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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗
DOE OSTI2020

NA-22 Quarterly Report: April-June, 2020

At LLNL we contribute to two projects in NNSA-IAEC Science and Technology Working Group Area V: Basic Science of Waste Management & Subsurface Science. The motivation behind Topic Area V is to evaluate the scientific and safety case for an intermediate borehole to house Israel’s nuclear waste. WM1 (SNL lead) is Thermomechanical Damage (initially called Damage Accumulation and Wellbore Stability). This work focuses on evaluating the damage to the host rock induced by excavation and heat released from a possible radioactive waste repository/borehole. WM2 (LLNL lead) is Radionuclide Facilitated Transport in Carbonate Rock (initially called Colloidal Transport of Radionuclides). This work focuses on better understanding the transport of radionuclides and colloids from a nuclear waste repository/borehole in a vadose zone environment through experimentation and modeling. In the second quarter (this report) our work has been delayed in part due to COVID-19. LLNL went into shelter in place (SIP), minimum safe operations mid-March. Operations at LLNL have been slowly ramping up with 50% of the workforce back onsite in limited capacity and telecommuting widely used. The good news is that our labs opened up the last week in June and experiments are expected to start up again for WM2 Radionuclide Facilitated Transport in July. LLNL’s contribution to WM1 Thermochemical Damage modeling efforts also started back up in late June. We expect to be able to catch up on much of our work by the end of next quarter. The three national laboratories communication on a bi-monthly basis to make sure Area V projects remain on track and we have a monthly conference call with our Israeli counterparts to make sure we are communicating priorities and coordinating project details.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

NA-22 Quarterly Report: April-June, 2020

At LLNL we contribute to two projects in NNSA-IAEC Science and Technology Working Group Area V: Basic Science of Waste Management & Subsurface Science. The motivation behind Topic Area V is to evaluate the scientific and safety case for an intermediate borehole to house Israel’s nuclear waste. WM1 (SNL lead) is Thermomechanical Damage (initially called Damage Accumulation and Wellbore Stability). This work focuses on evaluating the damage to the host rock induced by excavation and heat released from a possible radioactive waste repository/borehole. WM2 (LLNL lead) is Radionuclide Facilitated Transport in Carbonate Rock (initially called Colloidal Transport of Radionuclides). This work focuses on better understanding the transport of radionuclides and colloids from a nuclear waste repository/borehole in a vadose zone environment through experimentation and modeling. In the second quarter (this report) our work has been delayed in part due to COVID-19. LLNL went into shelter in place (SIP), minimum safe operations mid-March. Operations at LLNL have been slowly ramping up with 50% of the workforce back onsite in limited capacity and telecommuting widely used. The good news is that our labs opened up the last week in June and experiments are expected to start up again for WM2 Radionuclide Facilitated Transport in July. LLNL’s contribution to WM1 Thermochemical Damage modeling efforts also started back up in late June. We expect to be able to catch up on much of our work by the end of next quarter. The three national laboratories communication on a bi-monthly basis to make sure Area V projects remain on track and we have a monthly conference call with our Israeli counterparts to make sure we are communicating priorities and coordinating project details.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Reactor Performance and Safety Characteristics of Beryllium-Based Composite Moderators as Replacements for Graphite in mHTGRs

Here, this study evaluates beryllium-based two-phase composite moderators as an alternative to graphite in an evaluation of reactor performance and safety characteristics. Historically, modular high-temperature gas-cooled reactors (mHTGRs) use graphite as a moderator because of its high moderating ratio and reasonable thermal properties; however, graphite has unfavorable properties under irradiation, which can require component replacement and a significant radioactive waste burden. In this assessment, we explore advanced moderators comprised of magnesium oxide (MgO) as the host matrix and beryllium metal and/or beryllium oxide (Be and/or BeO) as the entrained moderating phase. For the reactor performance and thermal-hydraulic safety analysis, the core design model of the General Atomics mHTGR-350 was used to demonstrate the feasibility of a “drop-in” replacement of graphite using the beryllium-based moderators. We employed the neutronics code Serpent to analyze the moderating behavior of the composite moderators with comparisons drawn to graphite. We performed a scoping analysis of accidents for mHTGRs using RELAP to show that these moderators do not present impediments to safety and are expected to stay within temperature limits. Measured thermophysical properties of the composite moderators are used in the thermal-hydraulic assessments. Our analysis reveals that the two-phase composite MgO-matrix beryllium-based moderators are a suitable replacement for graphite.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

UNF-ST&DARDS Enhancements for RCCA Data in As-loaded Dual Purpose Cask Models

This report summarizes the work performed to enable detailed modeling of rod cluster control assembly (RCCAs) in dual purpose canisters (DPCs) in as-loaded configurations using the Used Nuclear Fuel – Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS). The goal of this project was to evaluate the reactivity impact that RCCAs have on k eff of DPCs with pressurized water reactor (PWR) fuel to potentially use the additional margin in future post-closure criticality safety analysis. This preliminary evaluation determined the number of DPCs that currently require compensatory actions prior to emplacement in a repository because of their high reactivity under post-closure criticality scenarios, which could be made acceptable by including the as-loaded RCCAs as specified in the Unified Database (UDB). This report briefly describes the modeling methods currently used within UNF-ST&DARDS and the modifications made to automate inclusion of the as-loaded RCCAs in the DPCs for post-closure criticality calculations for the loss of neutron absorber (NA) scenarios, or NA models. For the Zion site, the loss of basket scenarios, or degraded basket (DB) models, are also included. Discussion is also provided regarding the UDB data compared to available site-specific loading map data for the sites, which are specifically evaluated herein. This report compares the modifications to a similar evaluation for the Zion by site conducted by Walker as an initial validation. After the partial validation, all existing PWR sites with applicable NA models within the UDB were evaluated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Alkali–silica reaction (ASR) is a type of material degradation in concrete structures that leads to concrete cracking and rebar corrosion, thereby reducing the material’s structural integrity and the overall structure’s lifetime and raising safety concerns. Ultrasonic nondestructive evaluation (NDE) has been proven to be a valuable technique for assessing concrete properties and monitoring ASR progression in concrete. However, the deployment and analysis of ultrasonic NDE and its data requires specialized expertise, often relying on the engineer’s subjective interpretation. With the surge in computational power, artificial intelligence (AI) and machine learning (ML) algorithms have become popular in automating NDE data analysis. Various industrial sectors are increasingly adopting ML algorithms for NDE data analysis with a growing emphasis on AI–assisted automation. Regulatory agencies are also preparing for this technological shift, anticipating corresponding revisions in standards. Thus, there is an urgent need to identify the capabilities and limitations of current ML technologies for the evaluation of concrete material properties and damage status. Furthermore, the effects of various factors on ML model performance must be thoroughly investigated. The study summarized herein evaluated the effectiveness of two ML models (i.e., support vector regression (SVR) and deep neural network (DNN)) in predicting concrete material damage induced by ASR based on the long-term ultrasonic monitoring data. Four distinct concrete specimens were cast with artificially induced ASR, and over a period exceeding 500 days, ultrasonic signals and expansion data were continuously collected. For the SVR model, wave velocity and 12 other wave features were extracted from the ultrasonic signals, with 6 out of 13 features selected as input for the model. Different combinations of training and testing datasets were designed to explore factors influencing prediction performance, including the range of data within training and testing sets, in addition to various signal preprocessing methodologies. These findings suggest the importance of using a training dataset with a broader data range compared with testing datasets for improved model performance alongside consistent signal preprocessing across datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Health Physics Research Reactor Criticality Accident Alarm System Benchmark Overview [Slides]

This presentation discusses the Health Physics Research Reactor (HPRR) or Fast Burst Reactor (FBR) and how available data from Health Physics Research Reactor (HPRR) operation to can be used to create a benchmark report for inclusion in the ICSBEP, as a Criticality Accident Alarm System (CAAS) shielding benchmark. The evaluation of experimental data is also presented, specifically four experimental candidates are considered of potential value for the benchmark: 1. Neutron source estimation from a HPRR pulse (energy spectrum, fission yield), 2. Threshold Detector Unit (TDU) measurements at different distances from a HPRR pulse, shielded and unshielded, 3. Sulfur pellet activation at different distances from a HPRR pulse, shielded and unshielded, and 4. Total neutron fluence from a HPRR pulse measured by Bonner Sphere Spectrometry, shielded and unshielded. Additionally, a benchmark model overview is discussed as are sample calculation results.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

NDCS Group Meeting Work overview: NCSP Task ORNL IP&D-5 [Slides]

The presentation discusses the primary goal of the NCSP Task IP&D-5 which is to use available data from HPRR operation to create a benchmark report for inclusion in the ICSBEP, as a Criticality Accident Alarm System (CAAS) shielding benchmark. The presentation elaborates on the HPRR operation and how it relates to the KENO VI model, sulfur pellets measurements, and calculation methodology between KENO and MAVRIC FW-CADIS. The presentation concludes by stating that some work is still needed to evaluate uncertainties and to explain the discrepancy computation/experiment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Efficiency of Room Air Cleaners for Removal of Bioaerosols From Ambient Air

The COVID-19 pandemic necessitated the evaluation of commercial air purification units to protect worker safety in indoor spaces. This project evaluated the efficacy of commercial off-the-shelf air purification systems that utilize electrostatic precipitation for the collection and removal of particulates (including bioaerosols) from ambient air. An aerosol mist containing Escherichia coli (E. coli) and bacteriophage surrogate virus, MS2, was produced using a nebulizer and delivered to the front intake of the commercial system with the ionizer ON and OFF. Removal efficiency was evaluated by colony/plaque formation assays using a small agar plate (60 X 15 mm) to passively collect E. coli and MS2 exhausting from the electrostatic precipitation cell. The system was determined to remove 98-99% of the microorganism introduced to the system. Testing was performed with all pre- and post-electrostatic cell filters removed so further removal is anticipated.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Considerations regarding the Use of Computer Vision Machine Learning in Safety-Related or Risk-Significant Applications in Nuclear Power Plants

With the advancements made to date in the field of artificial intelligence (AI), significant potential exists to utilize AI capabilities for nuclear power plant (NPP) applications. AI can replicate human decision making and it is usually faster and more accurate than humans. For implementations that impact critical NPP applications (e.g., safety-related or non-safety systems that potentially affect overall plant risk), a deeper safety analysis of the AI methods is necessary. AI applied to NPP operations could resemble the use of digital I&C (DI&C) because such applications involve digital computer hardware and custom-designed software that input plant data, execute complex software algorithms, and output the results to a system or licensed human operator to potentially provoke an action. For AI methods to be compliant with current safety requirements for DI&C, AI compatibility must be evaluated, and AI-related gaps may exist that prevent the prompt deployment of AI in NPPs. This effort aims to evaluate how example AI technologies align with the DI&C safety framework, and discusses how they could be analyzed, modeled, tested, and validated in a manner similar to typical DI&C technologies. Because AI is a broad field that encompasses areas such as machine learning (ML), natural language processing, and computer vision, this research focused on a subset of methods categorized as the computer vision ML (CVML) methods. This report explores two CVML use cases, gauge reading and fire watch, considered relevant to the DI&C standards, as they could play a safety-critical role. For the gauge reading use case, a CVML-enabled technology that can read gauges at oblique angles is utilized. For the fire watch use case, a CVML-enabled technology is utilized that migrates fire watch from a manual (human) approach to automated fire detection. These use cases are mainly intended to give context to the CVML system discussion. This effort assumes the worst-case scenario, with the CVML system being used to replace a safety-related or risk-significant system, thus requiring evaluation. Evaluating CVML against most of the relevant safety requirements for DI&C yielded several CVML-specific considerations due to the uniqueness of its characteristics in comparison with typical DI&C systems. For example, CVML models often employ commonly used (open-source) datasets, and it is not always possible to determine the level of overlap among open-source datasets. Therefore, the independence of the developed CVML models when demonstrating diversity is questionable, therefore creating vulnerability to common cause failure (CCF). The design verification process is also impacted since the data overlap could result in overestimation of the software validation and verification (V&V) performance results. Section 2 of this report evaluates a list of the identified CVML-specific characteristics and discusses the resulting considerations and potential solutions in the context of each referenced requirement. A summation is provided in Section 3. This report is not to be used as a guideline. It was developed to identify and consider issues in the implementation of ML technologies used to augment activities that may have a bearing on plant operation. The report draws parallels to the use of DI&C technologies, for which many standards are available to guide their use in nuclear plant operation. It considers the technologies and some of the potential implications of their use in safety-related applications but is not intended to address regulatory or licensing related issues.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Benchmark Evaluation of one Dimensional Array of HEU Moderated and Reflected by Lucite [Poster]

Two critical experiments with HEU-Lucite were performed using the Planet Universal Critical Assembly Machine at Los Alamos National Laboratory in 2019. HEU foils were interleaved with Lucite in a column stack for moderation and reflection from the square Lucite plates. The evaluation of the experiments is presented. A Monte-Carlo (MCNP6) calculation model was developed for the determination of uncertainties and their effect on the multiplication factor. Experimental uncertainties were found to be low with the overall uncertainty in k eff approximately 240 pcm. The comparison of calculations with experimental data is presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Design of Critical Experiments Involving Graphite elements at the IPEN/MB-01 Plate-Type Core

A series of critical experiments were designed involving the use of characterized graphite elements in the new plate-type core of the IPEN/MB-01 reactor. Eleven configurations with different graphite quantities are considered, each producing distinct effects on neutron fluxes and offering opportunities to analyze varying sensitivities to k eff in the system. The analysis shows that the experiments can be performed with acceptably low k eff uncertainties considering an adequate characterization of the graphite elements to be used. The sensitivity results obtained using SCALE TSUNAMI calculations show that the experiments have a notable k eff sensitivity coefficient to graphite. In the configurations tested, the k eff sensitivity to graphite TSL is low. This experiment will be useful for criticality safety validation of applications using uranium fuel enriched to around 19.75 wt % 235 U, light water and graphite. It will also be useful to gain more insight into graphite material properties and their effect on k eff as a result of using the well-characterized graphite elements. The execution of the experiments is planned for the summer of 2025. The 11 designed configurations serve as a basis for the final design, and fewer configurations will be executed. Once executed, these critical experiments will be evaluated and submitted for publication in the International Criticality Safety Benchmark Evaluation Project Handbook, supporting the DOE/NRC Collaboration for Criticality Safety Support for Commercial-Scale HALEU for Fuel Cycles and Transportation project goal.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Updates to the n+ 63,65 Cu RRR+URR Evaluations [Slides]

This work updates neutron reactions on 63,65 Cu. It also discusses the motivations of the research which is to correct deficiencies in benchmark performance since critical assembly configurations use Cu as a reflector. This presentation discusses the incorporation of URR parameters fit to 2017 63 Cu(n, γ) measurement by Weigand et al., as well as preliminary work on angular distributions and states that validation against copper-sensitive ICSBEP benchmarks is ongoing. The presentation concludes by stating that R -matrix analysis coupled to consistent average parameters used in the URR have been performed for 63,65 Cu isotopes, that future work will focus on the angular distributions, and that an analysis of additional data measured on natural Cu sample is in progress.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Transportation Energy Analytics Dashboard (TEAD)

The investment decisions within the nation’s transportation sector have traditionally been prioritized and measured against safety and travel efficiency goals. Increasingly, federal, state, and local policies are requiring consideration of energy use and emissions in the design of our transportation infrastructure. Yet the processes, analytics, and knowledge to include these new metrics into transportation decisions are lacking. Although targeted studies and before and after analysis are performed from time to time, no comprehensive real-time monitoring systems provide energy and emissions to the same level as congestion and safety. To address this gap, the Center for Advanced Transportation Technology (CATT Lab) and the Maryland Transportation Institute (MTI) at the University of Maryland (UMD), in partnership with the National Renewable Energy Laboratory (NREL), have developed a Transportation Energy Analytics Dashboard, or TEAD, to raise awareness of the energy and emission impacts to the same level of observability as that of safety and mobility concerns. The overall goal of this project was to develop and demonstrate an online tool to monitor transportation energy use and emissions in real-time and to archive this data for retrospective analysis. By combining surface transportation energy and emissions evaluation capability with more traditional safety, mobility and reliability system evaluation, our goal is for TEAD to create a dynamic new paradigm in transportation systems management and performance analyses, for both real-time traffic operations, and longer-term planning for project and program investment decision-making.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Sensitivity/Uncertainty Comparison Study Involving IRSN, LANL, and ORNL Tools to Support Validation [Abstract]

Under a DOE Nuclear Criticality Safety Program (NCSP) task involving Analytical Methods, three Laboratories collaborated in a comparison of results obtained from Sensitivity/Uncertainty (S/U) packages relevant to validation of transport codes. The task involves Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Los Alamos National Laboratory (LANL), and Oak Ridge National Laboratory (ORNL) comparing results of MORET 5/MACSENS V3.0, MCNP6.2/Whisper-1.1, and SCALE 6.2.3/TSUNAMI/USLSTATS respectively. All Monte Carlo transport code results utilize nuclear data from ENDF/B-VII.1 evaluation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Updates to the n+ 63,65 Cu Evaluations in the Resolved Resonance Region [Slides]

This presentation discusses the motivation and background of the n+ 63,65 Cu Evaluations in the Resolved Resonance Region which is to study the interaction of neutrons with copper as it is important in nuclear applications since critical assembly configurations include metallic copper as reflector. In support to the U.S. Department of Energy (DOE) Nuclear Criticality Safety Program (NCSP), measurements and related evaluations of 63,65 Cu isotopes were selected to improve the agreement with the benchmarks and to assess the importance of the angular distribution data for reactor calculations. Previous and current evaluation work is supported by an experimental campaign initiated before 2010, the 63,65 Cu R-matrix analysis generated resonance parameters up to 300 keV. However, due to outstanding issues in the benchmark performance, ENDF/B-VIII.0 library released a truncated set of resonance parameters up to 100 keV. The goal of this work is to generate an updated set of resonance parameters in the 100-300 keV range to improve the benchmark performance of 63,65 Cu isotopes. In conclusion, R-matrix analysis to update 63,65 Cu evaluations was performed to simultaneously improve benchmark performance and extend the RRR to 300 keV. The benchmark calculations suggest the increased capture cross sections are beneficial, however, further investigation of the measured capture data is needed to understand the large normalization scaling factor needed to improve the reactivity. Also, the copper-reflected benchmarks indicate the need to further investigate angular distributions and extension of RRR to 300 keV is aided well by level statistics considerations. Work to refine the fit of individual resonances is ongoing.

07 ISOTOPE AND RADIATION SOURCES↗