{"product_id":"artificial-intelligence-applications-in-aeronautical-and-aerospace-engineering-hardback-9781394268764","title":"Artificial Intelligence Applications in Aeronautical and Aerospace Engineering (Hardback) 9781394268764","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eArtificial Intelligence Applications in Aeronautical and Aerospace Engineering\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eK. Sathish Kumar (Edited by), Kumar (Author), R. Naren Shankar (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394268764, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 10 October 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e448 pages\u003cbr\u003e28 x 19 x 2.6 cm, 0.862 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThis book is a comprehensive guide for anyone in the aeronautical and aerospace fields who wants to understand and leverage the transformative power of artificial intelligence to enhance safety, optimize performance, and drive innovation.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThe field of aeronautical and aerospace engineering is on the brink of a transformative revolution driven by rapid advancements in artificial intelligence (AI). This book analyzes AI’s multifaceted impact on the industry, exploring AI’s potential to address complex challenges, optimize processes, and push technological boundaries with a focus on enhancing safety, security, innovation, and performance. By blending technical insights with practical applications, it provides readers with a roadmap for harnessing AI to solve complex challenges and improve efficiency in aeronautics. Ideal for those seeking a deeper understanding of AI’s role in aeronautical and aerospace engineering, this book offers real-world applications, case studies, and expert insights, making it a valuable resource for anyone aiming to stay at the forefront of this rapidly evolving field. \u003c\/p\u003e\n\u003cp\u003eReaders will find this book: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExamines AI’s transformative role in aerospace and aeronautics, from enhancing safety to driving innovation and optimizing performance;\u003c\/li\u003e \u003cli\u003eHighlights real-time applications, addressing AI’s role in boosting operational efficiency and safety in the aerospace and aeronautical industries;\u003c\/li\u003e \u003cli\u003eOffers insights into emerging AI technologies shaping the future of aerospace and aeronautical systems;\u003c\/li\u003e \u003cli\u003eFeatures real-world case studies on AI applications in autonomous navigation, predictive maintenance of aircraft, and air traffic management.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eAeronautical and aerospace engineers, AI researchers, students, and industry professionals seeking to understand and apply AI solutions in areas like safety, security, and performance optimization.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Safety and Security 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Artificial Intelligence Based Habitual and Average DoS Attack Detection in Avionics and Necessity Estimators in Wireless Ad Hoc and Sensor Networks 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eC. R. Bharathi and D. Mahammad Rafi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eNomenclature 4\u003c\/p\u003e \u003cp\u003e1.1 Introduction 4\u003c\/p\u003e \u003cp\u003e1.2 Literature Survey 5\u003c\/p\u003e \u003cp\u003e1.3 MQTT’s Impact in Wired Sensor Networks (WSN) 8\u003c\/p\u003e \u003cp\u003e1.3.1 MQTT (Message Queuing Telemetry Transport) 8\u003c\/p\u003e \u003cp\u003e1.3.2 Mosquitto Broker 10\u003c\/p\u003e \u003cp\u003e1.4 Implementation 10\u003c\/p\u003e \u003cp\u003e1.4.1 Dataset Preparation 10\u003c\/p\u003e \u003cp\u003e1.4.2 Feature Set with Attribute Value and Type 11\u003c\/p\u003e \u003cp\u003e1.4.3 Classification 12\u003c\/p\u003e \u003cp\u003e1.4.4 Data Security of Avionics Systems 12\u003c\/p\u003e \u003cp\u003e1.4.5 Applications for Avionics Systems 14\u003c\/p\u003e \u003cp\u003e1.5 End Results and Talk 14\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 15\u003c\/p\u003e \u003cp\u003eReferences 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Artificial Intelligence Aerospace Based Penetrating Denial of Service Attack in Wireless Sensor Network 19\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eC. R. Bharathi and D. Mahammad Rafi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Overview 20\u003c\/p\u003e \u003cp\u003e2.2 Related Work 21\u003c\/p\u003e \u003cp\u003e2.3 Applications of Artificial Intelligence Based on DoS Detection 24\u003c\/p\u003e \u003cp\u003e2.3.1 Compiling and Modifying Data 24\u003c\/p\u003e \u003cp\u003e2.3.2 Choosing Features 25\u003c\/p\u003e \u003cp\u003e2.4 Attack Model 28\u003c\/p\u003e \u003cp\u003e2.4.1 Artificial Intelligence Aerospace Sensor Network Architecture 29\u003c\/p\u003e \u003cp\u003e2.4.2 Aerospace WSNs, Denial-of-Service Attacks 30\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 33\u003c\/p\u003e \u003cp\u003eReferences 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Application of Artificial Intelligence and Machine Learning in Computational Fluid Dynamics 37\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eG. Gowtham, S. Nithya and R. Sundharesan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 38\u003c\/p\u003e \u003cp\u003eMotivation for AI in CFD 39\u003c\/p\u003e \u003cp\u003eApplications of AI in CFD 40\u003c\/p\u003e \u003cp\u003eChallenges and Considerations 41\u003c\/p\u003e \u003cp\u003eData Collection 43\u003c\/p\u003e \u003cp\u003ePre-Processing 45\u003c\/p\u003e \u003cp\u003eAI Model Selection 46\u003c\/p\u003e \u003cp\u003eTraining Data Generation 49\u003c\/p\u003e \u003cp\u003eAI Model Training 51\u003c\/p\u003e \u003cp\u003eModel Validation 52\u003c\/p\u003e \u003cp\u003eCFD Prediction 54\u003c\/p\u003e \u003cp\u003ePost-Processing 55\u003c\/p\u003e \u003cp\u003eFuture Directions 56\u003c\/p\u003e \u003cp\u003eConclusion 58\u003c\/p\u003e \u003cp\u003eReferences 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Deep Learning Based Secure Predictive Maintenance Framework for Industrial Maintenance Using Autonomous Drones 61\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSharanya S., Karthikeyan S., Prabhakar E. and Manirao Ramachandrarao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Evolution of Industrial Maintenance 62\u003c\/p\u003e \u003cp\u003e4.1.1 Condition Monitoring in Industries 62\u003c\/p\u003e \u003cp\u003e4.1.2 Classification of Condition Monitoring 63\u003c\/p\u003e \u003cp\u003e4.2 Use Cases of Drone Technology in Industrial Activities 65\u003c\/p\u003e \u003cp\u003e4.3 Security Dimension of Drone Technology 67\u003c\/p\u003e \u003cp\u003e4.3.1 Cyberattacks on Drones 68\u003c\/p\u003e \u003cp\u003e4.3.2 Counter-Drone Measures 69\u003c\/p\u003e \u003cp\u003e4.4 Cybersecurity Framework for Deploying Drones in Predictive Maintenance 70\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 76\u003c\/p\u003e \u003cp\u003eReferences 76\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Role of Artificial Intelligence in the Life Cycle of Aircraft 79\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKarthikeyan S., Sharanya S., Manirao Ramachandrarao and N. Dilip Raja\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 80\u003c\/p\u003e \u003cp\u003e5.1.1 Why Aircraft Manufacturing is Very Expensive? 81\u003c\/p\u003e \u003cp\u003e5.2 AI for Aircraft Design 83\u003c\/p\u003e \u003cp\u003e5.3 AI in Determining Aircraft Shape 85\u003c\/p\u003e \u003cp\u003e5.4 AI in Aircraft Production 87\u003c\/p\u003e \u003cp\u003e5.5 AI in Aircraft Assembly Line 89\u003c\/p\u003e \u003cp\u003e5.6 AI in Aircraft Performance Improvement 90\u003c\/p\u003e \u003cp\u003e5.7 Predictive Maintenance in Aircrafts 93\u003c\/p\u003e \u003cp\u003e5.8 Conclusions 95\u003c\/p\u003e \u003cp\u003eReferences 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Artificial Intelligence for Aeronautical and Aerospace Applications Using Fuzzy Logic Controller 99\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnumula Swarnalatha and R. Asad Ahmed\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 99\u003c\/p\u003e \u003cp\u003e6.2 Fuzzy Logic Controllers Used in Aircraft 100\u003c\/p\u003e \u003cp\u003e6.3 Advantages of Fuzzy Logic Controllers in Aerospace 102\u003c\/p\u003e \u003cp\u003e6.4 Applications 103\u003c\/p\u003e \u003cp\u003e6.4.1 Fuzzy Logic Controller Design for an Aircraft 103\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 106\u003c\/p\u003e \u003cp\u003eReferences 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Revolutionizing Aerospace Quality Control: Harnessing AI for Defect Detection 109\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNaveen R., Rakesh Kumar C., Kowsalya, Fadhilah Mohd Sakri and Prasad G.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 110\u003c\/p\u003e \u003cp\u003e7.1.1 Aerospace Quality Control Background 110\u003c\/p\u003e \u003cp\u003e7.1.2 The Imperative for Quality Control Transformation 110\u003c\/p\u003e \u003cp\u003e7.1.3 The Role of AI in the Aerospace Sector 110\u003c\/p\u003e \u003cp\u003e7.2 Traditional Quality Control Methods 111\u003c\/p\u003e \u003cp\u003e7.2.1 Limitations and Challenges 111\u003c\/p\u003e \u003cp\u003e7.2.1.1 Manual Inspection Processes 112\u003c\/p\u003e \u003cp\u003e7.2.1.2 Time-Consuming Procedures 112\u003c\/p\u003e \u003cp\u003e7.2.2 Case Studies on Conventional Approaches 113\u003c\/p\u003e \u003cp\u003e7.2.2.1 Case Study 1: Manual Inspection Failures 113\u003c\/p\u003e \u003cp\u003e7.2.2.2 Case Study 2: Time-Related Complications 114\u003c\/p\u003e \u003cp\u003e7.3 AI in Aerospace: A Paradigm Shift 115\u003c\/p\u003e \u003cp\u003e7.3.1 Overview of AI Technologies 115\u003c\/p\u003e \u003cp\u003e7.3.1.1 Machine Learning Algorithms 115\u003c\/p\u003e \u003cp\u003e7.3.1.2 Computer Vision 116\u003c\/p\u003e \u003cp\u003e7.3.2 Integration of AI in Aerospace Manufacturing 116\u003c\/p\u003e \u003cp\u003e7.3.2.1 Design Optimization 116\u003c\/p\u003e \u003cp\u003e7.3.2.2 Real-Time Monitoring 117\u003c\/p\u003e \u003cp\u003e7.3.3 Advantages of AI for Quality Control 117\u003c\/p\u003e \u003cp\u003e7.3.3.1 Real-Time Monitoring 117\u003c\/p\u003e \u003cp\u003e7.4 Defect Detection with AI 118\u003c\/p\u003e \u003cp\u003e7.4.1 Understanding Defects in Aerospace Components 118\u003c\/p\u003e \u003cp\u003e7.4.1.1 Types of Defects 118\u003c\/p\u003e \u003cp\u003e7.4.2 AI Algorithms for Defect Detection 119\u003c\/p\u003e \u003cp\u003e7.4.2.1 Convolutional Neural Networks (CNNs) for Image Analysis 119\u003c\/p\u003e \u003cp\u003e7.4.2.2 Anomaly Detection Algorithms 119\u003c\/p\u003e \u003cp\u003e7.5 Implementation Strategies 120\u003c\/p\u003e \u003cp\u003e7.5.1 Challenges in Implementing AI for Quality Control 120\u003c\/p\u003e \u003cp\u003e7.5.1.1 Technical Challenges 120\u003c\/p\u003e \u003cp\u003e7.5.1.2 Organizational Challenges 120\u003c\/p\u003e \u003cp\u003e7.5.2 Best Practices and Lessons Learned 120\u003c\/p\u003e \u003cp\u003e7.5.2.1 Collaborative Cross-Functional Teams 121\u003c\/p\u003e \u003cp\u003e7.5.2.2 Incremental Implementation 121\u003c\/p\u003e \u003cp\u003e7.5.3 Regulatory and Ethical Considerations 121\u003c\/p\u003e \u003cp\u003e7.5.3.1 Compliance with Standards 121\u003c\/p\u003e \u003cp\u003e7.5.3.2 Ethical AI Practices 121\u003c\/p\u003e \u003cp\u003e7.6 Future Trends and Innovations 121\u003c\/p\u003e \u003cp\u003e7.6.1 Evolving Landscape of Aerospace Quality Control 121\u003c\/p\u003e \u003cp\u003e7.6.1.1 Integration of Advanced Sensors 122\u003c\/p\u003e \u003cp\u003e7.6.2 Potential Advances in AI for Defect Detection 122\u003c\/p\u003e \u003cp\u003e7.6.2.1 Explainable AI 122\u003c\/p\u003e \u003cp\u003e7.6.3 Implications for the Future of Aerospace Manufacturing 123\u003c\/p\u003e \u003cp\u003e7.6.3.1 Shift in Workforce Skills 123\u003c\/p\u003e \u003cp\u003e7.7 Impact of AI Techniques on Defect Detection 123\u003c\/p\u003e \u003cp\u003e7.7.1 Improvement in Defect Detection with AI Techniques 124\u003c\/p\u003e \u003cp\u003e7.7.2 Specific Outcomes Influenced by AI 124\u003c\/p\u003e \u003cp\u003e7.7.3 Enhancing Defect Detection with AI: A Comparative Analysis 125\u003c\/p\u003e \u003cp\u003e7.7.3.1 Traditional Defect Detection Methods 125\u003c\/p\u003e \u003cp\u003e7.7.3.2 Advantages of AI in Defect Detection 125\u003c\/p\u003e \u003cp\u003e7.7.4 Case Studies Highlighting AI Improvements 126\u003c\/p\u003e \u003cp\u003e7.8 Conclusion and Recommendations 129\u003c\/p\u003e \u003cp\u003e7.8.1 Recap of Key Findings 129\u003c\/p\u003e \u003cp\u003e7.8.1.1 Evolution of Quality Control 129\u003c\/p\u003e \u003cp\u003e7.8.1.2 Impact of AI 129\u003c\/p\u003e \u003cp\u003e7.8.1.3 Future Trends and Innovations 130\u003c\/p\u003e \u003cp\u003e7.8.2 The Path Forward: Recommendations for Industry Stakeholders 130\u003c\/p\u003e \u003cp\u003e7.8.2.1 Embrace Continuous Learning 130\u003c\/p\u003e \u003cp\u003e7.8.2.2 Collaborative Research and Development 130\u003c\/p\u003e \u003cp\u003e7.8.2.3 Regulatory Engagement 130\u003c\/p\u003e \u003cp\u003e7.8.3 Final Thoughts on the Future of Aerospace Quality Control 130\u003c\/p\u003e \u003cp\u003e7.8.4 Scope of the Future Work 131\u003c\/p\u003e \u003cp\u003eReferences 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Utilizing AI Techniques for Detecting Damage in Aerospace Applications 133\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRakesh Kumar C., Naveen R., Kowsalya, Fadhilah Mohd Sakri and Prasath M.S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 134\u003c\/p\u003e \u003cp\u003e8.2 Detection of Damage in Composite Materials for Aircraft Components 136\u003c\/p\u003e \u003cp\u003e8.2.1 Enhanced Defect Detection with AI: Comparative Analysis 136\u003c\/p\u003e \u003cp\u003e8.2.2 Recent Studies on AI in Aerospace Engineering 138\u003c\/p\u003e \u003cp\u003e8.3 AI-Based Aircraft Composite Damage Detection 139\u003c\/p\u003e \u003cp\u003e8.3.1 Data Collection 140\u003c\/p\u003e \u003cp\u003e8.3.2 Image Recognition and Computer Vision 141\u003c\/p\u003e \u003cp\u003e8.3.3 Sensor Data Analysis 141\u003c\/p\u003e \u003cp\u003e8.3.4 Feature Extraction 141\u003c\/p\u003e \u003cp\u003e8.3.5 Machine Learning Models 142\u003c\/p\u003e \u003cp\u003e8.3.6 Anomaly Detection 143\u003c\/p\u003e \u003cp\u003e8.3.7 Integration of Multiple Data Sources 143\u003c\/p\u003e \u003cp\u003e8.3.8 Real-Time Monitoring 143\u003c\/p\u003e \u003cp\u003e8.3.9 Human-in-the-Loop Validation 144\u003c\/p\u003e \u003cp\u003e8.3.10 Continuous Learning and Improvement 144\u003c\/p\u003e \u003cp\u003e8.3.11 Regulatory Compliance 145\u003c\/p\u003e \u003cp\u003e8.3.12 Discussion on the Application and Effectiveness of AI in Detecting Damage 145\u003c\/p\u003e \u003cp\u003e8.3.13 Improved Detection Accuracy 145\u003c\/p\u003e \u003cp\u003e8.3.14 Reduced False Positives and False Negatives 145\u003c\/p\u003e \u003cp\u003e8.3.15 Enhanced Predictive Capabilities 146\u003c\/p\u003e \u003cp\u003e8.3.16 Comparison with Traditional Methods 146\u003c\/p\u003e \u003cp\u003e8.3.17 Limitations and Challenges 146\u003c\/p\u003e \u003cp\u003e8.4 AI Methodologies for Defect Detection in Aerospace Manufacturing 147\u003c\/p\u003e \u003cp\u003e8.4.1 AI Algorithms 147\u003c\/p\u003e \u003cp\u003e8.4.2 Metrics and Evaluation Criteria 147\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 148\u003c\/p\u003e \u003cp\u003eReferences 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Sense and Avoid System for Navigation of Micro Aerial Vehicle in Cluttered Environments 151\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnbarasu B., Anitha G., Balaji G., Shabahat Hasnain Qamar, Sathish Kumar K., Naren Shankar R. and Santhosh Kumar G.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 152\u003c\/p\u003e \u003cp\u003e9.2 Related Works 153\u003c\/p\u003e \u003cp\u003e9.3 Proposed Methodology 154\u003c\/p\u003e \u003cp\u003e9.4 Sense and Avoid Algorithm 155\u003c\/p\u003e \u003cp\u003e9.4.1 Raw Disparity to Depth Conversion 155\u003c\/p\u003e \u003cp\u003e9.4.2 Obstacle Detection 156\u003c\/p\u003e \u003cp\u003e9.4.3 Collision Avoidance 157\u003c\/p\u003e \u003cp\u003e9.5 Experimental Results and Discussions 157\u003c\/p\u003e \u003cp\u003e9.6 Conclusions 165\u003c\/p\u003e \u003cp\u003eReferences 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Technological Advancements and Innovations 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 A Review on Mixed Reality and Artificial Intelligence for Smart Aviation Sector: Current Trends, Opportunities, and Challenges 171\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eG. Jegadeeswari, B. Kirubadurai, Jaganraj R. and Vinoth Thangarasu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 172\u003c\/p\u003e \u003cp\u003e10.2 A Mixed Reality for Smart Aerospace Engineering 174\u003c\/p\u003e \u003cp\u003e10.3 Integrated Reality to Enhance the Passenger Experience 177\u003c\/p\u003e \u003cp\u003e10.4 Opportunities and Challenges During and Post COVID-19 179\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 181\u003c\/p\u003e \u003cp\u003eAcknowledgments 182\u003c\/p\u003e \u003cp\u003eReferences 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 A Comprehensive Assessment of Unmanned Aerial Vehicles’ Fuel Cell Electric Propulsion Systems 189\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKirubadurai B., Jaganraj R., Jegadeeswari G. and Vinoth Thangarasu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 190\u003c\/p\u003e \u003cp\u003e11.2 Fuel Cell Types 191\u003c\/p\u003e \u003cp\u003e11.3 Machine Learning Technique 192\u003c\/p\u003e \u003cp\u003e11.4 Problems with UAVs Powered by FC 192\u003c\/p\u003e \u003cp\u003e11.4.1 Issues of On-Board Hydrogen Storage 192\u003c\/p\u003e \u003cp\u003e11.4.2 Problem with Limited Power Output 193\u003c\/p\u003e \u003cp\u003e11.4.3 Slow-Response Issue 194\u003c\/p\u003e \u003cp\u003e11.4.4 Efficiency Issue of FC Propulsion Systems 195\u003c\/p\u003e \u003cp\u003e11.4.5 Reinforcement Learning 196\u003c\/p\u003e \u003cp\u003e11.5 UAV Hardware Design and Integration 200\u003c\/p\u003e \u003cp\u003e11.5.1 Electrical System Diagram Excluding Super Capacitor and Fuel Cell Stack 201\u003c\/p\u003e \u003cp\u003e11.6 UAV in the Machine Learning Environment 202\u003c\/p\u003e \u003cp\u003e11.6.1 Wireless Network\/Computer 202\u003c\/p\u003e \u003cp\u003e11.6.2 Smart Cities and Military 202\u003c\/p\u003e \u003cp\u003e11.6.3 Agriculture 203\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 204\u003c\/p\u003e \u003cp\u003eReferences 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 AI-Powered Prediction of Centerline Total Pressure Variations in Coaxial Nozzles by Varying the Lip Thickness 211\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eR. Naren Shankar, Irish Angelin S., Bakiya Ambikapathy, K. Sathish Kumar and Parvathy Rajendran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 212\u003c\/p\u003e \u003cp\u003e12.2 Methodology 213\u003c\/p\u003e \u003cp\u003e12.3 Results and Discussions 218\u003c\/p\u003e \u003cp\u003e12.4 Conclusion 223\u003c\/p\u003e \u003cp\u003eReferences 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Enhancing Jet Noise Reduction: AI-Powered Predictions of Core Length and Total Pressure Variations in Coaxial Nozzles 225\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eR. Naren Shankar, Irish Angelin S., Bakiya Ambikapathy, K. Sathish Kumar and Parvathy Rajendran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 226\u003c\/p\u003e \u003cp\u003e13.2 Methodology 227\u003c\/p\u003e \u003cp\u003e13.3 Results and Discussions 233\u003c\/p\u003e \u003cp\u003e13.4 Conclusion 238\u003c\/p\u003e \u003cp\u003eReferences 238\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Application of Artificial Intelligence and Machine Learning in Composite Material Design 241\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eG. Gowtham, S. Nithya and J. V. Saiprasanna Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 242\u003c\/p\u003e \u003cp\u003eOverview 243\u003c\/p\u003e \u003cp\u003eAI Uses in Different Sectors 246\u003c\/p\u003e \u003cp\u003eChallenges and Considerations 249\u003c\/p\u003e \u003cp\u003eAI Use in Aircraft Materials 250\u003c\/p\u003e \u003cp\u003eMaterial Discovery and Design 251\u003c\/p\u003e \u003cp\u003eMaterial Optimization 252\u003c\/p\u003e \u003cp\u003eQuality Control 254\u003c\/p\u003e \u003cp\u003ePredictive Maintenance 255\u003c\/p\u003e \u003cp\u003eComposite Material Design 256\u003c\/p\u003e \u003cp\u003eMaterial Recycling 257\u003c\/p\u003e \u003cp\u003eData Analytics for Performance Monitoring 259\u003c\/p\u003e \u003cp\u003eSupply Chain Management 259\u003c\/p\u003e \u003cp\u003eEnergy Efficiency and Sustainability 261\u003c\/p\u003e \u003cp\u003eConclusion 263\u003c\/p\u003e \u003cp\u003eReferences 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Design Optimization Study of UAV Propeller Using Aeroacoustics 265\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePrem Kumar P.S., Kirthika S., Kishore Kumar S. and Hariharasubramaniyan A.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eNomenclature 266\u003c\/p\u003e \u003cp\u003eIntroduction 266\u003c\/p\u003e \u003cp\u003eMethodology 268\u003c\/p\u003e \u003cp\u003eComputational Implementation 268\u003c\/p\u003e \u003cp\u003eDomain Generation 269\u003c\/p\u003e \u003cp\u003eMeshing 270\u003c\/p\u003e \u003cp\u003eSolver Setup and Boundary Conditions 271\u003c\/p\u003e \u003cp\u003eResults and Discussion 272\u003c\/p\u003e \u003cp\u003eBase Propeller 272\u003c\/p\u003e \u003cp\u003eSerration Design 1 272\u003c\/p\u003e \u003cp\u003eSerration Design 2 272\u003c\/p\u003e \u003cp\u003eSerration Design 3 274\u003c\/p\u003e \u003cp\u003eConclusion and Future Work 275\u003c\/p\u003e \u003cp\u003eReferences 275\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Autonomous Mapping and AI-Based Navigation Using Deep Learning, SLAM, and Optical Flow for Micro Aerial Vehicle 277\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eB. Anbarasu, S. Seralathan and A. Muthuram\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 278\u003c\/p\u003e \u003cp\u003e16.2 Related Work 281\u003c\/p\u003e \u003cp\u003e16.2.1 AI-Based MAV Navigation 281\u003c\/p\u003e \u003cp\u003e16.3 Methodology 282\u003c\/p\u003e \u003cp\u003e16.3.1 SLAM System for UAV Navigation 283\u003c\/p\u003e \u003cp\u003e16.3.2 US City Block Dataset for MAV Navigation 284\u003c\/p\u003e \u003cp\u003e16.3.3 Data Collection for MAV Navigation 286\u003c\/p\u003e \u003cp\u003e16.3.4 CNN Model and Preprocessing for MAV Navigation 289\u003c\/p\u003e \u003cp\u003e16.3.4.1 CNN Model Training 290\u003c\/p\u003e \u003cp\u003e16.3.5 Gunnar-Farnebäck Algorithm 291\u003c\/p\u003e \u003cp\u003e16.4 Results and Discussions 292\u003c\/p\u003e \u003cp\u003e16.5 Conclusion 298\u003c\/p\u003e \u003cp\u003eReferences 300\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Performance And Efficiency Optimization 303\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 The Essential Phases in Aircraft Component Manufacturing Using Artificial Intelligence 305\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBoopathy G., Rajamurugu N., Siva Prakasam P. and Sai Prasanna Kumar J.V.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 306\u003c\/p\u003e \u003cp\u003e17.1 Introduction 306\u003c\/p\u003e \u003cp\u003e17.2 Precision in Engineering and Design for the Fabrication of Aircraft Components 308\u003c\/p\u003e \u003cp\u003e17.2.1 Role of Aerospace Engineers in Production of Aircraft Parts 310\u003c\/p\u003e \u003cp\u003e17.2.2 Design Software Utilized in Fabrication of Aircraft Parts 310\u003c\/p\u003e \u003cp\u003e17.2.3 Standards for Precision in Performance and Safety of Aircraft Parts 311\u003c\/p\u003e \u003cp\u003e17.2.4 Potential of Digital Twins in the Manufacturing of Aircraft Components 312\u003c\/p\u003e \u003cp\u003e17.3 Material Selection and Characteristics of Aircraft Parts 313\u003c\/p\u003e \u003cp\u003e17.3.1 Significance of Lightweight and Resilient Materials 315\u003c\/p\u003e \u003cp\u003e17.3.2 Environmentally Harsh Resistance of Materials 316\u003c\/p\u003e \u003cp\u003e17.3.3 Common Materials Used in Aircraft Component Manufacturing 317\u003c\/p\u003e \u003cp\u003e17.3.4 Predictive Procurement: Utilizing AI for Strategic Supply Chain Optimization 320\u003c\/p\u003e \u003cp\u003e17.4 Manufacturing Techniques and Quality Control Measures 320\u003c\/p\u003e \u003cp\u003e17.4.1 Statistical Process Control Using AI for Real-Time Quality Assurance 322\u003c\/p\u003e \u003cp\u003e17.5 Assembly Processes and Integration of Aircraft 323\u003c\/p\u003e \u003cp\u003e17.6 Routine Maintenance and Inspection of Aircraft Parts 325\u003c\/p\u003e \u003cp\u003e17.7 Conclusion 327\u003c\/p\u003e \u003cp\u003eReferences 328\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Artificial Intelligence in Failure Prediction of Aircraft Components and Inventory Leveraging 333\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVinu Ramadhas, Krishnadhas Subash and K. Vijayaraja\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 334\u003c\/p\u003e \u003cp\u003e18.2 Inspection and Defects 334\u003c\/p\u003e \u003cp\u003e18.2.1 Routine Inspections 334\u003c\/p\u003e \u003cp\u003e18.2.2 Aircraft Defects 335\u003c\/p\u003e \u003cp\u003e18.3 Platform-Centric Data 336\u003c\/p\u003e \u003cp\u003e18.3.1 Routine Inspection Database 336\u003c\/p\u003e \u003cp\u003e18.3.2 Repair and Component Replacement Database 336\u003c\/p\u003e \u003cp\u003e18.3.3 Operational Database 338\u003c\/p\u003e \u003cp\u003e18.3.4 Spare FOL Consumption 338\u003c\/p\u003e \u003cp\u003e18.3.5 Incident\/Accident Details 338\u003c\/p\u003e \u003cp\u003e18.3.6 HUMS Database 339\u003c\/p\u003e \u003cp\u003e18.4 Asset-Centric Data 339\u003c\/p\u003e \u003cp\u003e18.4.1 Aircraft Variant and Numbers 339\u003c\/p\u003e \u003cp\u003e18.4.2 Operational and Maintenance Staff 340\u003c\/p\u003e \u003cp\u003e18.4.3 Critical Component Float 341\u003c\/p\u003e \u003cp\u003e18.4.4 Test Sets and NDT Equipment 341\u003c\/p\u003e \u003cp\u003e18.4.5 Mandatory Spare Availability 341\u003c\/p\u003e \u003cp\u003e18.5 Fault Tree Analysis 342\u003c\/p\u003e \u003cp\u003e18.6 AI-Assisted Application 344\u003c\/p\u003e \u003cp\u003e18.6.1 Inspection and Maintenance Changes 344\u003c\/p\u003e \u003cp\u003e18.6.2 Modification and Lifing Analysis 345\u003c\/p\u003e \u003cp\u003e18.6.3 Exploitation and Operational Limitations 345\u003c\/p\u003e \u003cp\u003e18.7 Conclusion 346\u003c\/p\u003e \u003cp\u003eReferences 346\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Performance Analysis and Optimization of Eppler- 398\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnmanned Aerial Vehicle Using Machine Learning Techniques 349\u003cbr\u003e \u003ci\u003eR. Manikandan, A. Parthiban, T. Gopalakrishnan and Mandeep Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 350\u003c\/p\u003e \u003cp\u003e19.1.1 Eppler Profile 353\u003c\/p\u003e \u003cp\u003e19.1.2 Artificial Intelligence Role in Network-Based UAV 356\u003c\/p\u003e \u003cp\u003e19.1.3 Wireless Network Issues 356\u003c\/p\u003e \u003cp\u003e19.1.4 Design of Network Issues 357\u003c\/p\u003e \u003cp\u003e19.1.5 Localization and Trajectory 357\u003c\/p\u003e \u003cp\u003e19.2 Experimental Methods 358\u003c\/p\u003e \u003cp\u003e19.2.1 Design Phase and Wind Tunnel Testing 358\u003c\/p\u003e \u003cp\u003e19.2.2 Flow Visualization Techniques 358\u003c\/p\u003e \u003cp\u003e19.3 Computational Model 359\u003c\/p\u003e \u003cp\u003e19.3.1 Simulation Setup 359\u003c\/p\u003e \u003cp\u003e19.3.2 Aerodynamic Characteristics 360\u003c\/p\u003e \u003cp\u003e19.3.3 Airfoil Geometric Creation 361\u003c\/p\u003e \u003cp\u003e19.3.4 Grid Generation 362\u003c\/p\u003e \u003cp\u003e19.3.5 Applications of Machine Learning in UAV Using Artificial Neural Network (ANN) 364\u003c\/p\u003e \u003cp\u003e19.3.6 AI Techniques are Used to Identify and Classify High-Risk Areas and Motion Characteristics of UAVs 367\u003c\/p\u003e \u003cp\u003e19.4 Results of Smooth, Bump, and Upper Surface Bumped Eppler-398 Airfoil 368\u003c\/p\u003e \u003cp\u003e19.4.1 Validation 375\u003c\/p\u003e \u003cp\u003e19.4.2 Flow Visualization Techniques 376\u003c\/p\u003e \u003cp\u003e19.5 Ann 377\u003c\/p\u003e \u003cp\u003e19.5.1 Enhancing Security and Privacy in UAV Networks with AI 382\u003c\/p\u003e \u003cp\u003e19.5.2 Optimizing UAV Network Performance Through Intelligent AI Networking 383\u003c\/p\u003e \u003cp\u003e19.5.3 Predictive Maintenance in UAV Networks via AI 384\u003c\/p\u003e \u003cp\u003e19.5.4 AI-Driven Localization and Trajectory Planning in UAV Operations 385\u003c\/p\u003e \u003cp\u003e19.5.5 Tackling Technical Challenges in AI-UAV Network Integration 385\u003c\/p\u003e \u003cp\u003e19.6 Summary and Future Work 386\u003c\/p\u003e \u003cp\u003eReferences 388\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Navigation of Unconventional Drones — Autonomous Ornithopter 391\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSyam Narayanan S., P. Rajalaksmi, Yogesh Gangurde, Akshith Mysa and Satyajit Movidi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Ornithopters 392\u003c\/p\u003e \u003cp\u003e20.1.1 Conventional Versus Unconventional UAVs 392\u003c\/p\u003e \u003cp\u003e20.1.2 Brief History 395\u003c\/p\u003e \u003cp\u003e20.2 Autonomous Navigation 396\u003c\/p\u003e \u003cp\u003e20.2.1 Navigation and Control 396\u003c\/p\u003e \u003cp\u003e20.3 Autonomous Navigation for Ornithopters 402\u003c\/p\u003e \u003cp\u003e20.3.1 GPS-Based and GPS-Denied Navigation — Comparative Overview 403\u003c\/p\u003e \u003cp\u003e20.3.2 Software Systems 404\u003c\/p\u003e \u003cp\u003e20.3.2.1 Simultaneous Localization and Mapping (SLAM) 404\u003c\/p\u003e \u003cp\u003e20.3.2.2 ORBSLAM3 for Ornithopters 405\u003c\/p\u003e \u003cp\u003e20.3.2.3 ROS (Robot Operating System) 407\u003c\/p\u003e \u003cp\u003e20.3.2.4 ROS Control and Its Use in Ornithopters 408\u003c\/p\u003e \u003cp\u003e20.4 Artificial Intelligence for Ornithopters 410\u003c\/p\u003e \u003cp\u003e20.4.1 AI in Navigation 410\u003c\/p\u003e \u003cp\u003e20.4.2 AI in Control 410\u003c\/p\u003e \u003cp\u003e20.5 Ultra-Wide Band-Based Indoor GPS System for Ornithopters (Case Study) 411\u003c\/p\u003e \u003cp\u003e20.5.1 Ultra-Wide Band Technology for Localization 411\u003c\/p\u003e \u003cp\u003e20.5.1.1 Advantages of UWB for Localization 412\u003c\/p\u003e \u003cp\u003e20.5.2 Indoor GPS Setup 413\u003c\/p\u003e \u003cp\u003e20.5.3 Methodology 413\u003c\/p\u003e \u003cp\u003e20.5.4 Scope of Navigation Using UWB 415\u003c\/p\u003e \u003cp\u003eConclusion 416\u003c\/p\u003e \u003cp\u003eReferences 416\u003c\/p\u003e \u003cp\u003eIndex 419\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mechanical engineering \u0026amp; materials [\u003ca title=\"See our other books on Mechanical engineering \u0026amp; materials\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mechanical%20engineering%20\u0026amp;%20materials%20%5BTG%5D%22\"\u003eTG\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433242423576,"sku":"9781394268764","price":166.98,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394268764.jpg?v=1784852898","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/artificial-intelligence-applications-in-aeronautical-and-aerospace-engineering-hardback-9781394268764","provider":"Freshly Printed Books","version":"1.0","type":"link"}