Artificial Intelligence Applications in Aeronautical and Aerospace Engineering.

Format:

Book

Author/Creator:

Kumar, K. Sathish.

Contributor:

Shankar, R. Naren.

Language:

English

Subjects (All):

Aerospace engineering.

Physical Description:

1 online resource (446 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2025.

Summary:

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.

Contents:

Cover

Series Page

Title Page

Copyright Page

Contents

Preface

Part 1: Safety and Security

Chapter 1 Artificial Intelligence Based Habitual and Average DoS Attack Detection in Avionics and Necessity Estimators in Wireless Ad Hoc and Sensor Networks

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

Chapter 2 Artificial Intelligence Aerospace Based Penetrating Denial of Service Attack in Wireless Sensor Network

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

Chapter 3 Application of Artificial Intelligence and Machine Learning in Computational Fluid Dynamics

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

Chapter 4 Deep Learning Based Secure Predictive Maintenance Framework for Industrial Maintenance Using Autonomous Drones

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

Chapter 5 Role of Artificial Intelligence in the Life Cycle of Aircraft

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

Chapter 6 Artificial Intelligence for Aeronautical and Aerospace Applications Using Fuzzy Logic Controller

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

Chapter 7 Revolutionizing Aerospace Quality Control: Harnessing AI for Defect Detection

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

Chapter 8 Utilizing AI Techniques for Detecting Damage in Aerospace Applications

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

Chapter 9 Sense and Avoid System for Navigation of Micro Aerial Vehicle in Cluttered Environments

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

Part 2: Technological Advancements and Innovations

Chapter 10 A Review on Mixed Reality and Artificial Intelligence for Smart Aviation Sector: Current Trends, Opportunities, and Challenges

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

Chapter 11 A Comprehensive Assessment of Unmanned Aerial Vehicles' Fuel Cell Electric Propulsion Systems

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

Chapter 12 AI-Powered Prediction of Centerline Total Pressure Variations in Coaxial Nozzles by Varying the Lip Thickness

12.1 Introduction

12.2 Methodology

12.3 Results and Discussions

12.4 Conclusion

Chapter 13 Enhancing Jet Noise Reduction: AI-Powered Predictions of Core Length and Total Pressure Variations in Coaxial Nozzles

13.1 Introduction

13.2 Methodology

13.3 Results and Discussions

13.4 Conclusion

Chapter 14 Application of Artificial Intelligence and Machine Learning in Composite Material Design

Overview

AI Uses in Different Sectors

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-394-26879-3

1-394-26878-5

9781394268788

OCLC:

1543500243