Stochastic reduced order models for uncertainty quantification of intergranular corrosion rates

被引：30

作者：

Sarkar, Swarnavo ^{[1
]}

Warner, James E. ^{[1
]}

Aquino, Wilkins ^{[1
]}

Grigoriu, Mircea D. ^{[2
]}

机构：

[1] Duke Univ, Dept Civil & Environm Engn, Durham, NC 27708 USA

[2] Cornell Univ, Dept Civil & Environm Engn, Ithaca, NY 14853 USA

来源：

CORROSION SCIENCE | 2014年 / 80卷

基金：

美国国家科学基金会;

关键词：

Alloy; Electrochemical calculation; Modelling studies; Intergranular corrosion; MICROSTRUCTURAL EVOLUTION; INITIATION; BEHAVIOR; AL;

D O I：

10.1016/j.corsci.2013.11.032

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

We present a stochastic reduced order model (SROM) approach for quantifying uncertainty in systems undergoing corrosion. A SROM is a simple random element with a small number of samples that approximates the statistics of another target random element. The parameters of a SROM are selected through an optimization problem. SROMs can be used to propagate uncertainty through a mathematical model of a corroding system in the same way as in Monte Carlo methods. We use SROMs to estimate the statistics of corrosion current density, considering randomness in anode-cathode sizes. We compare the performance of SROMs against the more common Monte-Carlo approach. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：257 / 268

页数：12

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