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Title: Models for the prediction of rear-arc and forward-arc fan broadband noise in turbofan engines
Author: Jenkins, G.
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2013
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This thesis investigates three elements necessary for the prediction of the broadband noise from a turbofan engine due to the interaction between the turbulent rotor wakes with the Outlet Guide Vanes (OGVs). These are (i) the sound radiation from a cascade of closely spaced blades interacting with rotor wake turbulence, (ii) an analysis of the behaviour of hotwire velocity data from a Large Scale Fan Rig (LSFR), (iii) the development of a scheme for the prediction of the blockage due to the transmission of multi mode sound across the rotor necessary for the prediction of noise in the forward-arc. (i) Cascade noise model A noise model is presented for the prediction of rotor wake turbulence with a cascade of OGVs. Similar to other approaches of this kind, computation time becomes excessive at high frequencies as the number of modes required increases. This thesis shows that at sufficiently high frequencies, when at least two modes are cut-on between adjacent blades, the acoustic blade coupling is weak and the cascade sound radiation closely approximates to that of an isolated aerofoil whose radiation can be computed efficiently using single airfoil theory, thereby greatly reducing computation time. (ii) Characteristics of rotor wake turbulence One factor currently limiting accurate fan broadband noise predictions is an understanding of rotor wake turbulence at the OGV leading edge. This thesis analyses in detail recent hotwire velocity data measured in the interstage of an LSFR. The focus here is on assessing the extent of self-preservation in the rotor wake, whereby the mean and turbulent wake characteristics can be deduced at any position downstream of the rotor and at any operating condition from a limited number of measurements. Unlike as previously assumed, this analysis demonstrates insufficient self-preserving behaviour to justify further pursuit of this approach. Rotor wake turbulence must therefore be measured or predicted at each operating condition separately. An analysis procedure is developed by which the characteristics of individual wakes, necessary for broadband noise predictions, may be inferred from rotor wake velocity measurements in situations in which there is significant overlap between adjacent wakes. (iii) Multi mode rotor blockage Noise generated by the OGV propagates to the forward arc by passing upstream through the spinning rotor. This thesis presents a model for the sound power transmission loss associated with crossing the rotor that includes modal frequency scattering effects. It is shown that the results obtained using exact cascade scattering closely agree at low and high frequencies with the results from a relatively simple prediction scheme that assumes that only plane waves propagate through the cascade, thereby ignoring modal scattering effects. The advantage of making this approximation is that the computation is considerably more efficient than a full cascade calculation. At low frequencies, where only plane waves propagate in the gap, exact agreement is obtained between the exact and plane wave models. Close agreement is also observed in the high frequency limit where a large number of cascade modes are cut-on, most of which are well cut-on and hence whose behaviour tends that of the plane wave mode. The three components of the prediction procedure outlined above are combined to perform a prediction of the rear-arc and forward-arc broadband noise from an LSFR. Comparison of the measured and predicted noise spectra are in reasonable agreement with variations with working line and fan speed being reasonably well captured.
Supervisor: Joseph, Phillip Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: TA Engineering (General). Civil engineering (General) ; TJ Mechanical engineering and machinery