Artificial Intelligence and Computational Modeling in Heat Transfer and Fluid Dynamics

Mukesh Kumar Awasthi, PhD is an Assistant Professor in the Department of Mathematics at Babasaheb Bhimrao Ambedkar University. He has published more than 125 research publications in journal and conference articles and book chapters, as well as ten books. His expertise lies in viscous potential flow, electro-hydrodynamics, magnetohydrodynamics, and heat and mass transfer. Reshu Gupta, PhD is an Associate Professor in the Applied Science Cluster at the University of Petroleum and Engineering Studies with more than 20 years of teaching experience. She has published several papers in international journals and conference proceedings and three books. Her research areas include fluid dynamics, differential equations, heat and mass transfer, nanofluids, entropy, and artificial neural networks.

• Preface xvii1 Artificial Intelligence in Heat Transfer and Fluid Dynamics: Innovations, Applications, and Future Directions 1R. Sakthikala and R. Revathi1.1 Introduction 21.2 Theoretical Foundations of Heat Transfer and Fluid Dynamics 41.3 Artificial Intelligence in Engineering: Methods and Techniques 51.4 Artificial Intelligence Applications in Heat Transfer 111.5 Artificial Intelligence in Fluid Dynamics 131.6 Practical Implementations 171.7 Challenges and Future Directions 191.8 Conclusion 212 Machine Learning Applications in Fluid Mechanics 23A. Ahadi, P. Hosseini Baei and M. Sheikholeslami2.1 Introduction 242.2 The Basics of Machine Learning 262.3 Fluid Mechanics Machine Learning Influenced by Physics 302.4 Methods for Modeling Turbulence 362.5 Machine Learning in Fluid Dynamics: Obstacles and Prospects 402.6 Summary 413 Artificial Intelligence-Enhanced Developments in Computational Fluid Dynamics 51Tushar Sagar, Sachin Kumar, Dinesh Kumar Patel, Gaurav Nandan and Vipin Kumar Sharma3.1 Introduction 523.2 An Overview of Artificial Intelligence in Computational Fluid Dynamics 553.3 Methodology of Artificial Intelligence-Driven Enhancement in Computational Fluid Dynamics 663.4 Discussion 773.5 Case Study 803.6 Conclusion 814 Artificial Neural Network-Based Analysis of Natural Convection in Ag-TiO©ü/H©üO Hybrid Nanoliquids 93Madhavarao Kulkarni4.1 Introduction 944.2 Mathematical Modeling 964.3 Methods of Solution 994.4 Results and Discussion 1044.5 Conclusions 1135 Artificial Intelligence-Based Optimization of Heat Transfer in Gyrotactic-Nanofluid Flow 117Priyanka Chandra and Raja Das5.1 Introduction 1185.2 Mathematical Modeling 1195.3 Numerical Method 1225.4 Results and Discussions 1235.5 Conclusions 1376 Artificial Intelligence-Based Heat Exchanger Design and Optimization 141Sachin Mishra, Raj Kumar, Shailendra Singh Rathore, Sakshi Saxena, Pushpendra Sharma, Shubhra Khare and Kuldeep Chauhan6.1 Introduction 1426.2 Artificial Intelligence-Based Heat Exchanger Design and Optimization 1436.3 Principles of Heat Exchangers 1436.4 Two-Pipe Heat Exchangers 1456.5 Performance and Optimization Metrics 1466.6 Worldwide Market for Heat Exchangers 1466.7 Basic Equation of Heat Transfer 1476.8 Designing Heat Exchangers Thermally 1516.9 Issue with Thermal Design of Heat Exchanger 1536.10 The Need for Artificial Intelligence in Heat Exchanger Design and Optimization 1536.11 Artificial Intelligence in Heat Exchanger Design 1546.12 Benefits of Artificial Intelligence in Heat Exchanger Design and Optimization 1556.13 Artificial Intelligence Applications in Different Types of Heat Exchangers 1566.14 Significance of Artificial Intelligence in Heat Exchanger Design 1576.15 Key Aspects of Artificial Intelligence in Heat Exchanger Design 1576.16 Applications of Artificial Intelligence in Heat Exchanger Design 1596.17 Upcoming Developments and Trends 1606.18 Heat Exchange Design 1626.19 Innovation in Heat Exchanger Design 1666.20 Challenges in Artificial Intelligence-Based Heat Exchanger Design 1676.21 Conclusion 1697 Artificial Intelligence-Driven Energy Optimization in Heating, Ventilation, and Air Conditioning Systems 177G. Gandhimathi, C. Chellaswamy, S. Sridevi and Mohamed M. Awad7.1 Introduction 1787.2 Literature Review 1837.3 Game Theory Structure of Liquid Flow 1857.4 Liquid Flow of Fluid-Structural System 1887.5 Result and Discussion 1937.6 Conclusion 2098 Artificial Neural Network Model for Radiative Heat Transfer in a Magnetized Tapered Stenosed Artery 213Haris Alam Zuberi, Naveen Kumar and Nurul Amira Zainal8.1 Introduction 2148.2 Mathematical Modeling 2178.3 Methodology: Implementation of a Physics-Informed Neural Network Model in MATLAB 2218.4 Results and Discussion 2238.5 Validation of a Physics-Informed Neural Network Model 2298.6 Conclusions 2318.7 Medical Applications and Future Prospects 2329 Artificial Intelligence-Driven Flow Optimization in Renewable Energy Systems 237Sachin Kumar, Vipin Kumar Sharma, Dinesh Kumar Patel, Gaurav Nandan and Tushar Sagar9.1 Introduction 2389.2 Artificial Intelligence in Wind Energy Systems 2419.3 Artificial Intelligence in Hydroelectric Power Systems 2549.4 Artificial Intelligence in Solar Power Systems 2599.5 Challenges and Future Directions 2649.6 Conclusion 26710 Artificial Intelligence-Driven Flow Optimization for Enhanced Efficiency in Renewable Energy Systems 277Kavita Sanjay Singh, V. Shanmugapriya, Siddharth Shankar Mishra and Manvendra Singh10.1 Introduction 27810.2 Fundamentals of Flow Dynamics in Renewable Energy Systems 28010.3 Artificial Intelligence Technologies in Renewable Energy 28610.4 Artificial Intelligence Models for Flow Prediction and Optimization 29110.5 Optimizing Hydrodynamic Processes in Hydropower 29410.6 Artificial Intelligence-Enhanced Solar Energy Systems 29610.7 Challenges and Future Prospects 29910.8 Conclusion 30311 Artificial Intelligence for Flow Optimization in Renewable Energy Systems 307Devanshi Srivastava and Adarsh Kumar Arya11.1 Introduction 30811.2 Artificial Intelligence, Deep Learning, and the Sustainable Development Goals 30811.3 Analysis of Artificial Intelligence Technologies in Sustainable Power 31011.4 Technology for Energy Efficiency 31211.5 Recently Developed Artificial Intelligence-Powered Optimization Methods 31611.6 Applications of Artificial Intelligence and Deep Learning for Ecological Well-Being 32011.7 Using Artificial Intelligence and Deep Learning for Energy Efficiency in Smart Buildings 32111.8 Application of Artificial Intelligence in Solid Waste Management Systems and Predictive Analysis Model in Solar Synergy 32211.9 Ethical Concerns, Limitations, and Potential Biases in AI-Driven Environmental Solutions 32311.10 Obstacles and Prospective Pathways 32411.11 Conclusions 32512 Physics-Informed Neural Networks for Exothermic Reactions in Porous Media 333Pavan Patel and Saroj R. Yadav12.1 Introduction 33412.2 Mathematical Model 33512.3 The Building Block of Physics-Informed Neural Networks 33612.4 Experiments’ Results and Discussion 33712.5 Conclusion 34013 Machine Learning for Magnetohydrodynamic Nanofluid Flow: Artificial Neural Networks vs. Traditional Methods 343B.C. Rout, Bijoylakshmi Boruah, Utpal Kumar Saha, Madhusudan Senapati, Sakambari Mishra, Vikash Kumar and Bhimanand Pandurang Gajbhare13.1 Introduction 34513.2 Problem Description 34713.3 Results and Discussion 35013.4 Conclusion 36214 Case Studies of Artificial Intelligence in Industrial Fluid and Thermal Processes 365Abdulhalim Musa Abubakar, Kiran Batool, Muhammad Asif and Baudilio Coto14.1 Introduction 36614.2 Artificial Intelligence Techniques in Fluid Flow and Heat Transfer 36714.3 Artificial Intelligence in Chemical Processing Industries 36914.4 Artificial Intelligence in Power Generation 37314.5 Artificial Intelligence in Manufacturing and Electronic Components 37414.6 Challenges, Limitations, and Recommended Solutions 37714.7 Conclusion 38115 Artificial Intelligence in Microfluidics and Nanofluidics 395Ashish Mathur, Souradeep Roy and Rabab Fatima15.1 Introduction 39615.2 Case Study 40015.3 Predictive Modeling of Fluid Behaviour 40215.4 Real-Time Control Systems 40215.5 Artificial Intelligence and Edge Computing for Real-Time Applications 40315.6 Environmental Sensors 40515.7 Challenges and Limitations 40615.8 Future Directions 40715.9 Regulatory and Ethical Considerations of Artificial Intelligence in Microfluidics 40915.10 Conclusion 410References 411About the Editors 415Index 417