Optimising the informativeness of test data used for computational model updating
Artikel i vetenskaplig tidskrift, 2005
In advance of a computational model updating or an error localisation, it can be advantageous to make a preparatory error localisation using data from a nominal analytical model. The purpose is then to select parameters for quantifying model errors and also to design effective tests for determining the best parameter setting. For successful subsequent error localisation, the test data must be informative with respect to the model parameters chosen when such data become available after test. The demand for test data informativeness puts requirements on the experiment with regard to spatial resolution of sensors, bandwidth of excitation, signal-to-noise ratios, etc. Optimising a test design is a huge task, sometimes impossible in practice, due to its combinatorial nature. The number of possible sensor/actuator placement combinations grows rapidly as the number of sensor and actuator candidates increases. For industrial sized problems, finding a sub-optimal solution may be a more realistic target. Such solutions are sought in this work. The aim of this study is to quantify data informativeness, shown to relate to the Fisher information matrix, with respect to physical parameters that are used in error localisation and model updating. Deterministic finite-element models in combination with stochastic noise models are used for assessing data informativeness, and a procedure for test design optimisation with respect to this is devised.