SHARP TH Simulation Effort

Paul Fischer Mathematics and Computer Science DivisionArgonne National Laboratory

J. Lottes, A. Siegel, S. Thomas, C. Verma

Work sponsored by U.S. Department of Energy Office of Nuclear Energy, Science & Technology

SHARP TH MODELING

Outline

Long term objectives / Overview

2007 Accomplishments:

– Code Development• Nek5000• Low-Dimensional Code

– Simulations• DNS• LES• RANS• Low-Dimensional Models

SHARP TH MODELING

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

SHARP TH MODELING

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

SHARP TH MODELING

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.

SHARP TH MODELING

Targeted Range of Simulation Capabilities

Target Platform Model

Desktop SubchannelModeling

Conservative low-resolutionDG codes

RANS

LES

Petaflops DNS

SHARP TH MODELING

Targeted Range of Simulation Capabilities

Target Platform Model Current Capabilities / Efforts

Desktop Subchannel SAS (T. Fanning)

Modeling

Conservative Starting w/ Nek (S. Thomas)

low-resolutionDG codes

RANS Star CD (D. Pointer)

LES Nek (F., D. Sheeler,A. Siegel)

Petaflops DNS Prism (C. Pantano-UIUC)

SHARP TH MODELING

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

107 p. per channel

105 p. per channel – steady state

100 p. per channel – steady state

SHARP TH MODELING

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)

SHARP TH MODELING

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 = 9 N = 11 N = 15

y+

u+

SHARP TH MODELING

Rod Bundle Validation: Nek5000 Comparison w/ Experimental Data

(F. & Tzanos, 05)

SHARP TH MODELING

Outline

Long term objectives / Overview

2007 Accomplishments:

– Code Development• Nek5000• Low-Dimensional Code

– Simulations• DNS• LES• RANS• Low-Dimensional Models

SHARP TH MODELING

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

SHARP TH MODELING

Simulations 07

First Simulation Study: wire-wrapped fuel pins

– DNS

– LES

– RANS

– Low-Dimensional Models

SHARP TH MODELING

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

H

Fuel Pinand Wire

CornerSubchannel

EdgeSubchannel

InteriorSubchannel

Duct Wall

Fuel Pin D

P

Wire Wrap

SHARP TH MODELING

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.

SHARP TH MODELING

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

107 p. per channel

105 p. per channel – steady state

100 p. per channel – steady state

SHARP TH MODELING

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 •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)

SHARP TH MODELING

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

SHARP TH MODELING

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)

SHARP TH MODELING

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)

SHARP TH MODELING

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

kz = 50

kz = 200

SHARP TH MODELING

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

SHARP TH MODELING

Subchannel Interchange Velocities

Interchange velocity distributions

left: instantaneous

right: time-averaged

flow

SHARP TH MODELING

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

flow

H/D = 26.8

20.1

13.4

SHARP TH MODELING

7 Pin Simulatons:

E=132,000, N = 7

nv ~ 44 M

np ~ 28 M

niter ~ 30 / step

SHARP TH MODELING

7 Pin Visualization

Time-averaged axial (top) and transverse (bottom) velocity distributions.

A A

A A

Snapshot of axial velocity

SHARP TH MODELING

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

SHARP TH MODELING

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 Distributions7-Pin Distributions, H/D = 17.7

D-D

C-C

A-A

B-B

H/D = 17.7

SHARP TH MODELING

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

SHARP TH MODELING

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

SHARP TH MODELING

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

SHARP TH MODELING

Fine Polyhedral Mesh Results

Re=15000 (Vmean= 1, Dpin=1)

H/D = 26.6

SHARP TH MODELING

Coarse Polyhedral Mesh Results

Re=15000 (Vmean= 1, Dpin=1)

H/D = 26.6

SHARP TH MODELING

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

SHARP TH MODELING

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…

SHARP TH MODELING

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)

SHARP TH MODELING

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