Date of Award

9-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Aeronautics and Astronautics

First Advisor

Ramana V. Grandhi, PhD

Abstract

The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm reduces highdimensional airflow data to a low-dimensional sparse approximation to identify an optimal placement for any number of sensors on airfoil or wing models of arbitrary shape and size, and outperforms conventional optimization techniques in accuracy and speed. For 2D flow this algorithm found a sensor placement solution (design point, or DP) which predicts AoA to within 0.10 degrees and ranks within the top 1 percent of the design space. On 3D wing models SSPOP found four-sensor DPs ranked well within the top 0.10 percent. Experimental validation with velocity and pressure sensors confirmed the relative and absolute performance of five DPs: Best Possible (true optimum), SSPOP, Expert Opinion, and two Random DPs.

AFIT Designator

AFIT-ENY-DS-24-S-127

Comments

A 12-month embargo was observed for posting this work on AFIT Scholar.

Distribution Statement A, Approved for Public Release. PA case number on file.

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