Expected Improvement applied to an industrialcontext -- Prediction of new geometries increasing theefficiency of fans

TL;DR

This paper applies Kriging and Expected Improvement algorithms to design new automotive fan geometries that enhance efficiency, demonstrating an innovative approach with promising results in a competitive industry.

Contribution

It introduces a novel application of Kriging and Expected Improvement for optimizing fan geometries in the automotive sector.

Findings

Successful prediction of high-efficiency fan geometries

Demonstrated improvement over traditional design methods

Potential for significant efficiency gains in automotive fans

Abstract

In automotive industry, client needs evolve quickly in a competitiveness context, particularly, regarding the fan involved in the engine cooling module. This study has been done in cooperation with the automotive supplier Valeo. Here, we propose to use the Kriging interpolation and the Expected Improvement algorithm to provide new fan designs with high performances in terms of eciency. As far as we know, such a use of Kriging and Expected Improvement methodologies are innovative and provide really promising results.