Using designer confidence and a dynamic Monte Carlo simulation tool to evaluate uncertainty in system models
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000.
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Format: | Thesis |
Language: | eng |
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Massachusetts Institute of Technology
2006
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Online Access: | http://hdl.handle.net/1721.1/32262 |
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author | Lyons, Jeffrey M. (Jeffrey Michael), 1973- |
author2 | David Wallace. |
author_facet | David Wallace. Lyons, Jeffrey M. (Jeffrey Michael), 1973- |
author_sort | Lyons, Jeffrey M. (Jeffrey Michael), 1973- |
collection | MIT |
description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000. |
first_indexed | 2024-09-23T14:12:55Z |
format | Thesis |
id | mit-1721.1/32262 |
institution | Massachusetts Institute of Technology |
language | eng |
last_indexed | 2024-09-23T14:12:55Z |
publishDate | 2006 |
publisher | Massachusetts Institute of Technology |
record_format | dspace |
spelling | mit-1721.1/322622019-04-11T14:11:27Z Using designer confidence and a dynamic Monte Carlo simulation tool to evaluate uncertainty in system models Lyons, Jeffrey M. (Jeffrey Michael), 1973- David Wallace. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Mechanical Engineering. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000. Includes bibliographical references (p. 75). As the use of distributed engineering models becomes more prevalent, engineers need tools to evaluate the quality of these models and understand how subsystem uncertainty affects predictions of system behavior. This thesis develops a tool that enables designers and engineers to specify their perceptions of confidence. These data are then translated into appropriate probability distributions. Monte-Carlo-based methods are used to automatically provide correct propagation of these distributions within an integrated modeling environment. A case study using an assembly tolerance problem is shown and different confidence modeling methods are compared. The methods benchmarked are: worst case; statistical; conventional Monte Carlo simulation; and the dynamic Monte Carlo tool developed in this thesis. Finally the dynamic Monte Carlo tool is used together with surrogate modeling techniques. Comparisons based on implementation time, model execution time, and robustness are provided. by Jeffrey M. Lyons. S.M. 2006-03-29T18:28:47Z 2006-03-29T18:28:47Z 2000 2000 Thesis http://hdl.handle.net/1721.1/32262 56025036 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 75 p. 3857853 bytes 3856138 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
spellingShingle | Mechanical Engineering. Lyons, Jeffrey M. (Jeffrey Michael), 1973- Using designer confidence and a dynamic Monte Carlo simulation tool to evaluate uncertainty in system models |
title | Using designer confidence and a dynamic Monte Carlo simulation tool to evaluate uncertainty in system models |
title_full | Using designer confidence and a dynamic Monte Carlo simulation tool to evaluate uncertainty in system models |
title_fullStr | Using designer confidence and a dynamic Monte Carlo simulation tool to evaluate uncertainty in system models |
title_full_unstemmed | Using designer confidence and a dynamic Monte Carlo simulation tool to evaluate uncertainty in system models |
title_short | Using designer confidence and a dynamic Monte Carlo simulation tool to evaluate uncertainty in system models |
title_sort | using designer confidence and a dynamic monte carlo simulation tool to evaluate uncertainty in system models |
topic | Mechanical Engineering. |
url | http://hdl.handle.net/1721.1/32262 |
work_keys_str_mv | AT lyonsjeffreymjeffreymichael1973 usingdesignerconfidenceandadynamicmontecarlosimulationtooltoevaluateuncertaintyinsystemmodels |