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Research Papers: Design Automation

Cityplot: Visualization of High-Dimensional Design Spaces With Multiple Criteria

[+] Author and Article Information
Nathan Knerr

Mem. ASME
Mechanical and Aerospace Engineering,
Cornell University,
405 Rhodes Hall,
Ithaca, NY 14853
e-mail: nsk52@cornell.edu

Daniel Selva

Mem. ASME
Mechanical and Aerospace Engineering,
Cornell University,
212 Upson Hall,
Ithaca, NY 14853
e-mail: ds925@cornell.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received August 18, 2015; final manuscript received June 14, 2016; published online July 21, 2016. Assoc. Editor: Massimiliano Gobbi.

J. Mech. Des 138(9), 091403 (Jul 21, 2016) (9 pages) Paper No: MD-15-1578; doi: 10.1115/1.4033987 History: Received August 18, 2015; Revised June 14, 2016

In the early-phase design of complex systems, a model of design performance is coupled with visualizations of competing designs and used to aid human decision-makers in finding and understanding an optimal design. This consists of understanding the tradeoffs among multiple criteria of a “good” design and the features of good designs. Current visualization techniques are limited when visualizing many performance criteria and/or do not explicitly relate the mapping between the design space and the objective space. We present a new technique called Cityplot, which can visualize a sample of an arbitrary (continuous or combinatorial) design space and the corresponding single or multidimensional objective space simultaneously. Essentially a superposition of a dimensionally reduced representation of the design decisions and bar plots representing the multiple criteria of the objective space, Cityplot can provide explicit information on the relationships between the design decisions and the design criteria. Cityplot can present decision settings in different parts of the space and reveal information on the decision → criteria mapping, such as sensitivity, smoothness, and key decisions that result in particular criteria values. By focusing the Cityplot on the Pareto frontier from the criteria, Cityplot can reveal tradeoffs and Pareto optimal design families without prior assumptions on the structure of either. The method is demonstrated on two toy problems and two real engineered systems, namely, the NASA earth observing system (EOS) and a guidance, navigation and control (GNC) system.

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Copyright © 2016 by ASME
Topics: Design , Visualization
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Figures

Grahic Jump Location
Fig. 1

Example plot. The data cursor window (text box) displays the design and the criteria values of the selected design (square near center figure). The legend labels road distances.

Grahic Jump Location
Fig. 2

Hamming distance demonstration. Skyscrapers (from left, each city): (blue) criteria 1, (red) criteria 2, (green) criteria 3 (see figure online for color). Legend indicates original distances and labels roads.

Grahic Jump Location
Fig. 3

Exponentially weighted distance function. Skyscrapers (from left, each city): (blue) criteria 1, (red) criteria 2, (green) criteria 3 (see figure online for color). Legend indicates original distances and labels roads.

Grahic Jump Location
Fig. 4

Continuous toy problem Cityplot. Skyscrapers (from left, each city): (blue) criteria 1, (red) criteria 2, (green) criteria 3, (black) criteria 4, (cyan) criteria 5, (yellow) criteria 6 (see figure online for color). Legend indicates original distances and labels roads. (Top) taller building correspond to optimized criteria values (bottom) flipped heights so taller building correspond to sacrificed criteria values.

Grahic Jump Location
Fig. 5

Cityplot of EOS partitioning problem. Skyscrapers (from left, each city): (blue) science return, (red) cost, (green) operational risks (black) launch risks (see figure online for color). Legend indicates original distances and labels roads.

Grahic Jump Location
Fig. 6

Cityplot of a random selection of designs in the EOS partitioning problem. Skyscrapers (from left, each city): (blue) science return, (red) cost, (green) operational risks, (black) launch risks (see figure online for color). Legend indicates original distances and labels roads.

Grahic Jump Location
Fig. 7

Decision hierarchy for the GNC problem

Grahic Jump Location
Fig. 8

GNC when connections expensive. Skyscrapers (from left, each city): (blue) weight, (red) log reliability (see figure online for color). Legend indicates original distances and labels roads.

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