Revisiting RANS turbulence modelling used in built-environment CFD simulations

Serra, Nuno

doi:10.1016/j.buildenv.2023.110333

Cited by 9 publications

(3 citation statements)

References 63 publications

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“…The scientific literature includes examples of numerous research articles related to the numerical modeling of turbulent airflows in ventilated buildings. For many years, the influence of turbulence models from the RANS, URANS, and LES groups on the accuracy of predicting air distribution in rooms has been tested [17][18][19][20][21][22][23][24]. Various boundary conditions were also considered, i.e., inlet geometry, supply air velocity, Reynolds number, isothermal or non-isothermal flows, as well as inlet turbulence quantities: kinetic energy, intensity, dissipation rate, and scale length.…”

Section: Impact Of Turbulence Model On Cfd Resultsmentioning

confidence: 99%

Eddy–Viscosity Reynolds-Averaged Navier–Stokes Modeling of Air Distribution in a Sidewall Jet Supplied into a Room

Hurnik,

Ciuman,

Popiolek

2024

Energies

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Air velocity is one of the key parameters affecting the sensation of thermal comfort. In mixing ventilation, the air is most often supplied above the occupied zone, and the air movement in a room is caused by jets that generate recirculating flows. An effective tool for predicting airflow in a room is CFD numerical modeling. In order to reproduce the air velocity distribution, it is essential to select a proper turbulence model. In this paper, seven Eddy–Viscosity RANS turbulence models were used to carry out CFD simulations of a sidewall air jet supplied into a room through a wall diffuser. The goal was to determine which model was the most suitable to adopt in this type of airflow. The CFD results were validated using experimental data by comparing the gross and integral parameters, along with the parameters of the quasi-free jet model. The numerical results obtained for Std k-ε and EVTM models were most consistent with the measurements. Their error values slightly exceeded 15%. On the contrary, the k-ω and RNG k-ε models did not reproduce the quasi-free jet parameters correctly. The research findings can prove beneficial for simulating air distribution in supplied air jets during the initial conceptual phases of HVAC system design.

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Section: Impact Of Turbulence Model On Cfd Resultsmentioning

confidence: 99%

Eddy–Viscosity Reynolds-Averaged Navier–Stokes Modeling of Air Distribution in a Sidewall Jet Supplied into a Room

Hurnik,

Ciuman,

Popiolek

2024

Energies

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“…In practice, the construction of this PDF is avoided and a more economical thinning-and-rejection algorithm [14] is used. A size-𝑙 eddy event is probabilistically accepted with local rate 𝜏 −1 = 𝐶 √ 2𝐸∕𝑙 2 , where 𝐸 = 𝐸 kin − 𝑍𝐸 vp is the current specific available eddy energy that for incompressible shear flow only takes kinetic energy (𝐸 kin ) and viscous 'penalty' energy (𝐸 vp ) contributions. In the present application, the free ODT model parameters are fixed at 𝐶 = 6.5 and 𝑍 = 300 (following Glawe [27]), which is a reasonable selection for low-Reynolds number channel and boundary-layer type flows [28][29][30].…”

Section: Overview Of the Map-based Stochastic One-dimensional Turbule...mentioning

confidence: 99%

“…Low-fidelity modeling based on the Reynolds-averaged Navier-Stokes (RaNS) equations is state-of-the-art for numerical simulation of complex flows at high Reynolds number, which is in particular the case for atmospheric boundary-layer flows [1,2], among other industrial applications. RaNS provides fast turn-around times at reasonable accuracy for applications that are well described by some calibrated set-up and dedicated modeling approach, especially for the wall [3,4].…”

Section: Introductionmentioning

confidence: 99%

Stochastic deconvolution of wall statistics in Reynolds‐averaged Navier–Stokes simulations based on one‐dimensional turbulence

Glawe,

Klein,

Schmidt

2023

Proc Appl Math and Mech

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Reynolds‐averaged Navier–Stokes simulation (RaNS) is state‐of‐the‐art for numerical analysis of complex flows at high Reynolds number. Standalone RaNS may yield a reasonable estimate of the wall‐shear stress and turbulent drag if a proper wall‐function is prescribed, but detailed turbulence statistics cannot be obtained, especially at the wall. This lack in modeling is addressed here by a stochastic deconvolution strategy based on a stochastic one‐dimensional turbulence (ODT) model. Here, a one‐way coupling strategy is proposed in which a forcing term is computed from the balanced RaNS solution that is in turn utilized in the ODT model. The temporally developing ODT solution exhibits turbulent perturbations but relaxes toward the local RaNS solution due to resolved molecular‐diffusive processes. It is demonstrated that the approach is able to recover the distribution of positive wall‐shear stress fluctuations in turbulent channel flow. When formulated as post‐processing tool, it is suggested that RaNS can be enhanced by ODT providing economical means for local high‐fidelity numerical modeling based on a low‐fidelity flow solution.

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Can windcatcher's natural ventilation beat the chill? A view from heat loss and thermal discomfort

Liu,

Almazmumi,

Cao

et al. 2024

Building and Environment

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Revisiting RANS turbulence modelling used in built-environment CFD simulations

Cited by 9 publications

References 63 publications

Eddy–Viscosity Reynolds-Averaged Navier–Stokes Modeling of Air Distribution in a Sidewall Jet Supplied into a Room

Eddy–Viscosity Reynolds-Averaged Navier–Stokes Modeling of Air Distribution in a Sidewall Jet Supplied into a Room

Stochastic deconvolution of wall statistics in Reynolds‐averaged Navier–Stokes simulations based on one‐dimensional turbulence

Can windcatcher's natural ventilation beat the chill? A view from heat loss and thermal discomfort

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