1. Inverse method for static load reconstruction with automatic filtering for optimal sensor placement.
- Author
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Hanekom, M. and Venter, G.
- Abstract
The direct measurement of loads is not always feasible; thus, other means for quantifying imposed loads are required to evaluate the strength of a structure. Static load reconstruction is the process of transforming a structure into its own force transducer using the measured structural strain response to inversely calculate the imposed loads. Commercial software products provide functionality that allows for load reconstruction, but is not freely available. An open-source framework for inverse static load reconstruction for multiple load cases is provided in this paper. The relationship between input loading and output strain is determined with a unit load model concept, the optimal sensor positions are determined by a D-Optimal design of experiments algorithm and the imposed loads are approximated with a least-square estimate. The D-Optimal design selects the optimal sensor position and orientation that span the maximum volume of the set of potential sensor placement options, but makes no consideration for the practical aspects associated with mounting the sensors. Filters were designed and implemented to automatically exclude specific areas of the structure from consideration by the D-optimal algorithm for optimal sensor placement. The strain gradient filter, specifically, proposes a newly developed approach to identify where sensors would not be able to provide accurate results due to large strain gradients. The load reconstruction procedure, including the filters, was tested experimentally. Loads were satisfactorily reconstructed, but the accuracy can be improved if the numerical and the actual models were better correlated. The discrepancy between both models was due to large strain gradients, which implies that the current strain gradient filter is too conservative in its approach to classify large strain gradient areas. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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