A NOVEL STATISTICAL BASED APPROACH TO NON-LINEAR MODEL UPDATING USING RESPONSE FEATURES

This research presents a new method to improve analytical model fidelity for non-linear systems. The approach investigates several mechanisms to assist the analyst in updating an analytical model based on experimental data and statistical analysis of parameter effects. The first is a new approach at data reduction called feature extraction. This is an expansion of the 'classic' update metrics to include specific phenomena or characters of the response that are critical to model application. This is an extension of the familiar linear updating paradigm of utilizing the eigen- parameters or frequency response functions (FRFs) to include such devices as peak acceleration, time of arrival or standard deviation of model error. The next expansion of the updating process is the inclusion of statistical based parameter analysis to quantify the effects of uncertain or significant effect parameters in the construction of a meta- model. This provides indicators of the statistical variation associated with parameters as well as confidence intervals on the coefficients of the resulting meta-model. Also included in this method is the investigation of linear parameter effect screening using a partial factorial variable array for simulation. This is intended to aid the analyst in eliminating from the investigation the parameters that do not have a significant variation effect on the feature metric. Finally an investigation of the model to replicate the measured response variation is examined. 1. MOTIVATION The updating and validation of complex non-linear models to reflect not only 'real world' data but also its variability is of strong interest in the aerospace, automotive and aviation industries ((1), (2), (3)). The higher objective is to improve confidence in the model within and beyond the experimental range, since it is often impractical to test over the full operational range of a system. An additional objective is to develop an understanding and identification of the relation between significant input parameters, such as Young's Modulus, and critical response data components (features). Construction of a 'meta-model' between the input and output is developed to aide in this understanding and to reduce the computational load of investigating parameter variation. 2. METHODOLOGY