Research Papers

Rave: A Computational Framework to Facilitate Research in Design Decision Support

[+] Author and Article Information
Matthew J. Daskilewicz, Brian J. German

 School of Aerospace Engineering,  Georgia Institute of Technology, Atlanta, GA 30332mdaskilewicz@asdl.gatech.edu

Rave can be downloaded from http://www.rave.gatech.edu.

When used in this sense, “Analysis” will be capitalized to distinguish from the more general meaning of the word.

J. Comput. Inf. Sci. Eng 12(2), 021005 (Apr 24, 2012) (9 pages) doi:10.1115/1.4006464 History: Received June 24, 2011; Revised March 07, 2012; Published April 23, 2012; Online April 24, 2012

The cognitive challenges in the design of complex engineered systems include the scale and scope of decision problems, nonlinearity of the trade space, subjectivity of the problem formulation, and the need for rapid decision making. These challenges have motivated an active area of research in design decision-support methods and the development of commercial and openly available design frameworks. Although these frameworks are extremely capable, most are limiting as a basis for research relating to design decision support because they offer little user flexibility for incorporating and evaluating new features or techniques. This paper describes Rave (www.rave.gatech.edu), a computational framework designed specifically as a research platform for design decision-support methods. Rave has been structured to be flexible and adaptable, handle data with systematic data structures and descriptive metadata, facilitate a wide spectrum of visualization types, provide features to enable user interactivity and linking of graphics, and incorporate surrogate modeling and optimization as enabling capabilities. This framework is envisioned to provide the research and industrial communities an easily expandable and customizable baseline capability to facilitate investigation of further design decision-support advancements.

Copyright © 2012 by American Society of Mechanical Engineers
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Figure 4

A collection of linked continuous visualizations: Prediction profiler, surface plot, contour plot matrix, independent variable sliders. Changing variable values using sliders or by dragging crosshairs on graphs causes functional models to re-evaluate and all graphics to automatically update with new values.

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Figure 1

Rave user interface, (1) tabbed controls, (2) navigator, (3) workspace, (4) data table (inset)

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Figure 2

Parallel coordinates plot and preference (weight) sliders being used to interactively explore a TOPSIS ranking’s sensitivity to weights

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Figure 3

Example of linking among visualizations in the same Analysis. Recoloring data in one graph instantly updates linked graphs with new colors.




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