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Abstract Details
Activity Number:
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157
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract - #304702 |
Title:
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Experimental Design for Engineering Dimensional Analysis
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Author(s):
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Christopher Nachtsheim*+
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Companies:
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University of Minnesota-Minneapolis
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Address:
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3-150 Carlson School of Mgt, Minneapolis, MN, 55455, United States
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Keywords:
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D-optimal designs ;
Uniform designs ;
model robust designs ;
coordinate exchange ;
Nonparametric design ;
Buckingham Pi Theorem
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Abstract:
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Dimensional Analysis (DA) is a fundamental method in the engineering and physical sciences for analytically reducing the number of experimental variables affecting a given phenomenon prior to experimentation. Two powerful advantages associated with the method, relative to standard design of experiment (DOE) approaches are: (1) a priori dimension reduction, (2) scalability of results. The latter advantage permits the experimenter to effectively extrapolate results to similar experimental systems of differing scale. Unfortunately, DA experiments are underutilized because very few statisticians are familiar with it, and because the experimental designs that are recommended in engineering texts are always expensive and inefficient. In this paper, we provide an overview of DA and give basic recommendations for designing DA experiments.
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Authors who are presenting talks have a * after their name.
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