Augmenting Conceptual Design Trajectory Tradespace Exporation with Graph Theory

Abstract

Within conceptual design changes occur rapidly due to a combination of uncertainty and shifting requirements. To stay relevant in this fluid time, trade studies must also be performed rapidly. In order to drive down analysis time while improving the information gained by these studies, surrogate models can be created to represent the complex output of a tool or tools within a specified tradespace. In order to create this model however, a large amount of data must be collected in a short amount of time. By this method, the historical approach of relying on subject matter experts to generate the data required is schedule infeasible. However, by implementing automation and distributed analysis the required data can be generated in a fraction of the time. Previous work focused on setting up a tool called multiPOST capable of orchestrating many simultaneous runs of an analysis tool assessing these automated analyses utilizing heuristics gleaned from the best practices of current subject matter experts. In this update to the previous work, elements of graph theory are included to further drive down analysis time by leveraging data previously gathered. It is shown to outperform the previous method in both time required, and the quantity and quality of data produced.

Automation of POST Cases via External Optimizer and “Artificial p2” Calculation

“…In the case where the vehicles under analysis are chosen by DOE to take part in a large trade-space study such as the applications in [13,19], then UR is the way to go. If the vehicles under analysis are to be used in the creation of a surrogate model describing the entirety of the design space then the off-optimality of any one vehicle reported will be smoothed out by the end model.…”

“…The multiPOST software developed by the authors, presented in [13] and extended in [12,19,20] is used for all trials and limited to 80 simultaneous threads running across several Windows 7 machines.…”

Improvement of Automated POST Case Success Rate using Support Vector Machines

“…In addition, the example problem is expanded to include execution of the graph method from reference [4]. This allows for a full comparison between the SVM and random repetitions approaches in terms of surrogate model fitting.…”

“…This ultimately allows for a relatively short execution of random repetitions prior to execution of cases through the SVM. A comparison is given to show the time savings of this combined approach versus running straight random repetitions.In addition, the example problem is expanded to include execution of the graph method from reference [4]. This allows for a full comparison between the SVM and random repetitions approaches in terms of surrogate model fitting.…”

“…This work initially focused on implementation of analyst heuristics to enable closure of cases in an automated fashion, with the goal of applying the concepts of design of experiments (DOE) and surrogate modeling to enable near instantaneous throughput of vehicle cases. 3 As noted in [4] work was then completed to improve the DOE process by utilizing a graph theory based approach to connect similar design points.…”