Preface xvii
Part 1: Safety and Security 1
1 Artificial Intelligence Based Habitual and Average DoS Attack Detection in Avionics and Necessity Estimators in Wireless Ad Hoc and Sensor Networks 3
C. R. Bharathi and D. Mahammad Rafi
Nomenclature 4
1.1 Introduction 4
1.2 Literature Survey 5
1.3 MQTT's Impact in Wired Sensor Networks (WSN) 8
1.3.1 MQTT (Message Queuing Telemetry Transport) 8
1.3.2 Mosquitto Broker 10
1.4 Implementation 10
1.4.1 Dataset Preparation 10
1.4.2 Feature Set with Attribute Value and Type 11
1.4.3 Classification 12
1.4.4 Data Security of Avionics Systems 12
1.4.5 Applications for Avionics Systems 14
1.5 End Results and Talk 14
1.6 Conclusion 15
References 15
2 Artificial Intelligence Aerospace Based Penetrating Denial of Service Attack in Wireless Sensor Network 19
C. R. Bharathi and D. Mahammad Rafi
2.1 Overview 20
2.2 Related Work 21
2.3 Applications of Artificial Intelligence Based on DoS Detection 24
2.3.1 Compiling and Modifying Data 24
2.3.2 Choosing Features 25
2.4 Attack Model 28
2.4.1 Artificial Intelligence Aerospace Sensor Network Architecture 29
2.4.2 Aerospace WSNs, Denial-of-Service Attacks 30
2.5 Conclusion 33
References 34
3 Application of Artificial Intelligence and Machine Learning in Computational Fluid Dynamics 37
G. Gowtham, S. Nithya and R. Sundharesan
Introduction 38
Motivation for AI in CFD 39
Applications of AI in CFD 40
Challenges and Considerations 41
Data Collection 43
Pre-Processing 45
AI Model Selection 46
Training Data Generation 49
AI Model Training 51
Model Validation 52
CFD Prediction 54
Post-Processing 55
Future Directions 56
Conclusion 58
References 58
4 Deep Learning Based Secure Predictive Maintenance Framework for Industrial Maintenance Using Autonomous Drones 61
Sharanya S., Karthikeyan S., Prabhakar E. and Manirao Ramachandrarao
4.1 Evolution of Industrial Maintenance 62
4.1.1 Condition Monitoring in Industries 62
4.1.2 Classification of Condition Monitoring 63
4.2 Use Cases of Drone Technology in Industrial Activities 65
4.3 Security Dimension of Drone Technology 67
4.3.1 Cyberattacks on Drones 68
4.3.2 Counter-Drone Measures 69
4.4 Cybersecurity Framework for Deploying Drones in Predictive Maintenance 70
4.5 Conclusion 76
References 76
5 Role of Artificial Intelligence in the Life Cycle of Aircraft 79
Karthikeyan S., Sharanya S., Manirao Ramachandrarao and N. Dilip Raja
5.1 Introduction 80
5.1.1 Why Aircraft Manufacturing is Very Expensive? 81
5.2 AI for Aircraft Design 83
5.3 AI in Determining Aircraft Shape 85
5.4 AI in Aircraft Production 87
5.5 AI in Aircraft Assembly Line 89
5.6 AI in Aircraft Performance Improvement 90
5.7 Predictive Maintenance in Aircrafts 93
5.8 Conclusions 95
References 96
6 Artificial Intelligence for Aeronautical and Aerospace Applications Using Fuzzy Logic Controller 99
Anumula Swarnalatha and R. Asad Ahmed
6.1 Introduction 99
6.2 Fuzzy Logic Controllers Used in Aircraft 100
6.3 Advantages of Fuzzy Logic Controllers in Aerospace 102
6.4 Applications 103
6.4.1 Fuzzy Logic Controller Design for an Aircraft 103
6.5 Conclusion 106
References 106
7 Revolutionizing Aerospace Quality Control: Harnessing AI for Defect Detection 109
Naveen R., Rakesh Kumar C., Kowsalya, Fadhilah Mohd Sakri and Prasad G.
7.1 Introduction 110
7.1.1 Aerospace Quality Control Background 110
7.1.2 The Imperative for Quality Control Transformation 110
7.1.3 The Role of AI in the Aerospace Sector 110
7.2 Traditional Quality Control Methods 111
7.2.1 Limitations and Challenges 111
7.2.1.1 Manual Inspection Processes 112
7.2.1.2 Time-Consuming Procedures 112
7.2.2 Case Studies on Conventional Approaches 113
7.2.2.1 Case Study 1: Manual Inspection Failures 113
7.2.2.2 Case Study 2: Time-Related Complications 114
7.3 AI in Aerospace: A Paradigm Shift 115
7.3.1 Overview of AI Technologies 115
7.3.1.1 Machine Learning Algorithms 115
7.3.1.2 Computer Vision 116
7.3.2 Integration of AI in Aerospace Manufacturing 116
7.3.2.1 Design Optimization 116
7.3.2.2 Real-Time Monitoring 117
7.3.3 Advantages of AI for Quality Control 117
7.3.3.1 Real-Time Monitoring 117
7.4 Defect Detection with AI 118
7.4.1 Understanding Defects in Aerospace Components 118
7.4.1.1 Types of Defects 118
7.4.2 AI Algorithms for Defect Detection 119
7.4.2.1 Convolutional Neural Networks (CNNs) for Image Analysis 119
7.4.2.2 Anomaly Detection Algorithms 119
7.5 Implementation Strategies 120
7.5.1 Challenges in Implementing AI for Quality Control 120
7.5.1.1 Technical Challenges 120
7.5.1.2 Organizational Challenges 120
7.5.2 Best Practices and Lessons Learned 120
7.5.2.1 Collaborative Cross-Functional Teams 121
7.5.2.2 Incremental Implementation 121
7.5.3 Regulatory and Ethical Considerations 121
7.5.3.1 Compliance with Standards 121
7.5.3.2 Ethical AI Practices 121
7.6 Future Trends and Innovations 121
7.6.1 Evolving Landscape of Aerospace Quality Control 121
7.6.1.1 Integration of Advanced Sensors 122
7.6.2 Potential Advances in AI for Defect Detection 122
7.6.2.1 Explainable AI 122
7.6.3 Implications for the Future of Aerospace Manufacturing 123
7.6.3.1 Shift in Workforce Skills 123
7.7 Impact of AI Techniques on Defect Detection 123
7.7.1 Improvement in Defect Detection with AI Techniques 124
7.7.2 Specific Outcomes Influenced by AI 124
7.7.3 Enhancing Defect Detection with AI: A Comparative Analysis 125
7.7.3.1 Traditional Defect Detection Methods 125
7.7.3.2 Advantages of AI in Defect Detection 125
7.7.4 Case Studies Highlighting AI Improvements 126
7.8 Conclusion and Recommendations 129
7.8.1 Recap of Key Findings 129
7.8.1.1 Evolution of Quality Control 129
7.8.1.2 Impact of AI 129
7.8.1.3 Future Trends and Innovations 130
7.8.2 The Path Forward: Recommendations for Industry Stakeholders 130
7.8.2.1 Embrace Continuous Learning 130
7.8.2.2 Collaborative Research and Development 130
7.8.2.3 Regulatory Engagement 130
7.8.3 Final Thoughts on the Future of Aerospace Quality Control 130
7.8.4 Scope of the Future Work 131
References 131
8 Utilizing AI Techniques for Detecting Damage in Aerospace Applications 133
Rakesh Kumar C., Naveen R., Kowsalya, Fadhilah Mohd Sakri and Prasath M.S.
8.1 Introduction 134
8.2 Detection of Damage in Composite Materials for Aircraft Components 136
8.2.1 Enhanced Defect Detection with AI: Comparative Analysis 136
8.2.2 Recent Studies on AI in Aerospace Engineering 138
8.3 AI-Based Aircraft Composite Damage Detection 139
8.3.1 Data Collection 140
8.3.2 Image Recognition and Computer Vision 141
8.3.3 Sensor Data Analysis 141
8.3.4 Feature Extraction 141
8.3.5 Machine Learning Models 142
8.3.6 Anomaly Detection 143
8.3.7 Integration of Multiple Data Sources 143
8.3.8 Real-Time Monitoring 143
8.3.9 Human-in-the-Loop Validation 144
8.3.10 Continuous Learning and Improvement 144
8.3.11 Regulatory Compliance 145
8.3.12 Discussion on the Application and Effectiveness of AI in Detecting Damage 145
8.3.13 Improved Detection Accuracy 145
8.3.14 Reduced False Positives and False Negatives 145
8.3.15 Enhanced Predictive Capabilities 146
8.3.16 Comparison with Traditional Methods 146
8.3.17 Limitations and Challenges 146
8.4 AI Methodologies for Defect Detection in Aerospace Manufacturing 147
8.4.1 AI Algorithms 147
8.4.2 Metrics and Evaluation Criteria 147
8.5 Conclusion 148
References 149
9 Sense and Avoid System for Navigation of Micro Aerial Vehicle in Cluttered Environments 151
Anbarasu B., Anitha G., Balaji G., Shabahat Hasnain Qamar, Sathish Kumar K., Naren Shankar R. and Santhosh Kumar G.
9.1 Introduction 152
9.2 Related Works 153
9.3 Proposed Methodology 154
9.4 Sense and Avoid Algorithm 155
9.4.1 Raw Disparity to Depth Conversion 155
9.4.2 Obstacle Detection 156
9.4.3 Collision Avoidance 157
9.5 Experimental Results and Discussions 157
9.6 Conclusions 165
References 165
Part 2: Technological Advancements and Innovations 169
10 A Review on Mixed Reality and Artificial Intelligence for Smart Aviation Sector: Current Trends, Opportunities, and Challenges 171
G. Jegadeeswari, B. Kirubadurai, Jaganraj R. and Vinoth Thangarasu
10.1 Introduction 172
10.2 A Mixed Reality for Smart Aerospace Engineering 174
10.3 Integrated Reality to Enhance the Passenger Experience 177
10.4 Opportunities and Challenges During and Post COVID-19 179
10.5 Conclusion 181
Acknowledgments 182
References 182
11 A Comprehensive Assessment of Unmanned Aerial Vehicles' Fuel Cell Electric Propulsion Systems 189
Kirubadurai B., Jaganraj R., Jegadeeswari G. and Vinoth Thangarasu
11.1 Introduction 190
11.2 Fuel Cell Types 191
11.3 Machine Learning Technique 192
11.4 Problems with UAVs Powered by FC 192
11.4.1 Issues of On-Board Hydrogen Storage 192
11.4.2 Problem with Limited Power Output 193
11.4.3 Slow-Response Issue 194
11.4.4 Efficiency Issue of FC Propulsion Systems 195
11.4.5 Reinforcement Learning 196
11.5 UAV Hardware Design and Integration 200
11.5.1 Electrical System Diagram Excluding Super Capacitor and Fuel Cell Stack 201
11.6 UAV in the Machine Learning Environment 202
11.6.1 Wireless Network/Computer 202
11.6.2 Smart Cities and Military 202
11.6.3 Agriculture 203
11.7 Conclusion 204
References 204
12 AI-Powered Prediction of Centerline Total Pressure Variations in Coaxial Nozzles by Varying the Lip Thickness 211
R. Naren Shankar, Irish Angelin S., Bakiya Ambikapathy, K. Sathish Kumar and Parvathy Rajendran
12.1 Introduction 212
12.2 Methodology 213
12.3 Results and Discussions 218
12.4 Conclusion 223
References 223
13 Enhancing Jet Noise Reduction: AI-Powered Predictions of Core Length and Total Pressure Variations in Coaxial Nozzles 225
R. Naren Shankar, Irish Angelin S., Bakiya Ambikapathy, K. Sathish Kumar and Parvathy Rajendran
13.1 Introduction 226
13.2 Methodology 227
13.3 Results and Discussions 233
13.4 Conclusion 238
References 238
14 Application of Artificial Intelligence and Machine Learning in Composite Material Design 241
G. Gowtham, S. Nithya and J. V. Saiprasanna Kumar
Introduction 242
Overview 243
AI Uses in Different Sectors 246
Challenges and Considerations 249
AI Use in Aircraft Materials 250
Material Discovery and Design 251
Material Optimization 252
Quality Control 254
Predictive Maintenance 255
Composite Material Design 256
Material Recycling 257
Data Analytics for Performance Monitoring 259
Supply Chain Management 259
Energy Efficiency and Sustainability 261
Conclusion 263
References 263
15 Design Optimization Study of UAV Propeller Using Aeroacoustics 265
Prem Kumar P.S., Kirthika S., Kishore Kumar S. and Hariharasubramaniyan A.
Nomenclature 266
Introduction 266
Methodology 268
Computational Implementation 268
Domain Generation 269
Meshing 270
Solver Setup and Boundary Conditions 271
Results and Discussion 272
Base Propeller 272
Serration Design 1 272
Serration Design 2 272
Serration Design 3 274
Conclusion and Future Work 275
References 275
16 Autonomous Mapping and AI-Based Navigation Using Deep Learning, SLAM, and Optical Flow for Micro Aerial Vehicle 277
B. Anbarasu, S. Seralathan and A. Muthuram
16.1 Introduction 278
16.2 Related Work 281
16.2.1 AI-Based MAV Navigation 281
16.3 Methodology 282
16.3.1 SLAM System for UAV Navigation 283
16.3.2 US City Block Dataset for MAV Navigation 284
16.3.3 Data Collection for MAV Navigation 286
16.3.4 CNN Model and Preprocessing for MAV Navigation 289
16.3.4.1 CNN Model Training 290
16.3.5 Gunnar-Farnebäck Algorithm 291
16.4 Results and Discussions 292
16.5 Conclusion 298
References 300
Part 3: Performance And Efficiency Optimization 303
17 The Essential Phases in Aircraft Component Manufacturing Using Artificial Intelligence 305
Boopathy G., Rajamurugu N., Siva Prakasam P. and Sai Prasanna Kumar J.V.
Abbreviations 306
17.1 Introduction 306
17.2 Precision in Engineering and Design for the Fabrication of Aircraft Components 308
17.2.1 Role of Aerospace Engineers in Production of Aircraft Parts 310
17.2.2 Design Software Utilized in Fabrication of Aircraft Parts 310
17.2.3 Standards for Precision in Performance and Safety of Aircraft Parts 311
17.2.4 Potential of Digital Twins in the Manufacturing of Aircraft Components 312
17.3 Material Selection and Characteristics of Aircraft Parts 313
17.3.1 Significance of Lightweight and Resilient Materials 315
17.3.2 Environmentally Harsh Resistance of Materials 316
17.3.3 Common Materials Used in Aircraft Component Manufacturing 317
17.3.4 Predictive Procurement: Utilizing AI for Strategic Supply Chain Optimization 320
17.4 Manufacturing Techniques and Quality Control Measures 320
17.4.1 Statistical Process Control Using AI for Real-Time Quality Assurance 322
17.5 Assembly Processes and Integration of Aircraft 323
17.6 Routine Maintenance and Inspection of Aircraft Parts 325
17.7 Conclusion 327
References 328
18 Artificial Intelligence in Failure Prediction of Aircraft Components and Inventory Leveraging 333
Vinu Ramadhas, Krishnadhas Subash and K. Vijayaraja
18.1 Introduction 334
18.2 Inspection and Defects 334
18.2.1 Routine Inspections 334
18.2.2 Aircraft Defects 335
18.3 Platform-Centric Data 336
18.3.1 Routine Inspection Database 336
18.3.2 Repair and Component Replacement Database 336
18.3.3 Operational Database 338
18.3.4 Spare FOL Consumption 338
18.3.5 Incident/Accident Details 338
18.3.6 HUMS Database 339
18.4 Asset-Centric Data 339
18.4.1 Aircraft Variant and Numbers 339
18.4.2 Operational and Maintenance Staff 340
18.4.3 Critical Component Float 341
18.4.4 Test Sets and NDT Equipment 341
18.4.5 Mandatory Spare Availability 341
18.5 Fault Tree Analysis 342
18.6 AI-Assisted Application 344
18.6.1 Inspection and Maintenance Changes 344
18.6.2 Modification and Lifing Analysis 345
18.6.3 Exploitation and Operational Limitations 345
18.7 Conclusion 346
References 346
19 Performance Analysis and Optimization of Eppler- 398
Unmanned Aerial Vehicle Using Machine Learning Techniques 349
R. Manikandan, A. Parthiban, T. Gopalakrishnan and Mandeep Singh
19.1 Introduction 350
19.1.1 Eppler Profile 353
19.1.2 Artificial Intelligence Role in Network-Based UAV 356
19.1.3 Wireless Network Issues 356
19.1.4 Design of Network Issues 357
19.1.5 Localization and Trajectory 357
19.2 Experimental Methods 358
19.2.1 Design Phase and Wind Tunnel Testing 358
19.2.2 Flow Visualization Techniques 358
19.3 Computational Model 359
19.3.1 Simulation Setup 359
19.3.2 Aerodynamic Characteristics 360
19.3.3 Airfoil Geometric Creation 361
19.3.4 Grid Generation 362
19.3.5 Applications of Machine Learning in UAV Using Artificial Neural Network (ANN) 364
19.3.6 AI Techniques are Used to Identify and Classify High-Risk Areas and Motion Characteristics of UAVs 367
19.4 Results of Smooth, Bump, and Upper Surface Bumped Eppler-398 Airfoil 368
19.4.1 Validation 375
19.4.2 Flow Visualization Techniques 376
19.5 Ann 377
19.5.1 Enhancing Security and Privacy in UAV Networks with AI 382
19.5.2 Optimizing UAV Network Performance Through Intelligent AI Networking 383
19.5.3 Predictive Maintenance in UAV Networks via AI 384
19.5.4 AI-Driven Localization and Trajectory Planning in UAV Operations 385
19.5.5 Tackling Technical Challenges in AI-UAV Network Integration 385
19.6 Summary and Future Work 386
References 388
20 Navigation of Unconventional Drones - Autonomous Ornithopter 391
Syam Narayanan S., P. Rajalaksmi, Yogesh Gangurde, Akshith Mysa and Satyajit Movidi
20.1 Ornithopters 392
20.1.1 Conventional Versus Unconventional UAVs 392
20.1.2 Brief History 395
20.2 Autonomous Navigation 396
20.2.1 Navigation and Control 396
20.3 Autonomous Navigation for Ornithopters 402
20.3.1 GPS-Based and GPS-Denied Navigation - Comparative Overview 403
20.3.2 Software Systems 404
20.3.2.1 Simultaneous Localization and Mapping (SLAM) 404
20.3.2.2 ORBSLAM3 for Ornithopters 405
20.3.2.3 ROS (Robot Operating System) 407
20.3.2.4 ROS Control and Its Use in Ornithopters 408
20.4 Artificial Intelligence for Ornithopters 410
20.4.1 AI in Navigation 410
20.4.2 AI in Control 410
20.5 Ultra-Wide Band-Based Indoor GPS System for Ornithopters (Case Study) 411
20.5.1 Ultra-Wide Band Technology for Localization 411
20.5.1.1 Advantages of UWB for Localization 412
20.5.2 Indoor GPS Setup 413
20.5.3 Methodology 413
20.5.4 Scope of Navigation Using UWB 415
Conclusion 416
References 416
Index 419
