Nanosecond Pulsed Die-Away Experiments for Nuclear Data Validation
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Nuclear science and engineering↗
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Verified, Archived Library of Inputs and Data (VALID) Online Repository
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Nuclear Criticality Safety Program (NCSP)↗
Discovery of AMPX Thermal Scattering Law Processing Issue for Solid Moderators
The 2020 edition of The International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook includes a newly produced plastic-moderated evaluation, identified as PU-MET-MIXED-002 and referred to as PMM002. The 2021 edition should include another new plastic-moderated experiment, HEU-MET-THERM-004, referred to as HMT-004. These evaluations are of particular interest, as PMM-002 is moderated with polyethylene, and HMT-004 is moderated with polymethyl methacrylate (Lucite), allowing for investigation of the differences in the thermal scattering laws (TSLs) available for these materials. ENDF/B-VIII.0 includes newly produced data for 1 H-based scattering materials, including Lucite and yttrium hydride. Other available 1 H TSLs include polyethylene, light water, zirconium hydride. The unbound 1 H (free-gas) cross sections were also used. ENDF/B-VIII.0 also includes revisions made to the ENDF/B-VII.1 light water and polyethylene evaluations. Zirconium hydride was unchanged from ENDF/B-VII.1. As of this writing, there are no benchmarks included in the Verified, Archived Library of Inputs and Data (VALID) that are primarily moderated with any solid moderator, so validation that includes the PMM-002 and HMT-004 evaluations would expand the coverage to two new moderators. A study was undertaken at Oak Ridge National Laboratory with two purposes: to provide validation data based on systems that are primarily moderated with polyethylene and Lucite, and to demonstrate the reactivity changes that can result from use of different 1 H TSLs in these plastic-moderated systems. The first objective was mainly to test data and code for SCALE and AMPX, whereas the second objective was to provide useful data for practitioners on the potential variability of predicted $k_{eff}$ based on TSL changes. The use of an exactly correct TSL is often not possible given the materials involved (e.g., lubricants), but this study was intended to provide some indication of the magnitude of the changes among similar materials that can manifest through application of different TSLs in hydrogenous systems over a range of different neutron energy spectra. Unfortunately, the nominal $k_{eff}$ results exposed deviations between SCALE and Monte Carlo N-Particle (MCNP). Some of the results using different TSLs were also unexpected and difficult to explain. An investigation revealed a processing issue in AMPX that was caused by an ambiguous description of the data for incoherent elastic scattering in the ENDF manual. This issue is discussed in detail in a SCALE User Notification, and it affects some solid moderators, but it does not apply to 1 H bound in water. This issue highlights the importance of validating all TSLs used in safety analysis calculations. The remainder of this paper provides a more detailed examination of the results that triggered the investigation and a summary of the findings of that investigation.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Synthesizing pseudo-Kossel lines from neutron transmission data. II. Validation with neutron diffraction data
A new method to recover the orientation matrix of a single crystal with a known unit cell by analyzing synthesized pseudo-Kossel lines from time-of-flight neutron transmission data has been outlined in a companion article [Dessieux et al. (2023). J. Appl. Cryst. 56, https://dx.doi.org/10.1107/S1600576723001346; referred to here as Article I]. In this work, validation of this new technique is presented by employing experimental neutron transmission and diffraction measurements performed on two copper single-crystal specimens. Time-of-flight spectra were recorded during rotation (ω) of the single crystals about a vertical axis perpendicular to the incident neutron beam. The λ–ω maps recorded in transmission are utilized to determine the crystal orientation with respect to the neutron beam, following the procedure presented in Article I. Further, to validate the indexing procedure, the crystal orientations are compared with those obtained via conventional methods using the diffraction data. The resulting pseudo-Kossel lines across the 2D detectors are also observed for the first time.
36 MATERIALS SCIENCE↗
ARC Software Validation Work for the FFTF Reactor
Extensive efforts have been carried out at ANL for the verification and validation of the Argonne Reactor Codes (ARC) software package currently used for the design of Versatile Test Reactor (VTR). The ARC software package consists of steady state neutronics and thermal hydraulics modeling capabilities which are being used by the VTR program to develop most of the VTR reactor design details which will be part of the licensing application. It is anticipated that this software will continue to be used for the design work and for initial operations although additional software may be introduced at a later time. The validation work was focused primarily on obtaining validation data consistent with VTR and usable for the ARC software. Because no critical facilities or operating fast spectrum reactors are available to do experiments for the VTR, the next best option is to identify historical experimental data that can be used as validation data. Early on in VTR, the ZPPR-15 set of experiments was identified as good validation data because of 1) the availability and quality of the data, 2) existing staff that are already familiar with the experimental machine and measurements, 3) most of the ZPPR-15 loadings of interest have already been processed into ARC models, and 4) a full uncertainty quantification has already been done for several loadings of ZPPR-15. The FFTF startup and operations data was identified as the most consistent reactor type that has validation data usable for VTR. Finally, the EBR-II fuel depletion measurements were identified as the best available validation data for VTR. It is important to note that both the FFTF and EBR-II reactors typically come with higher uncertainties than the ZPPR. In the frame of the discussed verification and validation efforts, the present document discusses the analysis of selected FFTF measurements included in the benchmark specifications of the International Reactor Physics Experiment (IRPhE) handbook. The FFTF reactor core configurations from the benchmark specification are presented in Section 2. The analysis is performed with the use of the ARC code suite available at ANL for fast reactor studies and is discussed in Section 3. The reactor parameters from the benchmark include criticality, neutron spectra, effective delayed neutron spectra, control rod worth, isothermal temperature coefficient and low energy gamma-ray spectra. The calculated values and the comparison with the experimental data is discussed in Sections 4 to 9 for each considered reactor parameter. Finally, conclusions are presented in Section 10.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Why Lady Godiva Should be Replaced as the Default Validation Experiment for U-235 Nuclear Data
For the past seven decades the Lady Godiva benchmark (HEU-MET-FAST-001) has been the primary experiment used for 235 U nuclear data validation. The papers associated with large nuclear data library releases such as ENDF/B-VII.1 and ENDF/B-VIII.0 refer to it frequently. The reasons why HEU-MET-FAST-001 has been used as the primary validation experiment for 235 U nuclear data validation will be discussed in the following section. However, as discussed among the benchmark community, the standards associated with International Criticality Safety Benchmark Evaluation Project (ICSBEP) evaluations have changed throughout time; this is subject of the OECD/NEA WPEC (Working Party on Nuclear Criticality Safety) Subgroup 8. HEU-MET-FAST-001 is an older benchmark (issued during the inaugural year of ICSBEP in 1996 with only minor revisions occurring since then) and (along with many of the other benchmarks from this era) does not meet the standards for a modern benchmark. This work explores why HEU-MET-FAST-001 is useful for 235 U nuclear data validation and discusses other alternative validation experiments.
07 ISOTOPE AND RADIATION SOURCES↗
Uncertainty quantification of fireball features extracted from nuclear test films using computer vision
Films from the US’s historic nuclear testing era comprise the only extensive collection of imagery depicting high-yield detonations. These films offer unique insights into the characteristics of flows occurring on scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models used to describe nuclear detonations. In recent work, we implemented modern computer vision and machine learning techniques to extract features of the fireball following nuclear detonation. With a training dataset of fireball films, we fine-tuned a You Only Look Once 11 (YOLO11) model to detect and track the fireball. Applied to a video, the outer bounding box produced in each frame by YOLO11 is used as an input prompt to Meta’s Segment Anything Model 2 (SAM2), which is shown to accurately predict the boundary of the fireball over time with high resolution. These state-of-the-art computer vision foundation models exhibit impressive visual accuracy in their results but lack an output of values that robustly quantify uncertainty in scientific applications. In this paper, we develop procedures for uncertainty quantification of extracted fireball features. We outline the application of a parallel attention mechanism to calculate uncertainty ranges that complement and better pose model validation data. This higher quality fireball validation data may serve to improve prognostic models describing nuclear detonations in support of nuclear forensic and emergency response activities.
Khristy, Joel [ORNL] (ORCID:0000000209963060)↗
Summary report of the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis (FDPVA)
The objective of the Fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to artificial intelligence for automatization and improving reliability of control processes.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Validation and Data Evaluation Resource (VADER): A Tool for Criticality Safety Validation [Slides]
This presentation discusses the purpose of the criticality safety validation, decisions that were made during the validation exercise, the motivation that led to the creation of VADER, an explanation of USLSTATS, and an examination of VADER capabilities and interface.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Summary of the 5th IAEA technical meeting on fusion data processing, validation and analysis (FDPVA)
The purpose of the 5th International Atomic Energy Agency technical meeting on fusion data processing, validation and analysis (FDPVA) (Ghent University, Ghent, Belgium, 12–15 June 2023) was to provide a platform during which a set of topics relevant to FDPVA were discussed with the view of meeting the needs of next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics data analysis and synthetic diagnostics reported at the meeting, including concept description of new devices; fusion databases; integrated data analysis; inverse problems; uncertainty propagation, verification and validation; probabilistic methods and machine learning. The relevant results underline trends observed in the current major fusion confinement devices.
fusion databases↗
Initial Assessment of CTF for Time-at-Temperature Applications
The US nuclear industry is interested in improving the economics of their fleet of light-water reactors (LWRs) by uprating US plants. One option being considered is to regain lost margin from overly conservative fuel safety limits. The current limit requires avoidance of critical heat flux (CHF) and prevents further operation of fuel that experiences a dry-out in boiling water reactors (BWRs) or departure from nucleate boiling (DNB) in pressurized water reactors (PWRs); however, it has been shown that temporary, mild dry-out of the fuel does not necessarily increase the risk of fuel failure during its normal anticipated operating life. Such mild dry-out or DNB events may occur during a plant anticipated operational occurrence (AOO), such as a locked rotor in a PWR or a pump trip in a BWR. The time-at-temperature (TAT) approach to regulating fuel operation aims to demonstrate that the fuel rod’s integrity is not challenged during such a mild transient that leads to CHF in which the fuel operates at an elevated temperature for a brief period of time. However, implementing this approach will require extensive fuel material experimental data, as well as supporting modeling and simulation (M&S) predictions, to ensure that the predicted fuel response during AOOs, with all applicable uncertainty considered, will not threaten the safety of the fuel during the transient or the remainder of its anticipated lifecycle. To address this need, a comprehensive effort is being proposed that includes generating cladding material data under TAT conditions, assessment of available code capabilities for TAT conditions, development of new mechanistic models, and demonstration of the M&S capabilities for AOOs of interest. This will require a joint effort between the Nuclear Energy Advanced Modeling and Simulation (NEAMS) and Advanced Fuels Campaign (AFC) programs, as well as close collaboration with nuclear industry stakeholders. The outcome of this collaboration will result in development and assessment of capabilities that can be used by the nuclear industry to support qualification of a TAT-based fuel failure criteria safety limit. This report focuses on the thermal hydraulics (T/H) modeling capabilities and summarizes currently available data for validating the T/H subchannel code CTF for TAT conditions, as well as preliminary assessment results of the code. The initial assessment also resulted in implementation of an alternative post-CHF heat transfer package, which has been shown to significantly improve accuracy. This report is not a final assessment and does not consider all available validation data; it is intended that a future assessment will more fully validate the code for this application.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era
Large simulation efforts are required to provide synthetic galaxy catalogs for ongoing and upcoming cosmology surveys. These extragalactic catalogs are being used for many diverse purposes covering a wide range of scientific topics. In order to be useful, they must offer realistically complex information about the galaxies they contain. Hence, it is critical to implement a rigorous validation procedure that ensures that the simulated galaxy properties faithfully capture observations and delivers an assessment of the level of realism attained by the catalog. We present here a suite of validation tests that have been developed by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). We discuss how the inclusion of each test is driven by the scientific targets for static ground-based dark energy science and by the availability of suitable validation data. The validation criteria that are used to assess the performance of a catalog are flexible and depend on the science goals. We illustrate the utility of this suite by showing examples for the validation of cosmoDC2, the extragalactic catalog recently released for the LSST DESC second Data Challenge.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
GNPS Dashboard: collaborative exploration of mass spectrometry data in the web browser
Access to web-based platforms has enabled scientists to perform research remotely. A critical aspect of mass spectrometry data analysis is the inspection, analysis, and visualization of the raw data to validate data quality and confirm statistical observations. We developed the GNPS Dashboard, a web-based data visualization tool, to facilitate synchronous collaborative inspection, visualization, and analysis of private and public mass spectrometry data remotely.
59 BASIC BIOLOGICAL SCIENCES↗
Accounting for Training Data Error in Machine Learning Applied to Earth Observations
Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.
58 GEOSCIENCES↗
MARIAH PCAP data for Validation Demonstration
This dataset holds simulated PCAP (packet capture) data from the SCEPTRE validation demonstration model as a set of pairwise communications between devices via specific protocols. All connections should be assumed to be symmetric, as this data is an aggregation of the true PCAP. A mapping is also provided associating each IP address with its true device type.
cyber-physical system↗
Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in situ and remote sensing data sets
The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).
Cheng, Yanyan↗
A multi-scale time-series dataset of anthropogenic heat from buildings in Los Angeles County
The dataset contains hourly Anthropogenic heat (AH) from buildings in Los Angeles County, based on weather data from 2018. The hourly AH is aggregated at three spatial resolutions: 450m x 450m grid, 12km x 12km grid, and census tract. The AH is broken down into three components: building envelope surface convection, heating, ventilation, and air conditioning (HVAC) system heat release, and zone exfiltration and exhaust air heat loss. The dataset is created with the physics-based EnergyPlus building energy models to calculate individual buildings' AH considering WRF-UCM simulated microclimate conditions. Please refer to the paper "A multi-scale time-series dataset of anthropogenic heat from buildings in Los Angeles County" for more information about the data generation workflow and the data validation procedure. The data set contains two folders: the "output_data" folder holds the simulation results (EP_output and EP_output_csv), building metadata (building_metadata.geojson and building_metadata.csv), aggregated heat emission and energy consumption time-series data (hourly_heat_energy), and geographical data (geo_data) associated with the GEOID referenced in heat and energy consumption data. The "input_data" folder contains the raw data used to generate files in the "output_data" folder as well as data sets used in the validation. The code repository (https://github.com/IMMM-SFA/xu_etal_2022_sdata) holds the processing scripts for data curation, validation, and visualization.
Energy↗
Data for Validating Models for PV Module Performance
This data encompasses performance data measured for flat-plate photovoltaic (PV) modules installed in Cocoa, Florida; Eugene, Oregon; and Golden, Colorado. The data include PV module current-voltage curves and associated meteorological data for approximately one-year periods. The data was acquired with the NREL Performance and Energy Rating Testbed (PERT) and the mobile Performance and Energy Rating Testbed (mPERT).
PV, Validation, IV Curves, Field performance, mPER↗