Artificial Intelligence for Computational Fluid Dynamics serves as a comprehensive reference guide, providing up-to-date information on the utilization of high-performance computing and artificial intelligence (AI) in computational fluid dynamics (CFD). It caters to the needs of students and researchers, offering a single, comprehensive document encompassing machine learning, deep learning, neural networking, and their significance within the realm of CFD. The content not only covers the current state of the field but also provides insights into future research directions, emphasizing the importance of ongoing research and development. Additionally, the book introduces various scientific tools and software commonly employed in AI-based CFD applications. The newly amended CFD vision for 2030 receives specific attention, ensuring alignment with the latest advancements and industry trends. The editors and authors of this book are esteemed researchers with extensive experience in both teaching and research, establishing their expertise in the field.
1. Artificial Intelligence and Computational Fluid Dynamics: Background
2. Introduction to artificial intelligence and subsets
3. Artificial intelligence based computational fluid dynamics approaches
4. Enhanced reduced order modeling and accelerated direct numerical simulation
5. Machine learning/ Deep Learning architectures and Computational Fluid Dynamics
6. Turbulence Closure Modeling using Deep Learning
7. DNNs – CNNs/RNNs/PINNs/cPINNs/xPINNs
8. ANN as popular AI tool for CFD
9. Support Vector Machine (SVM) an important Supervised Learning Category
10. Current AI algorithms in CFD and implementation
11. AI for accelerated CFD and fluid flow optimization
12. Dynamic Model Decomposition of complex Fluid Flow Analysis using Machine Learning
13. Machine learning based optimal mesh generation and optimization
14. Machine learning based New sparse algorithms
15. Commercial and open source models/codes used in industry for AI and CFD
16. Modern tools, languages and systems available for implementing AI algorithms.
17. Aerodynamic Modeling in CFD using AI
18. Application of AI for Turbulence Modeling
19. Application of AI in CFD for Boundary layer and Multiphase Flows
20. AI in Heat and Mass Transfer using CFD
21. AI for CFD in materials industry and other applications
22. Operating challenges for AI in CFD and the available solutions
23. AI, CFD and CFD Vision 2030
23. Conclusion Remarks
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