Design Mode Analysis of Pareto Solution Set for Decision-Making Support

Abstract EN

Decision making in engineering design problems is challenging because they have multiple and conflicting criteria and complex correlation between design parameters.

This study proposes a decision-making support methodology named design mode analysis, which consists of data clustering and principal component analysis (PCA).

A design mode is indicated by the eigenvector obtained by PCA and reveals the dominant design parameters in a given dataset.

The proposed method is a general framework to obtain the design modes from high-dimensional and large datasets.

The effectiveness of the proposed method is verified on the conceptual design problem of the hybrid rocket engine.