Technology Characterization Models and Their Use in Systems Design

Abstract

Prior research suggests that setbased design representations can be useful for facilitating collaboration among engineers in a design project. However, existing setbased methods are limited in terms of how the sets are constructed and in their representational capability. The focus of this article is on the problem of modeling the capabilities of a component technology in a way that can be communicated and used in support of systemlevel decision making. The context is the system definition phases of a systems engineering project, when engineers still are considering various technical concepts. The approach under investigation requires engineers familiar with the componentor subsystemlevel technologies to generate a setbased model of their achievable technical attributes, called a technology characterization model (TCM). Systems engineers then use these models to explore systemlevel alternatives and choose the combination of technologies that are best suited to the design problem. Previously, this approach was shown to be theoretically sound from a decision making perspective under idealized circumstances. This article is an investigation into the practical effectiveness of different TCM representational methods under realistic conditions such as having limited data. A power plant systems engineering problem is used as an example, with TCMs generated for different technical concepts for the condenser component. Samples of valid condenser realizations are used as inputs to the TCM representation methods. Two TCM representation methods are compared based on their solution accuracy and computational effort required: a Krigingbased interpolation and a machine learning technique called support vector domain description (SVDD). The results from this example hold that the SVDDbased method provides the better combination of accuracy and efficiency.