Date of Award

Summer 2005

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Mechanical & Aerospace Engineering

Program/Concentration

Mechanical Engineering

Committee Director

Gene Hou

Committee Member

Han P. Bao

Committee Member

Laura J. Harrell

Call Number for Print

Special Collections; LD4331.E56 P377 2005

Abstract

The Latin hypercube sampling method, sometimes called the stratified sampling method, is a commonly used variance reduction method. Recently, the optimal Latin hypercube sampling method (OLH) and the modified optimal Latin hypercube sampling method (MOLH) are introduced in the literature, which use the genetic algorithm to adjust the distance between sampled points.

This paper will compare the basic Latin hypercube, the optimal Latin hypercube and the modified optimal Latin hypercube methods for generating the sample points for statistical application and curve fitting, in te1ms of accuracy and efficiency. Analytical and structural examples are used in this thesis to facilitate the study. The results show that the modified optimal Latin hypercube sampling method is more efficient and produces more uniformly distributed samples than other methods. Also, the optimal Latin hypercube sampling method and the modified optimal Latin hypercube sampling method provide a better estimate of statistical moments for a less number of sample points and give good approximation with 100 samples compared to 1000 Monte Carlo samples. The Matlab codes are developed for the optimal Latin hypercube sampling method and the modified optimal Latin hypercube sampling method.

This thesis has laid the groundwork for further development of a sampling method towards an efficient design tool.

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DOI

10.25777/x86m-mf97

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