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Submit a ProposalArtificial Intelligence and Computational Modeling in Heat Transfer and Fluid Dynamics
 | Edited by Mukesh Kumar Awasthi and Reshu Gupta Copyright: 2026 | Expected Pub Date: 2026 ISBN: 9781394433575 | Hardcover | 442 pages Price: $225 USD  |
One Line Description
Drive innovation in thermal sciences with this essential book that leverages artificial intelligence and machine learning to transcend traditional computational methods and solve complex, real-time problems in heat transfer and fluid dynamics.
Audience
Researchers, engineers, and students in the fields of thermal sciences and fluid dynamics looking to deepen their understanding of the integration of artificial intelligence and computational modeling.
Researchers, engineers, and students in the fields of thermal sciences and fluid dynamics looking to deepen their understanding of the integration of artificial intelligence and computational modeling.
Description
Traditionally, heat transfer and fluid dynamics have relied on classical computational methods like computational fluid dynamics, which employ numerical techniques to solve governing equations for fluid flow and thermal transport. However, these methods are often computationally intensive and limited in handling complex, real-time scenarios, especially in turbulence modeling, multiphase flows, and optimization tasks. This book explores the transformative impact of artificial intelligence in the fields of heat transfer and fluid dynamics. It covers a range of topics, including AI-based optimization techniques for thermal systems, machine learning applications in fluid dynamics, and the use of neural networks for modeling thermal systems. The book delves into advanced areas such as microfluidics, predictive maintenance, and real-time flow control, highlighting how AI enhances traditional computational fluid dynamics methods. It also presents case studies that illustrate successful implementations of AI in industrial processes, offering practical insights into its applications. By fostering an understanding of both theoretical and practical aspects, equips engineers and researchers with the tools necessary to leverage AI effectively in their work, ultimately driving innovation in thermal sciences.
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Traditionally, heat transfer and fluid dynamics have relied on classical computational methods like computational fluid dynamics, which employ numerical techniques to solve governing equations for fluid flow and thermal transport. However, these methods are often computationally intensive and limited in handling complex, real-time scenarios, especially in turbulence modeling, multiphase flows, and optimization tasks. This book explores the transformative impact of artificial intelligence in the fields of heat transfer and fluid dynamics. It covers a range of topics, including AI-based optimization techniques for thermal systems, machine learning applications in fluid dynamics, and the use of neural networks for modeling thermal systems. The book delves into advanced areas such as microfluidics, predictive maintenance, and real-time flow control, highlighting how AI enhances traditional computational fluid dynamics methods. It also presents case studies that illustrate successful implementations of AI in industrial processes, offering practical insights into its applications. By fostering an understanding of both theoretical and practical aspects, equips engineers and researchers with the tools necessary to leverage AI effectively in their work, ultimately driving innovation in thermal sciences.
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Author / Editor Details
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.
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.
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Table of Contents
Preface
1. Artificial Intelligence in Heat Transfer and Fluid Dynamics: Innovations, Applications, and Future Directions
R. Sakthikala and R. Revathi
1.1 Introduction
1.2 Theoretical Foundations of Heat Transfer and Fluid Dynamics
1.2.1 Governing Equations
1.3 Artificial Intelligence in Engineering: Methods and Techniques
1.3.1 Neural Networks
1.3.2 Reinforcement Learning
1.3.3 Physics-Informed Neural Networks
1.4 Artificial Intelligence Applications in Heat Transfer
1.4.1 Thermal System Optimization
1.4.2 Real-Time Monitoring
1.4.3 Material Discovery
1.5 Artificial Intelligence in Fluid Dynamics
1.5.1 Turbulence Modeling
1.5.2 Aerodynamic Optimization
1.6 Practical Implementations
1.6.1 Applications in Aerospace
1.6.2 Power Generation
1.6.3 Chemical Processing
1.7 Challenges and Future Directions
1.7.1 Data Scarcity
1.7.2 Model Interpretability
1.7.3 Integration with Quantum Computing
1.8 Conclusion
References
2. Machine Learning Applications in Fluid Mechanics
A. Ahadi, P. Hosseini Baei and M. Sheikholeslami
2.1 Introduction
2.2 The Basics of Machine Learning
2.2.1 Supervised Learning
2.2.1.1 Neural Networks
2.2.1.2 Random Forests and Support Vector Machines for Classification
2.2.2 Unsupervised Learning
2.2.2.1 Vector Quantization and Clustering
2.2.3 Semi-Supervised Learning
2.3 Fluid Mechanics Machine Learning Influenced by Physics
2.3.1 The Problem
2.3.1.1 Examples Related to Fluid Mechanics
2.3.2 The Data
2.3.2.1 Examples Related to Fluid Mechanics
2.3.3 The Architecture
2.3.3.1 Examples Related to Fluid Mechanics
2.3.4 The Loss Function
2.3.4.1 Examples Related to Fluid Mechanics
2.3.5 The Optimization Algorithm
2.3.5.1 Examples Related to Fluid Mechanics
2.4 Methods for Modeling Turbulence
2.4.1 Direct Numerical Simulation
2.4.2 Reynolds Averaged Navier–Stokes
2.4.3 Large Eddy Simulation
2.5 Machine Learning in Fluid Dynamics: Obstacles and Prospects
2.6 Summary
References
3. Artificial Intelligence-Enhanced Developments in Computational Fluid Dynamics
Tushar Sagar, Sachin Kumar, Dinesh Kumar Patel, Gaurav Nandan and Vipin Kumar Sharma
3.1 Introduction
3.2 An Overview of Artificial Intelligence in Computational Fluid Dynamics
3.2.1 Evolution of Computational Fluid Dynamics
3.2.2 Artificial Intelligence and Machine Learning Advances
3.2.3 Past Research on Artificial Intelligence in Computational Fluid Dynamics
3.3 Methodology of Artificial Intelligence-Driven Enhancement in Computational Fluid Dynamics
3.3.1 Data Collection
3.3.2 Artificial Intelligence Techniques
3.3.3 Computational Fluid Dynamics Simulation Framework
3.4 Discussion
3.4.1 Performance Metrics
3.4.1.1 Computational Efficiency
3.4.1.2 Accuracy
3.4.1.3 Generalization Ability
3.4.1.4 Performance Formula
3.5 Case Study
3.6 Conclusion
References
4. Artificial Neural Network-Based Analysis of Natural Convection in Ag-TiO2/H2O Hybrid Nanoliquids
Madhavarao Kulkarni
4.1 Introduction
4.2 Mathematical Modeling
4.3 Methods of Solution
4.4 Results and Discussion
4.4.1 Artificial Neural Network Modeling Graphs
4.5 Conclusions
References
5. Artificial Intelligence-Based Optimization of Heat Transfer in Gyrotactic-Nanofluid Flow
Priyanka Chandra and Raja Das
5.1 Introduction
5.2 Mathematical Modeling
5.3 Numerical Method
5.4 Results and Discussions
5.4.1 Optimization Process
5.4.1.1 Levenberg–Marquardt Algorithm
5.4.2 Optimization Analysis by the Central Composite Factorial Design and Artificial Neural Network, Central Composite Factorial Design
5.4.2.1 Designing an Artificial Neural Network Model for Nux()0 Against Nb, Pr, and n
5.5 Conclusions
References
6. Artificial Intelligence-Based Heat Exchanger Design and Optimization
Sachin Mishra, Raj Kumar, Shailendra Singh Rathore, Sakshi Saxena, Pushpendra Sharma, Shubhra Khare and Kuldeep Chauhan
6.1 Introduction
6.2 Artificial Intelligence-Based Heat Exchanger Design and Optimization
6.3 Principles of Heat Exchangers
6.3.1 Coaxial Heat Exchangers
6.3.2 Plate Heat Exchangers
6.3.3 Cooled by Air Heat Exchangers
6.4 Two-Pipe Heat Exchangers
6.5 Performance and Optimization Metrics
6.6 Worldwide Market for Heat Exchangers
6.7 Basic Equation of Heat Transfer
6.8 Designing Heat Exchangers Thermally
6.9 Issue with Thermal Design of Heat Exchanger
6.10 The Need for Artificial Intelligence in Heat Exchanger Design and Optimization
6.10.1 Complexity of Heat Exchanger Design
6.11 Artificial Intelligence in Heat Exchanger Design
6.12 Benefits of Artificial Intelligence in Heat Exchanger Design and Optimization
6.13 Artificial Intelligence Applications in Different Types of Heat Exchangers
6.13.1 Future Directions and Challenges
6.14 Significance of Artificial Intelligence in Heat Exchanger Design
6.15 Key Aspects of Artificial Intelligence in Heat Exchanger Design
6.15.1 Optimization of Heat Transfer Performance
6.15.2 Enhanced Simulation and Modeling
6.15.3 Design Space Exploration
6.15.4 Predictive Maintenance and Fault Detection
6.15.5 Customization and Personalization
6.15.6 Sustainability and Energy Efficiency
6.15.7 Automation of the Design Process
6.16 Applications of Artificial Intelligence in Heat Exchanger Design
6.16.1 Energy Recovery Systems
6.16.2 Real-Time Performance Monitoring
6.16.3 Heat Exchangers
6.17 Upcoming Developments and Trends
6.17.1 Cutting-Edge Materials
6.17.2 Nanomaterials
6.17.3 Composite Materials
6.17.4 Advanced Coating
6.18 Heat Exchange Design
6.18.1 Log-Mean Temperature Difference Method
6.18.2 Optimization of Heat Exchanger
6.18.3 Genetic Algorithm
6.19 Innovation in Heat Exchanger Design
6.19.1 Small and Miniaturized Designs
6.19.2 Enhanced Heat Transfer Technologies
6.19.3 Modular Designs
6.20 Challenges in Artificial Intelligence-Based Heat Exchanger Design
6.20.1 Quality and Availability of the Data
6.20.2 Computational Challenges
6.20.3 Model Interpretability and Trust
6.20.4 Industry Barriers
6.20.5 Strategies to Overcome Barriers or Solutions to this Problem
6.20.5.1 Enhancing Data Availability
6.20.5.2 Improving Computational Efficiency
6.20.5.3 Enhancing Model Interpretability
6.20.5.4 Facilitating Industry Adoption
6.21 Conclusion
Bibliography
7. Artificial Intelligence-Driven Energy Optimization in Heating, Ventilation, and Air Conditioning Systems
G. Gandhimathi, C. Chellaswamy, S. Sridevi and Mohamed M. Awad
7.1 Introduction
7.1.1 Objective of this Chapter
7.1.2 Motivation
7.2 Literature Review
7.2.1 Machine Learning in Fluid Mechanics and Heating, Ventilation, and Air Conditioning Systems
7.2.2 Game Theory in Energy Efficiency and System Optimization
7.2.3 Reinforcement Learning for Heating, Ventilation, and Air Conditioning Control
7.2.4 Optimization and Control of Fluid Flows
7.2.5 Predictive Maintenance and Energy Optimization
7.3 Game Theory Structure of Liquid Flow
7.3.1 Introduction of Matrix Format
7.4 Liquid Flow of Fluid-Structural System
7.4.1 Game Theory with Partial Differential Equations Model for Stabilization and Interference of Liquids
7.4.1.1 Overview of Dynamics
7.4.1.2 Hypotheses
7.4.1.3 Fixed Interval Intermission in the Liquid Flow
7.4.1.4 Perfect Game Theory for Liquid Structural Communications
7.5 Result and Discussion
7.5.1 Data Collection
7.5.2 Performance of Game Theory
7.5.3 Predictive Analysis Using Different Machine Learning Algorithms
7.5.4 Predictive Analysis Using Reinforced Algorithm
7.5.5 Performance Analysis of a Proposed Reinforced Model
7.5.6 Analyzing Industrial Pumps’ Performance Based on RL
7.5.7 Analyzing Industrial Pumps’ Performance Based on Graph Theory
7.6 Conclusion
References
8. Artificial Neural Network Model for Radiative Heat Transfer in a Magnetized Tapered Stenosed Artery
Haris Alam Zuberi, Naveen Kumar and Nurul Amira Zainal
8.1 Introduction
8.1.1 Blood Flow and Magnetic Field Effects
8.1.2 Radiative Heat Transfer in Blood Flow
8.1.3 Artificial Neural Networks in Hemodynamic and Thermal Modeling
8.1.4 Research Gaps and Study Objectives
8.2 Mathematical Modeling
8.2.1 Overview of the Problem
8.2.2 Governing Equations
8.2.3 Boundary Conditions
8.2.4 Physics-Informed Neural Network Model
8.3 Methodology: Implementation of a Physics-Informed Neural Network Model in MATLAB
8.4 Results and Discussion
8.5 Validation of a Physics-Informed Neural Network Model
8.6 Conclusions
8.7 Medical Applications and Future Prospects
References
9. Artificial Intelligence-Driven Flow Optimization in Renewable Energy Systems
Sachin Kumar, Vipin Kumar Sharma, Dinesh Kumar Patel, Gaurav Nandan and Tushar Sagar
9.1 Introduction
9.2 Artificial Intelligence in Wind Energy Systems
9.2.1 Turbine Blade Optimization
9.2.2 Pitch Control Systems
9.2.3 Predictive Maintenance
9.3 Artificial Intelligence in Hydroelectric Power Systems
9.3.1 Fluid Flow Modeling
9.3.2 Power Network Management
9.4 Artificial Intelligence in Solar Power Systems
9.4.1 Artificial Intelligence-Optimized Trackers
9.4.2 Combined Wind and Solar Systems
9.5 Challenges and Future Directions
9.5.1 Data Quality Issues
9.5.2 Data Quality Problems
9.5.3 Computational Costs
9.6 Conclusion
References
10. Artificial Intelligence-Driven Flow Optimization for Enhanced Efficiency in Renewable Energy Systems
Kavita Sanjay Singh, V. Shanmugapriya, Siddharth Shankar Mishra and Manvendra Singh
10.1 Introduction
10.2 Fundamentals of Flow Dynamics in Renewable Energy Systems
10.2.1 Basic Principles of Fluid Dynamics as Applicable to Wind and Hydro Energy Systems
10.2.2 Overview of Heat Transfer in Solar Thermal Systems
10.2.3 Challenges in Optimizing Natural and Forced Flow in Renewable Systems
10.3 Artificial Intelligence Technologies in Renewable Energy
10.3.1 Introduction to Artificial Intelligence and Its Subsets: Machine Learning, Deep Learning, and Reinforcement Learning
10.3.2 Current Artificial Intelligence Technologies Used in Renewable Energy
10.4 Artificial Intelligence Models for Flow Prediction and Optimization
10.4.1 Predictive Models for Flow and Performance Forecasting
10.4.2 Integration of Artificial Intelligence with Computational Fluid Dynamics
10.4.2.1 Enhancing Computational Efficiency
10.4.2.2 Increasing Accuracy and Predictive Capabilities
10.4.2.3 Adaptive Meshing
10.4.2.4 Real-Time Simulation and Control
10.4.2.5 Multi-Fidelity Modeling
10.5 Optimizing Hydrodynamic Processes in Hydropower
10.5.1 Artificial Intelligence Applications in Water Flow Prediction and Management
10.5.2 Enhancements in Turbine Efficiency and Maintenance through Artificial Intelligence
10.6 Artificial Intelligence-Enhanced Solar Energy Systems
10.6.1 Use of Artificial Intelligence in Optimizing Flow Processes in Solar Heating and Cooling Systems
10.6.2 Enhancements in Photovoltaic System Performance through AI-Based Predictive Maintenance
10.7 Challenges and Future Prospects
10.8 Conclusion
References
11. Artificial Intelligence for Flow Optimization in Renewable Energy Systems
Devanshi Srivastava and Adarsh Kumar Arya
11.1 Introduction
11.2 Artificial Intelligence, Deep Learning, and the Sustainable Development Goals
11.3 Analysis of Artificial Intelligence Technologies in Sustainable Power
11.3.1 Machine Learning and Deep Learning
11.3.2 Reinforcement Learning
11.3.3 Optimization Algorithms
11.4 Technology for Energy Efficiency
11.4.1 Zero-Emission Vehicles
11.4.2 Cogeneration
11.4.3 Virtual Power Plant Simulation
11.4.4 Modern Meter
11.5 Recently Developed Artificial Intelligence-Powered Optimization Methods
11.5.1 The Black Widow Technique
11.5.2 Sailfish Optimizer
11.5.3 Deer Hunting Algorithm
11.5.4 A Swarm Method for Tunicates
11.5.5 Artificial Electric Field Algorithm
11.5.6 Algorithm for Water Strider
11.5.7 Political Processes Optimizer (PO)
11.6 Applications of Artificial Intelligence and Deep Learning for Ecological Well-Being
11.7 Using Artificial Intelligence and Deep Learning for Energy Efficiency in Smart Buildings
11.8 Application of Artificial Intelligence in Solid Waste Management Systems and Predictive Analysis Model in Solar Synergy
11.9 Ethical Concerns, Limitations, and Potential Biases in AI-Driven Environmental Solutions
11.10 Obstacles and Prospective Pathways
11.11 Conclusions
References
12. Physics-Informed Neural Networks for Exothermic Reactions in Porous Media
Pavan Patel and Saroj R. Yadav
12.1 Introduction
12.2 Mathematical Model
12.3 The Building Block of Physics-Informed Neural Networks
12.4 Experiments’ Results and Discussion
12.5 Conclusion
References
13. Machine Learning for Magnetohydrodynamic Nanofluid Flow: Artificial Neural Networks vs. Traditional Methods
B.C. Rout, Bijoylakshmi Boruah, Utpal Kumar Saha, Madhusudan Senapati, Sakambari Mishra, Vikash Kumar and Bhimanand Pandurang Gajbhare
Nomenclature
13.1 Introduction
13.2 Problem Description
13.3 Results and Discussion
13.3.1 Analyzing Concentration Profiles with Varying Le Using Artificial Neural Networks and BVP4C
13.3.2 Results of Artificial Neural Networks and BVP4C Comparison
13.4 Conclusion
References
14. Case Studies of Artificial Intelligence in Industrial Fluid and Thermal Processes
Abdulhalim Musa Abubakar, Kiran Batool, Muhammad Asif and Baudilio Coto
14.1 Introduction
14.2 Artificial Intelligence Techniques in Fluid Flow and Heat Transfer
14.3 Artificial Intelligence in Chemical Processing Industries
14.3.1 Case Study 1: Optimizing Heat Exchangers
14.3.2 Case Study 2: Enhancing Fluid Mixing in Reactors
14.3.3 Case Study 3: Distillation Columns
14.4 Artificial Intelligence in Power Generation
14.4.1 Case Study 4: Improving Thermal Efficiency in Power Plants
14.4.2 Case Study 5: AI-Driven Cooling System Optimization
14.5 Artificial Intelligence in Manufacturing and Electronic Components
14.5.1 Case Study 6: Thermal Management of Electronic Components
14.5.2 Case Study 7: AI for Fluid Flow Optimization in Manufacturing Processes
14.6 Challenges, Limitations, and Recommended Solutions
14.7 Conclusion
References
15. Artificial Intelligence in Microfluidics and Nanofluidics
Ashish Mathur, Souradeep Roy and Rabab Fatima
15.1 Introduction
15.1.1 Microfluidics and Nanofluidics: An Overview
15.1.2 Integration of Artificial Intelligence in Microfluidics and Nanofluidics
15.1.3 Emerging Artificial Intelligence Techniques for Microfluidics and Nanofluidics
15.1.4 Machine Learning and Deep Learning Applications in Microfluidics and Nanofluidics
15.1.5 Challenges in the Field
15.2 Case Study
15.3 Predictive Modeling of Fluid Behaviour
15.4 Real-Time Control Systems
15.5 Artificial Intelligence and Edge Computing for Real-Time Applications
15.5.1 Automation of Experimental Workflows
15.5.2 Practical Applications
15.5.3 Artificial Intelligence-Driven Drug Delivery Systems
15.6 Environmental Sensors
15.7 Challenges and Limitations
15.8 Future Directions
15.8.1 Integration of AI with Quantum Computing for Fluidics
15.8.2 Sustainability and Green Artificial Intelligence in Fluidics
15.8.3 Edge Artificial Intelligence for Real-Time Applications
15.8.4 Quantum Computing
15.8.5 Interdisciplinary Collaborations
15.9 Regulatory and Ethical Considerations of Artificial Intelligence in Microfluidics
15.10 Conclusion
References
About the Editors
Index
Back to Top
Preface
1. Artificial Intelligence in Heat Transfer and Fluid Dynamics: Innovations, Applications, and Future Directions
R. Sakthikala and R. Revathi
1.1 Introduction
1.2 Theoretical Foundations of Heat Transfer and Fluid Dynamics
1.2.1 Governing Equations
1.3 Artificial Intelligence in Engineering: Methods and Techniques
1.3.1 Neural Networks
1.3.2 Reinforcement Learning
1.3.3 Physics-Informed Neural Networks
1.4 Artificial Intelligence Applications in Heat Transfer
1.4.1 Thermal System Optimization
1.4.2 Real-Time Monitoring
1.4.3 Material Discovery
1.5 Artificial Intelligence in Fluid Dynamics
1.5.1 Turbulence Modeling
1.5.2 Aerodynamic Optimization
1.6 Practical Implementations
1.6.1 Applications in Aerospace
1.6.2 Power Generation
1.6.3 Chemical Processing
1.7 Challenges and Future Directions
1.7.1 Data Scarcity
1.7.2 Model Interpretability
1.7.3 Integration with Quantum Computing
1.8 Conclusion
References
2. Machine Learning Applications in Fluid Mechanics
A. Ahadi, P. Hosseini Baei and M. Sheikholeslami
2.1 Introduction
2.2 The Basics of Machine Learning
2.2.1 Supervised Learning
2.2.1.1 Neural Networks
2.2.1.2 Random Forests and Support Vector Machines for Classification
2.2.2 Unsupervised Learning
2.2.2.1 Vector Quantization and Clustering
2.2.3 Semi-Supervised Learning
2.3 Fluid Mechanics Machine Learning Influenced by Physics
2.3.1 The Problem
2.3.1.1 Examples Related to Fluid Mechanics
2.3.2 The Data
2.3.2.1 Examples Related to Fluid Mechanics
2.3.3 The Architecture
2.3.3.1 Examples Related to Fluid Mechanics
2.3.4 The Loss Function
2.3.4.1 Examples Related to Fluid Mechanics
2.3.5 The Optimization Algorithm
2.3.5.1 Examples Related to Fluid Mechanics
2.4 Methods for Modeling Turbulence
2.4.1 Direct Numerical Simulation
2.4.2 Reynolds Averaged Navier–Stokes
2.4.3 Large Eddy Simulation
2.5 Machine Learning in Fluid Dynamics: Obstacles and Prospects
2.6 Summary
References
3. Artificial Intelligence-Enhanced Developments in Computational Fluid Dynamics
Tushar Sagar, Sachin Kumar, Dinesh Kumar Patel, Gaurav Nandan and Vipin Kumar Sharma
3.1 Introduction
3.2 An Overview of Artificial Intelligence in Computational Fluid Dynamics
3.2.1 Evolution of Computational Fluid Dynamics
3.2.2 Artificial Intelligence and Machine Learning Advances
3.2.3 Past Research on Artificial Intelligence in Computational Fluid Dynamics
3.3 Methodology of Artificial Intelligence-Driven Enhancement in Computational Fluid Dynamics
3.3.1 Data Collection
3.3.2 Artificial Intelligence Techniques
3.3.3 Computational Fluid Dynamics Simulation Framework
3.4 Discussion
3.4.1 Performance Metrics
3.4.1.1 Computational Efficiency
3.4.1.2 Accuracy
3.4.1.3 Generalization Ability
3.4.1.4 Performance Formula
3.5 Case Study
3.6 Conclusion
References
4. Artificial Neural Network-Based Analysis of Natural Convection in Ag-TiO2/H2O Hybrid Nanoliquids
Madhavarao Kulkarni
4.1 Introduction
4.2 Mathematical Modeling
4.3 Methods of Solution
4.4 Results and Discussion
4.4.1 Artificial Neural Network Modeling Graphs
4.5 Conclusions
References
5. Artificial Intelligence-Based Optimization of Heat Transfer in Gyrotactic-Nanofluid Flow
Priyanka Chandra and Raja Das
5.1 Introduction
5.2 Mathematical Modeling
5.3 Numerical Method
5.4 Results and Discussions
5.4.1 Optimization Process
5.4.1.1 Levenberg–Marquardt Algorithm
5.4.2 Optimization Analysis by the Central Composite Factorial Design and Artificial Neural Network, Central Composite Factorial Design
5.4.2.1 Designing an Artificial Neural Network Model for Nux()0 Against Nb, Pr, and n
5.5 Conclusions
References
6. Artificial Intelligence-Based Heat Exchanger Design and Optimization
Sachin Mishra, Raj Kumar, Shailendra Singh Rathore, Sakshi Saxena, Pushpendra Sharma, Shubhra Khare and Kuldeep Chauhan
6.1 Introduction
6.2 Artificial Intelligence-Based Heat Exchanger Design and Optimization
6.3 Principles of Heat Exchangers
6.3.1 Coaxial Heat Exchangers
6.3.2 Plate Heat Exchangers
6.3.3 Cooled by Air Heat Exchangers
6.4 Two-Pipe Heat Exchangers
6.5 Performance and Optimization Metrics
6.6 Worldwide Market for Heat Exchangers
6.7 Basic Equation of Heat Transfer
6.8 Designing Heat Exchangers Thermally
6.9 Issue with Thermal Design of Heat Exchanger
6.10 The Need for Artificial Intelligence in Heat Exchanger Design and Optimization
6.10.1 Complexity of Heat Exchanger Design
6.11 Artificial Intelligence in Heat Exchanger Design
6.12 Benefits of Artificial Intelligence in Heat Exchanger Design and Optimization
6.13 Artificial Intelligence Applications in Different Types of Heat Exchangers
6.13.1 Future Directions and Challenges
6.14 Significance of Artificial Intelligence in Heat Exchanger Design
6.15 Key Aspects of Artificial Intelligence in Heat Exchanger Design
6.15.1 Optimization of Heat Transfer Performance
6.15.2 Enhanced Simulation and Modeling
6.15.3 Design Space Exploration
6.15.4 Predictive Maintenance and Fault Detection
6.15.5 Customization and Personalization
6.15.6 Sustainability and Energy Efficiency
6.15.7 Automation of the Design Process
6.16 Applications of Artificial Intelligence in Heat Exchanger Design
6.16.1 Energy Recovery Systems
6.16.2 Real-Time Performance Monitoring
6.16.3 Heat Exchangers
6.17 Upcoming Developments and Trends
6.17.1 Cutting-Edge Materials
6.17.2 Nanomaterials
6.17.3 Composite Materials
6.17.4 Advanced Coating
6.18 Heat Exchange Design
6.18.1 Log-Mean Temperature Difference Method
6.18.2 Optimization of Heat Exchanger
6.18.3 Genetic Algorithm
6.19 Innovation in Heat Exchanger Design
6.19.1 Small and Miniaturized Designs
6.19.2 Enhanced Heat Transfer Technologies
6.19.3 Modular Designs
6.20 Challenges in Artificial Intelligence-Based Heat Exchanger Design
6.20.1 Quality and Availability of the Data
6.20.2 Computational Challenges
6.20.3 Model Interpretability and Trust
6.20.4 Industry Barriers
6.20.5 Strategies to Overcome Barriers or Solutions to this Problem
6.20.5.1 Enhancing Data Availability
6.20.5.2 Improving Computational Efficiency
6.20.5.3 Enhancing Model Interpretability
6.20.5.4 Facilitating Industry Adoption
6.21 Conclusion
Bibliography
7. Artificial Intelligence-Driven Energy Optimization in Heating, Ventilation, and Air Conditioning Systems
G. Gandhimathi, C. Chellaswamy, S. Sridevi and Mohamed M. Awad
7.1 Introduction
7.1.1 Objective of this Chapter
7.1.2 Motivation
7.2 Literature Review
7.2.1 Machine Learning in Fluid Mechanics and Heating, Ventilation, and Air Conditioning Systems
7.2.2 Game Theory in Energy Efficiency and System Optimization
7.2.3 Reinforcement Learning for Heating, Ventilation, and Air Conditioning Control
7.2.4 Optimization and Control of Fluid Flows
7.2.5 Predictive Maintenance and Energy Optimization
7.3 Game Theory Structure of Liquid Flow
7.3.1 Introduction of Matrix Format
7.4 Liquid Flow of Fluid-Structural System
7.4.1 Game Theory with Partial Differential Equations Model for Stabilization and Interference of Liquids
7.4.1.1 Overview of Dynamics
7.4.1.2 Hypotheses
7.4.1.3 Fixed Interval Intermission in the Liquid Flow
7.4.1.4 Perfect Game Theory for Liquid Structural Communications
7.5 Result and Discussion
7.5.1 Data Collection
7.5.2 Performance of Game Theory
7.5.3 Predictive Analysis Using Different Machine Learning Algorithms
7.5.4 Predictive Analysis Using Reinforced Algorithm
7.5.5 Performance Analysis of a Proposed Reinforced Model
7.5.6 Analyzing Industrial Pumps’ Performance Based on RL
7.5.7 Analyzing Industrial Pumps’ Performance Based on Graph Theory
7.6 Conclusion
References
8. Artificial Neural Network Model for Radiative Heat Transfer in a Magnetized Tapered Stenosed Artery
Haris Alam Zuberi, Naveen Kumar and Nurul Amira Zainal
8.1 Introduction
8.1.1 Blood Flow and Magnetic Field Effects
8.1.2 Radiative Heat Transfer in Blood Flow
8.1.3 Artificial Neural Networks in Hemodynamic and Thermal Modeling
8.1.4 Research Gaps and Study Objectives
8.2 Mathematical Modeling
8.2.1 Overview of the Problem
8.2.2 Governing Equations
8.2.3 Boundary Conditions
8.2.4 Physics-Informed Neural Network Model
8.3 Methodology: Implementation of a Physics-Informed Neural Network Model in MATLAB
8.4 Results and Discussion
8.5 Validation of a Physics-Informed Neural Network Model
8.6 Conclusions
8.7 Medical Applications and Future Prospects
References
9. Artificial Intelligence-Driven Flow Optimization in Renewable Energy Systems
Sachin Kumar, Vipin Kumar Sharma, Dinesh Kumar Patel, Gaurav Nandan and Tushar Sagar
9.1 Introduction
9.2 Artificial Intelligence in Wind Energy Systems
9.2.1 Turbine Blade Optimization
9.2.2 Pitch Control Systems
9.2.3 Predictive Maintenance
9.3 Artificial Intelligence in Hydroelectric Power Systems
9.3.1 Fluid Flow Modeling
9.3.2 Power Network Management
9.4 Artificial Intelligence in Solar Power Systems
9.4.1 Artificial Intelligence-Optimized Trackers
9.4.2 Combined Wind and Solar Systems
9.5 Challenges and Future Directions
9.5.1 Data Quality Issues
9.5.2 Data Quality Problems
9.5.3 Computational Costs
9.6 Conclusion
References
10. Artificial Intelligence-Driven Flow Optimization for Enhanced Efficiency in Renewable Energy Systems
Kavita Sanjay Singh, V. Shanmugapriya, Siddharth Shankar Mishra and Manvendra Singh
10.1 Introduction
10.2 Fundamentals of Flow Dynamics in Renewable Energy Systems
10.2.1 Basic Principles of Fluid Dynamics as Applicable to Wind and Hydro Energy Systems
10.2.2 Overview of Heat Transfer in Solar Thermal Systems
10.2.3 Challenges in Optimizing Natural and Forced Flow in Renewable Systems
10.3 Artificial Intelligence Technologies in Renewable Energy
10.3.1 Introduction to Artificial Intelligence and Its Subsets: Machine Learning, Deep Learning, and Reinforcement Learning
10.3.2 Current Artificial Intelligence Technologies Used in Renewable Energy
10.4 Artificial Intelligence Models for Flow Prediction and Optimization
10.4.1 Predictive Models for Flow and Performance Forecasting
10.4.2 Integration of Artificial Intelligence with Computational Fluid Dynamics
10.4.2.1 Enhancing Computational Efficiency
10.4.2.2 Increasing Accuracy and Predictive Capabilities
10.4.2.3 Adaptive Meshing
10.4.2.4 Real-Time Simulation and Control
10.4.2.5 Multi-Fidelity Modeling
10.5 Optimizing Hydrodynamic Processes in Hydropower
10.5.1 Artificial Intelligence Applications in Water Flow Prediction and Management
10.5.2 Enhancements in Turbine Efficiency and Maintenance through Artificial Intelligence
10.6 Artificial Intelligence-Enhanced Solar Energy Systems
10.6.1 Use of Artificial Intelligence in Optimizing Flow Processes in Solar Heating and Cooling Systems
10.6.2 Enhancements in Photovoltaic System Performance through AI-Based Predictive Maintenance
10.7 Challenges and Future Prospects
10.8 Conclusion
References
11. Artificial Intelligence for Flow Optimization in Renewable Energy Systems
Devanshi Srivastava and Adarsh Kumar Arya
11.1 Introduction
11.2 Artificial Intelligence, Deep Learning, and the Sustainable Development Goals
11.3 Analysis of Artificial Intelligence Technologies in Sustainable Power
11.3.1 Machine Learning and Deep Learning
11.3.2 Reinforcement Learning
11.3.3 Optimization Algorithms
11.4 Technology for Energy Efficiency
11.4.1 Zero-Emission Vehicles
11.4.2 Cogeneration
11.4.3 Virtual Power Plant Simulation
11.4.4 Modern Meter
11.5 Recently Developed Artificial Intelligence-Powered Optimization Methods
11.5.1 The Black Widow Technique
11.5.2 Sailfish Optimizer
11.5.3 Deer Hunting Algorithm
11.5.4 A Swarm Method for Tunicates
11.5.5 Artificial Electric Field Algorithm
11.5.6 Algorithm for Water Strider
11.5.7 Political Processes Optimizer (PO)
11.6 Applications of Artificial Intelligence and Deep Learning for Ecological Well-Being
11.7 Using Artificial Intelligence and Deep Learning for Energy Efficiency in Smart Buildings
11.8 Application of Artificial Intelligence in Solid Waste Management Systems and Predictive Analysis Model in Solar Synergy
11.9 Ethical Concerns, Limitations, and Potential Biases in AI-Driven Environmental Solutions
11.10 Obstacles and Prospective Pathways
11.11 Conclusions
References
12. Physics-Informed Neural Networks for Exothermic Reactions in Porous Media
Pavan Patel and Saroj R. Yadav
12.1 Introduction
12.2 Mathematical Model
12.3 The Building Block of Physics-Informed Neural Networks
12.4 Experiments’ Results and Discussion
12.5 Conclusion
References
13. Machine Learning for Magnetohydrodynamic Nanofluid Flow: Artificial Neural Networks vs. Traditional Methods
B.C. Rout, Bijoylakshmi Boruah, Utpal Kumar Saha, Madhusudan Senapati, Sakambari Mishra, Vikash Kumar and Bhimanand Pandurang Gajbhare
Nomenclature
13.1 Introduction
13.2 Problem Description
13.3 Results and Discussion
13.3.1 Analyzing Concentration Profiles with Varying Le Using Artificial Neural Networks and BVP4C
13.3.2 Results of Artificial Neural Networks and BVP4C Comparison
13.4 Conclusion
References
14. Case Studies of Artificial Intelligence in Industrial Fluid and Thermal Processes
Abdulhalim Musa Abubakar, Kiran Batool, Muhammad Asif and Baudilio Coto
14.1 Introduction
14.2 Artificial Intelligence Techniques in Fluid Flow and Heat Transfer
14.3 Artificial Intelligence in Chemical Processing Industries
14.3.1 Case Study 1: Optimizing Heat Exchangers
14.3.2 Case Study 2: Enhancing Fluid Mixing in Reactors
14.3.3 Case Study 3: Distillation Columns
14.4 Artificial Intelligence in Power Generation
14.4.1 Case Study 4: Improving Thermal Efficiency in Power Plants
14.4.2 Case Study 5: AI-Driven Cooling System Optimization
14.5 Artificial Intelligence in Manufacturing and Electronic Components
14.5.1 Case Study 6: Thermal Management of Electronic Components
14.5.2 Case Study 7: AI for Fluid Flow Optimization in Manufacturing Processes
14.6 Challenges, Limitations, and Recommended Solutions
14.7 Conclusion
References
15. Artificial Intelligence in Microfluidics and Nanofluidics
Ashish Mathur, Souradeep Roy and Rabab Fatima
15.1 Introduction
15.1.1 Microfluidics and Nanofluidics: An Overview
15.1.2 Integration of Artificial Intelligence in Microfluidics and Nanofluidics
15.1.3 Emerging Artificial Intelligence Techniques for Microfluidics and Nanofluidics
15.1.4 Machine Learning and Deep Learning Applications in Microfluidics and Nanofluidics
15.1.5 Challenges in the Field
15.2 Case Study
15.3 Predictive Modeling of Fluid Behaviour
15.4 Real-Time Control Systems
15.5 Artificial Intelligence and Edge Computing for Real-Time Applications
15.5.1 Automation of Experimental Workflows
15.5.2 Practical Applications
15.5.3 Artificial Intelligence-Driven Drug Delivery Systems
15.6 Environmental Sensors
15.7 Challenges and Limitations
15.8 Future Directions
15.8.1 Integration of AI with Quantum Computing for Fluidics
15.8.2 Sustainability and Green Artificial Intelligence in Fluidics
15.8.3 Edge Artificial Intelligence for Real-Time Applications
15.8.4 Quantum Computing
15.8.5 Interdisciplinary Collaborations
15.9 Regulatory and Ethical Considerations of Artificial Intelligence in Microfluidics
15.10 Conclusion
References
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