Risk Assessment of Scramjet Unstart Using Adjoint-Based Sampling Methods

Content Provider	Semantic Scholar
Author	Wang, Qiqi Duraisamy, Karthik Alonso, J. J. Iaccarino, Gianluca
Copyright Year	2010
Abstract	We demonstrate an adjoint based approach for accelerating Monte Carlo estimation of risk, and apply it to estimating the probability of unstart in a SCRamjet engine under uncertain conditions that are characterized by various Gaussian and non-Gaussian distributions. The adjoint equation is solved with respect to an objective function that is used to identify unstart and the adjoint solution is used to generate a linear approximation to the objective function. This linear surrogate is used to divide the uncertain input parameters into three different strata, corresponding to safe operation of the engine, uncertain operation and unstart. The probability of unstart within these strata is very different and as a result, stratified sampling significantly increases the efficiency of the risk assessment procedure by reducing the variance of the estimator. Using this technique, the Monte Carlo method was demonstrated to be accelerated by a factor of 5.4.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://web.stanford.edu/group/uq/pdfs/conferences/AIAA-2010-2921.pdf
Alternate Webpage(s)	http://web.mit.edu/qiqi/www/paper/AIAA-2010-2921.pdf
Language	English
Access Restriction	Open
Subject Keyword	Allocation Estimated Evaluation procedure Gaussian (software) Heat-Shock Proteins 70 Linear approximation Loss function Mathematical optimization Monte Carlo method Normal Statistical Distribution Optimization problem Population Parameter Risk assessment Sample Variance Sampling (signal processing) Sampling - Surgical action Shock Stratified sampling Variance reduction
Content Type	Text
Resource Type	Article