Search

Browse Subject Areas

For Authors

Submit a Proposal

Artificial Intelligence Applications in Aeronautical and Aerospace Engineering

Edited by K. Sathish Kumar and R. Naren Shankar
Copyright: 2025   |   Expected Pub Date:2025/08/30
ISBN: 9781394268764  |  Hardcover  |  
444 pages

One Line Description
This 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.

Audience
Aeronautical and aerospace engineers, AI researchers, students, and industry professionals seeking to understand and apply AI solutions in areas like safety, security, and performance optimization.

Description
The 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.
Readers will find this book:
• Examines AI’s transformative role in aerospace and aeronautics, from enhancing safety to driving innovation and optimizing performance;
• Highlights real-time applications, addressing AI’s role in boosting operational efficiency and safety in the aerospace and aeronautical industries;
• Offers insights into emerging AI technologies shaping the future of aerospace and aeronautical systems;
• Features real-world case studies on AI applications in autonomous navigation, predictive maintenance of aircraft, and air traffic management.

Back to Top
Author / Editor Details
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.

Back to Top

Table of Contents
Preface
Part 1: Safety and Security
1. Artificial Intelligence Based Habitual and Average DoS Attack Detection in Avionics and Necessity Estimators in Wireless Ad Hoc and Sensor Networks
C. R. Bharathi and D. Mahammad Rafi
Nomenclature
1.1 Introduction
1.2 Literature Survey
1.3 MQTT’s Impact in Wired Sensor Networks (WSN)
1.3.1 MQTT (Message Queuing Telemetry Transport)
1.3.2 Mosquitto Broker
1.4 Implementation
1.4.1 Dataset Preparation
1.4.2 Feature Set with Attribute Value and Type
1.4.3 Classification
1.4.4 Data Security of Avionics Systems
1.4.5 Applications for Avionics Systems
1.5 End Results and Talk
1.6 Conclusion
References
2. Artificial Intelligence Aerospace Based Penetrating Denial of Service Attack in Wireless Sensor Network
C. R. Bharathi and D. Mahammad Rafi
2.1 Overview
2.2 Related Work
2.3 Applications of Artificial Intelligence Based on DoS Detection
2.3.1 Compiling and Modifying Data
2.3.2 Choosing Features
2.4 Attack Model
2.4.1 Artificial Intelligence Aerospace Sensor Network Architecture
2.4.2 Aerospace WSNs, Denial-of-Service Attacks
2.5 Conclusion
References
3. Application of Artificial Intelligence and Machine Learning in Computational Fluid Dynamics
G. Gowtham, S. Nithya and R. Sundharesan
Introduction
Motivation for AI in CFD
Applications of AI in CFD
Challenges and Considerations
Data Collection
Pre-Processing
AI Model Selection
Training Data Generation
AI Model Training
Model Validation
CFD Prediction
Post-Processing
Future Directions
Conclusion
References
4. Deep Learning Based Secure Predictive Maintenance Framework for Industrial Maintenance Using Autonomous Drones
Sharanya S., Karthikeyan S., Prabhakar E. and Manirao Ramachandrarao
4.1 Evolution of Industrial Maintenance
4.1.1 Condition Monitoring in Industries
4.1.2 Classification of Condition Monitoring
4.2 Use Cases of Drone Technology in Industrial Activities
4.3 Security Dimension of Drone Technology
4.3.1 Cyberattacks on Drones
4.3.2 Counter-Drone Measures
4.4 Cybersecurity Framework for Deploying Drones in Predictive Maintenance
4.5 Conclusion
References
5. Role of Artificial Intelligence in the Life Cycle of Aircraft
Karthikeyan S., Sharanya S., Manirao Ramachandrarao and N. Dilip Raja
5.1 Introduction
5.1.1 Why Aircraft Manufacturing is Very Expensive?
5.2 AI for Aircraft Design
5.3 AI in Determining Aircraft Shape
5.4 AI in Aircraft Production
5.5 AI in Aircraft Assembly Line
5.6 AI in Aircraft Performance Improvement
5.7 Predictive Maintenance in Aircrafts
5.8 Conclusions
References
6. Artificial Intelligence for Aeronautical and Aerospace Applications Using Fuzzy Logic Controller
Anumula Swarnalatha and R. Asad Ahmed
6.1 Introduction
6.2 Fuzzy Logic Controllers Used in Aircraft
6.3 Advantages of Fuzzy Logic Controllers in Aerospace
6.4 Applications
6.4.1 Fuzzy Logic Controller Design for an Aircraft
6.5 Conclusion
References
7. Revolutionizing Aerospace Quality Control: Harnessing AI for Defect Detection
Naveen R., Rakesh Kumar C., Kowsalya, Fadhilah Mohd Sakri and Prasad G.
7.1 Introduction
7.1.1 Aerospace Quality Control Background
7.1.2 The Imperative for Quality Control Transformation
7.1.3 The Role of AI in the Aerospace Sector
7.2 Traditional Quality Control Methods
7.2.1 Limitations and Challenges
7.2.1.1 Manual Inspection Processes
7.2.1.2 Time-Consuming Procedures
7.2.2 Case Studies on Conventional Approaches
7.2.2.1 Case Study 1: Manual Inspection Failures
7.2.2.2 Case Study 2: Time-Related Complications
7.3 AI in Aerospace: A Paradigm Shift
7.3.1 Overview of AI Technologies
7.3.1.1 Machine Learning Algorithms
7.3.1.2 Computer Vision
7.3.2 Integration of AI in Aerospace Manufacturing
7.3.2.1 Design Optimization
7.3.2.2 Real-Time Monitoring
7.3.3 Advantages of AI for Quality Control
7.3.3.1 Real-Time Monitoring
7.4 Defect Detection with AI
7.4.1 Understanding Defects in Aerospace Components
7.4.1.1 Types of Defects
7.4.2 AI Algorithms for Defect Detection
7.4.2.1 Convolutional Neural Networks (CNNs) for Image Analysis
7.4.2.2 Anomaly Detection Algorithms
7.5 Implementation Strategies
7.5.1 Challenges in Implementing AI for Quality Control
7.5.1.1 Technical Challenges
7.5.1.2 Organizational Challenges
7.5.2 Best Practices and Lessons Learned
7.5.2.1 Collaborative Cross-Functional Teams
7.5.2.2 Incremental Implementation
7.5.3 Regulatory and Ethical Considerations
7.5.3.1 Compliance with Standards
7.5.3.2 Ethical AI Practices
7.6 Future Trends and Innovations
7.6.1 Evolving Landscape of Aerospace Quality Control
7.6.1.1 Integration of Advanced Sensors
7.6.2 Potential Advances in AI for Defect Detection
7.6.2.1 Explainable AI
7.6.3 Implications for the Future of Aerospace Manufacturing
7.6.3.1 Shift in Workforce Skills
7.7 Impact of AI Techniques on Defect Detection
7.7.1 Improvement in Defect Detection with AI Techniques
7.7.2 Specific Outcomes Influenced by AI
7.7.3 Enhancing Defect Detection with AI: A Comparative Analysis
7.7.3.1 Traditional Defect Detection Methods
7.7.3.2 Advantages of AI in Defect Detection
7.7.4 Case Studies Highlighting AI Improvements
7.8 Conclusion and Recommendations
7.8.1 Recap of Key Findings
7.8.1.1 Evolution of Quality Control
7.8.1.2 Impact of AI
7.8.1.3 Future Trends and Innovations
7.8.2 The Path Forward: Recommendations for Industry Stakeholders
7.8.2.1 Embrace Continuous Learning
7.8.2.2 Collaborative Research and Development
7.8.2.3 Regulatory Engagement
7.8.3 Final Thoughts on the Future of Aerospace Quality Control
7.8.4 Scope of the Future Work
References
8. Utilizing AI Techniques for Detecting Damage in Aerospace Applications
Rakesh Kumar C., Naveen R., Kowsalya, Fadhilah Mohd Sakri and Prasath M.S.
8.1 Introduction
8.2 Detection of Damage in Composite Materials for Aircraft Components
8.2.1 Enhanced Defect Detection with AI: Comparative Analysis
8.2.2 Recent Studies on AI in Aerospace Engineering
8.3 AI-Based Aircraft Composite Damage Detection
8.3.1 Data Collection
8.3.2 Image Recognition and Computer Vision
8.3.3 Sensor Data Analysis
8.3.4 Feature Extraction
8.3.5 Machine Learning Models
8.3.6 Anomaly Detection
8.3.7 Integration of Multiple Data Sources
8.3.8 Real-Time Monitoring
8.3.9 Human-in-the-Loop Validation
8.3.10 Continuous Learning and Improvement
8.3.11 Regulatory Compliance
8.3.12 Discussion on the Application and Effectiveness of AI in Detecting Damage
8.3.13 Improved Detection Accuracy
8.3.14 Reduced False Positives and False Negatives
8.3.15 Enhanced Predictive Capabilities
8.3.16 Comparison with Traditional Methods
8.3.17 Limitations and Challenges
8.4 AI Methodologies for Defect Detection in Aerospace Manufacturing
8.4.1 AI Algorithms
8.4.2 Metrics and Evaluation Criteria
8.5 Conclusion
References
9. Sense and Avoid System for Navigation of Micro Aerial Vehicle in Cluttered Environments
Anbarasu B., Anitha G., Balaji G., Shabahat Hasnain Qamar, Sathish Kumar K., Naren Shankar R. and Santhosh Kumar G.
9.1 Introduction
9.2 Related Works
9.3 Proposed Methodology
9.4 Sense and Avoid Algorithm
9.4.1 Raw Disparity to Depth Conversion
9.4.2 Obstacle Detection
9.4.3 Collision Avoidance
9.5 Experimental Results and Discussions
9.6 Conclusions
References
Part 2: Technological Advancements and Innovations
10. A Review on Mixed Reality and Artificial Intelligence for Smart Aviation Sector: Current Trends, Opportunities, and Challenges
G. Jegadeeswari, B. Kirubadurai, Jaganraj R. and Vinoth Thangarasu
10.1 Introduction
10.2 A Mixed Reality for Smart Aerospace Engineering
10.3 Integrated Reality to Enhance the Passenger Experience
10.4 Opportunities and Challenges During and Post COVID-19
10.5 Conclusion
Acknowledgments
References
11. A Comprehensive Assessment of Unmanned Aerial Vehicles’ Fuel Cell Electric Propulsion Systems
Kirubadurai B., Jaganraj R., Jegadeeswari G. and Vinoth Thangarasu
11.1 Introduction
11.2 Fuel Cell Types
11.3 Machine Learning Technique
11.4 Problems with UAVs Powered by FC
11.4.1 Issues of On-Board Hydrogen Storage
11.4.2 Problem with Limited Power Output
11.4.3 Slow-Response Issue
11.4.4 Efficiency Issue of FC Propulsion Systems
11.4.5 Reinforcement Learning
11.5 UAV Hardware Design and Integration
11.5.1 Electrical System Diagram Excluding Super Capacitor and Fuel Cell Stack
11.6 UAV in the Machine Learning Environment
11.6.1 Wireless Network/Computer
11.6.2 Smart Cities and Military
11.6.3 Agriculture
11.7 Conclusion
References
12. AI-Powered Prediction of Centerline Total Pressure Variations in Coaxial Nozzles by Varying the Lip Thickness
R. Naren Shankar, Irish Angelin S., Bakiya Ambikapathy, K. Sathish Kumar and Parvathy Rajendran
12.1 Introduction
12.2 Methodology
12.3 Results and Discussions
12.4 Conclusion
References
13. Enhancing Jet Noise Reduction: AI-Powered Predictions of Core Length and Total Pressure Variations in Coaxial Nozzles
R. Naren Shankar, Irish Angelin S., Bakiya Ambikapathy, K. Sathish Kumar and Parvathy Rajendran
13.1 Introduction
13.2 Methodology
13.3 Results and Discussions
13.4 Conclusion
References
14. Application of Artificial Intelligence and Machine Learning in Composite Material Design
G. Gowtham, S. Nithya and J. V. Saiprasanna Kumar
Introduction
Overview
AI Uses in Different Sectors
Challenges and Considerations
AI Use in Aircraft Materials
Material Discovery and Design
Material Optimization
Quality Control
Predictive Maintenance
Composite Material Design
Material Recycling
Data Analytics for Performance Monitoring
Supply Chain Management
Energy Efficiency and Sustainability
Conclusion
References
15. Design Optimization Study of UAV Propeller Using Aeroacoustics
Prem Kumar P.S., Kirthika S., Kishore Kumar S. and Hariharasubramaniyan A.
Nomenclature
Introduction
Methodology
Computational Implementation
Domain Generation
Meshing
Solver Setup and Boundary Conditions
Results and Discussion
Base Propeller
Serration Design 1
Serration Design 2
Serration Design 3
Conclusion and Future Work
References
16. Autonomous Mapping and AI-Based Navigation Using Deep Learning, SLAM, and Optical Flow for Micro Aerial Vehicle
B. Anbarasu, S. Seralathan and A. Muthuram
16.1 Introduction
16.2 Related Work
16.2.1 AI-Based MAV Navigation
16.3 Methodology
16.3.1 SLAM System for UAV Navigation
16.3.2 US City Block Dataset for MAV Navigation
16.3.3 Data Collection for MAV Navigation
16.3.4 CNN Model and Preprocessing for MAV Navigation
16.3.4.1 CNN Model Training
16.3.5 Gunnar-Farnebäck Algorithm
16.4 Results and Discussions
16.5 Conclusion
References
Part 3: Performance And Efficiency Optimization
17. The Essential Phases in Aircraft Component Manufacturing Using Artificial Intelligence
Boopathy G., Rajamurugu N., Siva Prakasam P. and Sai Prasanna Kumar J.V.
Abbreviations
17.1 Introduction
17.2 Precision in Engineering and Design for the Fabrication of Aircraft Components
17.2.1 Role of Aerospace Engineers in Production of Aircraft Parts
17.2.2 Design Software Utilized in Fabrication of Aircraft Parts
17.2.3 Standards for Precision in Performance and Safety of Aircraft Parts
17.2.4 Potential of Digital Twins in the Manufacturing of Aircraft Components
17.3 Material Selection and Characteristics of Aircraft Parts
17.3.1 Significance of Lightweight and Resilient Materials
17.3.2 Environmentally Harsh Resistance of Materials
17.3.3 Common Materials Used in Aircraft Component Manufacturing
17.3.4 Predictive Procurement: Utilizing AI for Strategic Supply Chain Optimization
17.4 Manufacturing Techniques and Quality Control Measures
17.4.1 Statistical Process Control Using AI for Real-Time Quality Assurance
17.5 Assembly Processes and Integration of Aircraft
17.6 Routine Maintenance and Inspection of Aircraft Parts
17.7 Conclusion
References
18. Artificial Intelligence in Failure Prediction of Aircraft Components and Inventory Leveraging
Vinu Ramadhas, Krishnadhas Subash and K. Vijayaraja
18.1 Introduction
18.2 Inspection and Defects
18.2.1 Routine Inspections
18.2.2 Aircraft Defects
18.3 Platform-Centric Data
18.3.1 Routine Inspection Database
18.3.2 Repair and Component Replacement Database
18.3.3 Operational Database
18.3.4 Spare FOL Consumption
18.3.5 Incident/Accident Details
18.3.6 HUMS Database
18.4 Asset-Centric Data
18.4.1 Aircraft Variant and Numbers
18.4.2 Operational and Maintenance Staff
18.4.3 Critical Component Float
18.4.4 Test Sets and NDT Equipment
18.4.5 Mandatory Spare Availability
18.5 Fault Tree Analysis
18.6 AI-Assisted Application
18.6.1 Inspection and Maintenance Changes
18.6.2 Modification and Lifing Analysis
18.6.3 Exploitation and Operational Limitations
18.7 Conclusion
References
19. Performance Analysis and Optimization of Eppler-398 Unmanned Aerial Vehicle Using Machine Learning Techniques
R. Manikandan, A. Parthiban, T. Gopalakrishnan and Mandeep Singh
19.1 Introduction
19.1.1 Eppler Profile
19.1.2 Artificial Intelligence Role in Network-Based UAV
19.1.3 Wireless Network Issues
19.1.4 Design of Network Issues
19.1.5 Localization and Trajectory
19.2 Experimental Methods
19.2.1 Design Phase and Wind Tunnel Testing
19.2.2 Flow Visualization Techniques
19.3 Computational Model
19.3.1 Simulation Setup
19.3.2 Aerodynamic Characteristics
19.3.3 Airfoil Geometric Creation
19.3.4 Grid Generation
19.3.5 Applications of Machine Learning in UAV Using Artificial Neural Network (ANN)
19.3.6 AI Techniques are Used to Identify and Classify High-Risk Areas and Motion Characteristics of UAVs
19.4 Results of Smooth, Bump, and Upper Surface Bumped Eppler-398 Airfoil
19.4.1 Validation
19.4.2 Flow Visualization Techniques
19.5 ANN
19.5.1 Enhancing Security and Privacy in UAV Networks with AI
19.5.2 Optimizing UAV Network Performance Through Intelligent AI Networking
19.5.3 Predictive Maintenance in UAV Networks via AI
19.5.4 AI-Driven Localization and Trajectory Planning in UAV Operations
19.5.5 Tackling Technical Challenges in AI-UAV Network Integration
19.6 Summary and Future Work
References
20. Navigation of Unconventional Drones — Autonomous Ornithopter
Syam Narayanan S., P. Rajalaksmi, Yogesh Gangurde, Akshith Mysa and Satyajit Movidi
20.1 Ornithopters
20.1.1 Conventional Versus Unconventional UAVs
20.1.2 Brief History
20.2 Autonomous Navigation
20.2.1 Navigation and Control
20.3 Autonomous Navigation for Ornithopters
20.3.1 GPS-Based and GPS-Denied Navigation — Comparative Overview
20.3.2 Software Systems
20.3.2.1 Simultaneous Localization and Mapping (SLAM)
20.3.2.2 ORBSLAM3 for Ornithopters
20.3.2.3 ROS (Robot Operating System)
20.3.2.4 ROS Control and Its Use in Ornithopters
20.4 Artificial Intelligence for Ornithopters
20.4.1 AI in Navigation
20.4.2 AI in Control
20.5 Ultra-Wide Band-Based Indoor GPS System for Ornithopters (Case Study)
20.5.1 Ultra-Wide Band Technology for Localization
20.5.1.1 Advantages of UWB for Localization
20.5.2 Indoor GPS Setup
20.5.3 Methodology
20.5.4 Scope of Navigation Using UWB
Conclusion
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page