Pressure flowfield and inlet flow distortion metrics reconstruction from velocity data

Date

2017-06-26

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AIAA

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Article

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0001-1452

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Pavlos K. Zachos, Michele Frascella, David G. MacManus, and Daniel Gil-Prieto, Pressure flowfield and inlet flow distortion metrics reconstruction from velocity data, AIAA Journal, Vol. 55, Issue 9, 2017, pp. 2929-2941

Abstract

Complex engine intakes are susceptible to unsteady flow distortions that may compromise the propulsion system operability. Hence, the need for high spatial and temporal resolution flow information is essential to aid the development of distortion tolerant, closely coupled propulsion systems. Stereoscopic PIV methods have been successfully applied to these flows offering synchronous velocity datasets of high spatial resolution across the Aerodynamic Interface Plane. However, total pressure distortion measurements are still typically provided by low bandwidth, intrusive total pressure rakes of low spatial resolution which results in limited characterisation of the total pressure distortion. This limitation can potentially be addressed by pressure field reconstruction from non-intrusive, high resolution velocity data. A range of reconstruction methods are assessed based on representative data from steady and unsteady computational simulations of an S-duct configuration. In addition to the reconstructed total pressure field, the impact on the key distortion metrics is assessed. The effect of Mach number is considered. Overall the reconstruction methods show that the distortion metrics can be determined with sufficient accuracy to indicate that there is a potential benefit from exploiting high resolution velocity measurements in evaluating total pressure distortion.

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Attribution-Non-Commercial 3.0 Unported (CC BY-NC 3.0) You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: Non-Commercial — You may not use the material for commercial purposes. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

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