Query: DoD digital twin

title: Blockchain based data management for digital twin of product
abstract: The concept of digital twin was originally proposed to assist product lifecycle management through the high fidelity virtual product. In product lifecycle, there are many participators constructing a complicated network with enormous product lifecycle data. It is difficult to manage data of digital twin efficiently and securely from the perspectives of data storage, data access, data sharing, and data authenticity among the complicated network. Moreover, the virtual product is always updated to the latest state of the physical product by overwriting the history virtual product that records the development process of digital twin. To address abovementioned data management problems simultaneously, a data management method for digital twin of product based on blockchain technology is proposed in this paper. A peer to peer network is constructed to enhance data sharing efficiency among participators. The basic structure of blockchain is introduced, including block, chain, and transaction. The blockchain is used for data storage through cryptography that ensuring only eligible participators can access the corresponding data. The change sensitive characteristic of blockchain can ensure data authenticity as well. In addition, the concept of smart contract can be used to execute some actions automatically to increase data sharing efficiency. A case study is presented to demonstrate the effectiveness of the proposed data management method. The results show that the proposed method can solve the abovementioned data management problems simultaneously.

title: The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action
abstract: Digital twins, Internet of Things (IoT) block chains, and Artificial Intelligence (AI) may redefine our imagination and future vision of globalization. Digital Twin will likely affect most of the enterprises worldwide as it duplicates the physical model for remote monitoring, viewing, and controlling based on the digital format. It is actually the living model of the physical system which continuously adapts to operational changes based on the real time data from various IoT sensors and devices and forecasts the future of the corresponding physical counterparts with the help of machine learning/artificial intelligence. We have investigated the architecture, applications, and challenges in the implementation of digital twin with IoT capabilities. Some of the major research areas like big data and cloud, data fusion, and security in digital twins have been explored. AI facilitates the development of new models and technology systems in the domain of intelligent manufacturing.

title: Review of digital twin about concepts, technologies, and industrial applications
abstract: Various kinds of engineering software and digitalized equipment are widely applied through the lifecycle of industrial products. As a result, massive data of different types are being produced. However, these data are hysteretic and isolated from each other, leading to low efficiency and low utilization of these valuable data. Simulation based on theoretical and static model has been a conventional and powerful tool for the verification, validation, and optimization of a system in its early planning stage, but no attention is paid to the simulation application during system run time. With the development of new generation information and digitalization technologies, more data can be collected, and it is time to find a way for the deep application of all these data. As a result, the concept of digital twin has aroused much concern and is developing rapidly. Dispute and discussions around concepts, paradigms, frameworks, applications, and technologies of digital twin are on the rise both in academic and industrial communities. After a complete search of several databases and careful selection according to the proposed criteria, 240 academic publications about digital twin are identified and classified. This paper conducts a comprehensive and in depth review of these literatures to analyze digital twin from the perspective of concepts, technologies, and industrial applications. Research status, evolution of the concept, key enabling technologies of three aspects, and fifteen kinds of industrial applications in respective lifecycle phase are demonstrated in detail. Based on this, observations and future work recommendations for digital twin research are presented in the form of different lifecycle phases.

title: The digital twin of discrete dynamic systems: Initial approaches and future challenges
abstract: This paper employs a discrete damped dynamic system to investigate the emerging concept of a digital twin. Dynamic systems are well understood across engineering and science domains, and represent a familiar and convenient platform for exploring the various aspects of a digital twin design. The aim is to create a framework accessible to engineering sciences related to the aerospace, electrical, mechanical and computational area. The virtual model of the physical system is expressed as a differential equation in two time scales, with the concept of a slow time being used to separate the evolution of the system properties from the instantaneous time. Cases involving stiffness variation and mass variation are considered, individually and together. It is assumed that the damped natural frequency and the time response are measured through sensors placed on the physical system. Issues of errors and reduced sampling rate in sensor measurements on the digital twin are investigated. The digital twin is expressed as an analytical solution through closed form expressions and the effect of sensor errors is brought out through the simulations. Several key concepts introduced in this paper are summarized and ideas for urgent future research needs are proposed. The current work breaks free from the qualitative description of digital twins pervading the literature and can be used as benchmark solutions to validate digital twin of experimental dynamic systems and their implementation using sensors, the internet of things and deployment on the cloud computing systems.

title: A generic tri model based approach for product level digital twin development in a smart manufacturing environment
abstract: Smart manufacturing, as an emerging manufacturing paradigm, leverages massive in context data from manufacturing systems for intelligent decision makings. In such context, Cyber Physical Systems (CPS) play a key role in digitizing manufacturing systems and integrating multiple systems together for collaborative works. Amongst different levels of smartness and connectedness of CPS, Digital Twin (DT) as an exact digital copy of a physical object or system including its properties and relationship with the environment, has a significant impact on realizing smart manufacturing. A DT constantly synchronizes with its physical system and provides real time high fidelity simulations of the system and offers ubiquitous control over the system. Despite its great advantages, few works have been discussed about DT reference models, let alone a generic manner to establish it for smart manufacturing. Aiming to fill the gap, this research introduces a generic CPS system architecture for DT establishment in smart manufacturing with a novel tri model based approach (i.e. digital model, computational model and graph based model) for product level DT development. The tri model works concurrently to simulate real world physical behaviour and characteristics of the digital model. To validate the proposed architecture and approach, a case study of an open source 3D printer DT establishment is further conducted. Conclusions and future works are also highlighted to provide insightful knowledge to both academia and industries at last.

title: Digital Twin Reference Model Development to Prevent Operators' Risk in Process Plants
abstract: In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space) while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation) During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms.

title: Digital twin for battery systems: Cloud battery management system with online state of charge and state of health estimation
abstract: Battery management is critical to enhancing the safety, reliability, and performance of the battery systems. This paper presents a cloud battery management system for battery systems to improve the computational power and data storage capability by cloud computing. With the Internet of Things, all battery relevant data are measured and transmitted to the cloud seamlessly, building up the digital twin for the battery system, where battery diagnostic algorithms evaluate the data and open the window into battery's charge and aging level. The application of equivalent circuit models in the digital twin for battery systems is explored with the development of cloud suited state of charge and state of health estimation approaches. The proposed state of charge estimation with an adaptive extended H infinity filter is robust and accurate for both lithium ion and lead acid batteries, even with a significant initialization error. Furthermore, a state of health estimation algorithm with particle swarm optimization is innovatively exploited to monitor both capacity fade and power fade of the battery during aging. The functionalities and stability of both hardware and software of the cloud battery management system are validated with prototypes under field operation and experimental validation for both stationary and mobile applications.

title: Digital twin based cyber physical production system architectural framework for personalized production
abstract: Personalized production is a manufacturing concept relevant to the fourth industrial revolution, which can satisfy various customer needs inexpensively. There are three main hurdles to the efficient implementation of this concept: access, cost, and performance. This paper proposes a digital twin based cyber physical production system (CPPS) architectural framework that overcomes the performance hurdle. The proposed architectural framework comprises five services that are solutions to the performance hurdle of personalized production, and it operates on information based on the proposed product, process, plan, plant, and resource (P4R) information model. This information model is manufacturing abstraction for personalized production and is presented on a detailed level with object orientation and the "type and instance" concept. Whereas previous studies on the digital twin concept considered one or several facilities and focused on the development of independent applications, this study focuses on the digital twin as a core technological element of the entire system and analyzes CPPS design and its operation from the system of systems perspective. The application of the digital twin based CPPS to a micro smart factory (MSF) provides an advanced solution for the personalized production of various products. An average makespan improvement of 26.87% was achieved using the implemented CPPS services in the MSF.

title: Digital twin based designing of the configuration, motion, control, and optimization model of a flow type smart manufacturing system
abstract: Digital twins can achieve hardware in the loop simulation of both physical equipment and cyber model, which could be used to avoid the considerable cost of manufacturing system reconfiguration if the design deficiencies are found in the deployment process of the traditional irreversible design approach. Based on the digital twin technology, a quad play CMCO (i.e. Configuration design Motion planning Control development Optimization decoupling) design architecture is put forward for the design of the flow type smart manufacturing system in the Industry 4.0 context. The iteration logic of the CMCO design model is expounded. Two key enabling technologies for enabling the customized and software defined design of flow type smart manufacturing systems are presented, including the generalized encapsulation of the quad play CMCO model and the digital twin technique. A prototype of a digital twin based manufacturing system design platform, named Digital Twin System, is presented based on the CMCO model. The digital twin based design platform is verified with a case study of the hollow glass smart manufacturing system. The result shows that the Digital Twin System based design approach is feasible and efficient.

title: Multi physics resolved digital twin of proton exchange membrane fuel cells with a data driven surrogate model
abstract: The development of multi physics resolved digital twins of proton exchange membrane fuel cells (PEMFCs) is significant for the advancement of this technology. Here, to solve this scientific issue, a surrogate modelling method that combines a state of the art three dimensional PEMFC physical model and data driven model is proposed. The surrogate modelling prediction results demonstrate that the test set relative root mean square errors (rRMSEs) of the multi physics fields range from 3.88% to 24.80% and can mirror the multi physics field distribution characteristics well. In summary, for multi physics field prediction, the data driven surrogate model has a comparable accuracy to the comprehensive 3D physical model; however, it considerably reduces the cost of computation and time and achieves the efficient multi physics resolved digital twin. Two model based designs based on the as developed digital twin framework, i.e. the PEMFC healthy operation envelope and the PEMFC state map, are demonstrated. This study highlights the potential of combining data driven approaches and comprehensive physical models to develop the digital twin of complex systems, such as PEMFCs.