Simulation Techniques of Digital Twin in Real-Time Applications : Design Modeling and Implementation

Simulation Techniques of Digital Twin in Real-Time Applications Design Modeling and Implementation

SIMULATION TECHNIQUES OF DIGITAL TWIN IN REAL-TIME APPLICATIONS 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. Divided into two parts with Par...

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Detalles Bibliográficos
Autor principal: Anand, Abhineet (-)
Otros Autores: Sardana, Anita, Kumar, Abhishek, Mohapatra, Srikanta Kumar, Gupta, Shikha
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2024.
Edición:1st ed
Materias:
Digital twins (Computer simulation)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009828035506719
Tabla de Contenidos:
  • Cover
  • Series Page
  • Title Page
  • Copyright Page
  • Dedication
  • Contents
  • Preface
  • Part 1: A Guide to Simulated Techniques in Digital Twin
  • Chapter 1 Introduction to Different Simulation Techniques of Digital Twin Development
  • 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
  • Chapter 2 Comprehensive Analysis of Error Rate and Channel Capacity of Fisher Snedecor Composite Fading Model
  • 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
  • Chapter 3 Implementation of Automatic Driving Car Test Approach Based on a Digital Twinning Technology and by Embedding Artificial Intelligence
  • 3.1 Introduction
  • 3.2 Literature Review
  • 3.3 Comparative Analysis
  • 3.4 Result
  • 3.5 Concluding Remarks and Future Scope
  • References
  • Chapter 4 Intelligent Monitoring of Transformer Equipment in Terms of Earlier Fault Diagnosis Based on Digital Twins
  • 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.
  • Chapter 5 Digital Twin System for Intelligent Construction of Large Span Assembly Type Steel Bridge
  • 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
  • Chapter 6 Digital Twin Application on System Identification and Control
  • 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
  • Chapter 7 Digital Twinning-Based Autonomous Take-Off, Landing, and Cruising for Unmanned Aerial Vehicles
  • 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
  • Chapter 8 Execution of Fully Automated Coal Mining Face With Transparent Digital Twin Self-Adaptive Mining System
  • 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
  • Chapter 9 MGF-Based BER and Channel Capacity Analysis of Fisher Snedecor Composite Fading Model
  • 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.
  • Chapter 10 Precision Agriculture: An Augmented Datasets and CNN Model-Based Approach to Diagnose Diseases in Fruits and Vegetable Crops
  • 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
  • Chapter 11 A Simulation-Based Study of a Digital Twin Model of the Air Purifier System in Chandigarh Using LabVIEW
  • 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
  • Chapter 12 Use of Digital Twin in Predicting the Life of Aircraft Main Bearing
  • 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
  • Chapter 13 Power Energy System Consumption Analysis in Urban Railway by Digital Twin Method
  • 13.1 Introduction
  • 13.2 Literature Review
  • 13.3 Method
  • 13.4 Implementation
  • 13.5 Conclusion.
  • References
  • Chapter 14 Based on Digital Twin Technology, an Early Warning System and Strategy for Predicting Urban Waterlogging
  • 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
  • Chapter 15 Advanced Real-Time Simulation Framework for the Physical Interaction Dynamics of Production Lines Leveraging Digital Twin Paradigms
  • 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.

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