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Research Papers

Design Space Exploration for Quantifying a System Model’s Feasible Domain

[+] Author and Article Information
Brad J. Larson

Department of Mechanical Engineering,  Brigham Young University, Provo, UT 84602brad.larson@byu.edu

Christopher A. Mattson

Department of Mechanical Engineering,  Brigham Young University, Provo, UT 84602mattson@byu.edu

J. Mech. Des 134(4), 041010 (Apr 04, 2012) (8 pages) doi:10.1115/1.4005861 History: Received May 13, 2011; Revised January 11, 2012; Published March 28, 2012; Online April 04, 2012

A major challenge in multidisciplinary system design is predicting the effects of design decisions at the point these decisions are being made. Because decisions at the beginning of system design, when the least is known about the new system, have the greatest impact on its final behavior, designers are increasingly interested in using compositional system models (system models created from independent models of system components) to validate design decisions early in and throughout system design. Compositional system models, however, have several failure modes that often result in infeasible or failed model evaluation. In addition, these models change frequently as designs are refined, changing the model domain (set of valid inputs and states). To compute valid results, the system model inputs and states must remain within this domain throughout simulation. This paper develops an algorithm to efficiently quantify the system model domain. To do this, we (1) present a formulation for system model feasibility and identify types of system model failures, (2) develop a design space exploration algorithm that quantifies the system model domain, and (3) illustrate this algorithm using a solar-powered unmanned aerial vehicle model. This algorithm enables systematic improvements of compositional system model feasibility.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figure 1

Example of a compositional system model

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Figure 2

Solar-powered UAV propulsion system model

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Figure 3

Design space exploration algorithm overview

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Figure 4

Feasibility exploration of a UAV system model

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Figure 5

Comparison of random, mean-squared error, and proposed feasibility design space exploration

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