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Optical quantitative detection of density fields of turbofan exhaust plumes

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posted on 2025-09-16, 08:27 authored by YuYao Wang, Xiaobing Sun, Wenyu CUI, Yuan HU, Changping YU, Changchun LIU, YiChen Wei, SHUN Yao, Ke HU
Accurately detecting the true spatial structure of exhaust plume density fields is important for optimising engine design, validating aerodynamic performance and advancing aircraft detection technologies. The fundamental difficulty in non-intrusive three-dimensional density field measurement lies in whether a density value at a specific point in free space can be mapped and whether its signal characteristics are practically observable. This paper uses the intensity of the light scattered instantaneously by an ultra-narrow laser pulse at a specific point within the flow field as an optical signal to characterise the density value at that point. On this basis, it proposes a non-intrusive method of measuring density and reconstructing the three-dimensional field for exhaust plume tomography using an active light source. A detection system was established and plume experiments on a small turbofan engine were performed, and cross-sectional images of absolute density distribution along the central axis of the plume were obtained with a spatial resolution of 3.75 cm. The experimental results revealed a hollow three-dimensional structure, wherein the gaseous medium of the small turbofan exhaust plume expands along the central axis and undergoes compression at the surrounding regions. The maximum density recorded was 3.90 kg/m³, which is 218.90% higher than the background mean. The minimum recorded density was 0.93 kg/m³, which is 24.04% lower than the background mean. The fluctuation level, or coefficient of variation, reached 44.05%, which is 14.4 times higher than the environmental background. This method provides a non-intrusive means of measuring flow field structures, serving fundamental research on complex fluids, diagnostic testing of aeroengines, and related engineering applications.

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Funder Name

Ministry of Science and Technology of the People's Republic of China (2022YFF0711703)

Preprint ID

127203

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