Uncertainty quantification and statistical model validation for an offshore jacket structure panel given limited test data and simulation model

Due to inherent variability (i.e., aleatory uncertainty) in material properties, loading conditions, manufacturing processes, etc., simulation output responses should follow certain distributions, which are the outcome of input distribution models and simulation models. Thus, uncertainty quantification (UQ) using simulation-based methods is accurate only if we have (1) accurate input distribution models and (2) an accurate simulation model. However, in practical engineering applications, only limited numbers of input test data are available for modeling input distributions. Moreover, the simulation model could be biased due to assumptions and idealizations in the modeling process. Thus, the simulation model needs to be validated to correctly predict the output response of the physical system. The statistical validation of the simulation model would require large numbers of physical test data, which is extremely expensive. This study presents a computational method to obtain a target output distribution, which is a good approximation of the true output distribution given limited test data and a biased model. Using Bayesian analysis, possible candidates of output distribution are obtained, and a target output distribution is selected at the posterior median. The target output distribution is used to measure the bias of the simulation models and the surrogate models for UQ and statistical model validation. Furthermore, the cumulative distribution function of the simulation model prediction error is presented to provide (1) the median of model prediction error and (2) model confidence at a user-specified error level. To demonstrate the proposed statistical model validation method, a simulation model of an offshore jacket structure panel is generated using ANSYS. This example has eight uncertain input parameters (six of them are geometrical parameters and two of them are material properties). For input distributions of geometrical parameters, the standard deviation of manufacturing tolerances is used, and three material tests are carried out to obtain input distributions of two material properties. The output response of interest is a reaction force. The simulation model is validated using three sets of material test data and experimental test data to determine model confidence on output prediction and the validity of the simulation model. The result of statistical model validation shows how much bias the simulation model has and how much confidence engineers can have in the model prediction at a user-specified error level.