Simulation Of Buoyancy Driven Flows Inside Heated Cavities Using LES And URANS Approaches

1. Code_Saturne users 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 AMMOUR Supervisors: Prof H. Iacovides and Dr T.J. Craft

2. Table of contents • Introduction • Objectives • Methods • Results • Coclusions

3. 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

4. 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_Saturnecan produce reliable predictions of this kind of flow.

5. Methods • Computational 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.

6. Modifications due to buoyancy • Momentum equation • Boussinesq approximation • Thermal expansion parameter 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

7. Computational meshes

8. RESULTS 2D RANS predictions of Vertical and inclined cavities at 60° Temperature profiles Nusselt number

9. Comparison of 2-D turbulent kinetic energy for the three angles of inclination (SSG prediction)

10. Comparison of 2-D and 3-D results (165° inclined cavity, High and Low Reynolds number models predictions) 3-D 2-D Parallel velocity profiles 2-D 3-D

11. LES Results (Smagorinsky subgrid model) 1. Inclined cavity at 165° Averaged parallel velocity profiles Averaged temperature profiles Iso-Q contours coloured by temperature Temperature Normal velocities

12. 2. Inclined cavity at 15° Averaged parallel velocity profiles Averaged normal velocity profiles Normal velocity Averaged temperature profiles Local Nusselt number Temperature

13. 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.