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Staff Slides - Advanced Reactor Stakeholder Meeting 12152022
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Advanced Reactor Stakeholder Public Meeting December 15, 2022 Microsoft Teams Meeting Bridgeline: 301-576-2978 Conference ID: 115 537 606#

Time Agenda Speaker 10:00 am - 10:10 am Opening Remarks / Adv. Rx Integrated Schedule NRC 10:10 am - 12:00 am Graphite Component Modeling NRC 12:00 pm -1:00 pm Lunch Break All 1:00 pm - 2:00 pm MACCS Reports NRC / Sandia 2:00 pm - 2:30 pm Operator Cold License Training for Advanced Reactors NEI 2:30 pm - 3:15 pm NuScale DC Review Lessons Learned NRC 3:15 pm - 3:25 pm Break 3:25 pm - 3:55 pm Workshops on Licensing Review Framework for Advanced Reactors Instrumentation and Controls NRC 3:55 pm - 4:00 pm Future Meeting Planning and Concluding Remarks NRC 2

Advanced Reactor Integrated Schedule of Activities The updated Advanced Reactor Integrated Schedule is publicly available on NRC Advanced Reactors website at:

https://www.nrc.gov/reactors/new-reactors/advanced/integrated-review-schedule.html 3

Advanced Reactor Integrated Schedule of Activities https://www.nrc.gov/reactors/new-reactors/advanced/integrated-review-schedule.html 4

Graphite Aging and Degradation Tool Christopher Ulmer and Joseph Bass RES/DE/REB Advanced Reactor Stakeholder Meeting December 15, 2022 5

Outline

Background on graphite aging and degradation tool development

Graphite properties and behavior overview

Graphite stress and oxidation modeling in MOOSE

ASME assessment review and implementation 6

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Background===

NRC staff are developing the regulatory framework and technical expertise to be able to regulate advanced non-light water reactors (ANLWRs)

Specifically, NRC staff are interested in developing expertise and tools to model graphite behavior in ANLWRs

Nonmetallic graphite and ceramic composite components for nuclear applications were added to the ASME Boiler Pressure and Vessel Code (BPVC) 7

Project Overview

INL provided training to NRC staff on graphite degradation, aging, and failure mechanisms as applied to the use of graphite in ANLWRs

INL developed a MOOSE-based tool that implements a graphite reliability model capable of performing component reliability and probability of failure (POF) analysis based on ASME BPVC III.5 graphite subsection HH subpart A requirements ML22346A082 8

Graphite for ANLWRs X-energy Xe-100 Terrestrial Energy IMSR

Graphite materials may be used in thermal spectrum advanced reactors

Graphite has excellent neutron moderation properties

Graphite is also used for core support components Examples:

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ASME Code

Graphite assessment methodology was added to ASME BPVC Section III Division 5 in subsection HH subpart A

Probability of failure in a graphite component is based on the inherent strength of a graphite grade and the applied stresses during operation

Considerations include oxidation and irradiation

NRC staff issued draft regulatory guide DG-1380 which endorses, with conditions, ASME BPVC III.5 10

Material Properties Material isotropy is processing dependent High thermal stability > 3000°C High heat capacity (thermal sink)

High thermal conductivity (better than metal)

Density: 15% - 20% porosity Purified graphite: Low activation Molten salt interaction is an area of current research Neutron moderator (thermal designs)

Easy machinability / cheap material High compressive / Low tensile strength Ceramic like material response Low fracture toughness (~ 1-2 MPa m)

Quasi-brittle cracking Property Nominal Range Density 1.7 - 1.9 g/cm3 Thermal Conductivity (at Room Temperature)

> 90 W/m/K Purity (Total Ash Content)

< 300 ppm Tensile Strength

> 15 MPa Compressive Strength

> 45 MPa Flexural Strength

> 20 MPa CTE (20°C to 500°C) 3.5 to 5.5 x 10-6 K-1 CTE Isotropy Ratio

< 1.10 Dynamic Elastic Modulus 8 - 15 GPa From ASTM D7219 11

Graphite Microstructure

Graphite microstructure has three phases: filler particles, binder phase, and pores (~20%)

Pores and pore structure play an important role in graphite irradiation behavior

Oxygen can penetrate the interior of the graphite pore microstructure 12

Irradiation Behavior

Dimensional change is a life-limiting behavior

Turnaround is a key parameter

Affected by microstructure and temperature

Internal stress build-up from dimensional changes

Less predictable behavior and (micro)cracking after turnaround

Irradiation creep can relieve internal stresses 13

Irradiation Effects on Graphite Properties Dimensional change Turnaround dose is key parameter Highly temperature dependent Density Graphite gets denser with irradiation until turnaround and then density decreases Formation of microcracks (molten salt consideration)

Strength and modulus Strength increases until turnaround dose and then decreases Coefficient of thermal expansion Initial increase but then decreases before turnaround Thermal conductivity Decreases quickly to ~30% of unirradiated values Oxidation rate Increases approximately 2-3 times over unirradiated rates Dimensional Change Strength & Modulus CTE Thermal Diffusivity 14

Graphite Oxidation

Oxidation is a complex relationship between reactivity and diffusion of oxygen

Reactive surface area is related to pore structure

Oxidation kinetics and gas diffusion are affected by temperature

It is important to know the strength remaining after oxidation

Temperature and microstructure affect oxidation rate and the extent of component internal oxidation 15

Graphite Modeling 16

Graphite Modeling Outline

Summary of modeling goals

- What can the model do?

- What cant the model do?

Modeling in MOOSE

- What is MOOSE?

- Introduction to using MOOSE

Modeling stresses in graphite

- Included physics, model formulation, and example problem

Modeling degradation (oxidation) in graphite

- Included physics, model formulation, and example problem

Applying the ASME assessments for graphite components 17 17

Summary of Modeling Goals The primary modeling goal is to provide a tool to help in the assessment of graphite components.

How can we assess a component?

Section III Division 5, subsection HH subpart A, of the ASME BPVC provides guidance on assessing a graphite component.

Two topics which this tool addresses (which are required/discussed in the ASME Code) are:

1)

Computing a stress distribution in the graphite component Accounting for thermal and irradiation effects in the stress calculation 2)

An analysis of the oxidation degradation Computing temperature-dependent density profiles.

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Model Limitations What can't the model do?

Material properties as a function of the states (temp, dose, mass loss) must be known.

The combined effect of oxidation and irradiation has not been well explored, so well characterized properties do not currently exist.

Post-turnaround properties have increased scatter and much less data. Consequently, the model should only be used up to turn around.

All grades of graphite behave differently, so graphite specific parameterizations are necessary.

1. Not all loads from the ASME Code can be directly implemented in the model.

Excludes: Fatigue, erosion, earthquake.

2. This is model is not a neutronics model. The evolution of the dose in the graphite must be determined separately.

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What is MOOSE?

20 Overview:

MOOSE (Multi-physics Object Oriented Simulation Environment) is an open-source, parallel finite element framework which has been under constant development at INL since 2008.

Purpose:

Designed to solve computational engineering problems and reduce the expense and time required to develop new applications.

Modules:

MOOSE contains modules which have been developed to simulate a variety of different physics and modeling methodologies including chemical reactions, contact, electromagnetics, heat conduction, phase-field, porous flow, tensor mechanics, XFEM and many others.

Image form: mooseframework.inl.gov MOOSE is the framework where the graphite tool was developed.

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MOOSE Organization 21 Applications which are built on the MOOSE framework are often given an animal name. Not all animals are open source, although anyone can request access.

The general organization of MOOSE including the physics modules and the more prominent animal is show in the figure below.

Image from MOOSE training slides https://mooseframework.inl.gov/workshop/index.html#/3/7 The graphite modeling capabilities discussed in this presentation are available in Grizzly. Everyone in the NRC has access to Grizzly through the Linux RESGC instances. External users must go through INLs Nuclear Computational Resource Center.

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Graphite Assessment Overview The flow chart on the right shows the various required pieces of performing a design by analysis assessment as outlined in the ASME code.

There are four distinct block types in the flow diagram

1. Required experimental data (brown blocks)

2. Parameters which are dependent on the design of the reactor (gray blocks)

3. Physics based models (green blocks)

4. ASME analysis methodology (purple block) 22 22

Considerations when Modeling Stresses in Graphite Oxidation effects Irradiation effects Properties:

As-manufactured properties graphite properties change as a function of the environment (temperature, oxidation, irradiation). This is exemplified in the plots on the right.

Eigenstrains:

The coefficient of thermal expansion is affected by dose as well as mass loss from oxidation.

Irradiation induced swelling is a function of dose as well as irradiation temperature.

Property Scatter:

Experimentally measured properties have more scatter post turn-around.

Scatter in graphite strength has led to probabilistic failure assessment methodologies 23 23

Thermo-mechanical Model: Overview (1/2)

Which type of problem is this model intended to solve?

This portion of the model computes stress and temperature distribution at the engineering scale.

Included Physics:

1.

Eigen strains generated from temperature and irradiation 2.

Irradiation creep 3.

Elastic strain 4.

Heat Transfer (thermal conduction) 5.

Material properties vary as a function of the states (temperature, dose, mass loss)

Temperature Dose Stress 24 24

Thermo-mechanical Model: Overview (2/2)

Model Inputs and Outputs:

1.

Material properties parameterized as a function of dose, temperature and mass loss.

2.

Dose profile evolution (dose and dose time derivative) 3.

Boundary conditions for variables.

Model Limitations 1.

The model is parametrized for IG-110. To use the model for other grades a new parameterization should be implemented.

2.

The model should only be used where experimental data exists (no combined irritation and oxidation, and the parameterization is not applicable past turn around)

Elastic Modulus CTE Irradiation Strain Thermal Conductivity 25 25

Thermo-mechanical Example Problem Geometry Temperature Profile Dose Profile Rate Problem Setup:

Assume we have the component geometry, temperature distribution, and dose evolution profile shown below:

The output of interest from the thermo-mechanical model is the stress distribution. The stress distribution is used in the structural assessment in the ASME Code.

The state variables in the thermo-mechanical model are strain, temperature, and dose. The model accounts for strain contributions from thermal, irradiation, and mechanical loads. In order to run an example problem, we must define the initial states in the system.

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Simulation Results The resultant stress distribution from the input temperature profiles, and irradiation effects are shown below. This results match well with previous studies which have simulated this problem.

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Dose Profile Temperature Profile Stress Distribution 27 All variables and auxvariables can be output (including the outputs relevant the ASME code assessments) 27

Why is oxidation modeling important?

1. The strength and other properties of oxidized graphite are a function of mass loss.

2. The ASME requires that the effect of oxidation is accounted for in the analysis of a graphite component.

What does the oxidation model do?

1. The model computes mass loss (density profiles) in a graphite component.

2. Computes temperature change caused by heat generation from graphite - oxygen reaction.

3. The oxidation model can be coupled to thermo-mechanical model (stress calculations).

Local material properties need to be computed as a function of local mass loss.

Oxidation Model Overview: Motivation for Model 28 28

Graphite oxidation is a multiscale phenomena, but for analysis, we are most interested in macroscale effects.

What physics is needed to model macroscale effects?

1.

Oxidant diffusion through the graphite

Controlled by graphite microstructure

Is a combination of Knudsen and bulk contributions

Evolves with the microstructure as oxidation increases This effect is modeled with a mass loss dependent effective diffusion coefficient (Deff) 2.

Reaction kinetics of the graphite

Is a function of the graphite crystallites (ASA cites density)

Will evolve with mass loss This effect is modeled with a mass loss dependent parameters which is proportional to the ASA (SA).

3.

Heat generation from graphite-oxygen reaction

Thermal conductivity evolves with mass loss

Reaction products are temperature dependent Oxidation Model: Background 29 Graphite basal plane showing the carbon atoms available for oxidation (zig-zag and arm-chair.)

Walker Diagram 29

There are three primary assumptions used in the development of the oxidation model:

Oxidation Model: Assumptions 30 Assumption Effects of Assumption At the atomistic scale, the reaction between graphite and the oxidant does not differ across grades.

We can conclude that the observed oxidation rate is a function of the microstructure and can be described by parameters which are a function of the microstructure.

Oxidation occurs on the pore wall in the open porosity.

The observed oxidation rate is controlled by pore surface area density (typically proportional to active surface area) and oxidation diffusion through the pores.

Impurities in the graphite do not have an appreciable effect.

The model will not account for rate effects which can be caused by impurities.

Note: Oxidation experiments on graphite powder were performed by Josh Kane in order to derive a generic oxygen-graphite reaction rate.

It is assumed that this derived reaction rate will be applicable to any graphite grade because the powdered graphite does not contain microstructural effects which would be present in a larger graphite specimen.

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Oxidation Model: Formulation The State variables in the oxidation model are the temperature, chemical species concentrations, and graphite density.

RSA evolution with mass loss Experimental testing is required to parameterize the model Gas diffusivity experimental testing Reactive surface area experimental testing State variable evolution:

The species concentrations and graphite density evolve through gas diffusion and chemical reactions.

The temperature evolves through heat transfer and the exothermic reaction between graphite and oxygen.

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The reaction of oxygen and graphite is exothermic which may affect temperature which in turn may affect the oxidation rate.

Several things need to be known to model the temperature effects of oxidation:

1. Thermal conductivity as a function of mass loss and temperature (experimental input)

2. Amount of heat generated

Reaction products

Reaction rate Modeling Oxidation Heating Generation 32 IG-110 Thermal Conductivity NBG-18 Thermal Conductivity Effect of heat generation on density profiles in oxidized cylinder 32

This example problem investigates the temperature-dependent density profiles generated in a graphite cylinder (2-inch length, L, and 1-inch diameter, D).

Oxidation Example Problem: Introduction 33 Problem model setup:

Problem run to 10% mass loss in IG-110 BCs:

1) Air is on the outside of the cylinder

2) Temperature of 564, 645, and 744 °C Wanted Result:

Density Profile Problem Geometry:

Air Air Air Air Symmetry R

Z D/2 L/2 In cylindrical coordinates Air Air 33

Example Problem: Results 34 To create the input file for this problem we must:

1. Generate a mesh

2. Specify boundary conditions (species concentrations and temperature)

3. Select a graphite grade (IG-110 or NBG-18)

4. Set a simulation run time The plot on the right shows the resultant density profiles at three temperatures each of which are at 10% mass loss. The main takeaways from this plot are:

1. The slope of the density profile increases with an increase in temperature

2. This is the experimentally observed temperature-dependent density profile behavior 34

ASME Assessments for Graphite Three methods are provided for assessing structural integrity

1. Simple Assessment: Allowable stress The computed peak equivalent stress is compared to an allowable value derived from Weibull theory and an allowable probability of failure (POF)

Conservative method based on Weibull derived ultimate strength

2. Full Assessment Method: Allowable Probability of Failure Weibull statistics are used to predict failure probability over the stress distribution in a component. It gives a full statistical analysis of the entire stress distribution through the component volume.

Typically, less conservative than the simplified assessment

3. Qualification by Testing Full-scale testing to demonstrate that failure probabilities meet all criteria of full-analysis method.

Dr. Mark Mitchell - PBMR Inc.

The ASME Code provides guidance on what phenomena should be considered in an analysis of a graphite component subjected to a high temperature nuclear environment.

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Applying the ASME Code A python script has been written which uses the outputs from the thermo-mechanical model and applies the simplified or full assessment. The python code and user manual on available upon request.

Simplified Assessment Inputs:

1) 2 parameter Weibull distribution of tensile strength 2)

Compressive strength 3)

Structural Reliability Class (SRC), which determined the allowable POF.

4)

Principal stresses computed from FEA Outputs:

1)

Computed allowable peak equivalent stress 2)

Pass or Fail Full Assessment Inputs:

1) 3 parameter Weibull distribution of tensile strength 2)

Compressive strength 3)

Structural Reliability Class (SRC), which determined the allowable POF.

4)

Principal stresses computed from FEA Outputs:

1)

Computed probability of failure 2)

Pass or Fail The assessments currently determine the probability of crack formation. The nonmetallic ASME working group is currently looking into the best way to advance the definition of failure in graphite.

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Summary 1.

NRC staff are interested in developing expertise and tools to model graphite behavior in ANLWRs 2.

INL developed a MOOSE-based tool to implement a graphite reliability model capable of performing component reliability analysis based upon ASME BPVC Section III Division 5, subsection HH subpart A requirements Capabilities for modeling stresses in graphite were developed Capabilities for modeling oxidation in graphite were developed Python codes were written to implement the full and simplified assessments.

3.

The graphite modeling capabilities discussed in this presentation are available in Grizzly. Everyone in the NRC has access to Grizzly through the Linux RESGC instances. External users must go through INLs Nuclear Computational Resource Center.

4.

A report detailing this work titled Graphite Degradation Modeling and Analysis is available. Example problems on modeling graphite are available within Grizzly.

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Questions?

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Lunch Break Meeting will resume at 1:00 pm EST Microsoft Teams Meeting Bridgeline: 301-576-2978 Conference ID: 115 537 606#

Advanced Reactor Stakeholder Public Meeting 39

Sandia National Laboratories is a multimission laboratory managed and operated by National Technology

& Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energys National Nuclear Security Administration under contract DE-NA0003525.

FY22 Advanced Reactor Code Development Updates for Consequence Analysis SAND2022-12467 C Kyle Clavier, PhD, SNL Dan Clayton, PhD, SNL Keith Compton, PhD, US NRC Advanced Reactor Stakeholder Meeting December 15, 2022 40

Code Development Plan for Consequence Analysis Potential MACCS code technical issues identified in Computer Code Development Plans for Severe Accident Progression, Source Term, and Consequence Analysis (NRC, 2020)

What have we accomplished in FY22?

What is our path forward?

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MACCS Technical Issues for Non-Light Water Reactors Near-Field Atmospheric Transport and Dispersion (Task CA1)

Based on the potential for non-LWRs to be located on sites with shorter site boundary distances than traditional LWR sites, improve MACCS near-field atmospheric transport and dispersion capability Radionuclide Screening (Task CA2)

Perform a screening analysis to identify which subset of radionuclides to include in MACCS calculations for each non-LWR type given the different mix of radionuclides that may be released in accidents from each type Chemical Form, Particle Size, and Shape Factor of Radionuclides and Impact on Atmospheric Transport and Dosimetry (Task CA3)

Evaluate potential differences in radionuclide releases from non-LWRs relative to LWRs including different aerosol size distributions, shape factors, and chemical forms. Based on the evaluation, improve MACCS capabilities for atmospheric transport and dosimetry to appropriately capture these issues for probabilistic consequence analysis. If necessary, consider a state-of practice resistance model for dry deposition.

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MACCS Technical Issues for Non-Light Water Reactors (Continued)

Tritium Modeling (Task CA4)

Develop MACCS model and/or dosimetry updates to better account for the unique behavior of tritium which is very mobile and can enter biological systems as part of water and organic molecules.

Evolution of Radionuclide Properties in the Atmosphere (Task CA5)

Identify whether non-LWR accident releases may be more subject to evolution in the atmosphere relative to LWR releases based on differences in hygroscopic properties or potential for chemical reactions during transport.

Impacts on Decontamination (Task CA6)

Based on the potential for non-LWRs to be sited in areas with different land use patterns than traditional LWR sites, develop updated decontamination cost models Chemical Hazards (Task CA7)

Examine issues associated with potential chemical releases to the environment. If appropriate, the staff will explore the use of the CHEM_MACCS tool for potential use with non-LWRs.

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FY22 Tasks Radionuclide Screening (Task CA2)

Chemical Form, Particle Size, and Shape Factor of Radionuclides and Impact on Atmospheric Transport and Dosimetry (Task CA3)

Tritium Modeling (Task CA4) 44 44

Radionuclide Screening (Task CA2)

Staff continued working on Task CA2 in FY22 Section 3.3 and 8.2.1 of Appendix VI of WASH-1400 (NRC, 1975) identifies a subset of radionuclides to be included in a consequence analysis This methodology includes consideration of:

Radionuclide half-life Emitted radiation type and energy Inventory Release fraction Elemental chemistry 45 45

Alpert et al. (1986) updated the list of radionuclides identified in WASH-1400 Estimation of release fractions was subject of considerable uncertainty at the time Alpert et al. developed a method to consider relative importance of individual elements to reactor accident consequences assuming equal release fractions Ultimately resulted in a list of 60 radionuclides This list was updated with the development of MACCS2 Explicitly includes 11 short lived decay progeny 71 radionuclides in total identified for LWR consequence analysis 46 Alpert et al., 1986 46

71 radionuclides were identified as important for LWRs based on inventory, half-life and potential biological hazard of radionuclides expected to be present in a large LWR 47 Chemical Group Isotope T1/2 Noble Gas Kr-85 10.72 yr Kr-85m 4.48 hr Kr-87 76.3 min Kr-88 2.84 hr Xe-133 5.25 d Xe-135 9.09 hr Xe-135m 15.3 min Alkali Metals Rb-86 18.7 d Rb-88 17.8 min Cs-134 2.062 yr Cs-136 13.1 d Cs-137 30.0 yr Alkaline Earths Sr-89 50.5 d Sr-90 29.1 yr Sr-91 9.5 hr Sr-92 2.71 hr Ba-137m 2.55 min Ba-139 82.7 min Ba-140 12.74 d Halogens I-131 8.04 d I-132 2.30 hr I-133 20.8 hr I-134 52.6 min I-135 6.61 hr Chemical Group Isotope T1/2 Chalcogens Te-127 9.35 hr Te-127m 109 d Te-129 69.6 min Te-129m 33.6 d Te-131 25.0 min Te-131m 30.0 hr Te-132 78.2 hr Platinoids Ru-103 39.3 d Ru-105 4.44 hr Ru-106 368.2 d Rh-103m 56.1 min Rh-105 35.4 hr Rh-106 29.9 sec Early Transition Elements Co-58 70.8 d Co-60 5.271 yr Nb-95 35.1 d Early Transition Elements Nb-97 72.1 min Nb-97m 1.0 min Mo-99 66.0 hr Tc-99m 6.02 hr Tetravalents Zr-95 64.0 d Zr-97 16.9 hr Ce-141 32.5 d Ce-143 33.0 hr Ce-144 284.3 d Np-239 2.35 d Pu-238 87.74 yr Pu-239 2.41E4 yr Pu-240 6.54E3 yr Pu-241 14.4 yr Chemical Group Isotope T1/2 Trivalents Y-90 64.0 d Y-91 58.5 d Y-91m 49.7 min Y-92 3.54 hr Y-93 10.1 hr La-140 40.3 hr La-141 3.9 hr La-142 92.5 min Pr-143 13.56 d Pr-144 17.3 min Pr-144m 7.2 min Nd-147 11.0 d Am-241 432.2 y Cm-242 162.8 d Cm-244 18.11 yr Cadmium Group Sb-127 3.85 d Sb-129 4.32 hr Source: NUREG/CR-7270 47

Advancing reactor technology has motivated an investigation into developing a similar subset of radionuclides relevant to advanced non-LWRs High-temperature gas reactors (HTGR)

Fluoride-salt-cooled high-temperature reactor (FHR)

Molten-salt reactors (MSR)

Sodium fast reactor (SFR)

Liquid metal fast reactors (LMR) 48 48

The NRC Vision and Strategy Document (Vol. 3) outlines a radionuclide screening effort (Task CA2)

Calls for the identification of a subset of radionuclides to be included in MACCS calculations for non-LWRs Radionuclide selection should be based upon factors such as:

Core inventory Nature of radioactivity Specific organ effects FY22 work expands upon previous qualitative efforts to screen advanced reactor radionuclides in Preliminary Radioisotope Screening for Off-site Consequence Assessment of Advanced Non-LWR Systems (Andrews et al., 2021)

Developed a preliminary, qualitative list of radionuclides for these reactors (57 radionuclides)

Identified knowledge gaps that still exist regarding reactor chemistry and system behavior 49 49

57 radionuclides identified in preliminary qualitative screening assessment (Andrews et al., 2021) 50 Chemical Group Isotope T1/2 Reactor Type New Proposed Group H-3 12.3 y HTGR, FHR, MSR, SFR C-14 5,730 y HTGR, FHR Alkali Metals Na-22 2.6 y SFR Na-24 15 h SFR Alkaline Earths Ra-224 3.66 d MSR Noble Gas Ar-41 110 m SFR Kr-83m 1.83 hr MSR Xe-131m 11.9 d MSR Xe-133m 2.2 d MSR Early Transition Elements Cr-51 27.7 d SFR Mn-54 312.3 d SFR Fe-59 44.5 d SFR Nb-93m 16.13 yr MSR Ta-182 114.4 d SFR Cadmium Group As-77 38.5 hr MSR Cd-113m 14.1 yr MSR Cd-115m 44.5 d MSR Sb-125 2.8 y HTGR, FHR Sb-126 12.3 d MSR Sb-128 9.01 hr MSR Chalcogens Se-81 18.4 m MSR Se-81m 57.3 m MSR Se-83 22.3 m MSR Te-125m 57.5 d MSR Te-133m 55.4 min MSR Te-134 41.8 min MSR Chemical Group Isotope T1/2 Reactor Type Halogens Br-83 2.4 hr MSR Br-84 31.8 min MSR Platinoids Pd-109 13.7 h MSR Pd-112 21.0 hr MSR Tin Group Ag-110m 250 d HTGR, FHR Ag-111 7.45 d MSR Sn-117m 13.7 d MSR Sn-119m 293 d MSR Sn-121m 43.9 yr MSR Sn-123 129 d MSR Trivalents Pr-146 24.2 hr MSR Pm-147 2.6 y HTGR, FHR, MSR Pm-148m 41.3 d MSR Pm-149 53.1 hr MSR Pm-151 28.4 hr MSR Sm-151 88.8 y HTGR, FHR, MSR Sm-153 46.3 hr MSR Eu-154 8.6 y HTGR, FHR, MSR Eu-155 4.8 y HTGR, FHR, MSR Eu-156 15.2 d MSR Eu-157 15.2 hr MSR Cm-243 29 y MSR, LMR Cm-245 8,500 y HTGR, FHR, MSR, LMR Cm-246 4700 y MSR, LMR Am-242m 150 y MSR, LMR Am-243 7400 y MSR, LMR Chemical Group Isotope T1/2 Reactor Type Tetravalents Th-228 1.91 y MSR Pa-233 27.0 d MSR, MSR, LMR Pu-242 373,300 y HTGR, FHR, MSR, LMR Uranium Group U-232 68.9 y MSR U-237 6.75 d MSR, LMR Source: Andrews et al., 2021. Preliminary Radioisotope Screening for Off-site Consequence Assessment of Advanced Non-LWR Systems, SAND2021-11703 50

Advanced reactor research is still underway, but some preliminary information does exist Information on half-life and potential biological hazard for 825 radionuclides in MACCS is available Still need reliable information for advanced reactors:

Inventories Transport pathways Chemistries Some preliminary inventories are available INL Heat Pipe Reactor Design See Walker et al. (2022) SCALE Modeling of the Fast-Spectrum Heat Pipe Reactor Available inventories allow us to illustrate a method that can be applied to identify a list of radionuclides for any advanced reactor technology, provided that a quantitative inventory is available Method analogous to Alpert et al. to estimate relative importance assuming equal release fraction Identify most important contributors based on relative importance Consider doses to multiple organs, scaled to that of I-131 (early phase) and Cs-137 (long-term phase) 51 51

MACCS 4.1 was used to assess the relative importance of advanced reactor radionuclide suite Step 1: calculate an activity-normalized dose of combined list of 57 preliminary radionuclides and heat pipe reactor suite Screen heat pipe reactor by eliminating radionuclides with short half-lives (<1 hour) and low contribution to the total inventory (<0.0001%)

> 1200 radionuclides reduced to 108 EARLY and CHRONC doses from a unit 1-Ci release were modeled for each of these radionuclides and normalized to equivalent releases of I-131 and Cs-137, respectively In this manner, a relative biological hazard list was developed Step 2: illustrate using a heat pipe reactor inventory to scale these hazard rankings by the inventory (relative to I-131 or Cs-137 as appropriate)

Identify radionuclides that, if released in sufficient quantities, may be important to early or long-term dose Step 3: additionally screen this list by eliminating radionuclides with effective and organ doses less than 1% of those of I-131 (early phase) and Cs-137 (late phase doses) 52 52

The MACCS assumptions used for this analysis mirrored those in Alpert et al. (1986)

L-ICRP60ED dosimetric quantity used as surrogate for potential latent health effects from both EARLY and CHRONC phase doses. A-RED MARR and A-LUNG used for surrogates of early health effects from early phase doses Constant, typical weather conditions - D stability, 4 m/s windspeed, no rain Release occurs outside of the growing season Doses from elements/isotopes include the effect of radioactive decay and in-growth or decay progeny during transport.

Nonbuoyant release from a single plume at 40 m elevation. Uniform 1-hour release immediately after accident initiation 0.002 m/sec dry dep. velocity, radiation protection factors of 0.75, 0.22, and 0.46 for cloudshine, groundshine, and inhalation and skin, respectively Uniform population distribution of approximate CONUS average No emergency protective actions, exposure duration of 7 days for EARLY, CHRONC duration of 1 year, no intermediate phase 53 53

Analysis suggests that 69 heat pipe reactor radionuclides may be of importance if released in sufficient quantities 48 of these radionuclides are already considered for LWR analyses 21 new radionuclides listed here Note: decay progeny not listed here 54 Isotope EARLY Relative ICRP60 Effective Dose EARLY Relative Red Marrow Dose EARLY Relative Lung Dose CHRONC Relative ICRP60 Effective Dose CHRONC Relative Red Marrow Dose CHRONC Relative Lung Dose Ag-111 0.04 Ag-112 0.01 Cd-115 0.03 Eu-155 0.017 Eu-156 0.031 0.063 Nb-95m 0.06 Nd-149 0.014 0.22 Pd-109 0.09 Pm-147 0.34 1.57 0.03 Pm-148m 0.015 Pm-149 0.035 0.97 Pm-151 0.012 0.043 0.396 Pr-145 0.03 1.58 Sb-125 0.02 0.017 0.04 0.04 0.04 Sm-153 0.173 Sn-121 0.011 Sn-125 0.0301 Sn-127 0.028 Te-125m 0.0107 U-234 0.40 0.663 0.03 U-237 0.035 0.041 0.663 54

Summary and Limitations - Radionuclide Screening A method for the identification of radionuclides of potential for advanced reactors - based on half-life, biological hazard, and relative abundance in a core - is provided and illustrated using a radiological inventory developed for a heat pipe reactor Radionuclides were progressively screened, first based on half-life and relative inventory, and then further based upon relative biological hazard to develop a list of 69 radionuclides (48 of which are already considered for LWR analyses and 21 of which are not currently considered) to include in the heat pipe reactor consequence analyses Method provides a traceable and transparent basis for selecting radionuclides for inclusion in advanced reactor consequence analysis In theory, this method can be applied to any advanced reactor inventory as they become available NRC staff considers work on Task CA2 to be completed based on the development of a quantitative methodology that can be applied to the diverse radiological inventories that may be present in advanced reactor design (see Clavier et al 2022a for summary report).

Further work may be undertaken in future years to continue refining the methodology.

Some radionuclides did not have dose coefficients in MACCS Complexities of H-3 and C-14 are generally unaccounted for in MACCS Food ingestion ignored Normalizing to doses of volatile isotopes of iodine and cesium means that large releases not associated with high elemental volatility may need to be reassessed Other thresholds for half-life, relative abundance, or relative biological hazard may be used 55 55

Chemical Form, Particle Size, and Shape Factor of Radionuclides and Impact on Atmospheric Transport and Dosimetry (Task CA3)

Staff began working on Task CA3 in FY22 In a nuclear accident scenario, it is possible that there are multiple chemical and physical forms of a given radionuclide released NRC non-LWR Vision and Strategy (NRC, 2020) calls for an investigation into how MACCS handles radionuclide size, shape and chemical form in atmospheric and dosimetry calculations Inform potential improvements to MACCS capabilities for atmospheric transport and dosimetry MACCS has robust, flexible modeling capabilities that are generally well-suited to accommodate diverse forms of a given isotope Understanding existing capabilities will help to inform improvements It is pertinent to investigate MACCS variables relevant to chemical and physical form modeling and how MACCS functionality facilitates the analysis of accident consequences from varying forms of a given isotope Look into dosimetry and transport assumptions relative to state of practice Technology neutral fashion 56 56

The MACCS dosimetry model EARLY: cloudshine, groundshine, direct/resuspension inhalation and skin deposition Difference dose coefficients for each pathway read from the DC file supplied with MACCS Generally, dose is a product of exposure and dose coefficient CHRONC: MACCS additionally considers food and water ingestion (in addition to groundshine and resuspension inhalation doses)

Indirect late-phase doses from ingestion may occur in different spatial elements than where the deposition processes occurred Food ingestion doses will depend on farmable land area and ground concentration Water ingestion will depend on direct deposition and washoff to freshwater bodies (simple secondary transport equation)

MACCS currently does not allow for multiple chemical forms for the same isotope Each isotope of a given element is attributed the same dose coefficient (for a given organ/pathway) 57 57

The MACCS deposition model MACCS allows user to assign radionuclides to various high-level chemical groups based on physical and chemical properties that are assumed to be identical Typically defined to be consistent with accident progression codes like MELCOR Assuming every isotope in a group behaves the same ignores the unique physical/chemical properties of radioactive molecules Particle size Transformation in the environment Hygroscopicity Agglomeration Density Aerosol size distribution is used to assign a dry deposition velocity Dry deposition a function of ground level air concentration and dry deposition velocity Particle size distribution are binned and each assigned dry deposition velocity Wet deposition functions independently of particle size 58 58

Other summary observations regarding the MACCS conceptual models The dose calculation for a given radioisotope in MACCS assumes chemical form does not change following release to the atmosphere No secondary environmental transport is assumed after deposition Risk coefficient uncertainty can vary considerably across applications E.g., inhalation risk may depend strongly on availability of information on the chemical/physical form inhaled FGR 13: the biokinetic, dosimetric, and radiation risk models generally have been derived from much less detailed and sometimes inconsistent databases (with) substantial uncertainties Default absorption types are recommended, but information regarding this selection is often limited and, in many cases, reflects occupational rather than environmental experience An analyst should consider the timescale in which chemical transformations are expected to happen Environmental transformation processes happen slowly E.g., is a given radionuclide expected to oxidize before substantially decaying?

59 59

Conceptual accounting for alternate physical/chemical forms in dose coefficient development EPA regularly publishes federal guidance reports to assist with radiation protection programs Federal Guidance Report (FGR) 13 (Eckerman et al., 1999) provides technical accounting for risk coefficients, dependent on age, gender, metabolism, dosimetry, radiogenic risk, and competing causes of death A supplement to FGR 13 provides the basis for current dose coefficients in MACCS Effective dose coefficients based on ICRP60 recommendations for tissue weighting Absorption type/clearance class in MACCS consistent with Runkle and Ostmeyer (1985), a study discussing dosimetric data for accidental radionuclide release from nuclear reactors Runkle and Ostmeyer (1985): the most probable chemical forms of the inhaled radionuclides are used to assign clearance classesexcept for Cs and I isotopes, most of the important inhaled radionuclides will be in the form of insoluble compounds (principally oxides and hydroxides) 60 60

More on the MACCS dosimetry lineage FGR 12 and ICRP 72 informed external and internal dose coefficients, respectively, included in FGR 13 External considered radionuclides with a half-life of at least 10 minutes or occurring in the decay chain of a radionuclide that does Internal excluded radionuclides with half-life less than 10 minutes and isotopes of noble gases No practical differences between ICRP 72 dose coefficients and those used to develop FGR 13 for isotopes of interest for consequence analysis 61 61

Clearance class and f1 development dates back to the 1950s ICRP 2 (1959) - provided a foundational, single compartment model for the lung to predict deposition, retention and clearance of inhaled aerosols ICRP 30 (1979) - supersedes ICRP 2, improved estimations of deposition in and clearance from the respiratory tract 3 anatomical compartments were used: nasopharyngeal, tracheobronchial and pulmonary Introduced the D, W, Y shorthand ICRP 66 (1994) - further revises ICRP 30 model and includes morphometry, respiratory physiology, radiation biology, deposition, clearance, and dosimetry Changed the D,W,Y clearance class convention to F,M,S Broad nature of clearance class timelines means the risk associated with some radionuclides may be over/underestimated (actual clearance times are on lower/upper end of clearance time bin) 62 62

Other relevant, newer documentation exists for dosimetry but are often associated with occupational intakes ICRP 68 supersedes ICRP 61 and provides dose coefficients for occupational intake of radionuclides Used the lung model from ICRP 66 Tabulates effective dose coefficients for inhalation for varying isotopes, clearance classes, and particle sizes Provides recommendations for clearance types and f1 values for various chemical compounds Beginning in 2015, Occupational Intakes of Radionuclides (OIR) reports published by ICRP to replace the ICRP 30 series, ICRP 54, 68, and 78 Contains more up-to-date and detailed information than ICRP 68 Detailed information on chemical forms commonly encountered in an occupational setting and associated clearance class and f1 values An update to FGR 13 may logically include a reference to the OIR series 63 63

Other deposition models exist, but MACCS is consistent with state-of-practice specifically for modeling variable chemical/physical forms EPA AERMOD model (see, e.g., Wesely et al. 2002) includes algorithms for both dry and wet deposition for particulates and gaseous emissions and relies on the resistance model Particulate deposition calculation method based on particle size Wet deposition depends on washout coefficient and precipitation rates NOAA HYSPLIT (see, e.g., Stein et al. 2015) uses either a user-specified velocity or calculated using a known particle diameter, air density and particle density Users can optionally select the resistance model Wet deposition model assumes a scavenging ratio to account for rainout and washout (removal constant)

Generally, the impact of chemical form on wet and dry particulate deposition is not currently account for in state-of-practice models for deposition, including MACCS 64 64

Additional work may be warranted to produce data necessary to inform model improvements Identify whether non-LWR accident releases contain chemical forms other than insoluble oxide or hydroxide forms, and what those forms are Conduct a sensitivity analysis for dose coefficients for variable chemical forms Expand the dose coefficient file and allow the user to define which chemical form should be used Enhance MACCS to allow a user to specify release fractions for different chemical forms of the same isotope Review non-LWR accident progression analyses to determine whether significant gaseous releases are likely Benchmark the MACCS dry and wet deposition models against alternate state of practice models 65 65

Summary and Limitations - Effects of Alternate Physical/Chemical Forms on Deposition and Dosimetry NRC staff considers Task CA3 to be completed based on identifying methodological issues that would need to be addressed in specific analyses (see Clavier and Clayton 2022b for summary report).

However, further work may be undertaken in future years, subject to the availability of information on reactor-specific chemical forms as well as the availability of staff and contractor resources. Such work may include:

Review non-LWR accident progression analyses to determine whether significant gaseous releases are likely Benchmark the MACCS dry and wet deposition models against alternate state of practice models Conducting sensitivity analyses of the effect on dose coefficients of alternate inhalation clearance classes to understand the uncertainty associated with alternate chemical forms Identifying which non-LWR accident releases may contain chemical forms other than the insoluble oxide or hydroxide forms characteristic of LWR releases.

Expanding the MACCS dose coefficient file to include dose coefficients for all chemical forms available in FGR-13 (and FGR-11 if computing TEDE) and allow the user to define which chemical form should be used Enhancing MACCS to allow a MACCS user to specify release fractions for different chemical forms of the same isotope.

66 66

Tritium Modeling (Task CA4)

Staff began working on Task CA4 in FY22 Tritium has highly unique chemical behavior in the environment and may become important for advanced reactor consequence analyses If released in large enough quantities, it may be warranted to update MACCS capabilities to model tritium fate and transport more effectively in an accident scenario Updates may be informed by existing modeling capabilities from other tritium models MACCS has been used in previous studies to model tritium releases 67 67

Tritium behaves similarly to hydrogen in the environment and biological processes Weak beta emitter - primary radiological hazard is through ingestion of tritiated organic molecules Chemical form can heavily influence radiological risk posed by tritium Inhaling gaseous tritium poses relatively limited radiological risk (low absorption, significant exhale). Dermal contact also limited Ingestion and dermal contact with HTO poses a comparatively larger risk (high biological uptake)

Meaningful concentrations of HTO can also be absorbed through the skin at a rate approximately half that of inhalation Can also bind to carbon through photosynthetic processes and create OBT 68 Tritium Form Dose Coefficient (Sv/Bq)

Organically Bound Tritium (Ingestion) 4.2 E-11 Tritiated Water (Ingestion) 1.8 E-11 Tritium Gas (Inhalation, Moderate Absorption) 1.8E-15 Organically Bound Tritium (Inhalation) 4.1E-11 Tritiated Water (Inhalation) 1.8E-11 68

Existing tritium models UFOTRI - the most comprehensive model regarding tritium releases, dispersion, deposition and the subsequent movement and transformation through the environment Similar Gaussian dispersion model to MACCS Differentiates between different forms of tritium in the environment, has a detailed reemission physics model, accounts for the conversion of tritium to HTO, uptake by plants, and conversion into OBT GENII - well documented dose and risk assessment model developed by EPA Utilizes special tritium models for acute and chronic exposures Chronic module depends on hydrogen content of plant/animal being contaminated Also accounts for OBT generation RSAC - Radiological Safety Analysis Computer program Uses different equations for calculating ingestion doses from tritium Assumed ratios of plant water and tritium concentration in plant water vs atmospheric water Other less documented/research models 69 69

Multiple pathways may exist for updates to MACCS to accommodate acute tritium releases The atmospheric transport processes are largely similar, but MACCS may benefit from updates to longer-term environmental process models Multiple pathways for updates:

Simply continue to use the most conservative dose coefficient for HTO in the existing MACCS code with no changes to the atmospheric transport or longer-term processes. Reduces the need to have an exact accounting of the chemical form of tritium releases Develop the capabilities for MACCS to identify multiple different chemical forms of tritium and associated transformation. Variable tiers of complexity for which this might be accomplished (e.g., incorporating a simple transfer rate model)

Introduce tritium accounting capabilities like those in UFOTRI and the more complex capabilities of other models.

This would require substantial effort but would provide more detailed estimations of tritium in an accident scenario NRC staff considers this task to be active (see Clavier and Clayton 2022c for current status) and expects efforts to evaluate MACCS capabilities for assessing tritium release consequences to continue beyond FY23.

In FY23, staff plans to conduct a model intercomparison study involving alternate state-of-practice tritium models (e.g., UFOTRI and GENII codes), coupled with a review of documented cases of tritium releases, to understand the degree to which differences in tritium modeling capabilities may impact severe accident dose assessments.

better understand the magnitude of tritium release necessary to yield significant doses at various distances. This will allow staff to understand which advanced reactor technologies may contain tritium inventories capable of resulting in significant doses.

70 70

Summary NRC staff considers Tasks CA1 to CA3 to be completed:

The MACCS code has been updated to improve modeling capability in the nearfield region (Task CA1)

A methodology has been developed to identify radionuclides for inclusion in consequence analyses for non-LWR cores (Task CA2)

MACCS code capabilities are consistent with state of practice for modeling effect of alternate chemical forms on dosimetry (Task CA3)

MACCS is consistent with the current state of practice for modeling deposition of particulate releases (Task CA3)

If significant gaseous releases are expected from advanced reactor technologies, MACCS may require updates to improve capability for modeling gaseous deposition (Task CA3)

Although Tasks CA2 and CA3 are considered complete insofar as the existing MACCS code capabilities are likely adequate, coordination is needed to determine whether the necessary information on the chemical and physical form of released radioactivity will be available to implement the recommendations for modeling impacts on aerosol dosimetry and deposition.

71 71

Path Forward In FY23, staff plans to Continue work on evaluating MACCS capabilities for assessing the consequences of severe accident tritium releases (Task CA4)

Begin work on the evolution of radionuclide properties in the atmosphere (Task CA5),

including a limited literature review to understand what types of chemical and physical transformations are possible and how these transformations are modeled in other state-of-practice codes for atmospheric transport, diffusion, and deposition.

The initial Task CA5 evaluation will be documented in a contractor report to be issued in September 2023, which will identify a more detailed plan for implementation in future years.

In FY25 and beyond, staff intends to begin work on Task CA6 to examine the impact of siting decisions on decontamination cost estimation, and on Task CA7 to examine issues associated with potential chemical releases to the environment.

72 72

Thank you!

Kyle Clavier, PhD, Sandia National Laboratories kaclavi@sandia.gov Dan Clayton, PhD, Sandia National Laboratories djclayt@sandia.gov Keith Compton, US Nuclear Regulatory Commission Keith.Compton@nrc.gov Authors would like to acknowledge the members of the larger MACCS team at SNL: Mariah Smith, Jennifer Leute, Joshua Dise, and John Fulton. Authors would like to additionally acknowledge the following NRC staff for their technical and programmatic contributions to this effort: Salman Haq, AJ Nosek and Nazila Tehrani.

73 73

REFERENCES U.S. Nuclear Regulatory Commission (NRC), 2020, NRC Non-Light Water Reactor (Non-LWR) Vision and Strategy, Volume 3: Computer Code Development Plans for Severe Accident Progression, Source Term, and Consequence Analysis, Revision 1 US Nuclear Regulatory Commission, Washington D.C., January 2020. ADAMS Accession Number ML20030A178 U.S. Nuclear Regulatory Commission, 1975, "Reactor Safety Study: Appendix VI (NUREG-75/014; WASH-1400)," U.S. Nuclear Regulatory Commission, Washington, D.C., 1975.

ADAMS Accession Number ML070600389 Alpert, D. J., D. I. Chanin, and L. T. Ritchie, Relative Importance of Individual Elements to Reactor Accident Consequences Assuming Equal Release Fractions. NUREG/CR-4467, SAND85-2575, Sandia National Laboratories, Albuquerque, NM, March 1986. ADAMS Accession Number ML20195C360 Andrews, N.C., M. Higgins, A. Taconi, and J. Leute, Preliminary Radioisotope Screening for Off-Site Consequence Assessment of Advanced Non-LWR Systems SAND2021-11703, Sandia National Laboratories, Albuquerque, NM, September 2021. ADAMS Accession Number ML21274A182 E. Walker, S. E. Skutnik, W. A. Wieselquist, A. Shaw, and F. Bostelmann (2022). SCALE Modeling of the Fast-Spectrum Heat Pipe Reactor, ORNL/TM-2021/2021, Oak Ridge National Laboratory, Oak Ridge, TN. (see https://code.ornl.gov/scale/analysis/non-lwr-models-vol3)

K. A. Clavier, D. J. Clayton, and C. Faucett (2022a). "Quantitative Assessment for Advanced Reactor Radioisotope Screening Utilizing a Heat Pipe Reactor Inventory (SAND2022-12018)". Sandia National Laboratories, Albuquerque, NM, September 2022. ADAMS Accession Number ML22270A046 Eckerman, K.F., Leggett, R.W., Nelson, C.B., Puskin, J.S., Richardson, A.C.B., 1999. Federal Guidance Report No. 13, Cancer Risk Coefficients for Environmental Exposure to Radionuclides (EPA 402-R-99-001). Oak Ridge National Laboratory Oak Ridge, TN, September 1999.

Runkle, G.E. and Ostmeyer, R. M. (1985) An Assessment of Dosimetry Data for Accidental Radionuclide Releases from Nuclear Reactors (NUREG/CR-4185, SAND85-0283),

Albuquerque, NM: Sandia National Laboratories, August 1985. ADAMS Accession Number ML20132F001 Wesely, M.L., Doskey, P.V., Shannon, J.D. (2002). Deposition Parameterizations for the Industrial Source Complex (ISC3) Model (ANL/ER/TR-01/003). Argonne National Lab.

(ANL), Argonne, IL Stein, A.F., Draxler, R.R., Rolph, G.D., Stunder, B.J.B., Cohen, M.D., Ngan, F. (2015). NOAAs HYSPLIT Atmospheric Transport and Dispersion Modeling System. Bulletin of the American Meteorological Society. 96 (12), 2059-2077 K. A. Clavier and D. J. Clayton (2022b). "Reviewing MACCS Capabilities for Modeling Variable Physiochemical Forms (SAND2022-12766)". Sandia National Laboratories, Albuquerque, NM, September 2022. ADAMS Accession Number ML22270A049 K. A. Clavier and D. J. Clayton (2022c). Reviewing MACCS Capabilities for Assessing Tritium Releases to the Environment (SAND2022-12016). Sandia National Laboratories, Albuquerque, NM, September 2022. ADAMS Accession Number ML22270A054 74

December 15, 2022 Rick Stadtlander Sr. Project Manager, NEI

There is a need to provide a method to acquire the knowledge and experience required for licensed operator duties during new plant construction & initial operations Focused on creating guidance for all Advanced Nuclear Reactors Not specific to any technology Includes LWR SMRs and non-LWRs

Background

©2022 Nuclear Energy Institute 77 NEI 06-13A, Appendix A Current NRC endorsed Cold License Training Plan Used with Vogtles AP1000 Does not allow for technology updates in the Advanced Nuclear Reactors being designed & licensed today

Background

Shorter construction times Smaller licensed operator staff Incorporates OE & lessons learned from Vogtle

Background

©2022 Nuclear Energy Institute 79 Focuses only on Cold License Training Like NEI 06-13A Provided paths for 3 positions RO Direct SRO SRO with previous experience Utilizing similar methodology for experience credit Enhancements:

Includes definition section for commonly used terms Provide flow charts to add clarity to the written descriptions NEI 23-01 vs NEI 06-13A

©2022 Nuclear Energy Institute 80 Reduced crew cumulative experience Based on smaller crew size Taking advantage of inherent and/or passive safety features Reduction in the number of Operator actions Associate degree or higher for direct SRO path Must be in science or applied science Degrees in communication, natural sciences, humanities, or social sciences are not credited Program Updates

©2022 Nuclear Energy Institute 81 Scheduling for initial license test No specified time frame in NEI 23-01 Discussed during pre-application meetings Licensed Operator Continuing Training Required within 90 days of passing initial license exam Maintain program until licenses issued and requalification training begins Systematically determined training to maintain operator knowledge of plant operation Program Updates

©2022 Nuclear Energy Institute 82 Part 53 General Licensed Operators Will be incorporated as the rule solidifies Update NEI 23-01 based on input from todays discussion Submit draft guidance to NRC around year end Schedule public meeting for about 1 month after submittal Plan to submit final guidance around mid March of 2023 for NRC review and endorsement Path forward for NEI 23-01

Questions & Comments 83

Using Lessons Learned to Optimize Advanced Reactor Licensing Activities December 15, 2022 Periodic Advanced Reactor Stakeholder Meeting 84

NuScale Lessons Learned Team established in November 2020 following August 2020 issuance of Final Safety Evaluation Report for NuScale Design Certification (DC)

February 19, 2021, letter from NuScale outlining its lessons learned from the DC Application (DCA) review process along with five recommendations for NRC staff consideration April 15, 2021, NRC staff acknowledgement of NuScale lessons learned letter March 30, 2022, NRC staff letter to A. Veil (NRR) documenting lessons learned from NuScale DCA review along with four recommendations and responses to NuScale and NEI recommendations November 14, 2022, NRC staff letter to R. Taylor (NRR) outlining how four recommendations are being addressed

Background

3 85

Recommendation 1: Design Finalization at Application and Changes During Licensing

- Guidance on pre-application engagement with NRC staff addresses impacts of substantive design changes

- Pre-application readiness assessment audits provide insights into design uncertainties and potential schedule impacts

Recommendation 2: Application of a Holistic, Risk-Informed Review Strategy

- Early establishment of multidisciplinary core review teams

- Pre-application review activities include reviews of draft PRA

- Guidance development and rulemaking activities underway Staff Responses to Recommendations 3

86

Recommendation 3: Enhancements to the Requests for Additional Information and Audit Processes

- Increased use of audits and requests for confirmation of information (RCIs) to ensure appropriate information is docketed in a timely manner

- Implementation of revised office instruction on RAIs (LIC-115)

Recommendation 4: Establishment and Management of Review Schedule and Resource Estimates

- Development of standardized dashboards

- Enhanced project controls established for accountability and to improve management of changes during project Staff Responses to Recommendations 3

87

Kairos Hermes Test Reactor

- Established 21-month review schedule based on robust preapplication engagement and a high-quality application

- Internal and external dashboards developed to provide stakeholders with up-to-date information on review status

- Use of a multidisciplinary core review team to ensure risk-informed review of the CP application

- Increased use of audits and RCIs to optimize the use of RAIs

- Leveraging collaborative tools to support more efficient concurrence process Optimizing and Applying Lessons Learned 3

88

SHINE Technologies Medical Isotope Facility

- Enhanced organizational engagement, including use of routine management meetings for enhanced project controls

- Expanded use of audits to resolve key issues (e.g., Technical Specifications)

- Centralized decision-making functions for the review

- Enhanced internal and external communications on project schedule Abilene Christian University Molten Salt Research Reactor

- ACU review is building off of the lessons learned from Kairos and Shine reviews (e.g.,

use of dashboards, maximizing the use of audits, internal project controls)

- Early engagement on key technical issues ensures that review is risk-informed and minimizes the potential for downstream schedule risk

Five key issues discussed conveyed in acceptance review letter

Public meeting to discuss these issues held in December 2022

- Enhanced communication with applicant on resource estimates and schedule Optimizing and Applying Lessons Learned 3

89

Pre-Application Activities by the Numbers 3

90 NRC staff are currently engaged with 15+ entities in pre-application activities 13+ current and potential applications by 2027 6+ potential operating licenses by 2027 51 topical reports and white paper reviews completed for 7 vendors 28 topical reports and white papers under review from 8 vendors

Questions?

25 92

Break Meeting will resume at 2:55 pm EST Microsoft Teams Meeting Bridgeline: 301-576-2978 Conference ID: 115 537 606#

Advanced Reactor Stakeholder Public Meeting 93

Workshops on Licensing Review Framework for Advanced Reactors Instrumentation and Controls Introduction to Proposed Future Workshops December 15, 2022 94

Final I&C DRG issued in February 2021 (ML21011A140) for non-LWR I&C design reviews by NRC staff

NRC staff reviews / pre-application engagements underway for a variety of potential LWR and non-LWR I&C designs

NRC staff engaged by industry interested in the background and details on the DRGand relationship to NEI documents

- Initial workshop planning ongoing and being coordinated with NEI

- Additional workshops on other I&C-related topics are intended to benefit all designers Requests for Workshop on Instrumentation and Control (I&C) Design Review Guide (DRG) Implementation 95

Overview of recent and ongoing initiatives:

- Licensing Modernization Project (LMP) and RG 1.233

- Technology-Inclusive Content of Application Project (TICAP)

- Advanced Reactor Content of Application Project (ARCAP)

Overview of I&C DRG

- Focus on safety-significant SSCs; Use of reliability / capability targets

- Developed for non-LWRs, but can be used for all reactor designs

- Specific industry challenges or questions Workshop #1 Proposed Topics 96

Codes and Standards

- How performance-based concepts can be applied to prescriptive requirements of endorsed codes and standards

- Applicability of IEEE 603 and related standards

- Use of international codes and standards

NRC staff review expectations

- I&C-specific Principal Design Criteria

- Fundamental I&C design principles

- I&C architecture and safety classification of I&C platforms Workshop #2 Proposed Topics 97

Content of Applications

- Clarity on applicability of Part 50/52 requirements

- Expectation for construction permit applications

- Non-power vs. power reactor applications

Use of NUREG-1537; Path forward for future power reactors Workshop #3 Proposed Topics 98

Questions?

For more information, contact:

Jordan.Hoellman2@nrc.gov 99

Future Meeting Planning

The next periodic stakeholder meeting will be scheduled for late January or February 2023.

If you have suggested topics, please reach out to Steve Lynch at Steven.Lynch@nrc.gov 100

How Did We Do?

Link to NRC public meeting feedback form:

101 https://www.nrc.gov/pmns/mtg?do=details&Code=20221170

Backup Slides 102

Graphite Background 103

Graphite Manufacturing

Graphite properties depend on the manufacturing process

Graphite is made using filler particles (coke) and a binder phase (pitch)

Billets are formed by extrusion, vibration molding, or isostatic molding 104

Graphite Microstructure Graphite sheets, Mrozowski cracks, crystallites, and pores 105

Graphite Oxidation

Oxidation occurs along the outer edges of the basal planes (zig-zag and arm-chair sites)

Oxygen can penetrate the interior of the graphite pore microstructure 106

Temperature Effect on Oxidation

Oxidation is a complex relationship between reactivity and diffusion of oxygen

Reactive surface area is related to pore structure

Oxidation kinetics and gas diffusion are affected by temperature 107

Oxidation Effects

It is important to know the strength remaining after oxidation

Temperature and microstructure affect oxidation rate and the extent of component internal oxidation 108

MOOSE Background 109

MOOSE Summary 110 MOOSE Overview

1. MOOSE is a finite element framework which can be used with a few or many processors to solve engineering problems.

2. MOOSE is broken into many systems which provides a modular approach which allows for coupling of previous code and simple application of new physics.

3. MOOSE has been under development at INL for over a decade and has had multiple modeling methodologies and physical behavior already implemented.

Getting Started with MOOSE:

1. Downloading a text editor with MOOSE plugins is a good first step

2. Instructions for downloading and getting started with MOOSE can be found on https://mooseframework.inl.gov/

3. The website also has training slides on MOOSE, a video of the last MOOSE training, as well as several other helpful features.

110

MOOSE Modularity and Pluggable Systems 111 MOOSE beaks up FEMs into multiple pluggable systems. This method has multiple advantages:

1. Promotes the reuse of objects

2. Allow for decoupling of code

3. Simplified addition of physics

4. Objects can be mixed and matched to achieve simulation goals There are multiple pluggable systems within MOOSE:

These pluggable systems correspond to different aspects which make up an FEM simulation Actions, AuxKernels, Base, BCs, Constraints, Controls, Dampers, DGKernels, DiracKernels, Distributions, Executioners, Functions, Geomsearch, ICs, Indicators Interface Kernels, Kernels, LineSearches, Markers, Materials, Mesh, MeshGenerators, MeshModifiers, Multiapps, NodalKernels, Outputs, Parser, Partitioner, Postprocessors, Preconditioners, Predictors, Problems, RelationshipManagers, Samplers, Splits, TimeIntegrators, TimeSteppers, Transfers, UserObject, Utils, Variables, VectorPostprocessors 111

How to use MOOSE 112 What inputs are needed for MOOSE?

A text file which include the following blocks (systems):

Mesh: Defines the geometry of the domain Variables: Defines the variables to be solved Kernels: Defines the equation(s) to solve BCs: Defines the boundary condition(s) of the problem Executioner: Defines how the problem will be solved Outputs: Defines how to output the results The mesh will often be generated external to MOOSE.

Choosing a text editor:

Any text editor will technically work, but some editors are better than others.

The current recommended editor is VSCode. Instructions on getting started with this editor can be found on the MOOSE framework website at: https://mooseframework.inl.gov/help/development/VSCode.html Example Variables Block 112

Generating Meshes Generating a mesh is required as part of running a problem in MOOSE.

There are multiple way to get a mesh in MOOSE:

Image from https://cubit.sandia.gov/

2) Generating meshes in other tools like CUBIT Other tools are needed to create more complex meshes

1) Generate the mesh internally to MOOSE using the mesh block below. These can be ideal when performing simple tests 113

Thermo-mechanical 114

Model Formulation The state variables in the thermo-mechanical model are strain, temperature, and dose. The model accounts for strain contributions from thermal, irradiation, and mechanical loads

= + + +

Where is the total stain, are eigen strains from thermal expansion, are strains from irradiation induced dimensional change, are strains from irradiation induced creep, and are elastic strains.

115

Irradiation Creep Implementation Two graphite creep models are currently available. If the secondary creep is the primary mechanism of concern, the creep can be modeled.

Material Block In general, the secondary creep will dominate the creep behavior for any significant doses (prior to turn around)

From:

INL/JOU-17-41026 116 This model assumes that the secondary creep coefficient is a constant.

What inputs are needed for this problem?

A text file which include the following blocks (systems):

Mesh: Defines the geometry of the domain Variables: Defines the variables to be solved Functions: For inputting functions Auxvariables: Auxvariables to be solved Materials: Defines material behavior Kernels: Defines the equation(s) to solve AuxKernels: Define AuxVariable equations BCs: Defines the boundary condition(s) of the problem Executioner: Defines how the problem will be solved Outputs: Defines how to output the results How do we set up this problem?

117 Mesh Generated in Cubit 117

Thermo-mechanical Example Problem Problem setup:

We have a known geometry, temperature profile, time dependent irradiation profile as shown below The output of interest is the stress distribution.

Geometry Temperature Profile Dose Profile Rate 118

Input File: Implementing the Temperature Profile Fit from on left from: Bratton, R., Modeling Mechanical Behavior of a Prismatic Replaceable Reflector Block, INL/EXT-09-15868, 2009.

Plot Fit = 480.1*exp(-.0001681 x) + 26.25*exp(-.004799 x)

Temperature profile from the literature Implementation in model Resultant Profile In Functions block In AuxVariables block In AuxKernels block 119

Input File: Implementing the Dose Profile Dose profile plot from: Bratton, R., Modeling Mechanical Behavior of a Prismatic Replaceable Reflector Block, INL/EXT-09-15868, 2009.

Plot Fit =.001912 exp(.03224 x)

Resultant Profile (dpa per year)

Dose Profile from the literature Implementation in model In Function block In AuxVariables block In AuxKernels block Note the dose time derivative needs to be implemented in the same way.

120

Tensor mechanics action An action from the tensor mechanics module can be used to set up much of the groundwork for the problem and reduce the input file complexity. An example is shown below.

The action is useful for declaring eigenstrains and making stress related outputs.

121

Eigenstrains from Irradiation and Temperature The eigenstrains will vary as a function of the states as shown in the plots below.

=.0006351 6.23 10 7.003476 4.26 10 7 0.00023242 Irradiation induced dimensional change Curve fit Coefficient of Thermal Expansion

= 4.827 10 6 3.9413 10 11 1.149 10 7 2.648 10 11 + 3 10 9 2 Curve fit 122

Implementing Eigenstrains from Irradiation and Temperature The method for implementing both thermal and irradiation eigenstrains is the same:

1.

Define the eigenstrains in the tensor mechanics action:

2.

Define AuxVariable and AuxKernels for the dimensional change.

3.

Define variable dependent eigenstrain object and compute the value (in the material block).

In AuxVariables In AuxKernels Anisotropic behavior can be included by adjusting the eigen_base 123 User input fit

Implementing an Isotropic Elastic Modulus The elastic modulus will vary as a function of the states.

= 12.41 0.0007386 + 2.839 0.00102 0.0753 2 ()

Modulus fit Code Implementation In Materials Block If an anisotropic modulus is needed, the material ComputeGraphiteElasticityTensor can be used. In this case, two DeriviativeParsedMaterials are required. One to define the modulus parallel to the gain and one to define the modulus perpendicular to the grain.

124 The material property fits are computed from experimental data

Model Outputs The full assessment required both element volumes and equivalent stresses which are computed from the principal stresses. These can be output in the following way Principal Stresses 1.

Include the principal stresses in the tensor mechanics action.

2.

In the VectorPostprocessor block, use the ElementValueSample 3.

Output the values in a csv file by setting csv= true in the Outputs block.

Element Volumes 1.

Create a volume AuxVariable and AuxKernel 2.

In the VectorPostprocessor block, use the ElementValueSample 3.

Output the values in a csv file by setting csv= true in the Outputs block.

125

Simulation Results The resultant stress distribution from the input temperature profiles, and irradiation effects are shown below. This results match well with previous studies which have simulated this problem.

=

+

Dose Profile Temperature Profile Stress Distribution 126 All variables and auxvariables can be output (including the outputs relevant the ASME code assessments)

Oxidation Model

Oxidation Model: Formulation 128

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[2 ] rx(x) 1 J. Kane et al. (2017). Understanding the reaction of nuclear graphite with molecular oxygen: Kinetics, transport, and structural evolution. Journal of Nuclear Materials, Volume (493), pp. 343-367.

Oxidation modeling formulation:

The primary physical considerations in the model are the diffusivity of the chemical species and local reaction kinetics.

The partial differential equations which describe this physics and are implemented in MOOSE are shown below.

Microstructural evolution effect:

As the graphite is oxidized the microstructure changes. Therefore, the effective diffusivity,,

thermal conductivity,, and active surface area,, are a function of the mass loss.

This example problem investigates the temperature-dependent density profiles generated in a graphite cylinder (2-inch length, L, and 1-inch diameter, D).

Oxidation Example Problem: Introduction 129 Problem model setup:

Problem run to 10% mass loss in IG-110 BCs:

1) Air is on the outside of the cylinder

2) Temperature of 564, 645, and 744 °C Wanted Result:

Density Profile Problem Geometry:

Air Air Air Air Symmetry R

Z D/2 L/2 In cylindrical coordinates Air Air 129

The coordinate system can be set to cylindrical by:

Example Problem: Input File Editing (1/6) 130 This cylindrical geometry is very simple, so it can be implemented in the input file and does not require external mesh generation:

130

In the Variables block the initial conditions can be set for the species concentrations and temperature.

Example Problem: Input File Editing (2/5) 131 Set initial conditions for the species concentrations Set initial Temperature in Kelvin Problem Setup Species Concentrations: The nitrogen is set at near the concentration of nitrogen in air.

Temperature: The temperature will have to be adjusted for each of the three simulated temperatures (564, 645, 744 °C).

131

Block which normally dont need editing:

AuxVariables, Kernels, AuxKernels Example Problem: Input File Editing (3/5) 132 D/2 L/2 Air Air Problem Setup The boundary condition block, BCs, editing:

The species concentration are set as constants approximately equal to air.

Editing the AuxVariables, Kernels, AuxKernels and BCs blocks BCs block 132

The model is parameterized for IG-110 and NBG-18.

Material Block Edits:

1. Input IG-110 or NBG-18 for graphite_type.

2. Check that the initial pore fraction (initial_porosity) and bulk density (initial_bulk_denity) in g/cc match the selected graphite.

3. Input the system pressure in Pa.

Note that the above steps are only valid for IG-110 and NBG-18, modeling other grades will require additional editing.

Example Problem: Input File Editing (4/5) 133

Example Problem: Input File Editing (5/5) 134 Executioner block Edits:

1. The end_time (seconds) should be set to the total simulation time.

2. A user may want to adjust the initial time step (dt). Low temperature simulations can be set with a larger time step than high temperature simulations.

Generally, the other inputs need not be adjusted unless there are convergence issues.

Example Problem: Results 135 To create the input file for this problem we must:

1. Generate a mesh

2. Specify boundary conditions (species concentrations and temperature)

3. Select a graphite grade (IG-110 or NBG-18)

4. Set a simulation run time The plot on the right shows the resultant density profiles at three temperatures each of which are at 10% mass loss. The main takeaways from this plot are:

1. The slope of the density profile increases with an increase in temperature

2. This is the experimentally observed temperature-dependent density profile behavior 135

In order to model oxidation, we must know how the reaction kinetics vary as a function of mass loss.

A macroscale understanding of the local reaction kinetics within a component can approximated using the following logic:

1. The local reaction rate within a component is a function of the reactive surface area density.

2. The reactive surface area density varies as function mass loss

3. At low temperatures, diffusion does not have an appreciable effect on the experimentally observed reaction rate Therefore, low temperature oxidation experiments can provide a relative reactive surface area density as a function of mass loss.

Modeling Reaction Kinetics 136 RSA evolution with mass loss 136

The oxidant diffusion in the model is controlled by a mass loss dependent effective diffusion,.

An effective diffusion of unoxidized graphite can be experimentally measured. Measurements used in this work were done by Josh Kane1.

Modeling Oxidant Diffusion 137 1 J. Kane et al. (2018) Effective gaseous diffusion coefficients of select ultra-fine, super-fine and medium grain nuclear graphite. Carbon, Volume (136), pp. 369-379.

The effective diffusivity is implemented as

= +

= 1 2 Here is the unoxidized diffusivity, is the bulk gas diffusion, and is the normalized density.

Experiments are currently being conducted to confirm the effect of mass loss on diffusivity.

The model assumes that the active surface area varies as a function of mass loss.

Model parameterization: Reaction kinetics 138 Needed Experiment Low temperature oxidation mass loss versus time data. This data can be used to:

1.

Set the parameter related to active surface area in the model 2.

Determine the evolution of the RSA Implementation The RSA evolution equation is input in the GraphiteThermalGaseous.C source file using the form:

_= init_area f(kalfa) where _is the reactive surface area variable. init_area is an internal parameter which need not be adjusted, and f(kalfa) is the RSA evolution equation where kalfa is the mass loss fraction.

The rate_scaling_factor in the GraphiteThermalGaseous material need to be adjusted so that the simulation matches the low temperature mass loss.

Diffusion Experiment An effective diffusion value needs to be determined. This can be done using experiments like the one shown on right.

Implementation In the GraphiteThermalGaseous.C source file the variable Z should be set to equal to the ratio of the unoxidized diffusivity to the bulk diffusivity Parameterization: Diffusion and Heat Generation 139 Heat Generation Experiment Thermal conductivity experiments as a function of mass loss should be run.

Implementation In the thermal conductivity, kT, should be input as a function of the states in the PorousMediaBase.C file. For example:

Simple Validation:

The model can be compared to mass loss at multiple temperatures to validate the oxidation model.

Validation of the Oxidation Model 140 Better Validation:

The best validation would be to compare the computed density profile to an experimentally determined density profile.

Molten Salt

MSR Modeling Currently the ASME Code does not provide significant guidance related to MSR graphite assessment. Most of the discussion on MSR graphite is shown to the right.

What do we need to know in order to model graphite in an MSR?

1.

Material properties

These must be determined as a function of the states (temperature, dose, salt concentration, etc.)

2.

Salt Penetration

Penetration depth will be grade dependent

This is essential as it will dictate where property changes occur.

3.

Degradation mechanisms

Chemical interaction (fluorination and chemical attack)

Abrasion/erosion rates Currently, we dont know which aspects of the interaction between graphite and molten salt are most important to model.

142

How could we implement MSR effect in the current tool?

We should model the interaction which has the largest effect on the graphite behavior.

The most important effect may be graphite grade and reactor design dependent.

If salt penetration is going to occur, we can model its effects by 1.

Introducing an AuxVariable and function which describe the salt concentration.

2.

Edit material properties as a function of salt_concentration auxvariable. For example, the CTE AuxKernel may look like:

AuxVariable block Function block AuxKernels block Note: abrasion and erosion may be modeled in a similar manner to regime 3 oxidation.

143

ASME

ASME Code Considerations

Weibull statistics for failure probability

Distribution of loading in the graphite component includes stresses from irradiation dimensional changes and temperature gradients

Oxidation loss of strength and irradiation property changes are to be considered ASME Code on modeling Stress 145

Graphite Data for Assessment

Graphite properties depend on the grade e.g., Form MDS-1 Material Data Sheet 146

The ASME Code does:

1. Identify what can cause oxidation

2. Identify how oxidation affects strength

3. Provide guidance on how an FEA analysis should be conducted on oxidized graphite

4. Identify limitations to the oxidation rules (oxidation rules are not applicable to graphite irradiated past.25 dpa )

The ASME Code does not:

Provide a method for assessing an oxidized component.

Therefore, it is the designer's responsibility to show that oxidation is appropriately accounted for.

Oxidation Modeling in the ASME Code 147

ML22343A279

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