Publications Details

Experimental uncertainty quantification of modal test data

Griffith, Daniel G.; Carne, Thomas G.

In this paper we present the results of a study to quantify uncertainty in experimental modal parameters due to test set-up uncertainty, measurement uncertainty, and data analysis uncertainty. Uncertainty quantification is required to accomplish a number of tasks including model updating, model validation, and assessment of unit-tounit variation. We consider uncertainty in the modal parameters due to a number of sources including force input location/direction, force amplitude, instrumentation bias, support conditions, and the analysis method (algorithmic variation). We compute the total uncertainty due to all of these sources, and discuss the importance of proper characterization of bias errors on the total uncertainty. This uncertainty quantification was applied to modal tests designed to assess modeling capabilities for emerging designs of wind turbine blades. In an example, we show that unit-to-unit variation of the modal parameters of two nominally identical wind turbine blades is successfully assessed by performing uncertainty quantification. This study aims to demonstrate the importance of the proper pre-test design and analysis for understanding the uncertainty in modal parameters, in particular uncertainty due to bias error.