Reynolds-Stress Model Prediction of 3-D Duct Flows

Gerolymos, G. A.; Vallet, I.

doi:10.1007/s10494-015-9648-7

Cited by 6 publications

(3 citation statements)

References 93 publications

(454 reference statements)

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“…In general, there are two different variants of the RSM model: ε -based and baseline (BSL) RSM models. The BSL RSM model, which has been discussed in detail by Gerolymos and Vallet [ 45 ], was applied in the present study due to a more accurate treatment of the near-wall region. In addition, the SST model with a curvature (rotational) correction (SST-CC) [ 46 ] was investigated as a middle step between the RSM and SST models.…”

Section: Methodsmentioning

confidence: 99%

Fluid-Solid Interaction Simulation Methodology for Coriolis Flowmeter Operation Analysis

Shavrina

Nguyen

Zeng

et al. 2021

Sensors

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Numerical simulation is a widely used tool for Coriolis flowmeter (CFM) operation analysis. However, there is a lack of experimentally validated methodologies for the CFM simulation. Moreover, there is no consensus on suitable turbulence models and configuration simplifications. The present study intends to address these questions in a framework of a fluid-solid interaction simulation methodology by coupling the finite volume method and finite element method for fluid and solid domains, respectively. The Reynolds stresses (RSM) and eddy viscosity-based turbulence models are explored and compared for CFM simulations. The effects of different configuration simplifications are investigated. It is demonstrated that the RSM model is favorable for the CFM operation simulations. It is also shown that the configuration simplifications should not include the braces neglect or the equivalent flowmeter tube length assumption. The simulation results are validated by earlier experimental data, showing a less than 5% discrepancy. The proposed methodology will increase the confidence in CFM operation simulations and consequently provide the foundation for further studies of flowmeter usage in various fields.

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Section: Methodsmentioning

confidence: 99%

Fluid-Solid Interaction Simulation Methodology for Coriolis Flowmeter Operation Analysis

Shavrina

Nguyen

Zeng

et al. 2021

Sensors

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“…8 Due to the presence of several interacting flow mechanisms, turbulent S-duct flows are very challenging to predict accurately. 11…”

Section: S-duct Flows Intrinsic S-duct Flow Regimementioning

confidence: 99%

Reynolds-averaged Navier–Stokes modelling in transonic S-ducts with passive flow control

Hickling

Ingram

2019

Proceedings of the Institution of Mechanical Engineers, Part A:

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S-duct diffusers are used in aircraft with embedded engines to route ambient air to the fan face. Sizing and stealth considerations drive a need for high curvature ducts, but the curvature causes complex secondary flows that lead to total pressure distortion and swirl velocities at the engine face. These must be controlled for stable engine operation. In this paper, tubercles, a novel bio-inspired passive flow control method, are analysed numerically in a duct with transonic flow. The results are compared to experimental data obtained as part of a campaign at the Royal Military College, Canada to investigate the effects of S-duct geometry and novel passive flow control devices on the performance of transonic S-ducts. The performance of Reynolds-averaged Navier–Stokes turbulence models in the S-ducts is assessed – Menter's shear stress transport model predicts excessive losses due to the overactivity of its stress limiter. The realisable k–ɛ model gives a significant improvement in the prediction of static pressure distributions, but losses and distortion characteristics are predicted poorly due to the model's inability to resolve the effects of unsteadiness in separated regions. Large tubercle geometries are found to trigger earlier separation in the centre of the duct by concentrating low momentum fluid in valleys, but they also act as boundary layer fences away from the duct centre. Smaller geometries are found to generate vortices that re-energise the boundary layer, delaying flow separation. Methods are recommended for future computational analyses of S-ducts and new designs of tubercles.

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“…Several models of this family have been assessed for the computation of complex 3D flows (Gerolymos and Vallet 2001, Jakirlić et al 2007, Cécora et al 2015 and are increasingly used to predict practical 3D configurations (Eisfeld 2015). Comparisons with measurements (Rumsey 2010) demonstrate the predictive improvement of 7-equation RSMs against standard 2-equation approaches, especially in presence of separation and/or secondary flows Vallet 2016b) but also highlight remaining challenges. In general RSMs cannot return the correct wall-asymptotic behaviour for all of the components of the Reynolds-stress tensor (Yakovenko and Chang 2007), and privileging the wall-normal components improves log-law prediction .…”

Section: Introductionmentioning

confidence: 99%

Further analysis of the budgets of the dissipation tensor ${\varepsilon }_{{ij}}$ in turbulent plane channel flow

Gerolymos¹,

Vallet²

2017

Fluid Dyn. Res.

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Abstract. Recent DNS results [Gerolymos G.A., Vallet I. : J. Fluid Mech. 807 (2016) have provided data for the terms in the transport equations for the components of the dissipation tensor ε ij in low-Reynolds turbulent plane channel flow. The present paper extends the previous results by a detailed analysis of the behaviour of various mechanisms in the ε ij -transport equations (production, diffusion, redistribution, destruction), with particular emphasis on the component-by-component comparison with the corresponding mechanisms in the transport equations for the Reynolds-stresses r ij . The splitting of the pressure terms for the wall-normal components into redistribution and pressure-diffusion reveals substantially different behaviour near the wall. The wall-asymptotics of different terms in the transport equations are studied in detail, and examined using the DNS data. Both DNS data and wall-asympotic analysis show that the anisotropy of the destruction-of-dissipation tensor εε ij is fundamentally different from that of r ij or ε ij , never approaching the 2-component (2-C) state at the solid wall.

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Reynolds-Stress Model Prediction of 3-D Duct Flows

Cited by 6 publications

References 93 publications

Fluid-Solid Interaction Simulation Methodology for Coriolis Flowmeter Operation Analysis

Fluid-Solid Interaction Simulation Methodology for Coriolis Flowmeter Operation Analysis

Reynolds-averaged Navier–Stokes modelling in transonic S-ducts with passive flow control

Further analysis of the budgets of the dissipation tensor ${\varepsilon }_{{ij}}$ in turbulent plane channel flow

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