Cody R. Godines and Randall D. Manteufel

University of Texas at San Antonio, San Antonio, Texas

Probabilistic Analysis and Density Parameter

Estimation Within Nessus

NASA/CR—2002-212008

December 2002
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Cody R. Godines and Randall D. Manteufel

University of Texas at San Antonio, San Antonio, Texas

Probabilistic Analysis and Density Parameter

Estimation Within Nessus

NASA/CR—2002-212008

December 2002

National Aeronautics and

Space Administration

Glenn Research Center

Prepared under Grant NAG3–2297
Acknowledgments

The authors greatly acknowledge the guidance and encouragement of Drs. Christos C. Chamis and Shantaram Pai

and the support of the NASA Glenn Research Center and the Minority University Research and Education

Program, NASA Headquarters. Dr. Ben Thacker and Mr. David Riha are recognized for their participation and

contributions to the two courses. Drs. Harry Millwater and Ronald Bagley are thanked for being on Mr. Cody

Godines’ thesis committee. Also acknowledged are Undergraduate Research Assistants, Mr. Henock Perez, Mr. Luis

Rangel, and Mr. Jody Cronenberger for their contributions to this project. The UTSA Alliance for Minority

Participation (AMP) program is also acknowledged for supporting Mr. Ronald Magharing as a collaborating

undergraduate student during the summer of 2000.

Available from

NASA Center for Aerospace Information

7121 Standard Drive

Hanover, MD 21076

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22100

The Aerospace Propulsion and Power Program at

NASA Glenn Research Center sponsored this work.

Available electronically at http://gltrs.grc.nasa.gov
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December 2002

NASA CR—2002-212008

E–13696

WBS–22–708–90–53

NAG3–2297

272

Probabilistic Analysis and Density Parameter Estimation Within Nessus

Cody R. Godines and Randall D. Manteufel

Probability; Structures; Stress; Analysis; Sensitivities

Unclassified -Unlimited

Subject Category: 39 Distribution: Nonstandard

University of Texas at San Antonio

6900 N Loop 1604 W

San Antonio, Texas 78249–0670

Project Manager, Christos C. Chamis, Research and Technology Directorate, NASA Glenn Research Center,

organization code 5000, 216–433–3252.

This NASA educational grant has the goal of promoting probabilistic analysis methods to undergraduate and graduate UTSA engineering students. Two

undergraduate-level and one graduate-level course were offered at UTSA providing a large number of students exposure to and experience in probabilistic techniques. The grant provided two research engineers from Southwest Research Institute the opportunity to teach these courses at UTSA, thereby

exposing a large number of students to practical applications of probabilistic methods and state-of-the-art computational methods. In classroom

activities, students were introduced to the NESSUS computer program, which embodies many algorithms in probabilistic simulation and reliability

analysis. Because the NESSUS program is used at UTSA in both student research projects and selected courses, a student version of a NESSUS manual

has been revised and improved, with additional example problems being added to expand the scope of the example application problems. This report

documents two research accomplishments in the integration of a new sampling algorithm into NESSUS and in the testing of the new algorithm. The new

Latin Hypercube Sampling (LHS) subroutines use the latest NESSUS input file format and specific files for writing output. The LHS subroutines are

called out early in the program so that no unnecessary calculations are performed. Proper correlation between sets of multidimensional coordinates can

be obtained by using NESSUS’ LHS capabilities. Finally, two types of correlation are written to the appropriate output file. The program enhancement

was tested by repeatedly estimating the mean, standard deviation, and 99th percentile of four different responses using Monte Carlo (MC) and LHS.

These test cases, put forth by the Society of Automotive Engineers, are used to compare probabilistic methods. For all test cases, it is shown that LHS

has a lower estimation error than MC when used to estimate the mean, standard deviation, and 99th percentile of the four responses at the 50 percent

confidence level and using the same number of response evaluations for each method. In addition, LHS requires fewer calculations than MC in order to

be 99.7 percent confident that a single mean, standard deviation, or 99th percentile estimate will be within at most 3 percent of the true value of the each

parameter. Again, this is shown for all of the test cases studied. For that reason it can be said that NESSUS is an important reliability tool that has a

variety of sound probabilistic methods a user can employ; furthermore, the newest LHS module is a valuable new enhancement of the program.