Buch, Englisch, 304 Seiten, Format (B × H): 239 mm x 159 mm, Gewicht: 548 g
An Architectural, Technical, and Software Perspective
Buch, Englisch, 304 Seiten, Format (B × H): 239 mm x 159 mm, Gewicht: 548 g
ISBN: 978-1-394-29830-3
Verlag: John Wiley & Sons Inc
Enables readers to understand the concept of digital twins from an engineering perspective
The Foundation for Digital Twins describes the technical characterizations of digital twins and explores the specifics of software and algorithms that facilitate a wide range of digital twin applications. The book discusses gaps between the current and future software solutions that must be put in place in order to fully exploit the digital twin capabilities, shows the relationship between digital twins and AI, provides an overall systematic approach to the construction and operation of digital twins, and delves into state-of-the-art applications of digital twins in smart cities and those focused on networking and cultural heritage.
This book includes information on: - Descriptive, predictive, and behavior models in the context of digital twins
- How to design a digital twin, covering distributed, interactive, top-down, and bottom-up approaches
- Centralized, edge-cloud, and totally distributed digital twin deployment
- Digital twins as an integral part of the metaverse and the most promising future applications of digital twins
The Foundation for Digital Twins is a timely, essential reference on the subject for professionals and researchers in software development and those involved in the implementation and management of digital twin platforms, frameworks, and related applications.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Foreword xiii
About the Authors xv
Preface xvii
Acknowledgments xix
Acronyms xxi
Introduction xxv
1 What Is a Digital Twin? 1
1.1 Introductory Concepts 1
1.1.1 Basic Definitions 1
1.1.2 Formalized Definitions 3
1.2 DT: A Rationale 4
1.2.1 Models and Modeling 4
1.2.2 DT as a Combination of Models 5
1.3 DT Definition: A Step Further 7
1.3.1 DT Properties 8
1.4 DT Representation, Model, and More 12
1.5 The Software Part of a DT 17
1.5.1 d Architecture and development Approach 17
1.5.2 An Introductory DT Architecture 18
1.6 Specification Methodologies 20
1.6.1 Designing a DT Based on Available Specifications of a PS 20
1.6.2 Designing a DT Without Previous System Specifications 21
1.6.3 Understanding the Physical System Behavior 21
1.7 Domain Knowledge and Operation in DTs 22
1.7.1 The Role of Domain-specific Knowledge and Technologies 22
1.7.2 Operational Context: Bridging IT and OT Domains 23
1.8 Clarifying Scope and Terminology for DT Architectures 23
1.8.1 d Architecture and Scope definition 24
1.8.2 Evolving Terminology in DT Research 25
2 Models and Modeling Aspects of a DT 27
2.1 The Representational Aspects of a DT 27
2.1.1 The Importance of Environment Representation 28
2.1.2 Physical System Life cycle and Development of DT 29
2.1.3 Modeling Aspects and Their Relationships 30
2.1.4 Cascade and Parallel Life cycles 31
2.2 Data Modeling 34
2.2.1 Data and Behavior Modeling 35
2.2.2 Data Models as Enablers of Passive DTs 38
2.3 Behavior Modeling 39
2.4 Predictive Models 41
2.5 Prognosis Model 43
2.6 Prescriptive Model 46
2.7 A Flexible Approach to Multi-facet Models 48
2.7.1 Relationships Between Different Models 48
2.7.2 Models and Design, the Complexity of DT Modeling 51
2.7.3 Modeling the Environment 52
2.7.4 Context and Situation 52
2.7.5 From Top or Bottom? 53
3 Foundational Data for a Digital Twin 55
3.1 Descriptive Models 55
3.2 Data-driven Architecture 58
3.3 Which Data to Include in a DT 60
3.4 Organizing Data into Data Models 62
3.4.1 Minimal DT Structure and Multiple Types of Data 62
3.4.2 Data Representations for the DT 64
3.4.3 Data Models and APIs 66
3.5 Data Models for a DT 69
3.5.1 Data Modeling Techniques and Semantic Enrichment in DTs 69
3.5.2 Existing Data Models for DTs 71
3.6 Extending Data Models to Fit the Stakeholders’ Needs 74
3.6.1 Stakeholder 1: Building Manager 74
3.7 Relationships Between Descriptive and Behavior Models 76
3.7.1 Data Exploitation Chain: From Passive to Prescriptive DT 77
4 Digital Twin as a Behavior Model of a Physical System 85
4.1 Behavior Modeling 85
4.1.1 An Example: The Traffic Light System Behavior 86
4.1.2 Behavior Modeling Challenges and Needs 88
4.2 Foundational Behavior Models 91
4.2.1 Types of Behavior Models 92
4.2.2 Functions of Behavior Models in a DT 92
4.2.3 Supporting Behavior Models with NGSI-LD 93
4.2.4 In the Context of Traffic Light Controller Example 93
4.3 Developing a Behavior Model 94
4.3.1 Implementing the Twin as a State Management Component 95
4.3.2 Environment and DT Structure 97
4.4 AI and Digital Twins 101
4.4.1 Iterative Refinement of DT Behavior Models Using Generative AI 104
4.4.2 Extraction of Behavior Rules from Data 107
4.4.3 Enhancing AI Explainability in DTs Through Behavior Models 109
4.4.4 Autonomic DTs and Agentic AI 111
4.5 Simulation Models, Behavior Validation, and GenAI Integration in DTs 112
4.5.1 Applicable Simulation Models 112
4.5.2 MetaModel for Unified Execution and Simulation 113
4.5.3 Evolving the Traffic Light DT with Situation-aware Behavior Modeling 115
4.6 Behavior Models for Prognosis and Prescriptive DTs 117
4.6.1 Behavior Models for Prognostic DTs 117
4.6.2 Prescriptive Model and Integration with Prognosis Capabilities 121
4.6.3 Recommendation Systems in DTs 122
4.6.4 The Role of Autonomics in Prescriptive DTs 122
4.6.5 Benefits of Behavior Models in Prescriptive DTs 124
4.7 Behavior Modeling Recap 125
5 Architecting and Implementing Digital Twin Systems: Approaches, Guidelines, and Best Practices 127
5.1 Requirements, Structural Concepts, and Terminology for a Digital Twin Architecture 127
5.1.1 Requirements and Properties of DTs 128
5.1.2 State of the Art in DT Architectures 128
5.1.3 Reimagining a Versatile DT Architecture 129
5.2 Software Interaction Paradigms for DT Implementation 132
5.2.1 Client-server Paradigm 133
5.2.2 Event-driven Paradigm 133
5.2.3 Agent-based Paradigm 133
5.2.4 Data-sharing Paradigm 133
5.3 Componentization of DT Architecture 135
5.3.1 System Engine 135
5.3.2 Data Management 136
5.3.3 DT Engine 137
5.3.4 DT Life-cycle Management 141
5.4 Model First 144
5.4.1 Alignment with the Reference Architecture 145
5.5 From Modules to Components and Microservices: A DT Perspective 149
5.5.1 Rationale for Further Decomposition: DT Specifics 150
5.5.2 Microservice Design Representation 151
5.5.3 Rationale for Microservice Decomposition: DT Advantages 154
5.5.4 Preparing the Components for Deployment 155
5.5.5 Comparison of the Architecture with ISO 23247 Standard 157
5.6 Testing and Validation of DTs 158
5.6.1 Importance of Testing and Validation 159
5.6.2 Continuous Impact Within the Enterprise 159
5.6.3 Techniques and Approaches for Testing and Validation 159
5.6.4 Value of a Validated DT 160
6 Deploying and Operating a Digital Twin 161
6.1 Distribution of DT Components and Functions 161
6.1.1 Centralized or Distributed DT 161
6.1.2 A Deployment Scenario 164
6.2 Operating the DT 165
6.2.1 Product and DT Life-cycle Management and Phase Transitions 165
6.2.2 Management of DT Functionalities 167
6.2.3 AI for DT Management and Operation 169
6.2.4 Data Management for the DT 170
6.2.5 Management of the System Infrastructure 172
6.2.6 Additional Relevant Topics 173
6.3 Example: Traffic Light Service DT 173
6.3.1 Deployment Mapping 174
6.3.2 Operational Workflow 175
6.3.3 Benefits 176
6.4 DT Impact on Organization Processes 177
6.4.1 Breaking Down Information Silos 177
6.4.2 Cross-departmental Cooperation and Commitment 177
6.4.3 Integration of Diverse Technologies 177
6.4.4 Organizational Change and Digital Maturity 178
6.4.5 Additional Organizational Challenges 178
6.5 Life-cycle Insights from Industrial Experiences 179
6.5.1 Life-cycle Phases 179
6.5.2 Exemplary Industrial DT Projects 181
6.5.3 Enablers and Barriers to Industrial Exploitation 182
6.5.4 Advantages and Enterprise Effort 184
7 Some Examples of Applicability of the Digital Twin Architecture 185
7.1 Introduction 185
7.2 Developing DTs for Smart Cities: A Bottom-up Approach 186
7.2.1 From Simple DTs to Specialized Behavior 186
7.2.2 Microservices and Component Flexibility 189
7.2.3 Integrating Heterogeneous Data Streams for Holistic Urban Insights 189
7.3 NDT: The Edge-cloud Continuum Representation 190
7.3.1 A Top-down Approach for NDT Design 190
7.3.2 NDT Template: Monitoring and Optimization 191
7.3.3 Stakeholder Views and Insights 191
7.3.4 NDT Architecture Overview 192
7.3.5 Optimization and Prognosis: Example Workflows 192
7.4 The Challenge of DTs for Cultural Heritage 194
7.4.1 A Hybrid Approach: Bottom-up and Top-down 195
7.4.2 Multi-view D for Artifacts 195
7.4.3 Web of Related DTs 196
7.4.4 Personalized and Adaptive Experiences 197
7.4.5 Case Study: Egyptian Scarabs and the Power of DTs 197
7.4.6 Context DT Architecture Support for Cultural Heritage 198
7.5 DT as an Integral Part of the Metaverse 199
7.5.1 State of the Art of Metaverse Platforms and Mapping to the Context Digital Architecture 200
7.5.2 Integration Points and Mutual Enhancement 201
7.5.3 Use Cases: Education, Tourism, and Factory Management 202
7.6 Implementing Services with the Envisaged Architecture 205
8 The Digital Twin of the Future 207
8.1 The Evolution of DT 207
8.2 Evaluating DTs as a General Solution 209
8.3 Promising Application Domains 210
8.3.1 Initial Criteria for DT Suitability 211
8.4 Anticipated Evolution of DT Technologies 211
8.4.1 DT Platforms 212
8.4.2 DT Creation and Development 213
8.4.3 Decomposition of Modules 215
8.4.4 Extensible Architecture 216
8.4.5 Integrated Methodologies 216
8.4.6 AI Integration 217
8.4.7 Testing and Assessment 219
8.5 Improved Operations 219
8.5.1 Life-cycle Management 220
8.5.2 Managing the Switch of States 220
8.5.3 Operations Tools 221
8.6 Interoperability and Standards 221
8.6.1 Standardization Efforts 222
8.6.2 Open APIs and Data Models 222
8.7 Ethical, Privacy, and Trust Considerations in DT Systems 223
8.8 Human-in-the-loop and User Experience 224
8.8.1 User-centric Design 225
8.8.2 Visualization and Immersive Interfaces 225
8.9 Sustainability and Societal Impact 225
8.9.1 DTs for Sustainable Development 225
8.9.2 Societal Impact and Digital Inclusion 226
8.10 Future Steps 226
A Listings and Details 229
A.1 Descriptive Modeling 229
A.1.1 Data Model and Application Programming Interfaces 229
A.1.2 Extending the Data Model to Fit Stakeholder Needs 235
A.2 Behavior Modeling 236
A.2.1 NGSI-LD Data Model for Traffic Light Behavior 236
References 239
Index 267
