Prediction of combustion noise for an aeroengine combustor

Combustion noise may become an important noise source for lean-burn gas turbine engines, and this noise is usually associated with highly unsteady flames. This work aims to compute the broadband combustion noise spectrum for a realistic aeroengine combustor, and to compare with available measured noise data on a demonstrator aeroengine. A low-order linear network model is applied to a demonstrator engine combustor to obtain the transfer function that relates to unsteadiness in the rate of heat release, acoustic, entropic and vortical fluctuations. A spectral model is used for the heat release rate fluctuation, which is the source of the noise. The mean flow of the aeroengine combustor required as input data to this spectral model is obtained from RANS simulations. The computed acoustic field for a low-medium power setting indicates that the models used in this study capture the main characteristics of the broadband spectral shape of combustion noise. Reasonable agreement with the measured spectral level is achieved.The current research has been conducted under UK Technology Strategy Board contract TP11/HVM/6/I/AB201K.This is the accepted manuscript. The final published version is available from ARC at http://arc.aiaa.org/doi/abs/10.2514/1.B34857. Copyright © 2013 by the authors. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission

Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound

We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties. While our approach holds for arbitrary distributions, we instantiate it with Dirichlet distributions: this allows for a closed-form and differentiable expression for the expected risk, which then turns the generalization bound into a tractable training objective.The resulting stochastic majority vote learning algorithm achieves state-of-the-art accuracy and benefits from (non-vacuous) tight generalization bounds, in a series of numerical experiments when compared to competing algorithms which also minimize PAC-Bayes objectives -- both with uninformed (data-independent) and informed (data-dependent) priors

Study of Blade/Vortex interaction using Computational Fluid Dynamics and Computational Aeroacoustics

Abstract A parametric study of the aerodynamics and the acoustics of parallel BVI has been carried out for different aerofoil shapes and vortex properties. Computing BVI using Computational Fluid Dynamics is challenging since the solution scheme tends to alter the characteristics of the vortex which must be preserved until the interaction. The present work uses the Compressible Vorticity Confinement Method (CVCM) for capturing the vortex characteristics, which is easier to implement and has minimal overhead in the performance of existing CFD solvers either in terms of CPU time or robustness during convergence. Apart from applying the CVCM method with an upwind solver, something not encountered in the literature, the present work couples CFD with Computational Aeroacoustics (CAA) and uses the strengths of both techniques in order to predict the nearfield and farfield noise. Results illustrate the importance of the aerofoil shape at transonic flow and show that the magnitude of the BVI noise depends strongly on the vortex strength and the miss-distance. The effect of the vortex core radius was also found to be important