In advance of a computational model updating or an er- ror localization, it can be advantageous to make a pre- paratory error localization using 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 suc- cessful error localization, the test data must be informa- tive with respect to the model parameters chosen. For dynamic computational models, the demand for test data informativeness puts limitations on the experiment with regard to spatial resolution of sensors, bandwidth of excitation, signal-to-noise ratios, etc.
Solving a full test design optimization problem is a huge task, sometimes impossible in practice, due to its com- binatorial nature. The number of possible sensor/actua- tor placement combinations grows rapidly as the number of sensor and actuator candidates increases. For industrial sized problems, finding a sub-optimal solu- tion may be a more realistic target.
The aim of this study is to quantify data informative- ness, shown to relate to the Fisher information matrix, with respect to physical parameters that are used in error localization and model updating. Deterministic finite- element models in combination with stochastic noise models are used for evaluating data informativeness, and a procedure for test design optimization with re- spect to this is devised.
Orlando, Florida, 2003.