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DOE OSTI2024

SMR Current Status: Development Needs and Global Perspectives

Defined as nuclear reactors with a power output up to 300 megawatts electrical (MWe) by the International Atomic Energy Agency (IAEA) and targeted for multipurpose applications, small modular reactors (SMRs) have been recognized as a very promising, clean, affordable, and sustainable energy source by many countries. At present, more than 80 SMRs are under design, development, demonstration, deployment, and beyond (4D+) phases worldwide. This study focuses on the current world status of SMRs and focuses on the necessary developments to accelerate the process of adopting SMRs as a major energy source globally. SMRs are not a new concept, but they do represent a new vision for older concepts if the challenges inherent within them are mitigated with strategic and realistic solution approaches. The major challenges for SMRs 4D+ like any new reactors are: (a) qualifying the advanced fuel-to-reactor design; (b) supporting rapid scaled/prototypic experimentations; (c) maintaining local and global codes, standards, and licensing; (d) supply chain issues;(e) effective cradle-to-grave nuclear fuel cycle and fuel material transportation, and (f) mitigating financial and environmental risks. These upfront challenges can be mitigated with a synergistic solution approach among the various stakeholders: industry, academia, research, government, and international entities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Presentation: SMR Current Status: Development Needs and Global Perspectives

Defined as nuclear reactors with a power output up to 300 megawatts electrical (MWe) by the International Atomic Energy Agency (IAEA) and targeted for multipurpose applications, small modular reactors (SMRs) have been recognized as a very promising, clean, affordable, and sustainable energy source by many countries. At present, more than 80 SMRs are under design, development, demonstration, deployment, and beyond (4D+) phases worldwide. This paper focuses on the current world status of SMRs and focuses on the necessary developments to accelerate the process of adopting SMRs as a major energy source globally. SMRs are not a new concept, but they do represent a new vision for older concepts if the challenges inherent within them are mitigated with strategic and realistic solution approaches. The major challenges for SMRs 4D+ like any new reactors are: (a) qualifying the advanced fuel-to-reactor design; (b) supporting rapid scaled/prototypic experimentations; (c) maintaining local and global codes, standards, and licensing; (d) supply chain issues; (e) effective cradle-to-grave nuclear fuel cycle and fuel material transportation, and (f) mitigating financial and environmental risks. These upfront challenges can be mitigated with a synergistic solution approach among the various stakeholders: industry, academia, research, government, and international entities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

SMR Current Status: Development Needs and Global Perspectives

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Calculation of neutron flux spectra of the VVER-1000 mock-up shielding benchmark with Monte Carlo code MCS utilizing mesh-based weight window

The measurements of neutron spectra compiled inside the NEA-1517/82 package from the Shielding Integral Benchmark Archive and Database (SINBAD) are chosen as benchmark cases to validate the variance reduction technique based on weight window in Monte Carlo Code MCS. A full 3D model for fixed source mode calculation with hexagonal lattice source definition is developed to simulate total of 6 points of measurements at the vicinity of the reactor and the reactor pressure vessel region. A code/code comparison against MCNP6 code is first conducted as verification element for the mesh-based weight window capability in MCS. Finally, the validation results are presented against measurements. The verification against MCNP6 code gives good agreement in addition of the insight to the importance of user understanding to determine proper reference point and reference lower weight bound for scaling which is not required in MCS code due to its capability of automatic scaling. The comparison of neutron spectra between MCS and measurements shows good agreement within 3 standard deviations for all of six detector positions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

NuGraph2 with context-aware inputs: physics-inspired improvements in semantic segmentation

Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geometry and track continuity, (ii) introducing auxiliary decoders to capture class-level correlations, and (iii) incorporating energy-based regularization terms motivated by Michel electron energy distributions. Experiments on MicroBooNE public datasets show that physics-inspired feature augmentation yields the largest gains, particularly boosting Michel electron precision and recall by disentangling overlapping latent space regions. In contrast, auxiliary decoders and energy-regularization terms provided limited improvements, partly due to the hit-level nature of NuGraph2, which lacks explicit particle- or event-level representations. Our findings highlight that embedding physics context directly into node-level inputs is more effective than imposing task-specific auxiliary losses, and suggest that future hierarchical architectures such as NuGraph3, with explicit particle- and event-level reasoning, will provide a more natural setting for advanced decoders and physics-based regularization. The code for this work is publicly available on Github at https://github.com/vitorgrizzi/nugraph_phys/tree/main_phys.

Other Experiments↗

DOE OSTI2024

TokaMaker: An open-source time-dependent Grad-Shafranov tool for the design and modeling of axisymmetric fusion devices

Here, in this paper, we present a new static and time-dependent MagnetoHydroDynamic (MHD) equilibrium code, TokaMaker, for axisymmetric configurations of magnetized plasmas, based on the well-known Grad-Shafranov equation. This code utilizes finite element methods on an unstructured triangular grid to enable capturing accurate machine geometry and simple mesh generation from engineering-like descriptions of present and future devices. The new code is designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. A detailed description of the numerical methods of the code, including a novel formulation of the boundary conditions for free-boundary equilibria, and validation of the implementation of those methods using both analytic test cases and cross-code validation is shown. Results show expected convergence across tested polynomial degree for analytic and cross-code test cases.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Upgrade of the Lyman-alpha diagnostic system on DIII-D for main chamber edge neutral studies

The LLAMA (Lyman Alpha Measurement Apparatus) pinhole camera diagnostic had previously been deployed on DIII-D to measure radial profiles of the Lyman-α (Ly-α) deuterium neutral line brightness across the plasma boundary in the lower chamber to infer neutral deuterium density and ionization rate profiles. This system has recently been upgraded with a new diagnostic head, named ALPACA, that also encloses two pinhole cameras and duplicates the LLAMA views in the upper chamber. Similar to LLAMA, ALPACA provides two times 20 lines of sight, viewing the plasma edge on the inboard and outboard sides with a radial resolution of ~2.5 cm (FWHM) and an effective time resolution of ~1 ms that allows for the investigation of inter-ELM dynamics. The extended Ly-α system provides better coverage to study neutrals in experiments with various plasma shapes utilizing both the upper and lower divertors. Furthermore, post-campaign calibration of the LLAMA diagnostic has successfully been demonstrated for the first time. This was facilitated by various upgrades to the calibration set-up and detailed measurements of the emissivity distribution of the Ly-α calibration source using a pinhole collimator. It was found that the sensitivity of the inboard LLAMA pinhole camera was reduced by a factor of 2.0 ± 0.2 over the course of six months of plasma operation in 2021. In conclusion, the upgraded Ly-α system, equipped with improved absolute calibration, will provide key input for neutral fueling and pedestal particle transport studies and for 2D edge transport code validation on the DIII-D tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

The inviscid incompressible limit of Kelvin–Helmholtz instability for plasmas

The Kelvin–Helmholtz Instability (KHI) is an interface instability that develops between two fluids or plasmas flowing with a common shear layer. KHI occurs in astrophysical jets, solar atmosphere, solar flows, cometary tails, planetary magnetospheres. Two applications of interest, encompassing both space and fusion applications, drive this study: KHI formation at the outer flanks of the Earth’s magnetosphere and KHI growth from non-uniform laser heating in magnetized direct-drive implosion experiments. Here, we study 2D KHI with or without a magnetic field parallel to the flow. We use both the GAMERA code, which solves the compressible Euler equations, and the STRATOSPEC code, which solves the Navier-Stokes equations under the Boussinesq approximation, coupled with the magnetic field dynamics. GAMERA is a global three-dimensional MHD code with high-order reconstruction in arbitrary nonorthogonal curvilinear coordinates, which is developed for a large range of astrophysical applications. STRATOSPEC is a three-dimensional pseudo-spectral code with an accuracy of infinite order (no numerical diffusion). Magnetized KHI is a canonical case for benchmarking hydrocode simulations with extended MHD options. An objective is to assess whether or not, and under which conditions, the incompressibility hypothesis allows to describe a dynamic compressible system. For comparing both codes, we reach the inviscid incompressible regime, by decreasing the Mach number in GAMERA, and viscosity and diffusion in STRATOSPEC. Here, we specifically investigate both single-mode and multi-mode initial perturbations, either with or without magnetic field parallel to the flow. The method relies on comparisons of the density fields, 1D profiles of physical quantities averaged along the flow direction, and scale-by-scale spectral densities. We also address the triggering, formation and damping of filamentary structures under varying Mach number or Atwood number, with or without a parallel magnetic field. Comparisons show very satisfactory results between the two codes. The vortices dynamics is well reproduced, along with the breaking or damping of small-scale structures. We end with the extraction of growth rates of magnetized KHI from the compressible regime to the incompressible limit in the linear regime assessing the effects of compressibility under increasing magnetic field. The observed differences between the two codes are explained either from diffusion or non-Boussinesq effects.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Reactor physics benchmark of Westinghouse PWR core design suite for high burnup/high enrichment fuel - Part 2: 2D and 3D core simulations

Westinghouse has performed a comprehensive set of code-to-code benchmark comparisons to corroborate application of its state-of-the-art NEXUS-based code suite to high burnup and high enrichment (HB/HE) fueled PWR core design and reload analysis. The benchmark consisted of a progression in complexity from modeling single 2D pin cells, through 2D lattices and finally to whole core 2D and then 3D scenarios. The accompanying paper (Part 1) presented the results for a variety of pin cell and lattices representative of high energy cores and established the ability of the MPACT code to accurately match the predictions of the Monte-Carlo code SERPENT2 such that the former may be used as a surrogate reference code in spatially complex whole core modeling applications where using Monte-Carlo methods is somewhat impractical. This paper focuses on comparisons at the whole core level using a representative high power density 3-loop Westinghouse NSSS core operating for 24 month cycles employing high enriched fuel with use of combined burnable absorbers to hold down excess reactivity. The evolution of the core reactivity and power distribution characteristics throughout the cycle are calculated using the Westinghouse NEXUS-based code suite and compared to the results using MPACT. The results of this whole core benchmark confirm the conclusions that the Westinghouse code suite accurately models HB/HE fuel designs, with very good prediction of core reactivity and power distribution throughout the cycle. The industry can be confident that the NEXUS code system can be deployed to support PWR reload safety and operational analyses for cores featuring HB/HE fuel and 24-month cycle operation. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Deterministic-Monte Carlo Hybrid Methods for Eigenvalue Sensitivity Coefficient Calculations

The TSUNAMI suite within the SCALE code package includes several methods for generating sensitivity data, including multigroup (MG) and continuous-energy (CE) capabilities. For generating sensitivities with CE data, three methods are available in SCALE 6.3.0: (1) the iterated fission probability (IFP) method with the KENO Monte Carlo transport solver, (2) IFP with the Shift Monte Carlo transport solver, and (3) the Contributon-Linked eigenvalue sensitivity/Uncertainty estimation via Tracklength importance Characterization (CLUTCH) with the KENO Monte Carlo transport solver. Currently, it is difficult to generate accurate sensitivities with large reflectors when using the CLUTCH method, specifically with fissionable and hydrogenous materials. To address this issue, the work presented herein examines a methodology to calculate the adjoint flux externally with the 3D deterministic SN transport code DENOVO in SCALE; the result is then read directly into the CLUTCH-TSUNAMI sequence. This hybridization method replaces the Monte Carlo F*(r) calculation in CLUTCH while still utilizing the forward calculation. The critical benchmark HEU-MET-FAST-028-001 is used to generate sensitivities based on the inability of CLUTCH to generate accurate sensitivities. Results from the hybrid method appear to generate sensitivity values that are in excellent agreement with direct perturbations. Although further testing is needed, the method provides promising results for the development and utility of a hybrid method for use in TSUNAMI.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Alloy 625 Qualification Pathway for ASME Section III Division 5 Class A construction

The American Society of Mechanical Engineers Boiler and Pressure Vessel Code Section III Division 5 provides construction and inspection rules to ensure the safety of nuclear components operating at elevated temperatures, defined as those operating above 700°F (370°C) or 800°F (425°C), depending on the material type. At present, there are six materials approved for Class A (high safety significance) component construction under ASME BPVC Section III Division 5. Out of these six materials, five materials are steels or iron-based alloys and one material, Alloy 617, is a nickel alloy. Nickel alloys are stronger than steels; however, Alloy 617 consists of 10-15% cobalt, and activation is a concern under irradiation. Therefore, there is a need to qualify new nickel alloy with lower cobalt content. Alloy 625 is one candidate which has similar mechanical properties at elevated temperature compared to Alloy 617 and has a maximum of 1% cobalt. Although Alloy 625 material properties have been developed in the past to support allowable stress development under ASME Section II, new test results would be needed to qualify this material for elevated temperature component construction under ASME Section III Division 5. The purpose of this report is to describe a path towards developing a Nuclear Code Case to qualify Alloy 625 (UNS N06625; Grade 1 and Grade 2) for elevated temperature nuclear use in accordance with the ASME BPVC rules. This report reviews the existing database on Alloy 625, presents proposed test campaign, and discusses potential paths for accelerating the accelerated material qualification process.

36 - MATERIALS SCIENCE↗

DOE OSTI2024

Large language model evaluation for high–performance computing software development

We apply AI-assisted large language model (LLM) capabilities of GPT-3 targeting high-performance computing (HPC) kernels for (i) code generation, and (ii) auto-parallelization of serial code in C ++, Fortran, Python and Julia. Our scope includes the following fundamental numerical kernels: AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG, and language/programming models: (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). Kernel implementations are generated using GitHub Copilot capabilities powered by the GPT-based OpenAI Codex available in Visual Studio Code given simple + + prompt variants. To quantify and compare the generated results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. For auto-parallelization, we use ChatGPT interactively giving simple prompts as in a dialogue with another human including simple “prompt engineering” follow ups. Results suggest that correct outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general-purpose Python can benefit from adding language keywords, while Julia prompts perform acceptably well for its Threads and CUDA.jl programming models. Finally, we expect to provide an initial quantifiable point of reference for code generation in each programming model using a state-of-the-art LLM. Overall, understanding the convergence of LLMs, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

TRISO Fuel Performance Modeling

Overview of TRISO fuel performance modeling TRISO fuel performance code PARFUME TRISO fuel performance modeling with BISON PARFUME and BISON comparison

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

TRISO Fuel Performance Modeling with PARFUME

Outline Introduction Overview of TRISO Fuel Performance Modeling TRISO fuel performance code PARFUME PARFUME application to support the AGR program Current Status and Future Work

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Cryogenics Python Coding Calculations for Nitrogen & Helium Loss

Helium and Nitrogen are two significant chemical elements that are used to advance cryogenic scientific breakthroughs. Helium is a colorless, odorless, and tasteless gas that becomes liquid at (−452 °F). Nitrogen is a gas at its standard conditions; It becomes solid once temperatures reach below (-346 °F), and it boils and becomes a gas once temperatures reach above (-320 °F). During this research project I used a web-based interactive computing platform called Jupyter Lab to modify various python programs that calculate, and display helium and nitrogen inventory lost from three cryo plants

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Quantum error correction from complexity in Brownian SYK

We study the robustness of quantum error correction in a one-parameter ensemble of codes generated by the Brownian SYK model, where the parameter quantifies the encoding complexity. The robustness of error correction by a quantum code is upper bounded by the “mutual purity” of a certain entangled state between the code subspace and environment in the isometric extension of the error channel, where the mutual purity of a density matrix ρAB is the difference $\mathcal{F}$ p ($A : B$) ≡ $\mathrm{T}$r $p^{2}_{AB}$ - $\mathrm{T}$r $p^{2}_{A}$ $\mathrm{T}$r $p^{2}_{B}$. We show that when the encoding complexity is small, the mutual purity is O(1) for the erasure of a small number of qubits (i.e., the encoding is fragile). However, this quantity decays exponentially, becoming O(1/N) for O(log N) encoding complexity. Further, at polynomial encoding complexity, the mutual purity saturates to a plateau of O(e -N ). We also find a hierarchy of complexity scales associated to a tower of subleading contributions to the mutual purity that quantitatively, but not qualitatively, adjust our error correction bound as encoding complexity increases. In the AdS/CFT context, our results suggest that any portion of the entanglement wedge of a general boundary subregion A with sufficiently high encoding complexity is robustly protected against low-rank errors acting on A with no prior access to the encoding map. From the bulk point of view, we expect such bulk degrees of freedom to be causally inaccessible from the region A despite being encoded in it.

1/N expansion↗

DOE OSTI2021

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (°C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a “0” if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the “time warp” period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503–2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100–103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278–283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Evaluation of AGR-3/4 In-pile Silver Release Predictions Against Post-Irradiation Examination Measurements

Fuel performance modeling codes that accurately predict the transport of radionuclides in high-temperature gas-cooled reactors that utilize tristructural isotopic (TRISO) fuel particles are an important aspect of reactor safety analyses. One objective of the Advanced Gas Reactor (AGR)-3/4 experiment was to assess the transport of fission products through fuel particles and their subsequent release into the compact matrix and structural graphite materials. This was accomplished by irradiating uranium oxycarbide (UCO) driver fuel particles and designed-to-fail (DTF) particles to serve as known sources of fission products. The fission product of particular interest when it comes to such transport is silver (Ag-110 m), as it has a 250-day half-life and has relatively high mobility in the TRISO coating layers. Furthermore, to assess the current modeling capabilities and diffusion parameters employed in the fuel performance codes PARFUME and BISON, the fractional release of silver release predicted by the two codes were compared against post-irradiation examination measurements from the AGR-3/4 experiment.

AGR-3/4 Experiment↗