Adaptive multi‐rate interface: development and experimental verification for real‐time hybrid simulation

Abstract

Real-time hybrid simulation (RTHS) is a powerful cyber-physical technique that is a relatively cost-effective method to perform global/local system evaluation of structural systems. A major factor that determines the ability of an RTHS to represent true system-level behavior is the fidelity of the numerical substructure. While the use of higher-order models increases fidelity of the simulation, it also increases the demand for computational resources. Because RTHS is executed at real-time, in a conventional RTHS configuration, this increase in computational resources may limit the achievable sampling frequencies and/or introduce delays that can degrade its stability and performance. In this study, the Adaptive Multi-rate Interface rate-transitioning and compensation technique is developed to enable the use of more complex numerical models. Such a multirate RTHS is strictly executed at real-time, although it employs different time steps in the numerical and the physical substructures while including rate-transitioning to link the components appropriately. Typically, a higher-order numerical substructure model is solved at larger time intervals, and is coupled with a physical substructure that is driven at smaller time intervals for actuator control purposes. Through a series of simulations, the performance of the AMRI and several existing approaches for multi-rate RTHS is compared. It is noted that compared with existing methods, AMRI leads to a smaller error, especially at higher ratios of sampling frequency between the numerical and physical substructures and for input signals with highfrequency content. Further, it does not induce signal chattering at the coupling frequency. The effectiveness of AMRI is also verified experimentally. Figure 7. Simulation results of transfer system tracking.Figure 8. Determining acceptable/unacceptable ranges for a specific multi-rate implementation error.Case study II: Two significant strengths of the AMRI are its effective performance for input signals with high-frequency content and large sampling frequency ratios. To evaluate the performance of the proposed interface, a series of simulated case studies are implemented in which the input is a sinusoidal signal with various frequencies between 1-49 Hz and sampling frequency ratios of 2, 4, 5, 8, and 10. The corresponding normalized tracking errors using Equation (17) are shown in Figure 8. The simulation results shown in Figure 8 allow the researcher to have a better understanding of the error stemming from the multi-rate implementation of a realtime hybrid simulation using the AMRI. In this analysis, the frequency spectrum of the command signal is assumed to be known. For instance, the shaded region in Figure 8 results in less than 5% transfer system tracking error using the AMRI ratetransitioning scheme. Moreover, Figure 8 shows that in the majority of cases, the normalized error is less than 1%. Case study III: Finally, to systematically compare the performance of the three existing methods (method I-III) and the AMRI, a set...

A Reflective Framework for Performance Management (REFORM) of Real-Time Hybrid Simulation

“…This multi-rate coordination block facilitates the use of complex and high-fidelity features. The Adaptive Multi-rate Interface ( Maghareh et al, 2016 ) is utilized to 86 meet this objective. The adaptive multi-rate interface (AMRI, hereafter) was initially developed as a mechanism to facilitate greater fidelity in the computational substructure by running the numerical model at a larger time-interval than what is used for the control system, thereby providing supporting use of a computationally demanding model.…”

Experimental evaluation of CV‐Voronoi based adaptive sampling for Kriging meta‐modeling of multiple responses through real‐time hybrid simulation

“…Along with above developments, various techniques, such as multi‐grid processing 26 ; sparse matrix storage schemes 27 ; and vector form intrinsic finite element (VFIFE) analysis 28 have been explored to enhance computational efficiency. The extrapolation and interpolation are also explored in RTHS when physical substructure and analytical substructure are executed in multi‐rate interface 29 . These developments in RTHS have enables its successful applications for response evaluation of the MR damper 30 and the nonlinear viscous damper 31 …”

Experimental verification of a frequency domain evaluation index‐based compensation for real‐time hybrid simulation

“…The extrapolation and interpolation are also used in RTHS when physical substructure and analytical substructure are executed in multirate interface. 12 In addition to the above challenges for integration algorithm and numerical substructure modeling, RTHS requires that the servo-hydraulic actuator synchronizes the interfaces between substructures and maintains the boundary compatibility. However, communication delay, computing time, and servo-hydraulic dynamics often lead to asynchronous responses of substructures.…”

“…Sparse matrix storage schemes 10 and vector form intrinsic finite element (VFIFE) 11 analysis have been proposed and applied to enhance computational efficiency. The extrapolation and interpolation are also used in RTHS when physical substructure and analytical substructure are executed in multirate interface 12 …”