Premium
A Combinatorial Approach to Tradespace Exploration of Complex Systems: A CubeSat Case Study
Author(s) -
Qiao Li,
Efatmaneshnik Mahmoud,
Ryan Michael
Publication year - 2017
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2017.00392.x
Subject(s) - computer science , multidimensional scaling , cubesat , cluster analysis , component (thermodynamics) , process (computing) , workload , visualization , artificial intelligence , machine learning , engineering , aerospace engineering , physics , satellite , thermodynamics , operating system
This paper presents a novel approach to tradespace exploration for large‐scale and multi‐dimensional tradespace, which reduce the cognitive workload in addressing numerous design options to a manageable level. Our approach enables exploration by classifying subsets of design solutions or designs of interest, which will in turn facilitate and accelerate the decision making process. As the design tradespace for products such as CubeSats is naturally large‐scale and multi‐dimensional, the proposed approach integrates computational data clustering algorithms with multi‐dimensional scaling visualization. In the proposed approach, k‐medoids clustering provides a grouping model where similar designs are grouped; uncovering hidden patterns and features within datasets; and the principal component analysis allow users to visualize design samples on two‐dimensional scatter plots. A case study tradespace consisting of 1200+ CubeSat design alternatives is used to demonstrate how this approach supports knowledge discovery of design options through tradespace exploration of multi‐dimensional data.