A comparison of two stochastic model updating methods using the DLR AIRMOD test structure

Abstract

The problem of stochastic model updating is addressed by means of the application of two methods (covariance and interval model updating) to the DLR AIRMOD structure which is repeatedly disassembled and reassembled to provide a database of modal variability due to uncertainty in joint and support stiffnesses and masses of cables and screws. The covariance method is based on an assumption of small uncertainty and implemented at each step of an iterative approach by forward propagation of uncertain parameters using a multivariate normal distribution. The interval approach is based on a Kriging meta-model, thereby providing a very efficient surrogate to replace the expensive full finite element model. This allows a mapping from multiple output measurements to define a hypercube bounded by intervals of parameter uncertainty. It is shown that the measured data is fully enclosed by the hyper-ellipses and hypercubes of the covariance and interval methods respectively. As expected, the interval method is found to be more conservative than the covariance approach but still provides useful estimates without restriction by any assumption of probability distribution.