Compressive Sensing for Reconstruction of Noise from an Aeroengine with Elliptic-Shaped Intake

Abstract

Based on our prior theoretical model for elliptical mode detection, compressive sensing is introduced in this work to overcome the limitations of Nyquist-Shannon sampling. The key contribution is showing how to apply compressive sensing to an elliptic duct, which holds potential for future blended-wing-body aircraft applications. The results show that the proposed method requires only about one-third of sensors under the traditional sampling theorem, while achieving comparable identification accuracy. Monte Carlo simulations further confirm that assigning a value of 2 to the coherence parameter between the sampling matrix and the sparse basis leads to a reconstruction error of 0.01. Furthermore, our work examines how various array configurations impact spinning elliptical mode detection, thereby informing the optimal design of sensor arrays for aeroengine applications of interest. This study proposes a compressive sensing method that minimizes sensor deployment for detecting acoustic modes in elliptical ducts, thereby enhancing the efficiency and scalability of aeroacoustic testing systems.