Research Papers

The Importance of Training for Interactive Trade Space Exploration: A Study of Novice and Expert Users

[+] Author and Article Information
David Wolf, Jennifer Hyland

Department of Mechanical and Nuclear Engineering,  The Pennsylvania State University, University Park, PA 16802tws8@psu.edu

Timothy W. Simpson1

Department of Mechanical and Nuclear Engineering,  The Pennsylvania State University, University Park, PA 16802tws8@psu.edu

Xiaolong (Luke) Zhang

Department of Information Sciences and Technology,  The Pennsylvania State University, University Park, PA 16802


Corresponding author.

J. Comput. Inf. Sci. Eng 11(3), 031009 (Sep 02, 2011) (11 pages) doi:10.1115/1.3615685 History: Received March 05, 2010; Revised April 11, 2011; Published September 02, 2011; Online September 02, 2011

Thanks to recent advances in computing power and speed, engineers can now generate a wealth of data on demand to support design decision-making. These advances have enabled new approaches to search multidimensional trade spaces through interactive data visualization and exploration. In this paper, we investigate the effectiveness and efficiency of interactive trade space exploration strategies by conducting human subject experiments with novice and expert users. A single objective, constrained design optimization problem involving the sizing of an engine combustion chamber is used for this study. Effectiveness is measured by comparing the best feasible design obtained by each user, and efficiency is assessed based on the percentage of feasible designs generated by each user. Results indicate that novices who watch a 5-min training video before the experiment obtain results that are not significantly different from those obtained by expert users, and both groups are statistically better than the novices without the training video in terms of effectiveness and efficiency. Frequency and ordering of the visualization and exploration tools are also compared to understand the differences in each group’s search strategy. The implications of the results are discussed along with future work.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 2

Example of attractor sampler

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Figure 1

Multidimensional data visualization examples

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Figure 8

Average NSP values for novice and expert groups

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Figure 7

Distribution of best feasible designs for novice and expert groups

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Figure 6

Brush/preference control settings for the geometry subsystem

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Figure 5

Definition of combustion chamber design variables [34]

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Figure 4

Example of Pareto sampler (Pareto points denoted by +)

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Figure 3

Example of preference-based sampler

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Figure 9

Comparison based on average percentage of feasible designs

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Figure 10

Scatter plot of percentage of feasible designs and NSP values

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Figure 11

Percentage of users utilizing each visualization and exploration tool

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Figure 12

State transition activity diagrams



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