Multifidelity Uncertainty Quantification of a Notional Munition in a Fire Scenario Using Reduced Order Models

Uncertainty quantification is important for the use of simulations for munitions design and testing. However, uncertainty quantification is a computationally expensive task. Multifidelity uncertainty quantification reduces that computational cost by introducing correlated models that sacrifice some accuracy for lower computational expense to supplement information obtained from high-fidelity simulations. This study explores the use of a multifidelity uncertainty quantification technique using reduced order models to accelerate the estimation of the mean and standard deviation of two quantities of interest for a simulation of a notional munition in a fire accident scenario with uncertain material properties. Results are obtained with multiple computational budgets with a multifidelity uncertainty quantification technique and the traditional Monte Carlo uncertainty quantification technique. The errors in these estimates are computed relative to a large-budget Monte Carlo uncertainty quantification study. It was found that the multifidelity technique produced mean estimates with significantly lower errors with smaller budgets and similar errors with the largest budget when compared to Monte Carlo estimates. The multifidelity technique and Monte Carlo both had similarly large errors in the estimates of the standard deviation with all budgets.