Investigation of the thermal mixing in a T-junction flow with different SRS approaches

Gritskevich, M. S.; Garbaruk, A. V.; Frank, Th.; Menter, F. R.

doi:10.1016/j.nucengdes.2014.03.010

Cited by 22 publications

(14 citation statements)

References 12 publications

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“…Each method was able to predict the flow and thermal mixing with reasonable accuracy with respect to experimental data, however they appear to slightly under perform when compared to the ELES case in Gritskevich et al [31], which employed a WMLES model, albeit at additional cost. Overall the findings of the two papers for ELES is encouraging.…”

Section: Thermal Mixing In a T-junctionmentioning

confidence: 95%

“…It is argued that thermal fatigue is the most common cause of unexpected failures within Nuclear Power Plants (NPP's) particularly within piping components such as mixing tee's [29]. Flows throughout complex piping geometries are highly unsteady and involve broad bandwidth, low frequency fluctuations that are poorly represented through standard URANS [31]. Westin et al [122] and Walker et al [120] illustrated that both steady and unsteady RANS calculations are unable predict realistic mixing between two flows [122].…”

Section: Nuclear Applicationsmentioning

confidence: 99%

“…Gritskevich et al [31] investigated the application of hybrid RANS-LES approaches for predicting heat transfer at the walls in a T-junction's mixing zone. Of particular interest here is the success of ELES, with Fig.…”

Section: Thermal Mixing In a T-junctionmentioning

confidence: 99%

“…It is suggested by Gritskevich et al [31] that the use of global schemes, such as Scale Adaptive Simulation (SAS) and DDES, could be problematic for the computation of flows where there is no natural geometrical or flow induced conversion to Scale Resolving Simulation (SRS) -i.e. there is no seperated flow.…”

Section: Thermal Mixing In a T-junctionmentioning

confidence: 99%

“…The work by Gritskevich et al [31] is later extended to include the combination of ELES with IDDES and SAS models so that the previous WMLES zones are occupied with either IDDES or SAS [33]; additionally the RANS to SRS interface is computed with both vortex method and a Generator of Synthetic Turbulence (GST) [1]. Each method was able to predict the flow and thermal mixing with reasonable accuracy with respect to experimental data, however they appear to slightly under perform when compared to the ELES case in Gritskevich et al [31], which employed a WMLES model, albeit at additional cost.…”

Section: Thermal Mixing In a T-junctionmentioning

confidence: 99%

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A Review of Embedded Large Eddy Simulation for Internal Flows

Holgate

Skillen

Craft

et al. 2018

Arch Computat Methods Eng

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When scale-resolving simulation approaches are employed for the simulation of turbulent flow, computational cost can often be prohibitive. This is particularly true for internal wall-bounded flows, including flows of industrial relevances which may involve both high Reynolds number and geometrical complexity. Modelling the turbulence induced stresses (at all scales) has proven to lack requisite accuracy in many situations. In this work we review a promising family of approaches which aim to find a compromise between cost and accuracy; hybrid RANS-LES methods. We place particular emphasis on the emergence of embedded large eddy simulation. These approaches are summarised and key features relevant to internal flows are highlighted. A thorough review of the application of these methods to internal flows is given, where hybrid approaches have been shown to offer significant benefits to industrial CFD (relative to an empirical broadband modelling of turbulence). This paper concludes by providing a cost-analysis and a discussion about the emerging novel use-modalities for hybrid RANS-LES methods in industrial CFD, such as automated embedded simulation and multi-dimensional coupling.

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Section: Thermal Mixing In a T-junctionmentioning

confidence: 95%