Artificial Intelligence Applications in Aeronautical and Aerospace Engineering

K. Sathish Kumar, PhD is a professor in the Department of Aeronautical Engineering at the Nehru Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India with over 15 years of research and teaching experience. He has authored numerous articles in international journals and serves as a mentor to several start-ups, fostering innovation in aerospace. His research focuses on jet mixing characteristics, nozzle design, and supersonic flow control. R. Naren Shankar, PhD is a professor in the Department of Aeronautical Engineering at Vel Tech Rangarajan Dr. Sagunthala Research and Development Institute of Science and Technology, India. He has published one book and 32 research articles, and has filed three patents. His research interests encompass high-speed jets, aerodynamics, propulsion, and unconventional energy engineering.

• Preface xviiPart 1: Safety and Security 11 Artificial Intelligence Based Habitual and Average DoS Attack Detection in Avionics and Necessity Estimators in Wireless Ad Hoc and Sensor Networks 3C. R. Bharathi and D. Mahammad RafiNomenclature 41.1 Introduction 41.2 Literature Survey 51.3 MQTT’s Impact in Wired Sensor Networks (WSN) 81.3.1 MQTT (Message Queuing Telemetry Transport) 81.3.2 Mosquitto Broker 101.4 Implementation 101.4.1 Dataset Preparation 101.4.2 Feature Set with Attribute Value and Type 111.4.3 Classification 121.4.4 Data Security of Avionics Systems 121.4.5 Applications for Avionics Systems 141.5 End Results and Talk 141.6 Conclusion 15References 152 Artificial Intelligence Aerospace Based Penetrating Denial of Service Attack in Wireless Sensor Network 19C. R. Bharathi and D. Mahammad Rafi2.1 Overview 202.2 Related Work 212.3 Applications of Artificial Intelligence Based on DoS Detection 242.3.1 Compiling and Modifying Data 242.3.2 Choosing Features 252.4 Attack Model 282.4.1 Artificial Intelligence Aerospace Sensor Network Architecture 292.4.2 Aerospace WSNs, Denial-of-Service Attacks 302.5 Conclusion 33References 343 Application of Artificial Intelligence and Machine Learning in Computational Fluid Dynamics 37G. Gowtham, S. Nithya and R. SundharesanIntroduction 38Motivation for AI in CFD 39Applications of AI in CFD 40Challenges and Considerations 41Data Collection 43Pre-Processing 45AI Model Selection 46Training Data Generation 49AI Model Training 51Model Validation 52CFD Prediction 54Post-Processing 55Future Directions 56Conclusion 58References 584 Deep Learning Based Secure Predictive Maintenance Framework for Industrial Maintenance Using Autonomous Drones 61Sharanya S., Karthikeyan S., Prabhakar E. and Manirao Ramachandrarao4.1 Evolution of Industrial Maintenance 624.1.1 Condition Monitoring in Industries 624.1.2 Classification of Condition Monitoring 634.2 Use Cases of Drone Technology in Industrial Activities 654.3 Security Dimension of Drone Technology 674.3.1 Cyberattacks on Drones 684.3.2 Counter-Drone Measures 694.4 Cybersecurity Framework for Deploying Drones in Predictive Maintenance 704.5 Conclusion 76References 765 Role of Artificial Intelligence in the Life Cycle of Aircraft 79Karthikeyan S., Sharanya S., Manirao Ramachandrarao and N. Dilip Raja5.1 Introduction 805.1.1 Why Aircraft Manufacturing is Very Expensive? 815.2 AI for Aircraft Design 835.3 AI in Determining Aircraft Shape 855.4 AI in Aircraft Production 875.5 AI in Aircraft Assembly Line 895.6 AI in Aircraft Performance Improvement 905.7 Predictive Maintenance in Aircrafts 935.8 Conclusions 95References 966 Artificial Intelligence for Aeronautical and Aerospace Applications Using Fuzzy Logic Controller 99Anumula Swarnalatha and R. Asad Ahmed6.1 Introduction 996.2 Fuzzy Logic Controllers Used in Aircraft 1006.3 Advantages of Fuzzy Logic Controllers in Aerospace 1026.4 Applications 1036.4.1 Fuzzy Logic Controller Design for an Aircraft 1036.5 Conclusion 106References 1067 Revolutionizing Aerospace Quality Control: Harnessing AI for Defect Detection 109Naveen R., Rakesh Kumar C., Kowsalya, Fadhilah Mohd Sakri and Prasad G.7.1 Introduction 1107.1.1 Aerospace Quality Control Background 1107.1.2 The Imperative for Quality Control Transformation 1107.1.3 The Role of AI in the Aerospace Sector 1107.2 Traditional Quality Control Methods 1117.2.1 Limitations and Challenges 1117.2.1.1 Manual Inspection Processes 1127.2.1.2 Time-Consuming Procedures 1127.2.2 Case Studies on Conventional Approaches 1137.2.2.1 Case Study 1: Manual Inspection Failures 1137.2.2.2 Case Study 2: Time-Related Complications 1147.3 AI in Aerospace: A Paradigm Shift 1157.3.1 Overview of AI Technologies 1157.3.1.1 Machine Learning Algorithms 1157.3.1.2 Computer Vision 1167.3.2 Integration of AI in Aerospace Manufacturing 1167.3.2.1 Design Optimization 1167.3.2.2 Real-Time Monitoring 1177.3.3 Advantages of AI for Quality Control 1177.3.3.1 Real-Time Monitoring 1177.4 Defect Detection with AI 1187.4.1 Understanding Defects in Aerospace Components 1187.4.1.1 Types of Defects 1187.4.2 AI Algorithms for Defect Detection 1197.4.2.1 Convolutional Neural Networks (CNNs) for Image Analysis 1197.4.2.2 Anomaly Detection Algorithms 1197.5 Implementation Strategies 1207.5.1 Challenges in Implementing AI for Quality Control 1207.5.1.1 Technical Challenges 1207.5.1.2 Organizational Challenges 1207.5.2 Best Practices and Lessons Learned 1207.5.2.1 Collaborative Cross-Functional Teams 1217.5.2.2 Incremental Implementation 1217.5.3 Regulatory and Ethical Considerations 1217.5.3.1 Compliance with Standards 1217.5.3.2 Ethical AI Practices 1217.6 Future Trends and Innovations 1217.6.1 Evolving Landscape of Aerospace Quality Control 1217.6.1.1 Integration of Advanced Sensors 1227.6.2 Potential Advances in AI for Defect Detection 1227.6.2.1 Explainable AI 1227.6.3 Implications for the Future of Aerospace Manufacturing 1237.6.3.1 Shift in Workforce Skills 1237.7 Impact of AI Techniques on Defect Detection 1237.7.1 Improvement in Defect Detection with AI Techniques 1247.7.2 Specific Outcomes Influenced by AI 1247.7.3 Enhancing Defect Detection with AI: A Comparative Analysis 1257.7.3.1 Traditional Defect Detection Methods 1257.7.3.2 Advantages of AI in Defect Detection 1257.7.4 Case Studies Highlighting AI Improvements 1267.8 Conclusion and Recommendations 1297.8.1 Recap of Key Findings 1297.8.1.1 Evolution of Quality Control 1297.8.1.2 Impact of AI 1297.8.1.3 Future Trends and Innovations 1307.8.2 The Path Forward: Recommendations for Industry Stakeholders 1307.8.2.1 Embrace Continuous Learning 1307.8.2.2 Collaborative Research and Development 1307.8.2.3 Regulatory Engagement 1307.8.3 Final Thoughts on the Future of Aerospace Quality Control 1307.8.4 Scope of the Future Work 131References 1318 Utilizing AI Techniques for Detecting Damage in Aerospace Applications 133Rakesh Kumar C., Naveen R., Kowsalya, Fadhilah Mohd Sakri and Prasath M.S.8.1 Introduction 1348.2 Detection of Damage in Composite Materials for Aircraft Components 1368.2.1 Enhanced Defect Detection with AI: Comparative Analysis 1368.2.2 Recent Studies on AI in Aerospace Engineering 1388.3 AI-Based Aircraft Composite Damage Detection 1398.3.1 Data Collection 1408.3.2 Image Recognition and Computer Vision 1418.3.3 Sensor Data Analysis 1418.3.4 Feature Extraction 1418.3.5 Machine Learning Models 1428.3.6 Anomaly Detection 1438.3.7 Integration of Multiple Data Sources 1438.3.8 Real-Time Monitoring 1438.3.9 Human-in-the-Loop Validation 1448.3.10 Continuous Learning and Improvement 1448.3.11 Regulatory Compliance 1458.3.12 Discussion on the Application and Effectiveness of AI in Detecting Damage 1458.3.13 Improved Detection Accuracy 1458.3.14 Reduced False Positives and False Negatives 1458.3.15 Enhanced Predictive Capabilities 1468.3.16 Comparison with Traditional Methods 1468.3.17 Limitations and Challenges 1468.4 AI Methodologies for Defect Detection in Aerospace Manufacturing 1478.4.1 AI Algorithms 1478.4.2 Metrics and Evaluation Criteria 1478.5 Conclusion 148References 1499 Sense and Avoid System for Navigation of Micro Aerial Vehicle in Cluttered Environments 151Anbarasu B., Anitha G., Balaji G., Shabahat Hasnain Qamar, Sathish Kumar K., Naren Shankar R. and Santhosh Kumar G.9.1 Introduction 1529.2 Related Works 1539.3 Proposed Methodology 1549.4 Sense and Avoid Algorithm 1559.4.1 Raw Disparity to Depth Conversion 1559.4.2 Obstacle Detection 1569.4.3 Collision Avoidance 1579.5 Experimental Results and Discussions 1579.6 Conclusions 165References 165Part 2: Technological Advancements and Innovations 16910 A Review on Mixed Reality and Artificial Intelligence for Smart Aviation Sector: Current Trends, Opportunities, and Challenges 171G. Jegadeeswari, B. Kirubadurai, Jaganraj R. and Vinoth Thangarasu10.1 Introduction 17210.2 A Mixed Reality for Smart Aerospace Engineering 17410.3 Integrated Reality to Enhance the Passenger Experience 17710.4 Opportunities and Challenges During and Post COVID-19 17910.5 Conclusion 181Acknowledgments 182References 18211 A Comprehensive Assessment of Unmanned Aerial Vehicles’ Fuel Cell Electric Propulsion Systems 189Kirubadurai B., Jaganraj R., Jegadeeswari G. and Vinoth Thangarasu11.1 Introduction 19011.2 Fuel Cell Types 19111.3 Machine Learning Technique 19211.4 Problems with UAVs Powered by FC 19211.4.1 Issues of On-Board Hydrogen Storage 19211.4.2 Problem with Limited Power Output 19311.4.3 Slow-Response Issue 19411.4.4 Efficiency Issue of FC Propulsion Systems 19511.4.5 Reinforcement Learning 19611.5 UAV Hardware Design and Integration 20011.5.1 Electrical System Diagram Excluding Super Capacitor and Fuel Cell Stack 20111.6 UAV in the Machine Learning Environment 20211.6.1 Wireless Network/Computer 20211.6.2 Smart Cities and Military 20211.6.3 Agriculture 20311.7 Conclusion 204References 20412 AI-Powered Prediction of Centerline Total Pressure Variations in Coaxial Nozzles by Varying the Lip Thickness 211R. Naren Shankar, Irish Angelin S., Bakiya Ambikapathy, K. Sathish Kumar and Parvathy Rajendran12.1 Introduction 21212.2 Methodology 21312.3 Results and Discussions 21812.4 Conclusion 223References 22313 Enhancing Jet Noise Reduction: AI-Powered Predictions of Core Length and Total Pressure Variations in Coaxial Nozzles 225R. Naren Shankar, Irish Angelin S., Bakiya Ambikapathy, K. Sathish Kumar and Parvathy Rajendran13.1 Introduction 22613.2 Methodology 22713.3 Results and Discussions 23313.4 Conclusion 238References 23814 Application of Artificial Intelligence and Machine Learning in Composite Material Design 241G. Gowtham, S. Nithya and J. V. Saiprasanna KumarIntroduction 242Overview 243AI Uses in Different Sectors 246Challenges and Considerations 249AI Use in Aircraft Materials 250Material Discovery and Design 251Material Optimization 252Quality Control 254Predictive Maintenance 255Composite Material Design 256Material Recycling 257Data Analytics for Performance Monitoring 259Supply Chain Management 259Energy Efficiency and Sustainability 261Conclusion 263References 26315 Design Optimization Study of UAV Propeller Using Aeroacoustics 265Prem Kumar P.S., Kirthika S., Kishore Kumar S. and Hariharasubramaniyan A.Nomenclature 266Introduction 266Methodology 268Computational Implementation 268Domain Generation 269Meshing 270Solver Setup and Boundary Conditions 271Results and Discussion 272Base Propeller 272Serration Design 1 272Serration Design 2 272Serration Design 3 274Conclusion and Future Work 275References 27516 Autonomous Mapping and AI-Based Navigation Using Deep Learning, SLAM, and Optical Flow for Micro Aerial Vehicle 277B. Anbarasu, S. Seralathan and A. Muthuram16.1 Introduction 27816.2 Related Work 28116.2.1 AI-Based MAV Navigation 28116.3 Methodology 28216.3.1 SLAM System for UAV Navigation 28316.3.2 US City Block Dataset for MAV Navigation 28416.3.3 Data Collection for MAV Navigation 28616.3.4 CNN Model and Preprocessing for MAV Navigation 28916.3.4.1 CNN Model Training 29016.3.5 Gunnar-Farnebäck Algorithm 29116.4 Results and Discussions 29216.5 Conclusion 298References 300Part 3: Performance And Efficiency Optimization 30317 The Essential Phases in Aircraft Component Manufacturing Using Artificial Intelligence 305Boopathy G., Rajamurugu N., Siva Prakasam P. and Sai Prasanna Kumar J.V.Abbreviations 30617.1 Introduction 30617.2 Precision in Engineering and Design for the Fabrication of Aircraft Components 30817.2.1 Role of Aerospace Engineers in Production of Aircraft Parts 31017.2.2 Design Software Utilized in Fabrication of Aircraft Parts 31017.2.3 Standards for Precision in Performance and Safety of Aircraft Parts 31117.2.4 Potential of Digital Twins in the Manufacturing of Aircraft Components 31217.3 Material Selection and Characteristics of Aircraft Parts 31317.3.1 Significance of Lightweight and Resilient Materials 31517.3.2 Environmentally Harsh Resistance of Materials 31617.3.3 Common Materials Used in Aircraft Component Manufacturing 31717.3.4 Predictive Procurement: Utilizing AI for Strategic Supply Chain Optimization 32017.4 Manufacturing Techniques and Quality Control Measures 32017.4.1 Statistical Process Control Using AI for Real-Time Quality Assurance 32217.5 Assembly Processes and Integration of Aircraft 32317.6 Routine Maintenance and Inspection of Aircraft Parts 32517.7 Conclusion 327References 32818 Artificial Intelligence in Failure Prediction of Aircraft Components and Inventory Leveraging 333Vinu Ramadhas, Krishnadhas Subash and K. Vijayaraja18.1 Introduction 33418.2 Inspection and Defects 33418.2.1 Routine Inspections 33418.2.2 Aircraft Defects 33518.3 Platform-Centric Data 33618.3.1 Routine Inspection Database 33618.3.2 Repair and Component Replacement Database 33618.3.3 Operational Database 33818.3.4 Spare FOL Consumption 33818.3.5 Incident/Accident Details 33818.3.6 HUMS Database 33918.4 Asset-Centric Data 33918.4.1 Aircraft Variant and Numbers 33918.4.2 Operational and Maintenance Staff 34018.4.3 Critical Component Float 34118.4.4 Test Sets and NDT Equipment 34118.4.5 Mandatory Spare Availability 34118.5 Fault Tree Analysis 34218.6 AI-Assisted Application 34418.6.1 Inspection and Maintenance Changes 34418.6.2 Modification and Lifing Analysis 34518.6.3 Exploitation and Operational Limitations 34518.7 Conclusion 346References 34619 Performance Analysis and Optimization of Eppler- 398Unmanned Aerial Vehicle Using Machine Learning Techniques 349R. Manikandan, A. Parthiban, T. Gopalakrishnan and Mandeep Singh19.1 Introduction 35019.1.1 Eppler Profile 35319.1.2 Artificial Intelligence Role in Network-Based UAV 35619.1.3 Wireless Network Issues 35619.1.4 Design of Network Issues 35719.1.5 Localization and Trajectory 35719.2 Experimental Methods 35819.2.1 Design Phase and Wind Tunnel Testing 35819.2.2 Flow Visualization Techniques 35819.3 Computational Model 35919.3.1 Simulation Setup 35919.3.2 Aerodynamic Characteristics 36019.3.3 Airfoil Geometric Creation 36119.3.4 Grid Generation 36219.3.5 Applications of Machine Learning in UAV Using Artificial Neural Network (ANN) 36419.3.6 AI Techniques are Used to Identify and Classify High-Risk Areas and Motion Characteristics of UAVs 36719.4 Results of Smooth, Bump, and Upper Surface Bumped Eppler-398 Airfoil 36819.4.1 Validation 37519.4.2 Flow Visualization Techniques 37619.5 Ann 37719.5.1 Enhancing Security and Privacy in UAV Networks with AI 38219.5.2 Optimizing UAV Network Performance Through Intelligent AI Networking 38319.5.3 Predictive Maintenance in UAV Networks via AI 38419.5.4 AI-Driven Localization and Trajectory Planning in UAV Operations 38519.5.5 Tackling Technical Challenges in AI-UAV Network Integration 38519.6 Summary and Future Work 386References 38820 Navigation of Unconventional Drones — Autonomous Ornithopter 391Syam Narayanan S., P. Rajalaksmi, Yogesh Gangurde, Akshith Mysa and Satyajit Movidi20.1 Ornithopters 39220.1.1 Conventional Versus Unconventional UAVs 39220.1.2 Brief History 39520.2 Autonomous Navigation 39620.2.1 Navigation and Control 39620.3 Autonomous Navigation for Ornithopters 40220.3.1 GPS-Based and GPS-Denied Navigation — Comparative Overview 40320.3.2 Software Systems 40420.3.2.1 Simultaneous Localization and Mapping (SLAM) 40420.3.2.2 ORBSLAM3 for Ornithopters 40520.3.2.3 ROS (Robot Operating System) 40720.3.2.4 ROS Control and Its Use in Ornithopters 40820.4 Artificial Intelligence for Ornithopters 41020.4.1 AI in Navigation 41020.4.2 AI in Control 41020.5 Ultra-Wide Band-Based Indoor GPS System for Ornithopters (Case Study) 41120.5.1 Ultra-Wide Band Technology for Localization 41120.5.1.1 Advantages of UWB for Localization 41220.5.2 Indoor GPS Setup 41320.5.3 Methodology 41320.5.4 Scope of Navigation Using UWB 415Conclusion 416References 416Index 419