Decomposition and Coordination to Support Tradespace Analysis for Ground Vehicle Systems Clemson University

Format:

Book

Conference/Event

Author/Creator:

de Castro, Philip, author.

Contributor:

Agusti, Rachel

Gorsich, David

Hartman, Gregory

Rizzo, Denise

Skowronska, Annette

Stewart, Hannah

Turner, Cameron

Wiecek, Margaret

Conference Name:

WCX SAE World Congress Experience (2022-04-05 : Detroit & Online, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2022

Summary:

Tradespace analysis is used to define the characteristics of the solution space for a vehicle design problem enabling decision-makers (DMs) to evaluate the risk-benefit posture of a vehicle design program. The tradespace itself is defined by a set of functional objectives defined by vehicle simulations and evaluating the performance of individual design solutions that are modeled by a set of input variables. Of special interest are efficient design solutions because their perfomance is Pareto meaning that none of their functional objective values can be improved without decaying the value of another objective. The functional objectives are derived from a combination of simulations to determine vehicle performance metrics and direct calculations using vehicle characteristics. The vehicle characteristics represent vendor specifications of vehicle subsystems representing various technologies. These functional objectives represent individual objectives in a multi-objective optimization problem (MOP). Since in vehicle design problems the number of functional objectives may exceed forty, the resulting MOP brings computational and decision-making challenges that are not typical when the number of objectives is lower. Among others, the solution to the overall problem in its entirety may be challenging to discover, or the selection of the preferred Pareto design solution may not be as straightforward for DMs due to the difficulty of comparing tradeoff decisions in high dimensions. In effect, in the presence of many objectives, decomposition of the MOP into MOPs with a smaller number of criteria becomes appealing provided solving the subproblems can be coordinated and related to solving the original MOP. In this preliminary study, a decomposition-coordination technique is proposed and applied to support tradespace analysis for a four-objective MOP to illustrate the anticipated benefits when dealing with MOPs having more objectives. An analysis of two lower-dimensional tradespaces leads to the selection of a preferred efficient design in the four-dimensional tradespace

Notes:

Vendor supplied data

Publisher Number:

2022-01-0370

Access Restriction:

Restricted for use by site license