The digital twin, a concept that aims to establish a real-time mapping between physical space and virtual space, can be used for real-time analysis, reliability assessment, predictive maintenance, and design optimization of products. This article presents an enabling technology named shape–performance integrated digital twin (SPI-DT) and takes a boom crane as an example to illustrate how to design the SPI-DT step by step for the structural analysis of complex heavy equipment. The SPI-DT contains different types of models, such as an analytical model, a numerical model, and an artificial intelligence (AI) model. In addition, it leverages multisource dynamic data obtained by placing different sensors at multiple measurement positions. In the SPI-DT, the AI model plays a central role, invoking the numerical model and sensor data as the input to predict the structural performance of key components of heavy equipment, while the analytical model analyzes the structure of noncritical components with sensor data as input. This significantly improves the computational efficiency of the digital twin used for the structural analysis of complex heavy equipment, making the digital twin computationally affordable, and thus can be used for the safety assessment and damage protection of the equipment in the operation, as well as the design optimization of next-generation products. Moreover, to visually demonstrate the models and data in the SPI-DT, a three-dimensional application used to display and record the shape and performance information in real time during the operation of the boom crane is developed.

Digital twin has the potential for increasing production, achieving real-time monitor, and realizing predictive maintenance by establishing a real-time high-fidelity mapping between the physical entity and its digital model. However, the high accuracy and instantaneousness requirements of digital twins have hindered their applications in practical engineering. This article presents a universal framework to fulfill the requirements and to build an accurate and trustworthy digital twin by integrating numerical simulations, sensor data, multifidelity surrogate (MFS) models, and visualization techniques. In practical engineering, the number of sensors available to measure quantities of interest is often limited, and complementary simulations are necessary to compute these quantities. The simulation results are generally more comprehensive but not as accurate as the sensor data. Therefore, the proposed framework combines the benefits of both simulation results and sensor data by using an MFS model based on moving least squares (MLS), named MLS-based multifidelity surrogate (MFS-MLS). The MFS-MLS was developed as an essential part to calibrate the continuous field of the simulation by limited sensor data to obtain accurate results for the digital twin. Then, single-fidelity surrogate models are built on the whole domain using the calibrated results of the MFS-MLS as training samples and sensor data as inputs to predict and visualize the quantities of interest in real time. In addition, the framework was validated by a truss test case, and the results demonstrate that the proposed framework has the potential to be an effective tool to build accurate and trustworthy digital twins.

Undetected fatigue and overload damages at the key locations of the crane boom are among the biggest threats in construction, leading to structural failure. Thus, the structural health of the crane boom should be monitored in real time to ensure that it works under the designed load capacity. In this study, we developed a lightweight digital twin by the multifidelity surrogate (MFS) model to improve the real-time monitoring and prediction accuracy of the structural safety of a crane boom. Digital twin technology, which can establish real-time mapping between the physical space and the digital space, has a promising potential for online monitoring and analysis of structures, equipment, and even human bodies. By combining the MFS model and sensor data, the lightweight digital twin can dynamically mirror the crane boom postures and predict its structural performance in real time. In this study, the structural analysis of the crane boom is limited to the linear elastic stage of materials. Numerical experiments showed that the accuracy of the lightweight digital twin was enhanced compared with that established by the single-fidelity surrogate model, and the computational cost of the lightweight digital twin was decreased with respect to the digital twin built by the numerical method. Meanwhile, the uncertainty from the physical space was analyzed to enhance the reliability of the lightweight digital twin. Thus, the lightweight digital twin developed in our work can ensure accurate safety prediction and design optimization for crane booms.

The concept of digital twins is to have a digital model that can replicate the behavior of a physical asset in real time. However, using digital models to reflect the structural performance of physical assets usually faces high computational costs, which makes it difficult for the model to satisfy real-time requirements. As a technique to replace expensive simulations, surrogate models have great potential to solve this problem. In practice, however, the optimal individual surrogate model (ISM) applicable to a given problem usually changes as factors change, and this can be mitigated by integrating multiple ISMs. Therefore, this paper proposes a scalable digital twin framework based on a novel adaptive ensemble surrogate model. This ensemble not only provides robust approximation but also reduces the additional cost brought by the ensemble by reducing the number of ISMs participating in the ensemble through multicriterion model screening. Moreover, based on the characteristics of the finite element method, a node rearrangement method, which provides scalability for the construction of a digital model, is proposed. That is, the distribution and number of nodes can be customized to not only decrease the computational cost by reducing nodes but also obtain the information at key positions by customizing the locations of nodes. Numerical experiments are employed to verify the performance of the proposed ensemble and node rearrangement method. A telehandler is used as an example to build a scalable digital twin, which proves the feasibility and effectiveness of the framework.

Along with the development of surrogate models, there is a growing need to use surrogate models instead of computationally intensive simulations to estimate real system responses. Compared with individual surrogate models, the ensemble of surrogate models is gradually drawing more attention due to its better applicability and robustness. Thus, this paper proposes an adaptive median-based ensemble of surrogate models (MID-ESMs). At first, construct a reference model using the median of the predicted values of several surrogate models. Then an adaptive weight ensemble strategy is proposed based on the reference model to integrate global trends and local features. Thirty test functions and a practical engineering case are used to evaluate the model performance. In addition, this paper investigates the effect of homoscedasticity noise and test functions of different dimensions on the proposed model. The results demonstrate that MID-ESM has higher accuracy and robustness than individual surrogate models and other ensembles of surrogate models, offering better applicability in engineering problems.

To optimize structures and monitor their health, it is essential to build an accurate dynamic analysis model. However, traditional modeling methods based solely on physical information or data-driven techniques may not suffice for many engineering applications. While physical models can accurately simulate complex equipment, they may also incur high computational time. On the other hand, data-driven models may improve computational efficiency but are subject to significant deviations due to the influence of training data. To address these challenges, the Physics-Informed Neural Network (PINN) has gained popularity for imposing physical constraints during the training process, leading to better generalization capabilities with fewer data samples. This paper proposes a physics-informed hybrid modeling (PIHM) approach that combines a reduced-order model, kernel functions, and dynamic equations to predict dynamic output with limited training data and physical information. The method integrates prior physics information into function approximation by incorporating the reduced dynamic equation into a surrogate modeling framework. The loss function considers inertial and damping effects, ensuring physical plausibility. Unlike traditional PINN applications, the proposed modeling method is more explainable, as the trained model can be expressed in function form with engineering interpretation. The approach is verified with a real-world engineering example (telehandler boom) under complex load conditions, demonstrating accuracy, efficiency, and physical plausibility. Overall, the proposed method offers promising capabilities in solving problems where high-fidelity simulation is challenging.