LAMPEAS, George, PASIALIS, Vasileios, LIN, Xiaoshan and PATTERSON, Eann (2015). On the validation of solid mechanics models using optical measurements and data decomposition. Simulation Modelling Practice and Theory, 52, 92-107. [Article]
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11867:37503
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Pasialis - On the validation of solid mechanics models - final manuscript.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Pasialis - On the validation of solid mechanics models - final manuscript.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
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Abstract
Engineering simulation has a significant role in the process of design and analysis of most
engineered products at all scales and is used to provide elegant, light-weight, optimized
designs. A major step in achieving high confidence in computational models with good predictive
capabilities is model validation. It is normal practice to validate simulation models
by comparing their numerical results to experimental data. However, current validation
practices tend to focus on identifying hot-spots in the data and checking that the experimental
and modeling results have a satisfactory agreement in these critical zones. Often
the comparison is restricted to a single or a few points where the maximum stress/strain
is predicted by the model. The objective of the present paper is to demonstrate a step-bystep
approach for performing model validation by combining full-field optical measurement
methodologies with computational simulation techniques. Two important issues of
the validation procedure are discussed, i.e. effective techniques to perform data compression
using the principles of orthogonal decomposition, as well as methodologies to quantify
the quality of simulations and make decisions about model validity. An I-beam with open holes under three-point bending loading is selected as an exemplar of the methodology.
Orthogonal decomposition by Zernike shape descriptors is performed to compress large amounts of numerical and experimental data in selected regions of interest (ROI)
by reducing its dimensionality while preserving information; and different comparison techniques including traditional error norms, a linear comparison methodology and a concordance coefficient correlation are used in order to make decisions about the validity of the simulation.
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