A Scalable Digital Twin Framework Based on a Novel Adaptive Ensemble Surrogate Model

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.