Optimization of Sparse Sensor Placement for Estimation of Wind Direction and Surface Pressure Distribution Using Time-Averaged Pressure-Sensitive Paint Data on Automobile Model

TL;DR

This paper develops a data-driven method to optimize sparse pressure sensor placement on an automobile model for accurate wind direction and pressure distribution estimation, using pressure-sensitive paint data.

Contribution

It introduces a sensor optimization approach based on greedy algorithms for sparse sensor placement to improve wind and pressure estimation accuracy.

Findings

Optimized sensors accurately predict yaw angle and pressure distributions.

Three greedy algorithms were compared for sensor placement effectiveness.

Few sensors can achieve high estimation accuracy.

Abstract

This study proposes a method for predicting the wind direction against the simple automobile model (Ahmed model) and the surface pressure distributions on it by using data-driven optimized sparse pressure sensors. Positions of sparse pressure sensor pairs on the Ahmed model were selected for estimation of the yaw angle and reconstruction of pressure distributions based on the time-averaged surface pressure distributions database of various yaw angles, whereas the symmetric sensors in the left and right sides of the model were assumed. The surface pressure distributions were obtained by pressure-sensitive paint measurements. Three algorithms for sparse sensor selection based on the greedy algorithm were applied, and the sensor positions were optimized. The sensor positions and estimation accuracy of yaw angle and pressure distributions of three algorithms were compared and evaluated. The results show that a few optimized sensors can accurately predict the yaw angle and the pressure distributions.