Explore the vast range of titles available.

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared. Code and preprocessed data for generating all the results and figures presented in the battery digital twin case study in part 2 of this review are available on Github .

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision- and policy-making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open-source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on Github .

Smart manufacturing becomes a major trend of manufacturing industry in the context of Industry 4.0. The integration of physical manufacturing machines and digitized virtual counterparts is promoted by emerging concepts and technologies of Digital Twin. Aiming to seamlessly integrate the cyberspace and physical world by constructing a bi-directional mapping system, Digital Twin can highly improve the user experience and production efficiency in smart manufacturing. So far little attention has been paid to the mapping from the cyberspace to the physical world in Digital Twin. Without this mapping, operations on the digitized virtual machines are incapable of working on the physical ones, which actually limits the applicability of Digital Twin to more manufacturing processes. In addition, the traditional 2D interactive interface in the cyberspace is limited in visualizing the large amount of digital data and providing concise information to improve the operation efficiency. To optimize the conventional Digital Twin mapping system, this paper proposes a modular-based Digital Twin system for smart manufacturing, where the bi-directional real-time mapping of the cyber–physical space is established through socket communication. Moreover, the proposed Digital Twin system aggregates the functions of remote control and virtual machining using virtual reality and augmented reality. These two essential functions are designed to provide an immersive and friendly operating environment as well as a vivid preview of machining outcomes to improve production efficiency, minimize machining cost, and avoid potential risks. The feasibility and effectiveness of the proposed Digital Twin system are demonstrated by implementing the system on a CNC milling machine where the control latency and virtual machining accuracy are verified. The proposed Digital Twin system can be utilized as an essential part of smart manufacturing, having high potential to be applied to various industrial machines and smart systems.

The design and operation of systems are conventionally viewed as a sequential decision-making process that is informed by data from physical experiments and simulations. However, the integration of these high-dimensional and heterogeneous data sources requires the consideration of the impact of a decision on a system’s remaining life cycle. Consequently, this introduces a degree of complexity that in most cases can only be solved through a simplified decision-making approach. In this perspective paper, we use the digital twin concept to formulate an integrated perspective for the design of systems. Specifically, we show how the digital twin concept enables the integration of system design decisions and operational decisions during each stage of a system’s life cycle. This perspective has two advantages: (i) improved system performance as more effective decisions can be made, and (ii) improved data efficiency as it provides a framework to utilize data from multiple sources and design instances. The novelty in the presented perspective is that it necessitates an approach that enables fleet-level (i.e., decisions that influence a plurality of systems) and system-level decisions. From a formal definition, we identify a set of eight capabilities that are vital constructs to bring about the potential, as defined in this paper, that the digital twin concept holds for the design of systems. Subsequently, by comparing these capabilities with the available literature on digital twins, we identify research questions and forecast their broader impact. By conceptualizing the potential that the digital twin concept holds for the design of systems, we hope to contribute to the convergence of definitions, problem formulations, research gaps, and value propositions in this burgeoning field. Addressing the research questions, associated with the digital twin-inspired formulation for the design of systems, will bring about more advanced systems that can meet some of the societies’ grand challenges.

In the absence of a high-fidelity physics-based prognostics model, data-driven prognostics methods are widely adopted. In practice, however, data-driven approaches often suffer from insufficient training data, which causes large training uncertainty that hinders the Digital twin (DT)-based decision-making. In such a case, the integration of low-fidelity physics with a data-driven method is highly demanded. This paper introduces physics-informed neural network (PINN)-based prognostics that can utilize low-fidelity physics information, such as monotonicity or the sign of curvature. Low-fidelity physics information is included as a constraint during the optimization process to reduce the training uncertainty in the neural network model by preventing unrealistic predictions. The proposed method is applied to two case studies to demonstrate the effect of reducing the prediction uncertainty and the robustness to the variability in test data. The two case studies show that PINN-based prognostics can successfully reduce the prediction uncertainty and yield more robust prognostics performance than the ordinary neural network.

Multi-objective optimization allows satisfying multiple decision criteria concurrently, and generally yields multiple solutions. It has the potential to be applied to structural damage identification applications which are oftentimes under-determined. How to achieve high-quality solutions in terms of accuracy, diversity, and completeness is a challenging research subject. The solution techniques and parametric selections are believed to be problem specific. In this research, we formulate a reinforcement learning hyper-heuristic scheme to work coherently with the single-point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-seed). The four low-level heuristics proposed can meet various optimization requirements adaptively and autonomously using the domination amount, crowding distance, and hypervolume calculations. The new approach exhibits improved and more robust performance than AMOSA, NSGA-II, and MOEA/D when applied to benchmark test cases. It is then applied to an active damage interrogation scheme for structural damage identification where solution diversity/completeness and accuracy are critically important. Results show that this approach can successfully include the true damage scenario in the solution set identified. The outcome of this research can potentially be extended to a variety of applications.

Aging bridges require regular inspection due to performance deterioration. For this purpose, numerous researchers have considered the use of unmanned aerial vehicle (UAV) systems for structural health monitoring and inspection. However, present UAV-based inspection methods only represent the type and extent of external damage, but does not assess the seismic performance. In this study, a seismic fragility analysis of deteriorated bridges employing a UAV inspection-based updated digital twin is proposed. The proposed method consists of two phases: (1) bridge condition assessment using UAV inspection for updating the digital twin and (2) seismic fragility analysis based on the updated digital twin. To update the digital twin, the bridge damage grade is assigned based on the UAV inspection, and subsequently, the corresponding damage index is calculated. The damage index is utilized as a percentage reduction in the stiffness of finite element (FE) model, based on a previously proposed research. Using the updated digital twin, the seismic fragility analysis is conducted with different earthquake motions and magnitudes. To demonstrate the proposed method, an inservice pre-stressed concrete box bridge is examined. In particular, the seismic fragility curves of deteriorated bridges are compared with those of intact bridges. The numerical results show that the maximum failure probability of the deteriorated bridges is 3.6% higher than that of intact bridges. Therefore, the proposed method has the potential to updated the digital twin effectively using UAV inspection, allowing for seismic fragility analysis of deteriorated bridges to be conducted.

Digital Twins (DTs) are widely used for design, manufacturing, prognostics, and decision support for operations. One critical challenge in optimizing DTs usually involves multi-fidelity (MF) models and data, such as multi-resolution computational simulation and experimental testing. The MF strategies provide advantages of high accuracy with low computational and experimental cost in DTs. A novel MF optimization framework is proposed in this paper to demonstrate and validate its potential application in DTs. First, MF data aggregation using convolutional neural networks (MDACNN) is introduced to integrate low-fidelity (LF) and high-fidelity (HF) models and data. It can fully utilize the LF data to learn the relationship across multiple fidelities. With MDACNN, predictions can be made with high accuracy compared to HF models. Next, MDACNN is integrated into the neural optimization machine (NOM), an optimization framework based on NNs. NOM is explicitly designed for optimizing NN objective functions based on the stochastic gradient descent method. The integrated model is named MF neural optimization machine (MFNOM). A numerical example is presented to illustrate the procedure of implementing MFNOM. Two engineering applications are presented for both MF simulation models and experimental data. The first problem focuses on the multi-resolution finite element simulation for structures and materials. Coarse and fine meshes are applied for simulation. The properties of multi-phase heterogeneous materials are optimized to minimize the stress in the simulation domain. The second problem investigates the internal defects in additively manufacturing. Low/high resolution and full/partial field scanning data are utilized to build DTs. MFNOM is used to design pore size and orientation to reduce the risk caused by irregular pores.

Digital twin that shows great potential in different fields may serve as the enabling technology for the health monitoring of aero-engine blade. However, due to the harsh conditions inside the aero-engine, one of the most challenging issues for the implementation of digital-twin-based blade health monitoring is the lack of an accurate connection method between the digital-twin model and the physical entity for rotating blade. Wherein, the key is how to measure the blade data accurately. The emerging blade tip timing (BTT), an effective non-contact measurement method for blades, has received extensive attention recently. Whereas, due to the limited probes that are allowed to be installed on the engine casing, the BTT signal is generally incomplete and under-sampling, which makes it very difficult to reconstruct the blade vibration parameters from the measured data. In this study, a novel paradigm for blade vibration parameter reconstruction with super-resolution from the undersampled BTT signal is proposed based on atomic norm soft thresholding (AST), which may offer accurate blade vibration information for the construction and updating of blade digital-twin model. Unlike the conventional reconstruction method that generally needs the interested signal to be sparse under a finite discrete dictionary for successful reconstruction, the proposed AST-based blade vibration parameter reconstruction method can take any continuous value in the frequency domain from the measurement data with fewer sampling numbers and higher under-sampling rate. Both numerical simulation and experimental verification are utilized to verify the validity of the proposed method. The comparative results indicate that the proposed method performs well in resisting “incomplete.” Meanwhile, the proposed method performs better than state-of-the-art methods under conditions with fewer data.

This paper pursues a probabilistic digital twin methodology for designing component health- and stress-aware system control, and demonstrates the proposed methodology for the problem of rotorcraft maneuver control. The probabilistic digital twin uses sensor data to infer up-to-date knowledge regarding the component’s current health state and the associated uncertainty, and predicts future degradation of the system and the stress experienced under specific operational trajectories. The stochastic optimization problem considers the stress predicted for the degrading system to decide suitable operational controls over time such that the system safely and reliably completes the desired task or mission. The operational optimization process using the probabilistic digital twin is demonstrated by conducting asset-specific simulation experiments for a light-weight rotorcraft and optimizing the pilot control actions.