Masters Theses

Abstract

"Model uncertainty quantification is mainly concerned with the problem of determining whether the observed data is consistent with the model prediction. In real world, there is always a disagreement between a simulation model prediction and the reality that the model intends to represent. Our increased dependence on computer models emphasizes on model uncertainty which is present due to uncertainties in model parameters, lack of appropriate knowledge, assumptions and simplification of processes. In addition, when models predict multi-variate data, the experimental observation and model predictions are highly correlated. Thus, quantifying the uncertainty has a basic requirement of comparison between observation and prediction. The comparison is costly on the observation side and computationally intensive on the other. The alternative approach presented in this thesis for model uncertainty quatification [sic] addresses the aforementioned problems. With the new methodology, the experiments performed according to measurement uncertainty standards will provide the experimental results in terms of expanded uncertainty. Thus, the experimental results for both model input and output will be expressed as intervals. Furthermore, interval predictions are procured from the simulation model by using the experimental results of input intervals only. The model uncertainty will then be quantified by the difference between experimental result for output interval and model prediction interval. The new methodology is easy to implement as the standards of measurement uncertainty are used which serve as a common framework for model builders and experimenters"--Abstract, page iii

Advisor(s)

Du, Xiaoping

Committee Member(s)

Hosder, Serhat
Chandrashekhara, K.

Department(s)

Mechanical and Aerospace Engineering

Degree Name

M.S. in Aerospace Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2011

Pagination

ix, 87 pages

Note about bibliography

Includes bibliographical references (pages 122-125).

Rights

© 2011 Harsheel Rajesh Shah, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Predictive control -- Mathematical models
Reliability (Engineering)
Uncertainty -- Mathematical models

Thesis Number

T 9944

Print OCLC #

794685182

Electronic OCLC #

752556485

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