Sangjoon Lee, M. Erden Yildizdag
Dimples offer a promising route to reducing drag on aerodynamic surfaces. However, whether such shallow concavities yield a net benefit depends sensitively on their topography, which demands systematic mapping and exploration of their comprehensive design space. In this study, dimple design is examined as a mixed-variable optimization over four design variables: dimple type, depth, in-plane scale, and streamwise stretch. The design space is explored using MixMOBO, a Bayesian optimizer, coupled with immersed-boundary large eddy simulations of channel flows at a constant flow rate corresponding to a flat channel at a friction Reynolds number of 180. The optimal solution, a relatively deep, fully packed, streamwise-elongated diamond dimple, attains a 13.2% drag reduction, notably above previously reported values. A Gaussian process metamodel sensitivity analysis identifies dimple topology as the dominant factor, with coverage and elongation acting mainly through interactions, and depth itself carrying no universal sign. A near-wall flow analysis links the leading designs to fully attached, groove-like flow, whereas poorer designs tend to produce local flow separation that incurs adverse form drag. From these findings, key design insights for drag-reducing dimples are provided.
@article{96a123ae-fa0d-4d3a-96d9-a8722b849b2a,
title={Bayesian optimization and topographic exploration of drag-reducing dimples for aerodynamic surfaces},
author={Sangjoon Lee and M. Erden Yildizdag},
year={2022},
language={en}
}TY - JOUR TI - Bayesian optimization and topographic exploration of drag-reducing dimples for aerodynamic surfaces AU - Sangjoon Lee AU - M. Erden Yildizdag PY - 2022 LA - en ER -
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