contributor author | Younes, Ahmad Bani | |
contributor author | Turner, James | |
date accessioned | 2017-11-25T07:20:31Z | |
date available | 2017-11-25T07:20:31Z | |
date copyright | 2016/08/19 | |
date issued | 2016 | |
identifier issn | 2332-9017 | |
identifier other | risk_2_4_041007.pdf | |
identifier uri | http://138.201.223.254:8080/yetl1/handle/yetl/4236507 | |
description abstract | In general, the behavior of science and engineering is predicted based on nonlinear math models. Imprecise knowledge of the model parameters alters the system response from the assumed nominal model data. One proposes an algorithm for generating insights into the range of variability that can be expected due to model uncertainty. An automatic differentiation tool builds the exact partial derivative models required to develop a state transition tensor series (STTS)-based solution for nonlinearly mapping initial uncertainty models into instantaneous uncertainty models. The fully nonlinear statistical system properties are recovered via series approximations. The governing nonlinear probability distribution function is approximated by developing an inverse mapping algorithm for the forward series model. Numerical examples are presented, which demonstrate the effectiveness of the proposed methodology. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Semi-Analytic Probability Density Function for System Uncertainty | |
type | Journal Paper | |
journal volume | 2 | |
journal issue | 4 | |
journal title | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering | |
identifier doi | 10.1115/1.4033886 | |
journal fristpage | 41007 | |
journal lastpage | 041007-7 | |
tree | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2016:;volume( 002 ):;issue: 004 | |
contenttype | Fulltext | |