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exasmr.md
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exasmr.md
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---
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author: Steven Hamilton
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title: ExaSMR Project KPP-1 Verification
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---
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# Science Challenge Problem Description
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The ExaSMR challenge problem is the simulation of a representative NuScale SMR
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core by coupling continuous-energy Monte Carlo neutronics with CFD. Features
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of the problem include the following:
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* representative model of the complete in-vessel coolant loop,
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* hybrid LES/RANS turbulence model or RANS plus an LES-informed momentum
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source for treatment of mixing vanes, and
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* pin-resolved spatial fission power and reaction rate tallies.
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Details on the challenge problem specification are given in the table.
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The simulation objective is to calculate reactor start-up conditions that
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demonstrate the initiation of natural coolant flow circulation through the
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reactor core and primary heat exchanger. The driver application, ENRICO,
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performs inline coupling of the Nek5000 CFD module with Monte Carlo through a
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common API that supports two Monte Carlo modules: Shift, which targets the
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Frontier architecture at ORNL, and OpenMC, which targets the Aurora system at
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ANL.
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Minimum neutronics requirements for the coupled simulation are as
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follows:
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* full-core representative SMR model containing 37 assemblies with $17\times
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17$ pins per assembly and 264 fuel pins per assembly item,
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* depleted fuel compositions containing $O(150)$ nuclides per material,
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* $10^{10}$ neutrons per eigenvalue iteration,
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* pin-resolved reaction rate with a single radial tally region and 20 axial
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levels, and
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* six macroscopic nuclide-independent reaction rate tallies.
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Minimum CFD requirements for the coupled simulation are as follows:
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* assembly bundle mesh models with momentum sources from a resolved CFD
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calculation on a representative spacer grid and
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* full-core mesh $200\times 10^6$ elements and $70\times 10^9$ DOF.
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Table: Challenge problem details
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------------------------------------------------------------------------------------
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Functional requirement Minimum criteria
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------------------------- ---------------------------------------------------------
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Physical phenomena and Eigenvalue form of the linear
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associated models Boltzmann transport equation with quasistatic
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nuclide neutronics coupled to hybrid RANS/LES
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(or equivalent accuracy incompressible CFD with
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Boussinesq approximation, or low-Mach
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incompressible CFD with nonzero thermal divergence.
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Numerical approach, The neutronics solver is an MC particle transport;
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and associated models the CFD solver is a spectral finite element on
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unstructured grids. Physics are coupled by using a
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quasistatic approximation.
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Simulation details The neutronics model has a minimum of 200,000 tally
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bins and 10 billion particle histories per
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eigenvalue iteration. The CFD model has a minimum
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of 200 million elements and 70 billion DOF.
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Demonstration calculation Run 30 eigenvalue cycles (10 inactive and 20 active)
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requirements to estimate the MC particle tracking rate and
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1000 time steps toward steady-state
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convergence in the CFD solve. Only one nonlinear
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(Picard) iteration is required. To facilitate
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comparison with the baseline measurement, the
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neutronics portion of this calculation requires six
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macroscopic reaction rates and one radial region
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per pin.
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------------------------------------------------------------------------------------
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# Demonstration Calculation
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## Facility Requirements
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All workflow and runtime requirements use standard ECP and facility-supported
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libraries (e.g., Trilinos, HDF5).
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## Input description and Execution
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### Problem Inputs
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To run ExaSMR coupled problems through the ENRICO driver, the following inputs
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are required:
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* Monte Carlo driver input (XML)
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* Monte Carlo geometry input file (XML or HDF5)
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* Monte Carlo material compositions file (HDF5)
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* CFD parameter file (text)
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* CFD mesh file (binary)
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* CFD C++ user-defined functions (text)
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* CFD OCCA user-defined kernels (text)
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* CFD restart file (binary, optional)
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* ENRICO driver input (XML)
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* Batch submission script (text)
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For the KPP-1 measurement case, the following inputs are used:
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-------------------------------------------------------------------------
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Component Filename Description
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------------ ------------------------------- --------------------------
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Monte Carlo singlerod_short.inp.xml Driver input
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singlerod_short.rtk.xml Geometry input
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singlerod_short_compositions.h5 Material definitions
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CFD rod_short.udf User-defined C++ functions
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rod_short.oudf User-defined OCCA kernels
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rod_short.re2 Mesh file
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rod_short.par Solver parmeters
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Driver enrico.xml Coupled driver settings
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Submission submit.lsf Batch submission script
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-------------------------------------------------------------------------
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### Resource Requirements
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Estimated compute requirements for KPP-1 verification are two hours using the
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full Frontier or Aurora machines.
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## Problem Artifacts
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Standard artifacts for coupled simulations include:
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* Screen output of submitted jobs
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* HDF5 output from Monte Carlo solver
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* NekRS state field output files (native binary format)
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The NekRS files will be made available upon request; however, it is anticipated
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that these files will be very large (>1 TB). It is therefore not likely that
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the NekRS files will be useful for verification and we suggest that the other
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artifacts be used as the primary confirmation of execution.
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Because the run to be used for the FOM measurement will not be able to
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fully converge a coupled simulation, we cannot check the accuracy of computed
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results, but we can assess whether the simulation meets several sanity checks
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related to consistency of the numerical models and that computed quantities
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obey certain physical characteristics. These checks, all of which are printed
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to the screen output, include:
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1. Verify consistency of the problem geometries. The ENRICO driver displays
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diagnostics related to volumetric mapping from the CFD mesh to the MC geometry,
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ensuring that the sum of the volume of the thermal/fluids elements matches the
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volume of the corresponding MC cell containing those elements.
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Exact agreement is not expected, but we anticipate the average volumetric error
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to be less than 1% and the maximum error for any element to be under 10% (some
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small regions may be slightly off due to modeling differences related to the
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gap between the fuel and clad in fuel pins). This check ensures that the physics
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models are geometrically equivalent and correctly aligned in space.
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2. The fuel temperature should be nonuniform, i.e., the minimum and maximum
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temperatures should be different. The minimum temperature should be greater than
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the coolant inlet temperature of 531.15K and the maximum temperature should be
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above this value. In a converged simulation, the fuel temperature is expected
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to reach a maximum of between 1000K and 1200K. For the FOM measurement, the
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actual temperature rise is expected to be much smaller, but the temperature
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should not exceed 1300K.
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3. The coolant/fluid temperature should be nonuniform, i.e., the minimum and
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maximum temperatures should be different. The minimum temperature should be
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approximately equal to the coolant inlet temperature of 531.15K and the maximum
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temperature should be above this value. In a converged simulation, the coolant
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temperature is expected to reach a maximum of around 590-610K. For the FOM
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measurement, the actual temperature rise is expected to be much smaller,
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but the coolant temperature should not exceed 620K.
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4. The eigenvalue (k-eff) computed by the MC solver should be approximately
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equal to 1. The actual value will depend on the temperature and coolant
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density distribution, but it is expected that it will likely be between 0.95
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and 1.05.
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In addition, we will provide visualization artifacts in the form of 1D lineout
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and 2D slices of the temperature and heat generation rate at several locations
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in the problem. These artifacts can be examined to confirm that the simulations
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are behaving as expected. Because the KPP-1 verification simulation will not be
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fully converged, certain aspects of these visualizations will not necessarily
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satisfy all physical characteristics of a converged solution. For example,
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the small number of time steps executed in the CFD calculation may result in
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the fluid temperature not being monotonically increasing with respect to the
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axial height in the problem. Therefore, to aid in the evaluation of these
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visualization artifacts, we will provide a series of equivalent plots showing
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the progression towards convergence on a similar (but smaller) example problem.
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These plots are intended to provide a basis for evaluating whether the results
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of the KPP-1 verification simulation are showing the expected progress towards
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a converged solution, even though the results themselves will not be converged.
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KPP-1 FOM measurement data can be obtained by running provided `python` script
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to extract performance data from above artifacts:
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```
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>>> python process_fom.py case_name
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```
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This script will extract timing data for each of the physics solvers and evaluate
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the ExaSMR FOM metric. The MC transport FOM uses the follow information:
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* the number of particle histories per eigenvalue cycle,
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* the number of active cycles (the MC FOM is only taken over active cycles),
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* the total time spent in the active cycles.
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All of these quantities are contained in the HDF5 output file.
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The FOM calculation for the CFD solver uses the following information:
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* the number of degrees of freedom in the problem,
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* the number of time steps,
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* the time spent in the CFD solve.
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This information is contained in the ENRICO screen output and will be parsed by
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the provided python script.
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## Verification of KPP-1 Threshold
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*Give evidence that*
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1. *The FOM measurement met threshold ($>50$)*
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2. *The executed problem met challenge problem minimum criteria*
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@ -1,131 +0,0 @@
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#!/usr/bin/env python3
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import argparse
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from math import pi
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from pathlib import Path
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import numpy as np
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import openmc
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from tqdm import tqdm
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from smr.materials import materials, clone
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from smr.surfaces import lattice_pitch, bottom_fuel_stack, top_active_core, \
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pellet_OR, active_fuel_length
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from smr.core import core_geometry
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from smr import inlet_temperature
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# Define command-line options
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parser = argparse.ArgumentParser()
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parser.add_argument('--multipole', action='store_true',
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help='Use multipole cross sections')
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parser.add_argument('--no-multipole', dest='multipole', action='store_false',
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help='Do not use multipole cross sections')
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parser.add_argument('--clone', action='store_true',
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help='Clone materials for each cell instance')
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parser.add_argument('--no-clone', dest='clone', action='store_false',
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help='Do not clone materials for each cell instance')
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parser.add_argument('--depleted', action='store_true',
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help='Whether UO2 compositions should represent depleted fuel')
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parser.add_argument('--no-depleted', dest='depleted', action='store_false',
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help='Whether UO2 compositions should represent depleted fuel')
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parser.add_argument('-r', '--rings', type=int, default=1,
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help='Number of annular regions in fuel')
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parser.add_argument('-a', '--axial', type=int, default=20,
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help='Number of axial subdivisions in fuel')
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parser.add_argument('-o', '--output-dir', type=Path, default=None)
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parser.set_defaults(clone=True, multipole=True, depleted=True)
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args = parser.parse_args()
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# Make directory for inputs
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if args.output_dir is None:
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if args.depleted:
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directory = Path('core-fom-depleted')
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else:
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directory = Path('core-fom-fresh')
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else:
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directory = args.output_dir
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directory.mkdir(exist_ok=True)
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if args.rings > 1:
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ring_radii = np.sqrt(np.arange(1, args.rings)*pellet_OR**2 / args.rings)
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else:
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ring_radii = None
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geometry = core_geometry(ring_radii, args.axial, args.depleted)
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h = active_fuel_length / args.axial
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fuel_mats = {}
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# Count the number of instances for each cell and material
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if args.clone:
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geometry.determine_paths(instances_only=True)
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fuel_volume = pi * pellet_OR**2 * h / args.rings
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for cell in tqdm(geometry.get_all_cells().values(),
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desc='Differentiating materials / assigning volume'):
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if cell.fill in materials:
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# Determine if this material is fuel
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name = cell.fill.name
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is_fuel = 'UO2 Fuel' in name
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# Determine volume of each fuel material
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if is_fuel:
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if args.clone:
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# Fill cell with list of "differentiated" materials if requested
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cell.fill = [clone(cell.fill) for i in range(cell.num_instances)]
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for mat in cell.fill:
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mat.volume = fuel_volume
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else:
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r_o = cell.region.bounding_box[1][0]
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if (name, r_o) not in fuel_mats:
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cell.fill = cell.fill.clone()
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cell.fill.volume = fuel_volume
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fuel_mats[name, r_o] = cell.fill
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else:
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cell.fill = fuel_mats[name, r_o]
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else:
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cell.fill.volume = 1.0
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#### Create OpenMC "materials.xml" file
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all_materials = geometry.get_all_materials()
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materials = openmc.Materials(all_materials.values())
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materials.export_to_xml(directory)
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#### Create OpenMC "geometry.xml" file
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geometry.export_to_xml(directory)
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#### Create OpenMC "settings.xml" file
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# Construct uniform initial source distribution over fissionable zones
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lower_left = [-7.*lattice_pitch/2., -7.*lattice_pitch/2., bottom_fuel_stack]
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upper_right = [+7.*lattice_pitch/2., +7.*lattice_pitch/2., top_active_core]
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source = openmc.source.Source(space=openmc.stats.Box(lower_left, upper_right))
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source.space.only_fissionable = True
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settings = openmc.Settings()
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settings.batches = 200
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settings.inactive = 100
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settings.particles = 10000
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settings.output = {'tallies': False, 'summary': False}
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settings.source = source
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settings.sourcepoint = {'write': False}
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settings.temperature = {
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'default': inlet_temperature,
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'method': 'interpolation',
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'range': (300.0, 1500.0),
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}
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if args.multipole:
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settings.temperature['multipole'] = True
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settings.temperature['tolerance'] = 1000
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settings.export_to_xml(directory)
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tallies = openmc.Tallies()
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tally = openmc.Tally(name='depletion tally')
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fuel_mats = [mat for mat in materials if 'UO2 Fuel' in mat.name]
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tally.filters = [openmc.MaterialFilter(fuel_mats)]
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tally.scores = ['total', 'absorption', 'scatter', '(n,gamma)', 'fission', '(n,2n)']
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tallies.append(tally)
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tallies.export_to_xml(directory)
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