A real-time hybrid simulation method based on multitasking loading

Due to the real-time loading property and the limitation of available loading facilities, both of the refined numerical substructure simulations and multiple experimental substructure tests are impossible for traditional real-time hybrid simulation method (RHSM). For improving the experimental accuracy under limited loading facilities, a RHSM based on multitasking loading (RHSM-ML) is proposed in this paper. In the proposed method, an inner-loop multitasking loading strategy is adopted for accurately reproducing the performance of multiple experimental substructures with limited available loading facilities, and an outer-loop force correction-based iteration strategy is adopted for further improving the experimental accuracy by allowing refined simulation of the numerical substructures while remaining real-time loading on the experimental substructures. Firstly, the methodology of the proposed RHSM-ML is presented. Furthermore, the numerical simulations were conducted for validating the effectiveness and accuracy of the proposed method. Finally, the influence of the structural model on the iterative convergence is analyzed. It is shown that the multitasking loading and the force correction-based iteration strategy are feasible for RHSM. It is shown from numerical simulations that with the contribution of the multitasking loading strategy, the correlation coefficients under different simulation conditions can up to 0.9999 within five round iterations by the RHSM-ML and the force correction-based iteration strategy of the RHSM-ML can significantly improve the iterative convergence accuracy. It is shown from iterative convergence analysis that under different structural models, the convergence of the RHSM-ML can be achieved within five round iterations.