A single-Stream jet noise prediction model for a family of chevron nozzles

This study develops a single-stream jet noise prediction model for a family of chevron nozzles. An original equation is proposed for the fourth-order space-time cross-correlations. They are expressed in flow parameters such as streamwise circulation and turbulent kinetic energy. The cross-correlations based on a Reynolds Averaged Navier-Stokes (RANS) flowfield showed a good agreement with those based on a Large Eddy Simulation (LES) flowfield. This proves that the proposed equation describes the cross-correlations accurately. With this novel source description, there is an excellent agreement between our model's far-field noise predictions and measurements1 for a wide range of frequencies and radiation angles. Our model captures the spectral shape, amplitude and peak frequency very well. This establishes that our model holds good for a family of chevron nozzles. As our model provides quick and accurate predictions, a parametric study was performed to understand the effects of a chevron nozzle geometry on jet noise and thrust loss. Chevron penetration is the underpinning factor for jet noise reduction. The reduction of jet noise per unit thrust loss decreases linearly with chevron penetration. The number of chevrons also has a considerable effect on jet noise.

Large eddy simulations in low-pressure turbines: Effect of wakes at elevated free-stream turbulence

The transition of a separated shear layer over a flat plate, in the presence of periodic wakes and elevated free-stream turbulence (FST), is numerically investigated using Large Eddy Simulation (LES). The upper wall of the test section is inviscid and specifically contoured to impose a streamwise pressure distribution over the flat plate to simulate the suction surface of a low-pressure turbine (LPT) blade. Two different distributions representative of a 'high-lift' and an 'ultra high-lift' turbine blade are examined. Results obtained from the current LES compare favourably with the extensive experimental data previously obtained for these configurations. The LES results are then used to further investigate the flow physics involved in the transition process.In line with experimental experience, the benefit of wakes and FST obtained by suppressing the separation bubble, is more pronounced in 'ultra high-lift' design when compared to the 'high-lift' design. Stronger 'Klebanoff streaks' are formed in the presence of wakes when compared to the streaks due to FST alone. These streaks promoted much early transition. The weak Klebanoff streaks due to FST continued to trigger transition in between the wake passing cycles.The experimental inference regarding the origin of Klebanoff streaks at the leading edge has been confirmed by the current simulations. While the wake convects at local free-stream velocity, its impression in the boundary layer in the form of streaks convects much slowly. The 'part-span' Kelvin-Helmholtz structures, which were observed in the experiments when the wake passes over the separation bubble, are also captured. The non-phase averaged space-time plots manifest that reattachment is a localized process across the span unlike the impression of global reattachment portrayed by phase averaging. © 2013 Elsevier Inc.

Unconstrained monocular 3D human pose estimation by action detection and cross-modality regression forest

This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts. © 2013 IEEE.