1. Comparative Study of Noise Control in Micro Turbojet Engines with Chevron and Ejector Nozzles Through Statistical, Acoustic and Imaging Insight
- Author
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Alina Bogoi, Grigore Cican, Mihnea Gall, Andrei Totu, Daniel Eugeniu Crunțeanu, and Constantin Levențiu
- Subjects
subsonic jet ,chevron nozzle ,ejector nozzle ,Schlieren image analysis ,statistics ,acoustics ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
In connection with subsonic jet noise production, this study investigates acoustic noise reduction in micro turbojet engines by comparing ejector and chevron nozzle configurations to a baseline. Through detailed statistical analysis, including assessments of stationarity and ergodicity, the current work validates that the noise signals from turbojet engines could be treated as wide-sense ergodic. This further allows to use time averages in acoustic measurements. Acoustic analysis reveals that the chevron nozzle reduces overall SPL by 1.28%, outperforming the ejector’s 0.51% reduction. Despite the inherent challenges of Schlieren imaging, an in-house code enabled a more refined analysis. By examining the fine-scale turbulent structures, one concludes that chevrons promote higher mixing rates and smaller vortices, aligning with the statistical findings of noise reduction. Schlieren imaging provided visual insight into turbulence behavior across operational regimes, showing that chevrons generate smaller, controlled vortices near the nozzle, which improve mixing and reduce noise. At high speeds, chevrons maintain a confined, high-frequency turbulence that attenuated noise more effectively, while the ejector creates larger structures that contribute to low-frequency noise propagation. Comparison underscores the superior noise-reduction capabilities of chevrons with respect to the ejector, particularly at high-speed. The enhanced Schlieren analysis allowed for new frame-specific insights into turbulence patterns based on density gradients, providing a valuable tool for identifying turbulence features and understanding jet flow dynamics.
- Published
- 2025
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