Abstract
Real-Time Hybrid Substructure (RTHS) testing is a commonly used method to investigate the dynamical influence of a component on a mechanical system. In RTHS, a part of the dynamical system is tested experimentally, while the remaining structure is simulated numerically in a co-simulation. There are several error sources in the RTHS loop that distort the test outcome. To investigate the reliability of the test, the fidelity of the test must be quantified. In many engineering applications, however, there is no reference solution available to which the test outcome can be validated against. This work reviews currently existing accuracy measures used in RTHS. Furthermore, using Artificial Neural Networks (ANN) to predict the fidelity of the RTHS test outcome when no reference solution is available is proposed. Appropriate input features for the network, such as dynamic properties of the system and existing error indicators, are discussed. ANN training was performed on a data set from a virtual RTHS (vRTHS) simulation of a dynamical system with contact. The training process was successful, meaning that the correlation between the ANN prediction and the true fidelity value was > 99 %. Then, the network was applied to data of experimental RTHS tests of the same dynamical system and achieved a correlation of 98 %, which proves that the relation found by the ANN captured the relation between the chosen input features and the error measure. The application of the trained ANN to data from a linear vRTHS test revealed that further improvement of the network and the choice of input features is necessary. This work suggests that ANNs could be a meaningful tool to predict the fidelity of the RTHS test outcome in the absence of a reference solution, especially if more data from different RTHS tests were aggregated to train them.
Highlights
In Real Time Hybrid Substructure (RTHS) Testing, a dynamical system is analyzed by splitting it into a numerically simulated and an experimentally tested part
This work proposed the application of Artificial Neural Networks (ANNs) to predict the test fidelity based on test data such as error indicators and dynamic properties when there is no reference solution available
The ANN was trained on that data set and the results revealed that the training process is successful, which indicates that a relation between the selected input features and the test fidelity, which was measured using the relative RMS reference error, exists
Summary
In Real Time Hybrid Substructure (RTHS) Testing, a dynamical system is analyzed by splitting it into a numerically simulated and an experimentally tested part. The substructures are coupled in real-time by a so-called transfer system that exchanges displacement/velocity and force information (flow and effort) between them [1]. The idea of Hybrid Substructuring was first proposed by Hakuno et al [2] (in Japanese, briefly summarized in English in [3]). It took until the early 1990s and the work of Nakashima et al [4] before more interest arose to RTHS. The controlled actuator performs the movement z (different from z in practice) and moves the experimental part, which
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