A Surrogate Model to Quantify Uncertainty in Thermal Protection Systems for Hypersonic Weapons
Modeling and simulation are key for the iterative development of thermal protection systems (TPS’s) for hypersonic weapons. In this work, the temperature-dependent flexural strength (FS) of α-SiC ceramic is predicted given Young’s modulus, Poisson’s ratio, and temperature. An artificial neural network (ANN) surrogate model is created to retain property-performance prediction while increasing computation speed.
