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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.
- Encompasses a wide range of examples, pictures, and experimental works from historical and contemporary AI research, incorporating computational fluid dynamics (CFD) in almost all its forms
- Serves as a comprehensive guide that explores the utilization of modern AI techniques and fluid dynamics computing methods, offering practical applications for researchers and students, both present and future
- Includes updates on the emerging field of quantum computing and its implications in the context of ongoing research, presenting its potential to replace high-performance computing (HPC) and its overall impact
- Fills a unique gap in the market as there has never been a publication specifically dedicated to the fusion of AI and computational fluid dynamics
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ISBN-13
978-0-443-29119-7 (9780443291197)
Schweitzer Classification
1. Artificial Intelligence and Computational Fluid Dynamics: Background2. Introduction to artificial intelligence and subsets3. Artificial intelligence based computational fluid dynamics approaches4. Enhanced reduced order modeling and accelerated direct numerical simulation5. Machine learning/ Deep Learning architectures and Computational Fluid Dynamics6. Turbulence Closure Modeling using Deep Learning7. DNNs - CNNs/RNNs/PINNs/cPINNs/xPINNs8. ANN as popular AI tool for CFD9. Support Vector Machine (SVM) an important Supervised Learning Category10. Current AI algorithms in CFD and implementation11. AI for accelerated CFD and fluid flow optimization12. Dynamic Model Decomposition of complex Fluid Flow Analysis using Machine Learning13. Machine learning based optimal mesh generation and optimization14. Machine learning based New sparse algorithms15. Commercial and open source models/codes used in industry for AI and CFD16. Modern tools, languages and systems available for implementing AI algorithms.17. Aerodynamic Modeling in CFD using AI18. Application of AI for Turbulence Modeling19. Application of AI in CFD for Boundary layer and Multiphase Flows20. AI in Heat and Mass Transfer using CFD21. AI for CFD in materials industry and other applications22. Operating challenges for AI in CFD and the available solutions23. AI, CFD and CFD Vision 2030¿¿¿23. Conclusion Remarks