Imaging systems for measuring surface displacement and strain fields such as stereoscopic Digital Image Correlation (DIC) are increasingly used in industry to validate model simulations. Recently, CEN has published a guideline for validation that is based on image decomposition to compare predicted and measured data fields. The CEN guideline was evaluated in an inter-laboratory study that demonstrated its usefulness in laboratory environments. This paper addresses the incorporation of the CEN methodology into an industrial environment and reports progress of the H2020 Clean Sky 2 project MOTIVATE. First, while DIC is a well-established technique, the estimation of its measurement uncertainty in an industrial environment is still being discussed, as the current approach to rely on the calibration uncertainty is insufficient. Second, in view of the push towards virtual testing it is important to harvest existing data in the course of the V&V activities before requesting a dedicated validation experiment, specifically at higher levels of the test pyramid. Finally, it is of uttermost importance to ensure compatibility and comparability of the simulation and measurement data so as to optimize the test matrix for maximum reliability and credibility of the simulations and a quantification of the model quality.
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Steps Towards Industrial Validation Experiments
Published: 09 May 2018 by MDPI in The Eighteenth International Conference of Experimental Mechanics session ICEM 2018
Keywords: validation; validation metric; orthogonal decomposition; DIC; calibration; CEN workshop agreement