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Probabilistic Error Estimation In Model-Based Predictions

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-04-M-0124
Agency Tracking Number: N041-137-0921
Amount: $69,930.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N04-137
Solicitation Number: 2004.1
Timeline
Solicitation Year: 2004
Award Year: 2004
Award Start Date (Proposal Award Date): 2004-04-07
Award End Date (Contract End Date): 2004-10-07
Small Business Information
2108 Maple Creek Circle
Ann Arbor, MI 48108
United States
DUNS: 134722656
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Nickolas Vlahopoulos
 Managing Director
 (734) 355-0084
 nickvlahopoulos@netscape.net
Business Contact
 Christina Vlahopoulos
Title: President
Phone: (734) 358-0777
Email: christinavl@juno.com
Research Institution
N/A
Abstract

Modern ship designs can only be successful if the ship can survive in a hostile environment. External threats for a ship can originate from underwater detonations, anti-ship missiles, and even low tech weapons like in the terrorist attack of USS Cole. These threats become even more important as the focal point for naval operations has shifted towards littoral areas, where ships are exposed to a higher risk. Current analysis methods do not provide high level of confidence and large structural design safety factors are used. Thus, ships are heavier and more expensive to construct and maintain than may actually be required. Models that quantify the level of confidence are required in order to provide meaningful and reliable information during the ship design stage. The proposed project will develop a system for probabilistic Error Estimation in model based predictions. It will utilize commercially available non-linear codes for shock analysis (LS-DYNA, ABAQUS, LS-DYNA/USA, etc.), a limited amount of test data, the level of uncertainty in model parameters and in the test data. Based on this information it will update the numerical model for improved probabilistic correlation to the test data and it will provide a probabilistic error estimate for the numerical results.

* Information listed above is at the time of submission. *

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