Investigation of the Effects of Lobe Number and Configuration on Mixing Performance in Lobed Mixer

Iper, Baran; Ergin, Cagdas C.; Cenik, Burak; Suer, Tacettin U.; Uslu, Sıtkı

doi:10.2514/6.2020-3778

Cited by 1 publication

(1 citation statement)

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“…14 One possible reason why the SST k À ω is popular in helicopter applications is that the model promotes convergence in a complex flowfield. 15 In the study by İper et al, 16 four turbulence models including the standard and realizable k À ϵ, as well as the standard k À ω and SST k À ω were used to predict the mixedness index of a lobed mixer. It was found that the SST k À ω predicted the best mixing of the flow, which is an important consideration as it affects the dissipation of streamwise and normal vortices downstream of the lobed mixer.…”

Section: Introductionmentioning

confidence: 99%

On the numerical assessment of engine exhaust plumes with a scaled cowling

Mohamed,

Teo,

New

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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The ability to predict the characteristics of engine exhaust is important to determining heat signatures on various components of the aircraft body. This is often obtained through numerical means such as RANS, which however relies closely on the choice of the turbulence model for accurate predictions. In this study, predictions of exhaust plumes from four turbulence models are compared against results from particle image velocimetry of a scaled helicopter engine exhaust. These models include the standard k − ϵ, realizable k − ϵ, shear-stress transport (SST) k − ω, and Durbin’s turbulence models. All four turbulence models managed to capture the general shape of the exhaust when analyzed through the velocity contours at two measurement windows. However, the comparisons of velocity contours fail to describe the shift of the predicted plume from the experiments, which is important for fuselage/tail impingement. To obtain further insights to the shifts, a visual correlation in the form of a confidence ellipse through principal component analysis (PCA) is introduced and plotted for the predicted plumes. All the models’ plume predictions showed quantifiable shifts in their mean-centers when compared to the measurements. In terms of matching the measurements’ statistical maximum variance of the plume distribution at the furthermost plane, it was found that the realizable k − ϵ performed the best among the models. On the other hand, the SST k − ω and Durbin’s model performed the best in predicting bivariate ( x and y coordinates) distribution of the plume at the furthermost plane.

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Section: Introductionmentioning

confidence: 99%