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Simulation Techniques of Digital Twin in Real-Time Applications

Design Modeling and Implementation

Edited by Abhineet Anand, Anita Sardana, Abhishek Kumar, Srikanta Kumar Mohapatra and Shikha Gupta
Copyright: 2024   |   Status: Published
ISBN: 9781394256976  |  Hardcover  |  
370 pages
Price: $195 USD
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One Line Description
The book gives a complete overview of implementing digital twin technology in real-time scenarios while emphasizing how this technology can be embedded with running technologies to solve all other issues.

Audience
Researchers, engineers, and students in computer science, software engineering and industrial engineering, will find this book to be very useful.

Description
Divided into two parts with Part 1 focusing on simulated techniques in digital twin technology and Part 2 on real-time applications of digital twin technology, the book collects a significant number of important research articles from domain-specific experts. The book sheds light on the various techniques of digital twin technology that are implemented in various application areas. It emphasizes error findings and respective solutions before the actual event happens. Most of the features in the book are on the implementation of strategies in real-time applications. Various real-life experiences are taken to show the proper implementation of simulation technologies. The book shows how engineers of any technology can input their research ideas to convert to real scenarios by using replicas. Hence, the book has a collection of research articles from various engineers with expertise in different technologies from many regions of the world. It shows how to implement the embedded real-time data into technologies.
Specifically, the chapters relate to the auto landing and cruising features in aerial vehicles, automated coal mining simulation strategy, the enhancement of workshop equipment, and implementation in power energy management for urban railways. This book also describes the coherent mechanism of digital twin technologies with deep neural networks and artificial intelligence.

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Author / Editor Details
Abhineet Anand, PhD, is a director at Chandigarh University, Punjab, India. His research focuses on cloud computing, load balancing, and clustering. He has published in several international and national journals and contributes to various conferences as a technical program committee member.

Anita Sardana, PhD, is an associate professor of computer science at Chandigarh University, Punjab, India. She has about 10 years of teaching experience and 8+ years of industry experience. Her industry experience includes patent searching, prosecution, and licensing for both Indian and US jurisdictions. She has filed four design patent applications, one utility patent application, and has published 18 research papers in national and international conferences and journals. Her one design patent was granted in 2023.

Abhishek Kumar, PhD, is an associate professor in the Computer Science & Engineering Department at Chandigarh University, Punjab, India, and is affiliated with the University of Castilla-La Mancha (UCLM), Toledo, Spain. His research areas include artificial intelligence, renewable energy, image processing, and machine learning. In total, he has more than 100 publications in peer-reviewed journals. Kumar is a keynote speaker and a member of various national and international societies in the field of engineering and research. He was awarded the CV Ramen National Award in 2018 in the young researcher and faculty category.

Srikanta Kumar Mohapatra, PhD, is an associate professor at Chitkara University, Institute of Engineering and Technology, Punjab, India. His research focuses on artificial intelligence, machine learning, and computational nanotech. Along with publishing 3 books and several research articles, Mohapatra is a member of the editorial board of several journals.

Shikha Gupta, PhD, is a professor in computer science at Chandigarh University, Punjab, India. Her research interests focus on data analytics, artificial intelligence, data mining, and machine learning. She has about 25 years of teaching experience and published several papers in national and international journals, and has authored books in computer science domains.

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Table of Contents
Preface
Part 1: A Guide to Simulated Techniques in Digital Twin
1. Introduction to Different Simulation Techniques of Digital Twin Development
Suvarna Sharma and Chetna Monga
1.1 Introduction
1.2 Literature Review
1.3 Digital Twin Simulation Techniques
1.3.1 Finite Element Analysis Simulation
1.3.2 Computational Fluid Dynamics Simulation
1.3.3 Discrete Event Simulation
1.3.4 Agent-Based Modeling Simulation
1.3.5 Multi-Body Dynamics Simulation
1.3.6 Monte Carlo Simulation
1.4 Conclusion
References
2. Comprehensive Analysis of Error Rate and Channel Capacity of Fisher Snedecor Composite Fading Model
Hari Shankar and Yogesh
2.1 Introduction
2.2 Fisher Snedecor Composite Fading
2.3 Mathematical Analysis
2.3.1 Error Rate Analysis
2.3.1.1 NCBFSK and BDPSK
2.3.1.2 BPSK, BFSK, and QPSK
2.3.1.3 MQAM
2.3.1.4 MPSK
2.3.1.5 MDPSK
2.3.1.6 NCMFSK
2.3.1.7 DQPSK
2.3.2 Channel Capacity Analysis
2.3.2.1 ORA
2.3.2.2 OPRA
2.3.2.3 CIFR
2.3.2.4 TIFR
2.4 Numerical Results
2.5 Conclusion
References
3. Implementation of Automatic Driving Car Test Approach Based on a Digital Twinning Technology and by Embedding Artificial Intelligence
Pranjal Shukla, Chahil Choudhary, Anurag and Jatin Thakur
3.1 Introduction
3.2 Literature Review
3.3 Comparative Analysis
3.4 Result
3.5 Concluding Remarks and Future Scope
References
4. Intelligent Monitoring of Transformer Equipment in Terms of Earlier Fault Diagnosis Based on Digital Twins
Satyabrata Sahoo
4.1 Introduction
4.2 Methodology
4.2.1 Arduino Uno
4.2.2 ESP32 Microcontroller
4.2.3 Data Acquisition
4.2.4 Blynk App
4.3 Machine Learning-Based Predictive Maintenance
4.4 Results and Discussion
4.5 Conclusion and Future Work
References
5. Digital Twin System for Intelligent Construction of Large Span Assembly Type Steel Bridge
Sucheta
5.1 Introduction
5.1.1 Digital Twin Technology
5.1.2 Technologies Used
5.1.3 Why Digital Twin?
5.1.4 Types of Digital Twins
5.2 Deep Learning
5.2.1 Types of Deep Neural Networks
5.2.2 Learning or Training in Neural Networks
5.3 Simulation vs. Digital Twin Technology
5.3.1 Integrating Deep Learning in Simulation Models
5.3.2 Benefits of Deep Learning Digital Twin
5.3.3 Applications of Digital Twin Technology
5.4 Literature Review
5.5 Conclusion
References
6. Digital Twin Application on System Identification and Control
Rakesh Kumar Pattanaik and Mihir Narayan Mohanty
6.1 Introduction
6.2 Digital Twin Technology and Its Application
6.2.1 Related Work on Digital Twin
6.2.2 DT Application
6.2.3 Different Levels of DT Models
6.2.3.1 Pre-Digital Twin
6.2.3.2 Model Design
6.2.3.3 Adaptive Model with DT Technology
6.2.3.4 The Process of Intelligent DT
6.2.4 Dynamic Model
6.2.5 Digital Twin and Machine Learning
6.3 Control and Identification: A Survey
6.3.1 Hierarchy of System Identification Methods
6.3.1.1 Parametric Methods
6.3.1.2 Nonparametric Methods
6.3.2 Machine Learning Approach
6.3.3 Deep Neural Network Approach
6.4 Proposed Methodology
6.4.1 DT Technology Application in Identification and Control
6.5 Result Analysis and Discussion
6.5.1 Case Study: Control Application
6.6 Conclusion and Future Work
References
Part 2: Real Time Applications of Digital Twin
7. Digital Twinning-Based Autonomous Take-Off, Landing, and Cruising for Unmanned Aerial Vehicles
Kiran Deep Singh, Prabhdeep Singh and Mohit Angurala
7.1 Introduction
7.1.1 Problem Statement
7.1.2 Research Objectives
7.2 Digital Twinning for UAV Autonomy
7.3 Challenges and Limitations
7.3.1 Manual Control and Pre-Programmed Flight Paths
7.3.2 Limited Adaptability to Dynamic Environments
7.3.3 Lack of Real-Time Decision-Making
7.3.4 Limited Perception and Situational Awareness
7.3.5 Computational Complexity and Processing Power
7.3.6 Calibration and Validation
7.4 Proposed Framework
7.4.1 Digital Twin Creation
7.4.2 Sensor Fusion and Data Acquisition
7.4.3 Environmental Analysis
7.4.4 Decision-Making and Control
7.4.5 Communication and Synchronization
7.4.6 Validation and Calibration
7.4.7 Iterative Improvement
7.5 Benefits and Feasibility
7.5.1 Improved Adaptability
7.5.2 Real-Time Decision-Making
7.5.3 Enhanced Safety
7.5.4 Feasibility Considerations
7.6 Conclusion and Future Directions
References
8. Execution of Fully Automated Coal Mining Face With Transparent Digital Twin Self-Adaptive Mining System
Bharat Tripathi, Nidhi Srivastava and Amod Kumar Tiwari
8.1 Introduction
8.2 Simulation Methods in Digital Twins
8.2.1 Computational Fluid Dynamics
8.2.1.1 Software Tools That are Being Used in Today’s Domain for CFD
8.2.1.2 Real-World Applications of CFD
8.2.2 Multibody Dynamics
8.2.3 Kinematics for Multibody Systems
8.3 Literature Review
8.3.1 Classification of MBD Simulations
8.3.2 Finite Element Analysis
8.4 Proposed Work
8.5 Conclusion
References
9. MGF-Based BER and Channel Capacity Analysis of Fisher Snedecor Composite Fading Model
Hari Shankar and Yogesh
9.1 Introduction
9.2 Fisher Snedecor Composite Fading Model
9.3 Performance Analysis Using MGF
9.3.1 ABER
9.3.1.1 BDPSK and NBFSK
9.3.1.2 BPSK and BFSK
9.3.1.3 MAM
9.3.1.4 Square MQAM
9.3.1.5 MPSK
9.3.2 NMFSK
9.3.3 Adaptive Channel Capacity
9.3.3.1 ORA
9.3.3.2 CIFR
9.4 Numerical Results
9.5 Conclusion
References
10. Precision Agriculture: An Augmented Datasets and CNN Model-Based Approach to Diagnose Diseases in Fruits and Vegetable Crops
Sparsh Mehta, Gurwinder Singh and Yogiraj Anil Bhale
10.1 Introduction
10.2 Literature Review
10.3 Major Fruit Diseases in the Valley
10.4 Methodology
10.5 Results and Discussion
10.6 Extended Experiment
10.7 Concluding Remarks
References
11. A Simulation-Based Study of a Digital Twin Model of the Air Purifier System in Chandigarh Using LabVIEW
Jyoti Verma, Monika Sethi, Vidhu Baggan, Manish Snehi and Jatin Arora
11.1 Introduction
11.1.1 Background Information on Chandigarh’s Air Pollution Problem
11.1.2 Digital Twin Technology and Its Relevance to Air Quality Monitoring
11.2 Literature Review
11.3 Methodology
11.4 Results
11.5 Discussion
11.6 Conclusion
References
12. Use of Digital Twin in Predicting the Life of Aircraft Main Bearing
Urvashi Kumari and Pooja Malhotra
12.1 Introduction
12.1.1 Background
12.1.2 Importance of Predictive Maintenance
12.1.3 Challenges in Aircraft Main Bearing Life Prediction
12.1.4 Digital Twin Technology in Aviation
12.2 Fundamentals of Digital Twin Technology
12.2.1 Components of a Digital Twin
12.2.2 Enabling Technologies for Digital Twin
12.3 Benefits of Digital Twin Technology
12.3.1 Aircraft Main Bearings: Structure and Failure Modes
12.4 Developing a Digital Twin for Aircraft Main Bearings
12.5 Predictive Analytics for Main Bearing Life Prediction
12.5.1 Machine Learning Algorithms for Predictive Modeling
12.5.2 Challenges of Digital Twin for Aircraft Health
12.5.3 Security Threats of the Digital Twin in Aircraft Virtualization
12.6 Future Prospects and Conclusion of Digital Twin for Aircraft Health
References
13. Power Energy System Consumption Analysis in Urban Railway by Digital Twin Method
K. Sreenivas Rao, P. Harini, Srikanta Kumar Mohapatra and Jayashree Mohanty
13.1 Introduction
13.2 Literature Review
13.3 Method
13.4 Implementation
13.5 Conclusion
References
14. Based on Digital Twin Technology, an Early Warning System and Strategy for Predicting Urban Waterlogging
Shweta Thakur
14.1 Introduction
14.1.1 Definition
14.1.2 Application Areas of Digital Twin Technology
14.2 Literature Review
14.3 Methodology
14.4 Discussion and Conclusion
References
15. Advanced Real-Time Simulation Framework for the Physical Interaction Dynamics of Production Lines Leveraging Digital Twin Paradigms
Neha Bhati, Narayan Vyas, Vishal Dutt, Ronak Duggar and Aradhya Pokhriyal
15.1 Introduction
15.2 Introduction to Advanced Simulation Frameworks
15.2.1 The Evolution of Production Line Simulations
15.2.2 The Promise of Real-Time Analysis
15.3 Digital Twins: A Comprehensive Analysis
15.3.1 What Defines a Digital Twin?
15.3.2 The Architecture and Components of Digital Twins
15.3.3 Advantages of Integrating Digital Twins in Manufacturing
15.4 Physical Interaction Dynamics in Production Lines
15.4.1 The Nature of Physical Interactions
15.4.2 The Role of Dynamics in Production Efficiency
15.4.3 Challenges in Traditional Simulation Methods
15.5 Building the Advanced Real-Time Simulation Framework
15.5.1 Core Principles and Design Objectives
15.5.2 Data Integration and Processing
15.5.2.1 Role of Sensors and IoT
15.5.2.2 Algorithmic Foundations for Feedback
15.6 Types of Algorithms
15.6.1 Pseudocode for Real-Time Adjustments
15.6.1.1 Initialization
15.6.1.2 Data Collection and Pre-Processing
15.6.1.3 Analysis Using Bayesian Inference
15.6.1.4 Anomaly Detection and Root Cause Analysis
15.6.1.5 Corrective Action Using Gradient Boosting
15.6.1.6 Update and Implement
15.6.1.7 Continuous Monitoring
15.7 Practical Implementations and Case Studies
15.7.1 Implementing the Framework: A Step-by-Step Guide
15.7.2 Measurable Benefits and Outcomes
15.8 Overcoming Challenges and Limitations
15.8.1 Potential Roadblocks in Framework Implementation
15.8.2 Solutions and Workarounds for Real-Time Challenges
15.8.3 Ensuring Data Security and Integrity
15.9 Regular Audits: Schedule Frequent Audits to Help Find Security Flaws Before They Cause Problems
15.10 The Future of Production Simulations With Digital Twins
15.10.1 Emerging Trends in Digital Twin Technology
15.10.2 Expanding the Scope of Real-Time Simulations
15.10.3 Ethical Considerations and Sustainability
15.11 Conclusion: Revolutionizing Production Lines With Advanced Simulations
References
Index

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