摘要

Presented is a design framework intended to support the introduction of changes resulting from unplanned iterations and affecting conceptual design. A novel methodology for an efficient exploration of the design space is proposed, which enables, in particular, the investigation of candidate alternatives for accommodating the required changes. The approach taken is to reuse the design information available from prior evaluations to generate surrogate models. These provide a means to obtain a prediction and the corresponding error estimate of the objectives and constraints functions at any point of interest across the design space. A reformulation of the problem is subsequently performed via the goal-attainment method, conducting an a priori articulation of preferences with respect to the achievement of desirable values of the design parameters. Ultimately, the exploration of optimal solutions is driven by a proposed extension of the sampling criteria associated with Bayesian global optimization, which enables the computation of points located in the vicinity of constraint boundaries. The effectiveness of the proposed methodology is demonstrated by an analytical example, whereas its functionality and applicability to problems of industrial relevance are validated by a test case concerning aircraft conceptual design.

  • 出版日期2013-2