Simulation Of Buoyancy Driven Flows Inside Heated Cavities Using LES And URANS
Approaches
School Of M.A.C.E.The University Of Manchester.
Presented by: DALILA AMMOURSupervisors: Prof H. Iacovides and Dr T.J. Craft
Code_Saturne users
Table of contents
Introduction
Objectives
Methods
Results
Coclusions
Introduction
Natural convection is defined as fluid motion where the flow arises
naturally from the effect of density differences which give rise to buoyancy
forces responsible for generating the flow.
Buoyancy-driven flows in enclosures have a number of technical
applications, ranging from cooling of electronic equipments, to the thermal
design of furnaces, energy storage systems and cooling of nuclear reactors.
Convection cell
Objectives
Predict 2-D and 3-D turbulent buoyant flow inside vertical (Betts) and
inclined cavities at 60°, 15° and 165° to the horizontal, using RANS and
LES approaches within Code_Saturne.
RANS simulation: test different RANS models and compare the results
obtained with recent experimental data.
LES simulation: Validate the RANS simulations and reproduce
accurate results.
Conclude which of the models tested within Code_Saturne can produce
reliable predictions of this kind of flow.
MethodsComputational meshes: Structured meshes (uniform coarse and non-
uniform fine grids for high and low Reynolds number models
respectively).
CFD code: Unstructured finite volume code Code_Saturne.
RANS models tested: high-Re κ-ε with wall functions, SST κ-ω
scheme, Re-stress transport models (LRR and SSG), ν²-f model and
the new version φ-α model.
Spatial discretization: RANS and LES, Second-order centered
scheme.
Time discretization: RANS, Implicit First-order Euler scheme. LES:
Second-order Crank- Nicolson scheme.
Physical properties: H/L=28.6, ΔT=18°C, Pr=0.71, Ra=0.86x106.
Modifications due to buoyancy
The Boussinesq approximation is used in the present study. It states that density differences are sufficiently small to be neglected, except where they appear in terms multiplied by the gravity
Momentum equation
Thermal expansion parameter
Boussinesq approximation
Computational meshes
RESULTS2D RANS predictions of Vertical and inclined cavities at 60°
Temperature profiles
Nusselt number
Comparison of 2-D turbulent kinetic energy for the three angles of inclination (SSG prediction)
Comparison of 2-D and 3-D results (165° inclined cavity, High and Low Reynolds number models predictions)
2-D3-D
Parallel velocity profiles2-D 3-D
LES Results (Smagorinsky subgrid model)1. Inclined cavity at 165°
Averaged temperature profiles
Averaged parallel velocity profilesIso-Q contours coloured by
temperature
Temperature Normal velocities
2. Inclined cavity at 15°
Averaged temperature profiles
Temperature
Normal velocity
Local Nusselt number
Averaged parallel velocity profiles
Averaged normal velocity profiles
Conclusions
2-D vertical and inclined cavity at 60°, heated from the upper side,
gave similar results for temperature, velocity and turbulent kinetic
energy, All RANS models succeed to predict the flow pattern. However
for inclined cavity at 165° RANS models disagree with experimental
data because of a multi cellular flow.
3-D RANS computation of inclined cavity at 165° have been
performed. High-Re-number models reproduce the unsteady structures
present in the flow however low Reynolds number models capture one
recirculation cell.
LES computation of 3-D inclined cavity at 165° and 15° have been
performed. Unsteady structures present in the flow are fully captured. In
general good agreements with measurements is shown.
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