Bayesian Surrogates Applied to Conceptual Stages of the Engineering Design Process

Abstract

During the conceptual design stages, designers often have incomplete knowledge about the interactions among design parameters. We are developing a methodology that will enable designers to create models with levels of detail and accuracy that correspond to the current state of the design process. Thus, designers can create a rough surrogate model when only a few data points are available and then refine the model as the design progresses and more information becomes available. These surrogates represent the system response when limited information is available and when few realizations of experiments or numerical simulations are possible. This paper presents a covariance-based approach for building multistage surrogates in the conceptual design stages when bounds for the response are not available a priori. We test the methodology using a one-dimensional analytical function and a heat transfer problem with an analytical solution, in order to obtain error measurements. We then illustrate the use of the methodology in a thermal design problem for wearable computers. The surrogate model enables the designer to understand the relationships among the design parameters.