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Screening for Fuel Economy: A Case Study of Supersaturated Designs in Practice

Keywords: Bayesian Variable Assessment, Supersaturated Design

A successful use of supersaturated design and analysis is demonstrated through a case study completed at The Lubrizol Corporation. In the study, a 28-run supersaturated design is used to screen the effects of more than 70 possible model terms (linear effects, quadratic effects, interactions, and measured covariates) on engine motor oil coefficient of friction (COF). Of the over 70 model terms of interest, 50 of them are two-way linear interactions. A Lubrizol-developed model-averaging technique known as Bayesian Variable Assessment (BVA) is used to identify the important high-level factors and model terms from the experiment.

Due to time and cost constraints, supersaturated designs are necessary to screen for phenomena such as gasoline-powered engine fuel economy. Based on the results from a 10 run follow-up experiment, the use of the supersaturated design analyzed using BVA is concluded to be a success in this case study.