Preface

Currently, a great amount of research is invested in the development of models and exploring their implementation. Digital twin technology is just the replica of an object in digital form. Generally, this technology improves the capability to receive real-time data and produce the data pool of the original object. This book is for researchers of diversified technologies, and the main objective is to showcase the proposed research models to a real-world audience.

This book collects a significant number of important research articles from domain-specific experts to present their works to the readers. A useful platform for both researchers and readers, this book gives a better understanding of how Digital twin technology may be the next big thing in the context of sustainable sectors to industrial sectors. This book sheds light on the various techniques of digital twin that are implemented in various application areas. It emphasizes error findings and respective solutions before the actual thing happens. Most of the aspects in this book are the implementation of strategies in real-time applications. Various real-life experiences are taken to show the proper implementation of simulation technologies. Overall, the book is for the readers to manage real-time applications or problems with the help of replicated models or digital twin technologies.

The book shows how authors 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 authors with expertise in different technologies from many regions of the world. It will give an idea to implement the real-time data embedded into technologies.

Specifically, the chapters herein 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.

Overall, the book gives a complete idea about the implementation of digital twin technology in real-time scenario. Furthermore, it emphasizes how this technology can be embedded with running technologies to solve all other issues.

This book comprises two parts: Part 1—“A Guide to Simulated Techniques in Digital Twin” and Part 2—“Real-Time Applications of Digital Twin”. In Part 1, Chapter 1 introduces digital twin modeling. Furthermore, it specifies that engineers and designers employ simulation, a key step in the development of digital twins, to generate and test various scenarios in a secure and controlled environment. Many simulation techniques are widely used in the development of digital twins, including FEA, CFD, DES, MBD, MCS, and ABM. There are pros and cons to each of these techniques, but they may all be used to imitate and enhance particular aspects of the physical system. As digital twin technology advances, new simulation techniques and tools will emerge, allowing engineers to create more accurate and comprehensive models.

Chapter 2 shines light on the future of today’s manufacturing lines. Furthermore, it clarifies that twin model is clearly headed toward advanced real-time simulation frameworks taking the lead. These frameworks, built on the digital twin principles, have ushered in a new era where real-time data synthesis and prompt feedback are not just useful but essential. Production simulations are now more realistic, precise, and comprehensive thanks to digital twin. The growing use of digital twin of traditionally physical systems has enabled industries to predict issues, make precise predictions, and base decisions on real-time data.

Chapter 3 discusses an air purifier system. The air quality, energy use, and cost-effectiveness of the air purifier system are all predicted by the LabVIEW simulation model. The digital twin concept can improve the efficiency, effectiveness, and cost-effectiveness of air purifier systems. The most effective and affordable air purifier system layouts can be found through analysis using the digital twin approach. The digital twin model might simulate how pollutants impact air quality and air treatment technologies. Research may enhance air quality, energy effectiveness, cost effectiveness, and air purification innovation.

Chapter 4 generated results that indicate that the suggested model did well on the classification dataset. It has very good accuracy, precision, recall, specificity, and F1-scores (between 96.85 and 99.3). The findings demonstrate that the model can accurately distinguish between those who genuinely have the illness and those who do not. The healthy class showed positive results, indicating that the model successfully distinguishes between healthy (normal) and damaged leaves.

Chapter 5 discusses various signaling methods, including BDPSK, BPSK, BFSK, QPSK, NCFSK, MPSK, MQAM, DQPSK, MDPSK, and NCMFSK over F fading channel, which have had error rate equations calculated for them in this study. The asymptotic, tightly bound, and approximate expressions of ABER have now been calculated. Additionally, the many expressions of capacities have been discovered. For the purposes of generalization and validation, a few reduction examples are also described. The analytical results have been acquired, and simulation results support them.

Chapter 6 looks at the effectiveness of the F model when combined with MGF. The expression for MGF is first derived. We have determined the expression for BER utilizing a variety of signaling schemes, including BDPSK, NBFSK, BPSK, BFSK, MSK, MAM, Square MQAM, MPSK, and NMFSK, using the suggested MGF. Additionally, the ORA and CIFR capacity expressions are computed. Through the use of Monte Carlo simulations and special case outcomes, the accuracy of the result has been verified. According to the study, higher-fading severity parameters perform better in terms of BER and channel capacity than lower ones.

To begin Part 2, Chapter 7 works on the creation of virtual replicas of real cars which is made possible by the use of digital twin technology, enabling prolonged testing in a controlled environment. By modeling numerous scenarios and driving conditions, developers can evaluate the performance and capabilities of autonomous driving systems without the need for real-world testing. This avoids wasting time and money and guarantees that the technology is thoroughly tested before being used on public roads.

Chapter 8 summarizes the study’s major conclusions and learnings in digital twin modeling. Real-time information, including voltage, current, and temperature, are gathered by sensors for transformer condition monitoring. Therefore, this information is sent to the computer. The use of a MATLAB-based ANN-based intelligent monitoring system that is connected with the hardware to produce digital twins demonstrates tremendous potential for enhancing the precision and effectiveness of power transformer failure analysis through temperature monitoring.

Chapter 9 gives a thorough explanation of the integrated deep learning digital twin approach. For the purpose of advancing digital twin technology, authors have examined several forms of digital twins and the ways that deep learning techniques are applied in various simulation models. They have researched a variety of current publications that use deep learning to enhance the functionality of digital twin models.

Chapter 10 discloses an online system identification or virtual modeling approach. There is huge potential for the use of digital twin (DT) in dynamical systems, including active control, health monitoring, diagnostics, prognosis, and computation of remaining useful life. However, the implementation of this technology in real time has lagged behind schedule, largely because there is a dearth of data pertinent to the application being used.

Chapter 11 describes UAVs that use digital twin-based techniques, which have a great chance of performing autonomous takeoff, landing, and cruising. The findings of this study add to the body of knowledge already available on UAV autonomy and highlight the need for more research in this field. Overcoming the challenges and researching the suggested future courses are necessary to fully realize the promise of digital twin-based autonomous operations and drastically transform how they are used in many businesses and sectors.

Chapter 12 explains digital twins and artificial intelligence (AI)-powered algorithms to increase productivity, safety, and sustainability in the mining industry, as the adoption of such a system alters coal mining operations. Overall, the DT is making headway, and thanks to its almost endless potential, it is becoming a more significant and well-liked competitor in the race. The authors are one step closer to making actual DTs with the development of its underlying technologies, which are constantly evolving.

Chapter 13 analyzes real-time data from the aircraft’s sensors and systems. Digital twins can foresee likely defects or maintenance needs. This proactive approach helps to reduce unscheduled downtime, enhance maintenance schedules, and improve aircraft availability and reliability. By combining digital twin technology with artificial intelligence and machine learning methods, the prediction abilities of Digital Twins will be enhanced.

Chapter 14 relates to energy consumption, which is closely related to power consumption in urban trains. The...