Abstract
An implementation of updating techniques similar to finite element updating in structural dynamics is developed for thermal material inspection using adaptive response surfaces to approximate experimental parameters. In general, thermal models contain high nonlinearities in their parameters, which influences updating accuracies. This is further investigated in this work. Several adaptive response surface regression methods are compared: interpolation, piecewise spline and polynomial regression functions. Next, the influence of the choice of optimisation parameters is discussed and compared with several global and local optimisation routines. Finally, a well-suited regression technique is investigated which transforms the dataset to a smaller, focused response model in each optimisation loop and delivers a proper regression accuracy. This results in data-reduction for the model to be optimised.
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Acknowledgements
This research has been funded by the University of Antwerp and the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT) by the support to the TETRA project ‘SINT’ with Project Number HBC.2017.0032. Furthermore, the research leading to these results has received funding from Industrial Research Fund FWO Krediet aan navorsers 1.5.240.13N and the FWO travel Grant V4.010.16N. The authors also acknowledge the Flemish government (GOA-Optimech) and the research council of the University of Antwerp (fti-OZC) for its funding.
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Peeters, J., Louarroudi, E., Bogaerts, B. et al. Active thermography setup updating for NDE: a comparative study of regression techniques and optimisation routines with high contrast parameter influences for thermal problems. Optim Eng 19, 163–185 (2018). https://doi.org/10.1007/s11081-017-9368-z
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DOI: https://doi.org/10.1007/s11081-017-9368-z