Download presentation

Presentation is loading. Please wait.

Published byLiam Fryer Modified about 1 year ago

1
Advanced Thermal Hydraulics Simulation – Part II David PointerCarlos Pantano - UIUC Jeff SmithHank Childs – LLNL Adrian Tentner Paul Fischer James Lottes Aleks Obabko Yulia Peet Andrew Siegel - Argonne National Laboratory

2
RANS-based simulations Commercial CFD codes STAR-CCM+ and STAR-CD –Finite volume solution of the well-known Reynolds-averaged form of the Navier-Stokes equations. 2 Second order solution using Face flux based differencing (COMET) SIMPLE predictor-corrector solution algorithm Algebraic multi-grid pre-conditioning two-equation realizable k-epsilon turbulence model

3
Allowed Mesh Types in Star-CCM+ Radial cross-sections showing computational mesh distributions in the 7-pin RANS simulations for: –(a) the block-structured mesh –(b) the trimmed cell mesh –(c) the generic polyhedral mesh. 3

4
Allowed Mesh Types in Star-CCM+ 4

5
5 LES vs RANS LES RANS Comparison of velocity magnitude distributions

6
Initial Comparison of LES / RANS Results RANS using Star CD k-eps Close comparison of results RANS can predict cross-flow velocities in the wire-wrap case. Pressure-drop comparisons underway. 6

7
Allowed Mesh Types in Star-CCM+ 7

8
19-pin simulations 8 Relative transverse velocity magnitude

9
Comparison of 3 Reynolds Numbers Re = 6000 Re=9000Re=50000 Relative transverse velocity magnitude

10
37-pin Simulations Relative transverse velocity magnitude 10

11
Predicted Dimensionless Pressure Loss Coefficient from RANS Simulations vs. Correlations The dimensionless pressure loss coefficient is the pressure drop normalized by the dynamic head, so that Cp=f (L/D). The Cheng & Todreas correlation assumes that there are three fundamental sub-channel types: interior, edge, and corner. Each of the three types of sub-channel frictional losses is calculated separately. The bundle friction factor is then averaged. The Rehme correlation is a simpler single equation formulation based on representative geometric parameters. 11 * Small 7-pin assemblies are not within the range of applicability of the correlations

12
217-pin heated assembly 12 Defined a “typical” SFR assembly for initial comparisons between CFD code predictions and SASSYS subchannel model predictions –217 pins 8 mm pin outer diameter Pitch-to-diameter ratio of 1.135 1.03 mm diameter wire wrap 20 cm wire wrap lead length –Assumed power distribution Uniform radial power Cosine axial distribution –Simplified boundary conditions Uniform inlet velocity – 5.8 m/s Constant pressure outlet Specified wall heat flux on pin surfaces Adiabatic can surface

13
Axially coarsened polyhedral meshes 13 Nominal Cell Aspect Ratio Number of CellsPredicted Pressure Loss (kPa) 1:16750724376.01 1:8976972277.00 1:41495552382.01 Changes in pressure loss are mostly form losses, coarsening of surface representation may be introducing artificial surface roughness.

14
Simplifed wire wrap representation Minimal effect on flow field –Slight increase in size of low velocity region on leeward side of wire 14 Transverse velocity magnitude (inlet velocity = 5.8 m/s)

15
217 pin SFR Assembly simulation Polyhedral mesh with simplified wire wrap representation 15 Coolant Temperature (K) Pin Surface Temperature (K)

16
217 pin SFR Assembly simulation Polyhedral mesh with simplified wire wrap representation 16

17
MAX – A Thermal Mixing Experiment LDRD funded project –Define validation requirements for high-fidelity methods –Evaluate/Demonstrate advanced measurement capabilities –Develop methods for analysis and correlation of large 4-D data sets 10-100 TeraBytes per test –Develop methods and visualization capabilities for validation comparisons of detailed 4-D data –Provide data set for initial validation of individual advanced methods and integrated multi-scale code systems

18
RANS Simulations of MAX Thermal Mixing Experiments The computational model of the nominal geometry was developed from a simplified CAD representation The surfaces defined by the CAD model are triangulated and used as the basis for a tetrahedral mesh. The tetrahedral are then collapsed into generic polyhedra to form the mesh shown. Boundary Conditions –uniform velocity, constant temperature inlets –constant pressure outlet –no-slip adiabatic walls Short turn-around time allows RANS to be applied to configuration studies

19
RANS Simulations of MAX Thermal Mixing Experiment Steady state simulations –default segregated flow solver –SIMPLE algorithm –Rhie- Chow interpolation for pressure- velocity coupling –algebraic multi-grid preconditioning. –1st-order upwind differencing scheme –secondary gradient terms in the diffusion step were retained –Realizable k-epsilon turbulence model with a two-layer all y+ wall treatment (Norris & Reynolds) RANS simulations predict the development of a very sharp interface between the two jets Complex mixing pattern in the surrounding fluid after the jets have impinged on the upper surface.

20
Extracted Data To facilitate quantitative comparisons of characteristic data from simulations of different design options, data was extracted along lines at the mid-plane of the “fish tank”

21
Extracted Data - Nominal Configuration

22
Mesh resolution studies 22

23
MAX Experiment for TH Validation 23 Initial RANS / LES Comparisons: –Average velocity distributions on two centerline cuts RANS: 2 million gridpoints, steady state LES: 23 million gridpoints, unsteady (still in initial transient) –TKE comparisons similar ABAB Pointer, Lomperski, Fischer., Validation of CFD Methods for Advanced SFR Design: Upper Plenum Thermal Striping and Stratification, ICONE17-75740, 2009 A B

24
Other Leveraged Efforts Very High Temperature Reactor Integrated Multi-Physics Simulation 2-Phase Boiling Model Coarse Finite Element Design Simulations 24

25
VHTR Single Fuel Block Column 25 Completed coupled CFD/Neutronics simluations of a single column of prismatic VHTR fuel blocks –DeCART 2D/1D MOC –STAR_CD Steady RANS CFD model uses 8.8 million computational cells –Polyhedral elements allow conformal meshing of solid and fluid components Improved numerical performance for conjugate heat transfer –Includes upper and lower plenum volumes –Flow splits between channels are simulated rather than specified

26
DeCART/STAR-CD Mesh Mapping 26 Initial mapping utility uses a simple approach in which DeCART zones are associated with all STAR-CD cells whose centroid falls within that zones. Global conservation is enforced within any single material across the entire domain

27
Coupled Full Block Model Results 27 Power distribution from DeCART, reflects temperature feedback from CFD Temperature feedback exaggerated, due to greatly increased F/M ratio for single block After CFD initialization, coupled simulation required 4.2 hours on 32 cores for 9 data exchanges T q’’’

28
2-Phase Boiling CFD Generation 1: Bubbly flow topology used in all cells. Spherical vapour bubbles surrounded by continuous liquid phase. Used for previous BFBT calculations Generation 2: Bubbly flow, Mist flow and Transition topologies Bubbly flow topology: Spherical bubbles surrounded by liquid Mist flow topology: Spherical droplets surrounded by vapor Preliminary liquid film models Used for current BFBT calculations Generation 3: Bubbly flow, Mist Flow, Transition, and Sharp Interface topologies Uses 2-D Topology Map based on α and α gradients Inter-phase surface transport planned Developed jointly by Adapco, Argonne, Sarov Labs, VNIIEF

29
BFBT BWR Benchmark Simulation Results Exit Quality = 25% Predicted void fraction Percent Error Predicted void fraction Percent Error Microscopic Distribution (CT Scan Data) Channel Averages (Calculated from CT Scan Data)

30
Coarse Finite Element Design Simulations Solve Convection- Diffusion Use imposed flow field based on RANS or LES simulations Relatively fast running –A few minutes on a few hundred nodes High-order numerical accuracy High-order spatial accuracy 30

31
Duplicate spacer grid design experiment methodology 31

32
Off-Set Injection Point Thermal Diffusion x 2 Thermal Diffusion x 4 No Spacers Effect of Nominal Spacer Effect of Spacer With 0.5 * H/D 32

33
CONCLUSIONS – RANS (and other) The applicability of commercial RANS-based CFD tools to hydrodynamic analysis of wire-wrapped sodium-cooled fast reactor fuel assemblies has been demonstrated RANS-based CFD predictions compare well with LES predictions Sensitivity of predictions to Reynolds number is low –Re=9000 is probably sufficient for hydrodynamic studies Broad axial and radial mesh density sensitivity studies have been completed for 7-, 37-, and 217-pin models Sensitivity of bulk predictions to mesh structure is low, but mesh structure impacts resolution of jet structures and recirculation regions on leeward side of pins. Completed initial benchmarking study comparing predicted pressure drop versus lumped parameter correlations and experimental data Completed study comparing predicted temperature distributions versus conventional sub-channel models based on lumped parameter correlations Demonstrated initial course mesh conduction diffusion solver 33

34
Current/Future Work – RANS (and other) Comparisons with legacy experimental data in progress –Turbulent diffusion in HEDL experiments 217 pin simulations with conjugate heat transfer in fuel, cladding and sodium bond gap Simulations of alternate spacer options –Engineered can walls –Reduced diameter edge row wire wrap Implementing thermophysical property functions for liquid sodium, clad and fuel. Beginning to extend simulations to ex-core regions Initiate development of standalone finite element subchannel simulation tool and finite element network flow solver 34

35
Questions? 35

36
Extra Slides 36

37
Central Injection Point Thermal Diffusion x 2 Thermal Diffusion x 4 No Spacers Effect of Nominal Spacer Effect of Spacer With 0.5 * H/D 37

38
Side Channel Injection Point Thermal Diffusion x 2 Thermal Diffusion x 4 No Spacers Effect of Nominal Spacer Effect of Spacer With 0.5 * H/D 38

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google