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Abstract Computer model calibration typically operates by fine-tuning parameter values in a computer model so that the model output faithfully predicts reality. By using performance targets in place of observed data, we show that calibration techniques can be repurposed for solving multi-objective design problems. Our approach allows us to consider all relevant sources of uncertainty as an integral part of the design process. We demonstrate our proposed approach through both simulation and fine-tuning material design settings to meet performance targets for a wind turbine blade. 

Abstract In order to accurately predict the performance of materials under dynamic loading conditions, models have been developed that describe the rate-dependent material behavior and irrecoverable plastic deformation that occurs at elevated strains and applied loads. Most of these models have roots in empirical fits to data and, thus, require the addition of specific parameters that reflect the properties and response of specific materials. In this work, we present a systematic approach to the problem of calibrating a Johnson-Cook plasticity model for 304L stainless steel using experimental testing in which the parameters are treated as dependent on the state of the material and uncovered using experimental data. The results obtained indicate that the proposed approach can make the presence of a discrepancy term in calibration unnecessary and, at the same time, improve the prediction accuracy of the model into new input domains and provide improved understanding of model bias compared to calibration with stationary parameter values. 

Purpose This paper aims to present an approach for calibrating the numerical models of dynamical systems that have spatially localized nonlinear components. The approach implements the extended constitutive relation error (ECRE) method using multi-harmonic coefficients and is conceived to separate the errors in the representation of the global, linear and local, nonlinear components of the dynamical system through a two-step process. Design/methodology/approach The first step focuses on the system’s predominantly linear dynamic response under a low magnitude periodic excitation. In this step, the discrepancy between measured and predicted multi-harmonic coefficients is calculated in terms of residual energy. This residual energy is in turn used to spatially locate errors in the model, through which one can identify the erroneous model inputs which govern the linear behavior that need to be calibrated. The second step involves measuring the system’s nonlinear dynamic response under a high magnitude periodic excitation. In this step, the response measurements under both low and high magnitude excitation are used to iteratively calibrate the identified linear and nonlinear input parameters. Findings When model error is present in both linear and nonlinear components, the proposed iterative combined multi-harmonic balance method (MHB)-ECRE calibration approach has shown superiority to the conventional MHB-ECRE method, while providing more reliable calibration results of the nonlinear parameter with less dependency on a priori knowledge of the associated linear system. Originality/value This two-step process is advantageous as it reduces the confounding effects of the uncertain model parameters associated with the linear and locally nonlinear components of the system.