Model Validation

Model validation is based on comparison to experimental data from camera-based instruments.
Starting Point: CEN Workshop Agreement
A recent CEN guideline recommends comparison by using data reduction techniques such as image decomposition. In MOTIVATE this procedure is applied to an aircraft sub-component test in an industrial environment.
Predicted (top left) & measured (bottom left) y-direction strain field in percentage strain in the web of an I-beam subject to three-point bending; corresponding plot (right) of normalised shape descriptors with a band of acceptability shown by the dashed lines (from Hack et al. ).

We develop novel methods for the comparison of predictive numerical models with full-field experimental data in order to achieve a robust, quantitative validation of the simulation. In particular an understanding will be gained of the uncertainties in simulation and experimental data and their influence on predictions and measurements.

Criteria allowing an easy comparison and interpretation of data will be embedded in novel correlation methods that allow confidence in simulations to be established, supported by the quantifcation of 'the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model', as the ASME definition of validation reads. Preliminary work at ULIV has demonstrated that feature vectors, obtained via image decomposition, can be used to generate relative error metrics. This preliminary work will be integrated with the prior work embedded in the CEN guideline to produce a validation methodology.