Bayesian Calibration of Coupled Aerothermal Models Using Time-Dependent Data

摘要

Accurately quantifying the uncertainty in coupled aerothermoelastic response predictions for hypersonic aircraft structures is a critical and difficult challenge. A dynamic Bayesian network is constructed to enable Bayesian model calibration and confidence assessment of transient, coupled aerothermal models. Time-dependent temperature data from historic hypersonic wind tunnel experiments are incorporated into the dynamic Bayesian network to calibrate uncertain model parameters and model discrepancies through time. Global and incremental model discrepancy implementations are investigated and compared using the Bayes factor metric.