Model error propagation from experimental to prediction configuration

[摘要] Full-scale system tests are often prohibitively expensive, therefore experiments on simpler configurations are used in engineering to gain insights that can be applied to the behavior prediction of the full-scale system. However, the transfer of information from the experimental configuration to the prediction configuration is not trivial. In this paper, we present a methodology for predicting the response of a dynamic system using data from tests conducted on a different experimental configuration. The experimental and prediction configurations are connected by a set of common model form errors, which are unrelated to geometry, boundary conditions, or loading. These errors cause discrepancies between the model prediction of system response and the corresponding measured values. The proposed methodology is based on estimating the model form errors for the experimental configuration, using the measured data, and propagating the estimated errors to the prediction configuration. The governing equations of the two systems - the experimental and prediction configurations - are discretized using the finite element method, using elements of the same type and dimensions. Bayesian state estimation is adopted to estimate the discrepancies in unmeasured responses of the tested configuration, and subsequently, these estimates are used to evaluate the model form errors. These errors are propagated to untested configurations using an artificial neural network (ANN). The proposed methodology is illustrated on predicting the deformation of a simply supported beam and a curved plate, and for predicting the temperature time histories along a rigid bar under one-dimensional heat conduction. (C) 2021 Elsevier Inc. All rights reserved.

[发布日期] 2021-10-15

[关键词] Model form errors;Discrepancy prediction;Bayesian state estimation;Artificial neural networks;Finite element discretization