Abstract

The presentation will briefly review some of the reasons for the recent renewed interest in the Design of Experiments (DoE) and some key developments which, in the author’s view and experience, underpin and enable this success. One is identified in the ability of combining classical DoE methods with substantially more sophysticated mathematical descriptions of the physics in the experiment being designed, thus putting the “model-based” firmly in front of DoE. Another, the main subject of the talk, is a better understanding of the relationship between desired performance and evaluation metric(s), leading to the disaggregation of a single “best” design objective into constituent components, and to much richer formulations of the design problem that can be tailored to specific situations. A final reason is the substantial improvement in the numerical and computing tools supporting the model-based design of experiments, but also and chiefly in the availaility of integrated modelling/solution environments which make the whole technology accessible to a much wider engineering community. The presentation will illustrate, with reference to examples, some of the new problem formulations that can be used to represents more sophysticated design requirements (including parameter precision, anti-correlation, robustness to uncertainty) and, briefly, some of the newer solution approaches (including design of parallel experiments, on-line re-design). It will also illustrate some successful applications in a variety of demanding industrial areas, ranging from fuel cells, to complex reactor design, to biomedical applications.