersection of cutting-edge technology and academic rigor, making it an indispensable reference for anyone in the field.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xiii 7. About the editors - p. xvii 8. Preface - p. xxi 9. Chapter 1. Artificial intelligence and computational fluid dynamics: background - p. 1 10. Chapter 2. An overview of artificial intelligence and its subsets - p. 29 11. Chapter 3. Computational fluid dynamics approaches leveraging artificial intelligence - p. 53 12. Chapter 4. Enhanced reduced-order modeling and accelerated direct numerical simulation - p. 91 13. Chapter 5. Machine learning and deep learning architectures for computational fluid dynamics - p. 131 14. Chapter 6. Turbulence closure modeling using deep learning - p. 163 15. Chapter 7. Artificial intelligence accelerated computational fluid dynamics and neural networks - p. 197 16. Chapter 8. Artificial neural network as popular artificial intelligence tool for computational fluid dynamics - p. 215 17. Chapter 9. Support vector machine, an important supervised learning category - p. 237 18. Chapter 10. State of current artificial intelligence algorithms in computational fluid dynamics and their implementation - p. 261 19. Chapter 11. Machine learning-based optimal mesh generation and optimization - p. 283 20. Chapter 12. Machine learning-based new sparse algorithms and reinforcement learning for computational fluid dynamics - p. 313 21. Chapter 13. Modern tools, languages, and systems available for implementing artificial intelligence algorithms - p. 345 22. Chapter 14. Applications of artificial intelligence in computational aerodynamics and modeling - p. 377 23. Chapter 15. Applications of artificial intelligence in turbulence modeling - p. 401 24. Chapter 16. Applications of artificial intelligence for boundary layer and multiphase flows - p. 423 25. Chapter 17. Applications of artificial intelligence in heat and mass transfer-based flow computations and materials processing - p. 449 26. Chapter 18. Operating challenges for artificial intelligence in computational fluid dynamics and the available solutions - p. 469 27. Chapter 19. Importance of artificial intelligence in Computational Fluid Dynamics Vision 2030—editor’s perspective - p. 495 28. Chapter 20. Concluding remarks - p. 523 29. Index - p. 543
Artificial Intelligence for Computational Fluid Dynamics offers a groundbreaking exploration of how high-performance computing and artificial intelligence (AI) revolutionize computational fluid dynamics (CFD). Designed for students and researchers alike, this book consolidates information on machine learning, deep learning, and neural networking, demonstrating their transformative potential in CFD. With an emphasis on both the current state of the field and future possibilities, the text serves as a vital resource for anyone looking to understand and leverage AI in fluid dynamics. This work not only educates but also inspires innovation within a rapidly evolving scientific landscape.
The book delves into future research directions, aligning with the newly amended CFD vision for 2030, and underscores the importance of continued development. It introduces scientific tools and software integral to AI-driven CFD applications, ensuring that readers gain practical knowledge alongside theoretical insights. Authored by renowned experts with extensive teaching and research experience, this work stands at the intersection of cutting-edge technology and academic rigor, making it an indispensable reference for anyone in the field.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xiii 7. About the editors - p. xvii 8. Preface - p. xxi 9. Chapter 1. Artificial intelligence and computational fluid dynamics: background - p. 1 10. Chapter 2. An overview of artificial intelligence and its subsets - p. 29 11. Chapter 3. Computational fluid dynamics approaches leveraging artificial intelligence - p. 53 12. Chapter 4. Enhanced reduced-order modeling and accelerated direct numerical simulation - p. 91 13. Chapter 5. Machine learning and deep learning architectures for computational fluid dynamics - p. 131 14. Chapter 6. Turbulence closure modeling using deep learning - p. 163 15. Chapter 7. Artificial intelligence accelerated computational fluid dynamics and neural networks - p. 197 16. Chapter 8. Artificial neural network as popular artificial intelligence tool for computational fluid dynamics - p. 215 17. Chapter 9. Support vector machine, an important supervised learning category - p. 237 18. Chapter 10. State of current artificial intelligence algorithms in computational fluid dynamics and their implementation - p. 261 19. Chapter 11. Machine learning-based optimal mesh generation and optimization - p. 283 20. Chapter 12. Machine learning-based new sparse algorithms and reinforcement learning for computational fluid dynamics - p. 313 21. Chapter 13. Modern tools, languages, and systems available for implementing artificial intelligence algorithms - p. 345 22. Chapter 14. Applications of artificial intelligence in computational aerodynamics and modeling - p. 377 23. Chapter 15. Applications of artificial intelligence in turbulence modeling - p. 401 24. Chapter 16. Applications of artificial intelligence for boundary layer and multiphase flows - p. 423 25. Chapter 17. Applications of artificial intelligence in heat and mass transfer-based flow computations and materials processing - p. 449 26. Chapter 18. Operating challenges for artificial intelligence in computational fluid dynamics and the available solutions - p. 469 27. Chapter 19. Importance of artificial intelligence in Computational Fluid Dynamics Vision 2030—editor’s perspective - p. 495 28. Chapter 20. Concluding remarks - p. 523 29. Index - p. 543
