Experimental Techniques

Journal Information
ISSN / EISSN : 0732-8818 / 1747-1567
Current Publisher: Springer Science and Business Media LLC (10.1007)
Former Publisher: Wiley (10.1111)
Total articles ≅ 3,610
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Latest articles in this journal

Ali Hussain Khan, Shib Sankar Sarkar, Kalyani Mali, Ram Sarkar
Experimental Techniques pp 1-13; doi:10.1007/s40799-021-00470-4

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T.Q. Thai, J. Ruesch, P.R. Gradl, T.T. Truscott,
Experimental Techniques; doi:10.1007/s40799-021-00481-1

Sushmita Deka, Abhishek Kamal, , Maneswar Rahang, Vinayak Kulkarni
Experimental Techniques; doi:10.1007/s40799-021-00472-2

J.P. Waldbjoern, A. Quinlan, H. Stang, C. Berggreen
Published: 28 April 2021
Experimental Techniques; doi:10.1007/s40799-021-00475-z

M. Kiani, V. Parvaneh, A. Dadrasi, M. Abbasi
Published: 28 April 2021
Experimental Techniques; doi:10.1007/s40799-021-00477-x

, D. J. Rixen
Published: 27 April 2021
Experimental Techniques; doi:10.1007/s40799-021-00466-0

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.
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