Using designer confidence and a dynamic Monte Carlo simulation tool to evaluate uncertainty in system models

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2000

Author dc:creator

• Lyons, Jeffrey M. (Jeffrey Michael), 1973-

Advisor dc:contributor.advisor

• David Wallace.

• Mechanical Engineering.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/32262

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/32262