1. SHARP TH Simulation Effort Paul Fischer Mathematics and Computer Science Division Argonne National Laboratory J. Lottes, A. Siegel, S. Thomas, C. Verma Work sponsored by U.S. Department of Energy Office of Nuclear Energy, Science & Technology

2. SHARP TH MODELING 2 Outline Long term objectives / Overview 2007 Accomplishments: –Code Development Nek5000 Low-Dimensional Code –Simulations DNS LES RANS Low-Dimensional Models

3. SHARP TH MODELING 3 Long Term Objectives Exploit DOE’s Petascale computing facilities ( P > 100,000 processors) and state of the art simulation tools to improve TH predictive capabilities at the design level –temperature distributions, under a broad range of loading conditions –pressure drops and flow resistance through the system Provide validated predictive capabilities based on a fidelity hierarchy: –DNS  LES  RANS  low-dimensional modeling –enable investigation of new designs (e.g., outside validated range of current codes) Coupled simulation capability: –spanning a range of scales, –integrated with other physics (e.g., neutronics, structural mechanics, …) –integrated with other codes Allow simultaneous coupling of say, LES in some areas + low- dimensional models elsewhere + neutronics Ultimately, simulate full reactor

4. SHARP TH MODELING 4 Petascale Computing at DOE Argonne: –100 Tflops IBM BG/P Nov. 07 32,000 processors, 850 MHz –500 Tflops IBM BG/PAug. 08 140,000 processors, 850 MHz Oak Ridge –100 Tflops Cray XT4Now 23,000 processors, 2.6 GHz –1 Petaflops Cray XT4Late 08 200,000 processors, 2.6 GHz It’s time to be thinknig about Exaflops

5. SHARP TH MODELING 5 Overview, SHARP Thermal-Hydraulics Plan Develop design & analysis capabilities that span desktop  Petaflop: “Design” – rapid turn-around; reactor scale “Analysis” – detailed simulations providing information previously accessible only through experiment. –Input to design codes –Understanding of basic phenomena (e.g., thermal striping) –Design validation: Large scale multiphysics simulations at reactor scale (out years, PFLOPS) Reduce # of experiments, not replace.

6. SHARP TH MODELING 6 Targeted Range of Simulation Capabilities Target PlatformModel DesktopSubchannel Modeling Conservative low-resolution DG codes RANS LES PetaflopsDNS

7. SHARP TH MODELING 7 Targeted Range of Simulation Capabilities Target PlatformModelCurrent Capabilities / Efforts DesktopSubchannelSAS (T. Fanning) Modeling Conservative Starting w/ Nek (S. Thomas) low-resolution DG codes RANS Star CD (D. Pointer) LESNek (F., D. Sheeler,A. Siegel) PetaflopsDNSPrism (C. Pantano-UIUC)

8. SHARP TH MODELING 8 Approaches to TH analysis of subassemblies DNS – direct numerical simulation of all scales parameter-free LES – large eddy simulation + dissipation parameter-free RANS – Reynolds-averaged Navier-Stokes tuning required Subchannel modeling empirical input 400 x 200 subchannels in the core: –Subchannel analysis will continue to be used for reactor design. –RANS will inform design process. –LES can help to validate / inform RANS and subchannel analysis. impractical 10 7 p. per channel 10 5 p. per channel – steady state 100 p. per channel – steady state

9. SHARP TH MODELING 9 Current TH Capabilities within ANL SHARP team: Nek5000 – ANL code for fluids / heat transfer(Fischer, Lottes, Thomas) –High-order accuracy –Scales to P > 10,000 processors –State of the art multilevel solvers –2 decades of development / verification / validation –Supports conjugate heat transfer, variable properties, MHD, ALE, URANS Extensive reactor TH experience:(Fanning, Pointer, Yang) –RANS modeling – Star CD –Subchannel codes (SAS)

10. SHARP TH MODELING 10 Validation: Nek5000 Computations Rod bundle flow at Re=30,000 w/ C. Tzanos (ANL) Low-speed streaks in a rod bundle: Log-law profiles: N = 9N = 11N = 15 y+y+ u+u+

11. SHARP TH MODELING 11 Rod Bundle Validation: Nek5000 Comparison w/ Experimental Data (F. & Tzanos, 05)

12. SHARP TH MODELING 12 Outline Long term objectives / Overview 2007 Accomplishments: –Code Development Nek5000 Low-Dimensional Code –Simulations DNS LES RANS Low-Dimensional Models

13. SHARP TH MODELING 13 Code Development Efforts 07 Nek5000: –Improved parallel coarse-grid solver for multigrid solution of pressure work in progress; low-memory – but not scaling as expected –Working with European collaborators on low-Mach number formulation for non-Boussinesq thermal expansion effects –New mesh reading capabilities for large element counts and non-native mesh generators –Coupled to VisIt (D. Bremer, LLNL) Low-Dimensional Modeling –Surrogate mass-conserving velocity fields derived from LES/RANS used for thermal transport in larger systems (i.e., full-length fuel assemblies) –Developing a conservative super-parametric formulation that will be volume preserving (non-faceted geometries) with few degrees-of-freedom

14. SHARP TH MODELING 14 Simulations 07 First Simulation Study: wire-wrapped fuel pins –DNS –LES –RANS –Low-Dimensional Models

15. SHARP TH MODELING 15 First TH Study: analysis of wire wrapped pins in subassembly Starting point for TH simulation development and deployment: –Uniformity of temperature controls peak power output –A better understanding of flow distribution (interior, edge, corner) can lead to improved subchannel models. –Wire wrap geometry is relatively complex

16. SHARP TH MODELING 16 Objectives for LES / RANS Potential surrogate for “benchtop” experiments Provide geometry-specific input to subchannel codes Consider sequence of 7, 19, …, 217 pins to provide a detailed picture of the hydrodynamics and heat transfer in a single assembly. From Bogoslovskaya et al.

17. SHARP TH MODELING 17 Approaches to TH analysis of subassemblies DNS – direct numerical simulation of all scales parameter-free LES – large eddy simulation + dissipation parameter-free RANS – Reynolds-averaged Navier-Stokes tuning required Subchannel modeling empirical input 400 x 200 subchannels in the core: –Subchannel analysis will continue to be used for reactor design. –RANS will inform design process. –LES can help to validate / inform RANS and subchannel analysis. impractical 10 7 p. per channel 10 5 p. per channel – steady state 100 p. per channel – steady state

18. SHARP TH MODELING 18 Direct Simulation of Wire in Turbulent Channel with Crossflow Carlos Pantano UIUC Channel-wire flow model Model turbulent flow around wires in reactor core Target large DNS with accurate spatio-temporal resolution Derive turbulence statistics for validation of RANS/LES models Preliminary results (spectral element code) Domain size: Lx=4 , Ly= 2, Lz=2  15 th order polynomial, 52 elements in x-y plane, 64 Fourier modes (750K grid points) Bulk Reynolds numbers: Re x =500 and Re z =1200 (  = 67 o ) Friction Reynolds numbers: 42 and 86 (core flow region)

19. SHARP TH MODELING 19 Flow visualization Presence of spiral recirculation bubbles ( isocontours of mean spanwise velocity and streamlines of transverse velocity ) Vorticity magnitude ( strong near walls and shear layer shed from the wire ) Average streamline visualizations

20. SHARP TH MODELING 20 Turbulence statistics Mean velocity components Mean Velocity Components Normal Reynolds stresses Kolmogorov scale in false color logarithmic scale (dark regions denote smaller  not fully converged statistics)

21. SHARP TH MODELING 21 LES of Single and 7 Pin Wire Wrap – Nek5000 Single Pin: –Mimics infinite array (no assembly walls) –Cheap, first case for exploratory convergence studies, etc. 7-Pin: –Geometry is current ARR design P/D = 1.135 H/D = 17.74 (2/3 of current ARR design)

22. SHARP TH MODELING 22 Relationship to Inflow / Outflow Configuration Flow establishes a fully turbulent state within ~ 1 flow-through time  spatial development length ~ H/D To be checked by multi-pitch inflow / outflow simulations k z = 50 k z = 200

23. SHARP TH MODELING 23 Cross-Sectional Velocity Distributions Flow tends to follow in the wake of the wire Near the contact point, the flow separates and forms a strong standing vortex in the assembly cross section, as also reported in RANS computations of Ahmad & Kim

24. SHARP TH MODELING 24 Subchannel Interchange Velocities Interchange velocity distributions left: instantaneous right: time-averaged flow

25. SHARP TH MODELING 25 Subchannel Interchange Velocities Close fit to sinusoid, with amplitudes: –H / D = 13.4:a ~ 0.290 U z –H / D = 20.1:a ~ 0.225 U z –H / D = 26.8:a ~ 0.150 U z Amplitude higher than predicted by geometric factors alone flow H/D = 26.8 20.1 13.4

26. SHARP TH MODELING 26 7 Pin Simulatons: E=132,000, N = 7 n v ~ 44 M n p ~ 28 M n iter ~ 30 / step

27. SHARP TH MODELING 27 7 Pin Visualization Time-averaged axial (top) and transverse (bottom) velocity distributions. A A A A Snapshot of axial velocity

28. SHARP TH MODELING 28 Subchannel Interchange Velocities – 7-Pin, with Sidewalls Inter-channel exchange is no longer a simple sinusoid Edge channels have non-zero mean  swirling flow 7-Pin Distributions, H/D = 17.7 D-D C-C A-A B-B A A B B C C D D

29. SHARP TH MODELING 29 Subchannel Interchange Velocities – 7-Pin, with Sidewalls Inter-channel exchange is no longer a simple sinusoid Edge channels have non-zero mean  swirling flow H/D = 26.8 20.1 13.4 Single- (Infinite-) Pin Distributions 7-Pin Distributions, H/D = 17.7 D-D C-C A-A B-B H/D = 17.7

30. SHARP TH MODELING 30 7-Pin RANS Using Star CD D. Pointer (ANL)

31. SHARP TH MODELING 31 Fine Polyhedral Mesh ~2.5 million cells Based on fine triangulated surface Surface extrusion layer not used in current cases to allow use of high Re and two-layer k-epsilon turbulence models. Will be used with low Re models. Generated from fine triangulated surface using Star-CCM+ meshing tools

32. SHARP TH MODELING 32 Coarse Polyhedral Mesh ~1 million cells Based on coarse triangulated surface Surface extrusion layer not used in current cases to allow use of high Re and two-layer k-epsilon turbulence models. Will be used with low Re models. Generated from coarse triangulated surface using Star- CCM+ meshing tools

33. SHARP TH MODELING 33 Fine Polyhedral Mesh Results Re=15000 (V mean = 1, D pin =1) H/D = 26.6

34. SHARP TH MODELING 34 Coarse Polyhedral Mesh Results Re=15000 (V mean = 1, D pin =1) H/D = 26.6

35. SHARP TH MODELING 35 LES / RANS Comparison Same basic features Significant scaling discrepancies (1.5 x due to different H/D, rest tbd) Star CD RANS Model (note scale difference ) 7-Pin Distributions, H/D = 17.7 D-D C-C A-A B-B H/D = 17.7 H/D = 26.6

36. SHARP TH MODELING 36 Low-Dimensional Representations A step towards subchannel modeling –allows full-core simulations –less geometric detail (no wire) Wire-induced transport compensated by interchannel exchange velocities –currently generated by helical forcing –future: projection onto LES/RANS results Intra-channel mixing – enhanced diffusion Allows rapid turn-around of coupled multi-physics simulations Some issues: –How to smear wire-wrap volume into reduced geometry? Increased clad thickness? Maintain cross-sectional area? Other…

37. SHARP TH MODELING 37 Low-Dimensional Models, Full Length Subassemblies Effects of interchannel mixing with –no-wire vs. wire-wrap –pin conductivity –thermal loading –large pin counts Sacrifices detailed intra- channel mixing Surrogate velocity field generated by spiral forcing to match effect of wire-wrap Desktop (or small cluster)

38. SHARP TH MODELING 38 Conclusions Software Development –Advances to Nek5000 to incorporate additional physics, low-resolution conservative formulations underway –Pushing the envelope on problem size and processor count –Continually comparing with commercial and other codes as reality check Simulations –First 7-pin LES study is near completion –RANS & LES comparison underway –19-pin simulations within the next few weeks (EDF) –Low-resolution TH w/ 7 pins ready to couple with UNIC –Low-resolution 217-pin simulation nearly ready