Rapid Uncertainty Propagation for High-Fidelity Prognostics Using SROMPy and Python

Abstract

This work introduces a practical approach for accelerating probabilistic, high-fidelity prognostics using the stochastic reduced order model (SROM) method and its availability in the open-source Python package, SROMPy. SROMs are used as an efficient Monte Carlo simulation (MCS) method, providing low-dimensional representations of random model inputs enabling rapid and non-intrusive uncertainty propagation. This study represents the first application of the SROM approach in the field of prognostics and health management and serves as a tutorial demonstration of the SROMPy software package. The relative ease of applying SROMs with SROMPy for uncertainty propagation is demonstrated on an example of probabilistic, non-planar crack growth simulation. Results show that the SROM approach agrees well with results from MCS while providing orders of magnitude computational speedup. The complete source code and input data required to reproduce the results in this paper are available online to facilitate further evaluation and adoption of the SROM method by researchers in the field.

Robust topology optimization under loading uncertainties via stochastic reduced order models

“…For the numerical examples in this article, . We note that several error metrics have been used in the literature 28,43 . The SROM size, m , indicates the number of model evaluations that will be used for uncertainty propagation and is fixed prior to solving Equation (39) based on available computational considerations.…”