Application of the Scale-Adaptive Simulation to a Hot Jet in Cross Flow

Duda, Benjamin M.; Menter, F. R.; Deck, Sébastien; Bézard, Hervé; Hansen, Thorsten; Esteve, Marie-Josephe

doi:10.2514/1.j052021

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…Comparisons to standard LES wake predictions and experiments for some generic aerodynamic cases, such as flow over a cavity and a jet in a crossflow, can be found in previous studies. 16,17 The approach is only recommended for strongly separated globally unstable wakes, where strong self-sustaining instabilities control the large-scale wake features that dominate wake spatial development and frequency content, which is the case here. Figure 2(a) shows the domain boundaries which, except for the road, were placed far enough from the cyclist that they had a negligible effect on the simulated results.…”

Section: Numerical Simulationmentioning

confidence: 99%

A numerical model for the time-dependent wake of a pedalling cyclist

Griffith

Crouch

Burton

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part P:

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A method for computing the wake of a pedalling cyclist is detailed and assessed through comparison with experimental studies. The large-scale time-dependent turbulent flow is simulated using the Scale Adaptive Simulation approach based on the Shear Stress Transport Reynolds-averaged Navier–Stokes model. Importantly, the motion of the legs is modelled by joining the model at the hips and knees and imposing solid body rotation and translation to the lower and upper legs. Rapid distortion of the cyclist geometry during pedalling requires frequent interpolation of the flow solution onto new meshes. The impact of numerical errors, that are inherent to this remeshing technique, on the computed aerodynamic drag force is assessed. The dynamic leg simulation was successful in reproducing the oscillation in the drag force experienced by a rider over the pedalling cycle that results from variations in the large-scale wake flow structure. Aerodynamic drag and streamwise vorticity fields obtained for both static and dynamic leg simulations are compared with similar experimental results across the crank cycle. The new technique presented here for simulating pedalling leg cycling flows offers one pathway for improving the assessment of cycling aerodynamic performance compared to using isolated static leg simulations alone, a practice common in optimising the aerodynamics of cyclists through computational fluid dynamics.

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Section: Numerical Simulationmentioning

confidence: 99%

A numerical model for the time-dependent wake of a pedalling cyclist

Griffith

Crouch

Burton

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part P:

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“…41 The performance advantage of SAS as compared with Unsteady Reynolds-averaged Navier-Stokes (URANS) for simulating jet in cross-flow is well documented. 42 Thermal mixing characteristics and temperature distributions are more accurately captured using SAS. It has also been reported that SAS can produce results that are more in agreement with experimental data, as compared with RANS.…”

Section: Introductionmentioning

confidence: 99%

Flow Field and Heat Transfer of an Impinging Synthetic Jet in the Presence of Cross-Flow: Application of SAS and DES Approaches

Bazdidi–Tehrani

Saadniya

Rashidzadeh

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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Nowadays, synthetic jets have various applications such as cooling enhancement and active flow control. In the present paper, the capability of two turbulence modelling approaches in predicting thermal performance of an impinging synthetic jet is investigated. These two approaches are scale adaptive simulation (SAS) and detached eddy simulation (DES). Comparisons between numerical data and experimental studies reveal that the ability of DES in predicting the asymmetrical trend of heat transfer profiles is better than SAS in almost all the study cases. Although, near the stagnation zone, the performance of SAS is superior. Results show that the effects of parameters such as frequency, cross-flow velocity and suction duty cycle factor are well predicted by both approaches. An increase of cross-flow velocity from 1.81 m/s to 2.26 m/s results in an improvement of [Formula: see text] near the stagnation point by almost 16.3% and 9.2% using DES and SAS, respectively.

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“…The transition at the interface is therefore not well defined, especially for weak flow instabilities as present in the shear layer at low momentum jets as has been shown by several studies. [12][13][14][15] Though, these investigations were mainly based on applied configurations. The present study aims to analyze the capabilities of SAS, URANS and LES turbulence models in a generic test case.…”

Section: Introductionmentioning

confidence: 99%