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SHARP TH Simulation Effort

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. Outline. Long term objectives / Overview

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SHARP TH Simulation Effort

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  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. Outline • Long term objectives / Overview • 2007 Accomplishments: • Code Development • Nek5000 • Low-Dimensional Code • Simulations • DNS • LES • RANS • Low-Dimensional Models

  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. Petascale Computing at DOE • Argonne: • 100 Tflops IBM BG/P Nov. 07 • 32,000 processors, 850 MHz • 500 Tflops IBM BG/P Aug. 08 • 140,000 processors, 850 MHz • Oak Ridge • 100 Tflops Cray XT4 Now • 23,000 processors, 2.6 GHz • 1 Petaflops Cray XT4 Late 08 • 200,000 processors, 2.6 GHz • It’s time to be thinknig about Exaflops

  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. Targeted Range of Simulation Capabilities Target Platform Model Desktop Subchannel Modeling Conservative low-resolution DG codes RANS LES Petaflops DNS

  7. Targeted Range of Simulation Capabilities Target Platform Model Current Capabilities / Efforts Desktop Subchannel SAS (T. Fanning) Modeling Conservative Starting w/ Nek (S. Thomas) low-resolution DG codes RANS Star CD (D. Pointer) LES Nek (F., D. Sheeler,A. Siegel) Petaflops DNS Prism (C. Pantano-UIUC)

  8. Approaches to TH analysis of subassemblies impractical 107 p. per channel 105 p. per channel – steady state 100 p. per channel – steady state • 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.

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

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

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

  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. Simulations 07 • First Simulation Study: wire-wrapped fuel pins • DNS • LES • RANS • Low-Dimensional Models

  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. Objectives for LES / RANS From Bogoslovskaya et al. • 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.

  17. Approaches to TH analysis of subassemblies impractical 107 p. per channel 105 p. per channel – steady state 100 p. per channel – steady state • 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.

  18. Direct Simulation of Wire in Turbulent Channel with CrossflowCarlos 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  • 15th order polynomial, 52 elements in x-y plane, 64 Fourier modes (750K grid points) • Bulk Reynolds numbers: Rex=500 and Rez=1200 ( = 67o) • Friction Reynolds numbers: 42 and 86 (core flow region)

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

  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. 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. kz = 50 kz = 200 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

  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. flow Subchannel Interchange Velocities Interchange velocity distributions left: instantaneous right: time-averaged

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

  26. 7 Pin Simulatons: E=132,000, N = 7 nv ~ 44 M np ~ 28 M niter ~ 30 / step

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

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

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

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

  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. 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. Fine Polyhedral Mesh Results • Re=15000 (Vmean= 1, Dpin=1) • H/D = 26.6

  34. Coarse Polyhedral Mesh Results • Re=15000 (Vmean= 1, Dpin=1) • H/D = 26.6

  35. 7-Pin Distributions, H/D = 17.7 C-C D-D B-B A-A 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) H/D = 26.6 H/D = 17.7

  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. 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. 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

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