Simulation Techniques of Digital Twin in Real-Time Applications E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 500

Veröffentlichungsjahr: 2024

Beschreibung

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

Leseprobe

Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Simulation Techniques of Digital Twin in Real-Time Applications

Design Modeling and Implementation

Edited by

Abhineet Anand

Computer Science and Engineering, Bahara University, Waknaghat, Himachal Pradesh, India

Anita Sardana

Dept. of Computer Science and Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

Abhishek Kumar

University of Castilla-La Mancha (UCLM), Toledo, Spain

Srikanta Kumar Mohapatra

Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

and

Shikha Gupta

Dept. of Computer Science Engineering, Chandigarh University, Mohali, Punjab, India

This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-25697-6

Cover image: Pixabay.ComCover design by Russell Richardson

Dedication

For our gurus, parents, and God, we dedicate our book. Their blessings are the reason for our motivation.

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 system’s energy performance demonstrates that energy in both reference instances is gradually rising. An error result of less than 1% is also displayed to demonstrate accuracy. Therefore, the aspect of concern is power consumption or energy utilization. The involved railroad authorities may find these results helpful in describing the user-label experience. These results also demonstrate that the simulation can use certain preventative measures.

Chapter 15 describes pipedream, which is a cutting-edge system that enables real-time hydraulic state forecasting and interpolation for urban fast-flood nowcasting. The pipedream toolset’s data assimilation method can be utilized by emergency management to estimate localized floods at ungauged locations, enabling rapid flood response and targeted emergency service dispatch.

We would like to express our gratitude to the writers and contributors for their efforts as well as to Wiley and Scrivener Publishing for their cooperation and assistance in the timely publication of this book.

The EditorsMarch 2024

Part 1A GUIDE TO SIMULATED TECHNIQUES IN DIGITAL TWIN

1Introduction to Different Simulation Techniques of Digital Twin Development

Suvarna Sharma and Chetna Monga*

Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

Abstract

Virtual models of actual systems, processes, or products are known as “digital twins” in technology. It replicates an object or system’s behavior, functionality, and responsiveness to various circumstances using a software version of the real-world counterpart. In addition to manufacturing, healthcare, construction, aerospace, and transportation, there are numerous additional sectors that use the technology. With the use of digital twins, businesses may increase productivity, decrease downtime, maximize resource use, and enhance decision-making. The following simulation methodologies can be applied in digital twin technology: computational fluid dynamics (CFD), finite element analysis (FEA), agent-based modeling (ABM), multi-body dynamics (MBD), discrete event simulation (DES), and monte Carlo simulation. The foundations of digital twin technology are covered in this chapter, along with the benefits they offer to real-world research applications. This chapter also discusses several simulation methods for the practical uses, advantages, and disadvantages that will be covered in later chapters.

Keywords: Digital-twin technology, agent-based modeling (ABM), finite element analysis (FEA), discrete event simulation (DES), computational fluid dynamics (CFD), Monte Carlo simulation, multi-body dynamics (MBD)

1.1 Introduction

Digital twins are frequently described as “a digital representation of a real world object with focus on the object itself” [13]. Figure 1.1 illustrates the extensive application areas of digital twin technology across various industries [1]. This visualization showcases the diverse utilization of digital twins in sectors such as manufacturing, healthcare, and urban planning, highlighting their significance in enhancing operational efficiency and facilitating predictive analysis.

A. Digital twin featuresTechnologies for creating, managing, and analyzing digital twins—virtual representations of actual systems or processes—are known as “digital twin technologies.” Among the crucial elements of digital twin technologies are the following:

Data integration: Digital twin technologies combine information from several sources, such as IoT devices, sensors, and other systems, to create a full image of the physical system or process.

Figure 1.1 Different application areas of digital twin.

Simulated and modeled learning: In order to build virtual representations of actual systems or processes, digital twin technologies employ simulation and modeling approaches. With the help of these models, the behavior of the physical system under various conditions may be forecasted and studied. Real-time monitoring: Digital twin technologies offer real-time observation of the physical system or process using data from sensors and other gears. Engineers and operators can now quickly identify and deal with any new issues as they arise.

Prevention-based maintenance: Based on information gathered from sensors and other devices, digital twin technology may be used to forecast when maintenance is necessary for the physical system or process. By doing so, downtime may be cut down and overall productivity can rise.

Optimization: The functionality of a physical system or process could be improved using technologies like digital twins through data analysis and the identification of issue areas. Increased effectiveness and lower costs may result from this.

Collaboration: Communication between numerous teams and departments is made feasible by digital twin technologies’ shared image of the physical system or process. This can improve dialogue and facilitate problem-solving.

Machine learning and artificial intelligence: By analyzing data and making predictions about how a physical system or process will behave, digital twin technologies may make use of machine learning and artificial intelligence algorithms. Making decisions will be more accurate and effective as a result.

The ability to create and maintain virtual representations of actual systems or activities is provided through a number of capabilities provided by digital twin technologies, which are used by engineers and operators. These characteristics can help with cost reductions, performance improvements, and efficiency increases.

B. Digital twin classificationDigital twin instance (DTI): A certain type of digital twin is referred to as a “digital twin instance” when it continuously depicts its physical counterpart across time in line with its specification [14]. As a result, the physical twin is continuously monitored, and any adjustments or changes it goes through will affect the digital twin. In this perception, the aim keeps track of and predicts the behavior of a process or product from the moment of its conception until the end of its existence. Validating a product’s or object’s predicted performance and behavior is helpful.

Digital twin prototype (DTP): A digital twin prototype gathers and stores important information and features about a product’s physical counterpart when it comes to the design and production of that product. Diagrams, computer-aided designs (CADs), and even information connecting persons involved in the production chain with the manufacturing process are examples of data [14]. Before the actual manufacturing process begins, the DTP may simulate production situations and carry out validation testing, assessments, and even quality control testing in accordance with DT requirements. By detecting defects or potential dangers in the physical twin before manufacturing, this method efficiently minimizes production costs and operating time. DTPs are also sometimes referred to as experimentable DTs in this sense, where, in accordance with [15], it is possible to access a virtual prototype whose degree of detail grows over time, and virtual test results offer an adequate evaluation of the design’s quality while reducing the need for normally affluent hardware architypes.

Performance digital twin (PDT): More realistic and unforeseen situations can be monitored, collected, and evaluated by the PDT than they can by physical twins [14]. The PDT can analyze the data being tracked from the physical equivalent by fusing its smart capabilities. The processing yields useful information that may be applied to design optimization, the creation of maintenance plans, and the drawing of inferences from a product’s capability [16].

1.2 Literature Review

A. Digital twin technology: Based on the fact that DT technology is still in its infancy, it will be crucial to overcome the many obstacles that a contemporary DT deployment faces, such as costs, information complexity and maintenance, a lack of standards and legislation, issues with cybersecurity and communications, and issues with standards and regulations. When assessing DTs in the three areas of technology, social readiness, and maturity, the maturity spectrum examination of major publications, as well as the TRL, SRL, and maturity analysis, are all very beneficial. The evolution of DT technology and maturity has only just started for the vast majority of applications. Although advanced DT uses are addressed in [1], more work has to be done before DTs may be fully enabled, accepted, and sustained in practical settings. The development of DTs will be aided by technologies and techniques for data processing and analysis. To address the issues brought up in this paper, future research should concentrate on the following areas: 1) computing complexity reduction through simulation and modeling, 2) 5G communication, 3) data from the Internet of Things can be processed and analyzed using big data, machine learning, and artificial intelligence, 4) the capacity of simulation, modeling, analysis, and visualization software to cooperate and integrate, and 5) recent technical advances including, but not limited to, the inclusion of edge and cloud computing potentialities in current microprocessors. One will be able to make a more accurate assessment of the current state of the art and the direction that technology is moving if they are aware of the whole picture of DTs across many crucial fields. It is crucial to take on the suggested research projects if DTs are to completely deliver on their promise for the future.

In [2], the conclusions are as follows:

A variety of naval equipment can be built using the full digital twin (DT) architecture, and the DT approach demonstrated for the horizontal axis tidal turbine (HATT) performs well in simulations and tank tests. Instructions for the DT model may be broadcast to the outside world in order to achieve self-learning, and data can be utilized to translate changes in the physical environment to the digital environment.

After several mechanical learning sessions, the substantial error in the DT data that the interpolation approach produced was decreased to less than 10%, which is regarded as being within an acceptable range and can be used as engineering reference values. The monitoring and performance assessment process for any marine equipment might be covered by this system.

Supercomputing is used to run a variety of simulations, develop a turbine DT full life cycle decision support system, and perform quick interpolation and data extraction. These systems also enable online monitoring of physical parameters like flow rate and pressure. Machine learning is combined with the optimization procedure for data with significant comparison result curve deviation.

B. Finite element analysis: The finite element analysis (FEA) method is used in the simulation model in conjunction with real-time data from the assembly line. Innovative engineering methods and open-source development have also made it easier for field engineers and operators to use technology. As the digital twin is expanded to additional production lines, there is a good chance that Arçelik’s manufacturing facilities may employ technology more frequently in the future. As a result, the overall cost of production and labor needs for refrigerators have decreased [3].

In [4], the use of a machine learning algorithm (decision tree) and digital twin technologies enables personalized medical care. The patient’s historical and real-time data are both captured by the model. In order to forecast, monitor, and create a model that might be used for additional diagnostics, doctors, healthcare organizations, nurses, and patients will follow the observations. With the use of these technologies, effective and customized medical care may be created, and patients will have access to more individualized care. Cybersecurity is the main issue one could run into when utilizing digital twin technology. Data loss is a result of the rise in cybercrimes. The enormous amount of data is being gathered from multiple sources, all of which could be potential weak points. Cloud-based simulation software and storage can be utilized to address these difficulties and stop data loss and leakage. HIPPA-like regulations, for example, can be used to impose data governance. If properly implemented, these techniques may lower the likelihood of data loss.

In [5], the measured current was the input used by the FEA model. Temperature estimates at the same spot as the sensor-probed point were made in the simulation; readings had a significant correlation with dimensions (maximum error of 9.5%). Additional support for the DT theory came from simulations that looked at temperature distribution, torque profile, resistive losses, and stator copper conductivity—parameters that are challenging to measure but have a strong correlation with the motor’s operating state. By demonstrating behavior that was consistent with theory and consistent with observations in the literature, these results provided support for the numerical model that had been created. The technique offers a tool that will aid in the monitoring of induction motor status in the future and produces accurate results under a continuous regime. The industry also benefits greatly from the advanced understanding because it means that the motor is only shut off when necessary for maintenance, which lowers expenses.

C. Computational fluid dynamics: Precision medicine’s goal is to provide treatments that are customized for each patient. This goal is made possible by our growing capacity to collect vast data on each patient. In [6], the second enabling aspect for achieving this aim, in the opinion of some, is the capacity of computers and algorithms to learn about, comprehend, and produce a patient’s “digital twin.” Future treatments will consider more factors in addition to the patient’s health right now and the information that is now accessible, but also a precise projection of the various routes for restoring health provided by model projections. The ability to make diagnoses and prognoses is being improved by computational models. Cardiovascular imaging and computational fluid dynamics can be utilized to provide diagnostic metrics and non-invasively characterize flow fields in the contexts of coronary artery disease, aortic aneurysm, aortic dissection, valve prosthesis, and stent design.

In [6], to give precision cardiology, information will be integrated with inductive and deductive reasons that are incorporated in each patient’s digital twin. Accurate predictions of the underlying causes of sickness and the best cures or treatments for health maintenance or restoration will serve as the cornerstone for treating and preventing cardiovascular diseases. In the domains of coronary artery disease, aortic aneurysm, aortic dissection, valve prosthesis, and stent design, non-invasive flow fields are characterized and diagnostic metrics are computed using cardiovascular imaging and computational fluid dynamics. Mechanistic and statistical models will work together in a synergistic manner to develop and validate these predictions. Although the first steps have already been made in this direction, the next ones will depend on a coordinated effort by stakeholders in the scientific, clinical, industrial, and regulatory areas to gather the necessary data and address the organizational and social problems described below.

D. Discrete event simulation: The need to predict asset behavior and make decisions practically instantly has caused DES to develop into an essential component of what are now known as the digital twins (DTs) in the Logistics 4.0 era [7]. Using real-time data produced by IoT devices implanted on the physical twin, or the automation, discrete event simulation programs, which act as the cyber twin, are utilized to run simulation software queries and update the simulation to the consequent system state. The integration of the real and virtual warehouse is made possible by DTs, which promote speedy and effective decision-making, planning, and management in the warehouse. Future DES and DT systems will have the ability to do simulations in real time and provide results that are almost real time, when “sensing” shop floor data is widely available, improving the efficiency of both production and logistics processes.

Article [6] develops the concept of a digital twin (DT) of medical services using a mix of discrete event simulation (DES) and the Internet of Things (IoT), which is a novel methodology described in this work. Realtime services data from several systems and devices are used to construct a predictive decision support model. By employing this technique, it is feasible to assess both the results of service improvements and the efficacy of the present healthcare delivery systems without having an impact on the hospital’s normal operations. A digital twin, also known as a virtual equivalent of the hospital, was developed to imitate a variety of crucial hospital healthcare services utilizing vital data that is obtained in real time. Despite the fact that the model first imitates four significant services to illustrate the idea, it also offers a core framework that may be adjusted to suit more services. By giving management and practitioners the opportunity to assess any model changes in order to predict the effectiveness or efficiency of services before they are put into practice, the proved proof-of-concept reveals how it enhances resource use and planning.

The overall patient count for each scenario, both input and output, is depicted in Figure 1.3, as evidenced by the fact that the number of patients serviced changes whenever resources vary. Similar to this, a hospital’s increased resources enhance the number of patients it can serve. The amount of staff members required in the hospital twin might also be simulated and tested. One may have observed that the number of inputs and patients are not equal. It is possible that some patients were still getting care at the conclusion of the simulation period, which would account for this. The 3D simulation model for hospital patient pathways is depicted in Figure 1.2. For each scenario in Table 1.1, Figure 1.2 describes the altered necessary resources. Variables may, of course, be changed to better reflect the resources used in this simulation—for instance, the number of input and output patients might be equalized without affecting the level of care.

Table 1.1 Description of each scenario’s available objects [8].

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Number of Receptionist

1

1

1

1

Number Physicians

1

1

1

2

Number RNs

1

1

1

2

Number of Exam Rooms (Exam Tables)

1

2

6

6

Number of Xray

1

1

1

1

Number of triage

1

1

1

1

Number of wheelchair

1

1

1

1

Number of waiting room

1

1

1

1

Number of patient Arrive door

1

1

1

1

Number of patient Exit door

1

1

1

1

Number of item queuing

1

1

1

1

Number of item processing

1

1

1

1

Figure 1.2 details the patients’ wait periods along their treatment pathway. The length of time the participants in this simulation wait depends on a number of parameters as was previously shown—for instance, beds or waiting areas as well as nurses and doctors.

In scenario 2, there is a longer line at the patients’ vital sign station, as seen in Figure 1.3. The fact that there is just one registered nurse (RN) is the cause of this. Additionally, in scenarios 3 and 4, nearly no time has passed while people have been waiting in the Exam Room area. This raises the issue of the six exam rooms present here as opposed to one or two in other settings.

Figure 1.2 Patient chart input–output [8].

Figure 1.3 Waiting time for each activity in minutes [8].

E. Agent-based modeling: The biggest challenges of today are dealing with uncertainty and numerous threats that can cause the network to collapse at any point while developing and running systems that can swiftly and efficiently manufacture a wide range of customized products. Due to agents’ potential ability to accurately represent this environment, agentbased models can solve optimization problems. A number of things may be accomplished using the virtual model, including comprehending the traits and connections between the system’s agents, assessing and predicting specific system qualities, and identifying and fixing flaws. Generally speaking, in many application fields, the interaction of autonomous agents can be demonstrated using an agent-based model [9, 46].

F. Multi-body dynamics: The root-mean-square metrics used to pinpoint the defects demonstrate that the root-mean-square characteristic metrics of the digital twin model perform better than those of the traditional model. The problem of data collection during real gear equipment degradation must be resolved. In order to be resolved, it will be possible to conduct additional research on planetary gear predictive maintenance on the basis of this model. The author created a more accurate model of digital twin gear depreciation in place of utilizing inadequate testing. Positive derivation and inverse identification are employed to extract precise meshing parameters, gearbox defects, and other mechanical data during the fault progression [10]. The aerospace and automotive industries, as well as large-scale industrial equipment, can all benefit from the answers provided by this research in terms of problem identification and preventative maintenance.

G. Monte Carlo simulation: The thorough part digital twin model (PDTM), which has been put forth [11], integrates many heterogeneous geometric models and converts assembly information from assembly semantics to geometry components in order to increase the effectiveness of assembly simulation. As a result, parts are automatically positioned during assembly. Calculating the impact on the assembled product’s important components takes into consideration manufacturing mistakes, assembly alignment difficulties, and mating surface distortion. A combination of the small displacement torsor (SDT) theory and the Monte Carlo method based on the modeling of the mating surfaces’ actual real-world mating state is used to mimic the uncertainty of the assembly position in a real assembly. The precision of the analytical results can be increased by superimposing the assembly-gap calculation result on the deformation of the mating surface. The effectiveness of the recommended strategy is illustrated using a case study that uses a prototype system and a load-sensitive multi-way valve assembly process.

Real-time Monte Carlo simulation models of operations with unknown operators may now be generated automatically, thanks to the development of a technology. The resulting real-time locating systems (RTLS)-based digital twin is flexible due to its real-time connections to the manufacturing execution system (MES), enterprise resource planning system (ERP), and real-time locating systems (RTLS) databases. The placement of workstations, how they are used, and the flow of manufacturing materials may all be learned from position and vibration data by applying data mining techniques. One may consider the online usage of this data in simulation software as a versatile depiction of the manufacturing process. A real-time digital twin’s output can be used to monitor productivity, particularly when activities are finished by personnel.

Digital twins that combine simulations with current sensor and production data should be the foundation of cyber-physical model-based solutions. The benefits of employing real-time locating systems (RTLS) to combine information are explored in the study [12], and it is shown how the examination of product-specific activity periods using simulation may be accomplished using position and acceleration data. The suggested digital twin is able to forecast the state of production in real time and offer data for assessing the effectiveness of the production process. RTLS and adaptable simulation model connections in real time are really appreciated. In the industrial case study that serves as the basis for this paper, we demonstrate how the notion of Simulation 4.0 aids in an analysis of the efficacy of human resources (HRE) in an assembly process.

1.3 Digital Twin Simulation Techniques

1.3.1 Finite Element Analysis Simulation

The combination of the power of computational modeling with real-time data integration has increased the accuracy and efficiency of finite element analysis (FEA) simulations with digital twin technology. An illustration of a digital twin simulation utilizing finite element analysis is the following:

Data collection: In order to get started, the digital twin collects real-time data from the physical system’s embedded sensors. Temperature, pressure, strain, and displacement are a few examples of characteristics that might be included in this data. Continuous data transfers are made to the digital twin platform [28].

Initialization of the model: The physical system is represented by a 3D model that includes information about its shape and composition. There are finite elements that make up the model, and these elements are joined at particular nodes [17].

Material characterization: Based on the physical characteristics of the actual system, each element is given a unique set of material qualities, such as elastic modulus, Poisson’s ratio, and thermal conductivity. Testing of the material or already-existing data sources are two ways to get this information.

Boundary conditions: The boundary conditions seen in the physical system are included into the digital twin. Forces, displacements, or heat inputs are a few examples of these situations. In order to depict the system’s current condition properly, sensor data is used [22].

Model-data integration: The FEA solver’s simulated results are combined with the real-time data received from the physical system through a process known as model-data integration. With the aid of this integration, which enables the digital twin to modify and update its model in response to observable behavior, the simulation can be validated to make sure it properly reflects the actual system [32].

Analysis and design optimization: The digital twin platform examines the simulation results to spot possible problems, gauge the effectiveness of the system, and improve its layout. As a result, preventative maintenance and performance enhancements are possible. It can also forecast how the system will behave in certain conditions [32].

Visualization and reporting: The simulation results and analytical findings are given in clear visualizations and reports so that stakeholders may comprehend the system’s behavior and make wise decisions about upkeep, design modifications, or other actions [26].

1.3.2 Computational Fluid Dynamics Simulation

To produce precise and dynamic simulations of fluid systems, computational fluid dynamics (CFD) simulation with respect to digital twin technology makes use of real-time data integration and sophisticated fluid flow modeling. Here is a CFD simulation utilizing digital twin technology as an example:

Data collection: The real-time data that is collected by the physical system’s sensors, which are utilized to monitor various parameters, includes flow rates, pressures, temperatures, and velocities. These data are delivered in real-time to the digital twin platform.

Geometry modeling: A 3D model of the fluid system’s geometry is made, including all necessary pipes, valves, pumps, and other geometric elements. The model can be generated from current data sources or constructed using CAD tools [37].

Mesh generation: The fluid system’s shape is discretized into smaller parts using a computational mesh that is produced by the digital twin. The mesh needs to be fine enough to correctly depict the flow behavior’s finer characteristics. There are several meshing methods that may be used, including structured and unstructured meshes.

Fluid properties and boundary conditions: The simulation model’s digital twin takes boundary conditions and fluid properties into consideration. Examples of these fluid qualities include density, viscosity, and thermal conductivity. Additionally, the relevant regions of the computational domain are subjected to the boundary conditions found in the physical system, such as inflow rates, temperatures, and pressure boundaries.

Initialization of the solver: The governing equations, including the Navier– Stokes equations for fluid flow, are repeatedly solved over discrete time steps in order to simulate the behavior of the fluid system [23].

Simulation of fluid flow: The CFD solver calculates the properties of the flowing fluid, such as velocity profiles, pressure distributions, temperature gradients, turbulence patterns, and other pertinent flow parameters. The solver takes into consideration variables including fluid viscosity, temperature impacts, and turbulence models to properly depict the flow behavior [2].

Data incorporation and validation: Along with the simulated outcomes from the CFD solver, real-time data gathered from the physical system is also incorporated. Through continual updating and model adjustments based on observed flow behavior, with the aid of this connection, the digital twin is able to update and evaluate the simulation, guaranteeing correctness and reliability [28].

Analysis and optimization: The effectiveness of the system is evaluated by the digital twin platform using the results of the simulation, spot any possible problems, and improve the fluid system’s conception and operation. Different scenarios may be simulated, and the effects of configuration modifications can be examined, resulting in insights that can be used to increase operating performance and efficiency while also reducing energy consumption [18].

Reporting and visualization: Reports and visualizations are used to communicate the outcomes of the simulation, the analysis, and the performance metrics. This enables stakeholders to comprehend the flow behavior, pinpoint areas for improvement, and make data-driven choices about system optimization, maintenance, or operational modifications [29].

1.3.3 Discrete Event Simulation

The capabilities of simulation modeling and real-time data integration are used in discrete event simulation (DES). It uses digital twin technology to portray complicated systems in a way that is dynamic and lifelike. As an example of a discrete event simulation based on a digital twin, consider the following:

Data collection: Sensors and other embedded data sources in the physical system are used to gather real-time data. This information could contain elements like the condition of the equipment, output rates, queue lengths, or resource use. The digital twin platform receives the data on a constant basis.

Model initialization: A simulation model capturing the physical system’s structure, operations, resources, and decision-making principles is developed. With the help of a simulation software, the model may be constructed utilizing components like entities, events, queues, and resources.

A connection is made between the simulation model and real-time data collected from the physical system. Due to this link, the accuracy of the digital twin’s representation of the system’s current status and of dynamic changes occurring in real time is ensured [43].

Event-driven simulation: The simulation model employs a discrete event approach, in which activities inside the system are initiated by events that take place at certain periods in time. The model replicates how things (such as items and consumers) move through different processes, capturing resource allocation, queueing behavior, and system interactions [42]. Real-time control and decision-making: Real-time data is used by the digital twin platform to operate the simulation model and make decisions that follow established rules or algorithms. Based on observed circumstances in the physical system, it may, for instance, alter resource allocation, give orders top priority, or start maintenance procedures [45].

Performance analysis: During the simulation run, the simulation model generates performance measures such as throughput, cycle time, waiting periods, or resource consumption. These indicators reveal the behavior of the system, its bottlenecks, and its potential areas of development [44].

Optimization and what-if analysis: The digital twin platform enables scenario analysis and optimization by changing the simulation model’s parameters, rules, or resource allocations. As a result, users may investigate various operational methods, assess the effects of changes, and find the best configurations or rules [21, 30].

Reporting and visualization: Reports and visualizations are used to display the outputs of the analysis, performance measurements, and simulation results. As a result, stakeholders are better able to comprehend the behavior of the system, evaluate performance, and decide how to enhance processes, allocate resources, or make operational changes.

1.3.4 Agent-Based Modeling Simulation

Using agent-based modeling (ABM) simulation, which is based on digital twin technology, complex systems may be accurately and dynamically simulated. Simulating agent-based modeling (ABM) blends real-time data integration with ABM’s modeling capabilities. This simulation makes use of agent-based modeling and digital twin technologies.

Data collection: Data is gathered in real time through sensors, IoT gadgets, or other data sources that are integrated into the physical system. The variables in this data include things like customer interactions, agent conduct, and environmental and resource circumstances. To the digital twin platform, the data is continually transferred.

Initialization of the model: A simulation model simulating the behavior of various actors or entities in the system is built, including agents that replicate their behavior. In addition to the environment in which the agents function, the model also depicts the structure, rules, and interactions among the agents [25].

Agent definition: According to their traits, activities, rules for making decisions, interactions with other agents, and interactions with their surroundings, agents are defined. Because of the fact that these characteristics are drawn from actual data and subject matter expertise, the behavior of the physical system can be accurately replicated by the digital twin.

Integration of real-time data: The simulation model is combined with the real-time data that has been gathered from the physical system. By adding real-time changes in agent behavior, environmental circumstances, or resource availability, this integration makes sure that the digital twin accurately depicts the system’s present state [38].

Execution of the simulation model: The simulation model may be run in real time or accelerated mode, enabling the agents to interact and make decisions in accordance with predefined rules and up-to-date information. The model accurately depicts the dynamics of the system, including agent mobility, decision-making procedures, resource distribution, and emergent behaviors at the systemic level [19].

Performance analysis: The simulation generates performance measures such as agent productivity, system throughput, client satisfaction, or resource use. These measurements show patterns in the system’s behavior, point out bottlenecks, and emphasize areas that need development [35].

Optimization and what-if analysis: The digital twin platform enables what-if analysis and scenario optimization by changing agent behaviors, environmental factors, or resource distributions inside the simulation model. Users are able to investigate various operational tactics, assess the effects of adjustments, and find the best configurations or rules as a result [20].

Reporting and visualization: Reports and visualizations are used to display the simulation results, performance data, and analytical findings. Individual agent behavior may be observed, system-level trends can be examined, and stakeholder-level choices about process optimization, resource allocation, or policy changes can be made with full knowledge of the situation [34].

1.3.5 Multi-Body Dynamics Simulation

Using digital twin technology, multi-body dynamics (MBD) simulation creates precise and dynamic models of mechanical systems by combining the capabilities of real-time data integration with multi-body dynamics modeling. Using digital twin technology, following is the process of simulation of multi-body dynamics:

Data collection: Information about the location, velocity, acceleration, and forces acting on various parts of the physical system is captured in real time by sensors integrated into the system. The digital twin platform receives this information on a constant basis.

Model initialization: The mechanical system’s shape, parts, and connections are represented by a 3D model that is built. Model elements such as bodies, joints, contacts, and forces are used to describe the system’s physical characteristics and restrictions. It may either be created with CAD software or retrieved from already existing data sources.