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NASA NTRSDate not supplied

Simulating High Energy Dynamic Impact of IM7/PEKK Continuous Fiber Laminated Thermoplastic Composite using Open Hole Coupon Experiments

The use of advanced thermoplastic composites (TPC) for structural applications in the aerospace and automotive industries has grown increasingly popular due to their high performance, high manufactured part output, and sustainability. Enhancing verification simulations of the progressive failure in TPCs at the coupon scale is needed to better validate advanced composite material models at larger length scales, such as element level panels. MAT_213, a tabulated composite material model, has been applied to simulate high velocity dynamic impact (HEDI) and is integrated in the LS-DYNA explicit finite element (FE) software. Previous efforts using MAT_213 have calibrated HEDI simulated failures with experimental data. Traditionally simulations have relied on a structured mesh to represent quasi-isotropic panels. For this study, a continuous fiber unidirectional tape, IM7/PEKK, was analyzed through a series of verification studies of notched laminate coupons, then validation analyses of quasi-isotropic panels under HEDI were compared with experimental data. Experiments using unidirectional TPC were performed to obtain tabulated stress vs strain curves using Digital Image Correlation (DIC) for input into MAT_213. Next, notched coupon experiments were used as an intermediate step to calibrate the damage and failure behavior in MAT_213. Lastly, investigations into differences between HEDI simulations using a fiber aligned mesh were compared with experimental failure patterns and damage sizes. The use of experimentally generated material behavior along with selective mesh layout improved HEDI predictions with minimal material parameter calibration.

Polymer matrix composites↗

NASA NTRSDate not supplied

Improved Assessment of Recent Trends in NOx and VOC Emissions and Ozone Production Sensitivity Regimes Using Satellite Data

This presentation highlights results from a NASA Aura Science Team and Atmospheric Composition Modeling and Analysis Program (ACMAP) project which study the capability to observe and model trends in ozone (O3) production regimes using spaceborne sensors. Ultraviolet– visible (UV–Vis) tropospheric column satellite retrievals of formaldehyde (HCHO) (a proxy for volatile organic compound [VOC] reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen oxides [NOx]) are frequently used to investigate the sensitivity of O3 production to emissions of NOx and VOCs. There are challenges that come from using satellite-derived ratios of HCHO and NO2 (FNR) to study O3 production sensitivity with the largest uncertainties associated with specific spaceborne sensor’s retrieval biases and errors. This study quantifies the differences and improvements in satellite retrievals of O3 production sensitivity regimes using FNRs when moving from legacy polar orbiting satellites such as the Ozone Monitoring Instrument (OMI) onboard NASA’s Aura satellite and Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) onboard the NASA/NOAA Suomi-NPP platform to newer, higher spatiotemporal resolution satellite sensors TROPOspheric Monitoring Instrument (TROPOMI) and eventually the recently launched NASA geostationary sensor Tropospheric Emissions: Monitoring of Pollution (TEMPO). Furthermore, we investigate how using retrievals of NO2 and HCHO from these different satellites to constrain model predictions impacts the ability to accurately simulate O3 chemistry including chemical production regimes. To this end, we have conducted inverse model simulations, using the WRF-CMAQ-DDM data assimilation system at 12 km × 12 km, to constrain emissions of NOx and VOCs over the contiguous United States (CONUS) when assimilating OMI and TROPOMI retrievals of NO2 and HCHO. Two advantages of this are that we a) account for each satellite’s errors/biases in the emission estimation and b) update the prior profile to ensure that only radiance information is used for optimizing the emissions. This presentation will demonstrate: a) the varying accuracy of different satellite retrieved FNRs and ability to capture known sub-annual emission trends (e.g., seasonal, weekend/weekday) and emission anomalies during the COVID-19 lockdown of 2020, b) the differences and improvements in top-down emission estimates of NOx and VOCs when constrained by newer satellite sensors compared to legacy systems, and c) multi-sensor optimized emission estimates of summer-time NOx and VOCs between 2019-2021.

Data↗

NASA NTRSDate not supplied

Investigating the Impacts of Land Use Change on Urban Heat and Vulnerability in Cali, Columbia

The urban heat island effect (UHI) is an environmental phenomenon where cities experience higher temperatures than rural areas due to increased pavement and decreased cooling from vegetation. Approximately 76% of people in Colombia live in urban areas, and the city of Santiago de Cali is facing UHI challenges exacerbated by land use change. Wetlands and forests formerly surrounded the city but were replaced by development and agriculture. The Colombian municipal government agency Departamento Administrativo de Gestión del Medio Ambiente and the community organization Fundacion Dinamizadores Ambientales partnered with NASA DEVELOP to evaluate communities in Cali most vulnerable to urban heat. This project illustrated the utility of using NASA Earth observations to evaluate the relationship between land use, temperature, and social factors in Cali, Colombia between 2013 and 2023. The team used Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), and Landsat 9 OLI-2/TIRS-2 to generate land surface temperature (LST), normalized difference vegetation index (NDVI), and albedo maps in Google Earth Engine. Heavy cloud cover limited the accuracy of the LST but incorporating up to three satellites for a median image reduced potential errors. Through further analysis in ArcGIS Pro, the team classified land use change using a deep learning model and found that LST was significantly higher in urban areas than in wetlands or forests. Using R studio, the team ran a principal component analysis to determine which social factors had the strongest correlation with LST. The team found that health care and green space access were negatively correlated, and Afro-Colombian ethnicity was positively correlated with LST. With awareness of the most impacted and vulnerable regions, the partner organizations can work to prioritize green space establishment in those areas to reduce the impacts of urban heat. Addressing the urban heat island effect will reduce environmental justice concerns within the city and improve overall health, air, and water quality for those who live there.

Brenna Bruffey↗

NASA NTRSDate not supplied

Variable Lidar Ratios for Marine and Dusty Marine Using MODIS AOD Constrained Retrievals and GOCART Model Simulations and their Impact on Level 2 CALIPSO Data Products in Version 5

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard CALIPSO provided global measurements of attenuated backscatter profiles of various tropospheric aerosol species from June 2006 through June 2023. Extinction profiles are retrieved from these backscatter measurements by assuming a value for the extinction-to-backscatter or lidar ratio (LR). Up until version V4.51 of the data products, only a constant global value for each of the species has been used, spatially and temporally. However, it has been well known from various ground-based measurements that LRs of each aerosol species can potentially vary spatially as well seasonally. For the forthcoming Version 5 of the CALIPSO products, the CALIPSO team will implement variable lidar ratios at 532 nm for the species classified as “marine” and “dusty marine”. Here we present the essential elements of this scheme, which involves estimating lidar ratios by constraining Fernald solutions for the CALIOP particulate backscatter profiles using aerosol optical depths (AOD) from collocated MODIS retrievals. To account for the lack of measurements in various regions and seasons (e.g., Arctic winters), we leverage collocated model sea-salt volume fractions (SSVF) simulated by the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model and use an empirical relationship between the SSVF and LR to build hybrid climatological maps for the entire globe, i.e., using the modelled values when reliable values of the constrained LRs are not available. Initial evaluation of the resulting extinction profiles and corresponding AODs, both globally and in specific regions are presented using a limited amount of data. The primary improvement in AOD occurs in the coastal areas, like the Arabian Sea and Bay of Bengal. The estimated marine LR values in these regions are significantly higher than the constant value of 23 sr used in previous data releases, likely due to mixing with offshore pollution which results in higher AODs. Initial validation results using the CALIPSO Ocean Derived Column Optical Depth (ODCOD) retrievals and ground-based AERONET AOD measurements are presented.

CALIPSO↗

DOE OSTI2021

Introducing the GeoRePORT Resource Size Tool: Reporting on Geothermal Resource Size Estimations Using the Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT): Preprint

The Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT) was developed with funding from the U.S. Department of Energy Geothermal Technologies Office to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. The assessment protocols used in GeoRePORT allow for comparison of project attributes across locations and geological settings to understand the feasibility of geothermal development. This work introduces the Resource Size Tool, a new feature within the GeoRePORT package that compiles two independent methods for estimating geothermal resource size in terms of energy capacity in MW. Energy production potential for twenty-three case studies was estimated with the Resource Size Tool in order to 1) generate a reasonable range of resource size estimates for a particular geothermal field; 2) illustrate the advantages and limitations of each methodology (such as data input requirements, estimate accuracy and precision, and the appropriate circumstances of use); and 3) test the ability of the resource size tool to provide useful and accurate information for geothermal stakeholders. The tool employs two methods widely used in the geothermal industry: (1) USGS Volumetric and (2) Power Density. Results from our case studies show general overlap between these two methods in terms of resource size estimates; however, they also reveal key differences between the two approaches that should be considered when using such estimates to drive development. First, the two methods rely on different input parameters and therefore one method may be more appropriate and/or accurate for a given project than the other. Second, the Power Density method was found to generate wider ranges of resource size predictions, more consistently aligning with actual power production of the field but with larger scales of error; whereas the USGS Volumetric method predicts narrower ranges but tends to overestimate when compared to current MW production. Future work will refine variables used in the methods with input data from other sections of GeoRePORT and modify uncertainty levels based on the particular datasets used for a given project.

geological↗

DOE OSTI2021

Cascaded systems and methods for controlling energy use during a demand limiting period

A cascaded control system is configured to control power consumption of a building during a demand limiting period. The cascaded control system includes an energy use setpoint generator and a feedback controller. The energy use setpoint generator is configured to use energy pricing data and measurements of a variable condition within the building to generate an energy use setpoint during the demand limiting period. The feedback controller is configured to use a difference between the energy use setpoint and a measured energy use to generate a control signal for building equipment that operate to affect the variable condition within the building during the demand limiting period.

Wenzel, Michael J.↗

DOE OSTI2021

MapSpace: POI-based Multi-Scale Global Land Use Modeling

Accurate and up-to-date land use maps are important to the study of human-environment interactions, urban morphology, environmental justice, etc. Traditional land use mapping approaches involve several surveys and expert knowledge of the region to be mapped. While traditional approaches generate accurate and authoritative maps, it is expensive and takes a long time to develop a new version of map. Besides, such maps have region-specific spatial embedding, making them difficult to benchmark and compare against other land use maps. This work introduces a scalable POI-based land use modeling approach to generate global land use maps at multiple spatial scales and different semantic granularities. In addition, our land use maps adhere to a unified land use categories and can be compared for accuracy and precision.

Thakur, Gautam Malviya↗

DOE OSTI2022

Probabilistic Modeling of Commercial Building Occupancy Patterns Using Location-Based Map Data: Preprint

Considering occupancy patterns is crucial to simulate buildings' energy use. Current energy models use inputs that simplify the actual diversity in occupancy into static occupancy patterns and are not able to represent the numerous variations in occupancy patterns between buildings and across different locations. Recently, inferring occupancy schedules from metered electricity consumption data was used to model occupancy in commercial buildings. However, the translation from metered data to occupancy schedules requires many assumptions that might not capture the reality, and the process is hindered by the availability of data from advanced metering infrastructure. With the development of information technologies, occupancy modeling should not be limited to traditional approaches. The prevalence of social networks and location services with real-time user feedback provides publicly accessible data via Maps Application Programming Interfaces (APIs) such as Google Maps, SafeGraph, Mapbox, Foursquare, etc. This paper presents an automated framework for modeling parametric occupancy patterns using such APIs to calibrate commercial district buildings' energy models. This process includes three main steps: data extraction and processing, parametric schedules generation, and schedules integration. We demonstrated this framework in districts where we used maps API to generate more accurate behavioral patterns for operations and electric vehicle charging events. We used these patterns to determine differences in energy use across key sociodemographic and spatial parameters. The presented method has the potential for worldwide applications. Users can utilize this framework to extract data for selected locations of interest to create more realistic behavioral patterns for commercial facilities across different districts.

building energy modeling↗

DOE OSTI2023

An Experimental Investigation of Students’ Learning Effects When Using a Simplified Nuclear Simulator

Securing enough data has been a main challenge in human reliability analysis (HRA). Many researchers and institutes have made a lot of efforts for collecting HRA data to produce reasonable human error probabilities (HEPs) as well as reduce the uncertainty of HRA quantification. Representatively, U.S. Nuclear Regulatory Commission (U.S. NRC), Korea Atomic Energy Research Institute (KAERI) and Idaho National Laboratory (INL) have led lots of empirical research regarding the HRA data collection. The U.S. NRC and KAERI have mainly carried out full-scope simulator research collecting HRA data through experiments using full-scope simulators with actual operators. In contrast, INL has experimentally collected the data using simplified simulators and student operators. INL has proposed the Simplified Human Error Experimental Program (SHEEP) framework to complement full-scope data collection efforts by suggesting a way to infer full-scope data based on experimental data collected from students operating simplified simulators, specifically the Rancor Microworld Simulator (Rancor) and the Compact Nuclear Simulator (CNS). The aim of the SHEEP framework is to lower the entry point for collecting useful HRA data by securing large sample sizes at a reasonable amount of cost and labor while also guaranteeing a high degree of freedom when designing experiments. The authors’ previous research investigated whether data collected from the SHEEP framework could support a representative full-scope study. Besides, human performance differences between professional and student operators when using Rancor and CNS have been analyzed to understand the lack of fidelity of the simplified simulators and student operators within the SHEEP study. As a follow up research, this study experimentally investigates students’ learning effects and the performance trends over a certain period when using Rancor. This study aims to find out 1) how much training or education is required to collect HRA data from non-experts (i.e., students) when using Rancor and 2) how much differences there are in human performance measures between students and professional operators. In this study, a longitudinal experiment is developed. The four experiment trials with two weeks interval are carried out for sixteen undergraduate students majoring nuclear engineering at Chosun University. Totally four scenarios randomly selected from ten Rancor scenarios are used in each experiment trial. Four human performance measurements (i.e., workload, situation awareness, time and error) are considered in the experiment. Lastly, the trend of students’ performance is compared with operator data having been collected from the previous experiment.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Using High-Resolution NSRDB Data to Evaluate Cloud Mask Forecast from WRF-Solar EPS

Validating spatiotemporal distributions of cloud forecasts using numerical weather prediction (NWP) models is difficult as this requires high-quality cloud-property at significantly high spatial and temporal resolution over extended periods of time. Observations of cloud properties, such as cloud mask, cloud optical thickness, and cloud type, are vital for assessing the capability of NWP models to forecast various types of clouds. Using the National Solar Radiation Database (NSRDB), this research evaluates ensemble cloud-mask predictions from the WRF-Solar ensemble prediction system (WRF-Solar EPS). From the WRF-Solar EPS, day-ahead solar forecasts for the contiguous United States (CONUS) for 2018 are simulated. Given the NSRDB data is accessible at a resolution of 2 km, we can calculate the cloud fraction across the 9-km grid of WRF-Solar EPS. This allows us to spatially assess the cloud-mask forecasts using two methods against the high-resolution NSRDB: (a) considering all 2-km NSRDB clouds in the forecast domain (EMAll), and (b) using a minimum cloud fraction threshold of 50% to designate a pixel as cloudy (EMP50). The low-resolution cloud masks from WRF-Solar EPS are evaluated directly against the cloud-resolving scale gridded observations from NSRDB using EMAll. With EMP50, we presume that scenes with less than 50% cloud cover from the 2-km NSRDB are clear. Thus, this assessment approach allows for a fair comparison with WRF-Solar EPS resolved to a 9-km grid. A method of point-by-point verification is used to evaluate dichotomous (yes/no) cloud mask predictions against the NSRDB. For each pixel of model extent, cloud frequency and traditional metrics (e.g., probability of detection, false alarm rate, and hit rate, etc.) are computed and compared with satellite-derived data sets. Mismatched cloud frequency (MCF) is computed to measure the present capability of WRF-Solar EPS in representing various types of clouds, which are categorized using three levels of cloud top height (CTH) and cloud optical depth (COD) across entire CONUS. Preliminary results show that the WRF-Solar EPS provides MCF values ranging from 9% to 46%, 16% to 33%, and 8% to 27% for low-level, middle-level, and high-level clouds, respectively, for three CTHs. The model produces MCFs ranging from 27% to 46%, 13% to 34%, and 8% to 19% for thin, medium-thickness, and thick clouds, respectively, for three CODs. The presentation will include a detailed description of the current outcomes as well as potential future extensions. The evaluation approach established in this study is readily extensible to the evaluation of cloud predictions from different ensemble NWP models. In addition, the findings of the suggested evaluation technique aid in identifying model weaknesses and will ultimately lead to advances in WRF-Solar EPS's skill in predicting clouds and solar irradiance.

cloud mask forecast↗

DOE OSTI2020

Use of Unmanned Aerial Systems for Post-closure Waste Site Maintenance and Surveillance - 20143

The P- and R-Area Reactor buildings, located at SRS near Aiken, SC, were in-situ decommissioned by grouting below-grade portions of the buildings and demolishing some above-grade structures to grade level. Other reactor building structures were left above-grade and sealed to prevent human or animal access. Because the above grade-structures are expected to continue in their present state for hundreds of years, the condition of the building roofs is critical to mitigate rainwater intrusion. For this reason, building roof areas were strengthened with high strength concrete to ensure the long-term integrity of the roof structure. Periodic inspections of the roof structures are required to ensure that the roofs are functioning properly and to identify damage areas. The traditional method of inspecting closed reactor buildings requires the use of a helicopter and photographer to generate photographs and videos for review. This method proved sufficient but lacked the resolution and clarity that is required for a thorough inspection of the building structure. Fortunately, the Savannah River National Laboratory (SRNL) has established a Small Unmanned Aircraft System (sUAS) program using commercially available and custom-built remote-controlled aircraft. EC and ACP contacted the SRNL program to evaluate whether the sUAS technology could be employed for periodic reactor building inspections. The sUAS can fly within a few meters of the reactor buildings and hover, allowing for a more thorough aerial inspection. The initial sUAS inspection of the P-Area Reactor building was completed in February 2018 and at the R-Area Reactor building in August 2018 and were successful in providing higher resolution photos and videos. The sUAS inspections also revealed that vegetation had begun to grow on the roofs that could potentially damage the structure integrity. SRNL partnered with Virginia Polytechnic Institute and State University to build a custom heavy lift sUAS that could be equipped with herbicides to remotely treat the vegetative growth. Herbicides were successfully applied to the R-Area Reactor building roof using the custom sUAS in September 2018. The use of unmanned aircraft systems at SRS to perform aerial inspections and herbicide treatment in otherwise inaccessible areas has proven to be an efficient and cost-effective technology that provides high value, increases knowledge of facility conditions, and provides for early detection of damage. Periodic inspections of the reactor building roofs using sUAS technology are performed safely, efficiently, and at a significant cost and schedule savings while reducing emissions, noise, and fossil fuel use. The use of sUAS equipment for building inspections and herbicide application at SRS is unique within the DOE complex. The goal of SRS is to apply this remote technology to other waste unit operations and maintenance activities in the future. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

The Use of Long-wave Infrared Cameras for Hazardous Waste Remediation - 20457

For over fifty years, infrared cameras have been used in military applications, nondestructive testing, condition monitoring, and predictive maintenance. As infrared cameras continue to become more sophisticated and less expensive, they are providing value in an ever-increasing variety of unique applications, including hazardous waste remediation. This paper describes several instances of how infrared cameras have been used within the US Department of Defense and the US Department of Energy in support of waste remediation projects, including the author's recent use of an infrared camera in support of the Calcine Retrieval Project at the Idaho National Laboratory. Infrared cameras provide images of infrared radiation, or heat energy, which is otherwise invisible to the unaided eye. Infrared radiation is part of the electromagnetic spectrum, which includes visible light. But unlike visible light, infrared has wavelengths longer than the human eye can detect. Infrared is emitted by everything with a temperature above absolute zero (-273 deg. C, or -459 deg. F); the higher the temperature, the greater the infrared thermal radiation, or heat, that is emitted. Even objects that feel cold to us, like ice, emit thermal radiation and can be imaged by infrared cameras. These cameras are typically used to look for abnormally hot or cold spots on a component or target area under normal operating conditions. The method provides a rapid, wide-area, noncontact technique for identifying problems associated with a temperature differential. All infrared cameras can provide qualitative thermal information by displaying relative differences in temperatures within a two-dimensional image. More expensive infrared cameras can also provide quantitative information where an absolute temperature value is assigned to each pixel associated with the displayed two-dimensional image. Proper camera calibration and a solid understanding of heat transfer and thermography techniques are required when using an infrared camera to obtain quantitative information. Case studies outlined in this paper include the rapid, non-intrusive detection of hazardous decontamination solution within one-ton shipping containers at Pine Bluff Arsenal, the non-intrusive identification of residual elemental sodium within the cooling loops of the Experimental Breeder Reactor II (EBR-II) reactor at the Idaho National Laboratory, process monitoring of heat exchanger melt-and-drain efforts during EBR-II decommissioning, and the remote detection of internal steel supports within calcine storage bins prior to bin penetration. For each use of thermography, the author describes a summary of the waste remediation effort, the infrared camera used, the thermal imaging technique employed, and the results obtained. The paper concludes with a discussion on common mistakes to avoid for similar applications of thermography. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Measurement of the muon spin precession frequency using the straw tracking detectors at the Fermilab Muon g-2 experiment

The measurement of the anomalous magnetic dipole moment of the muon ($a_{\mu}$) has long stood as an excellent precision test of the Standard Model (SM). The Fermilab Muon g-2 experiment has recently finished data-taking and in July 2023 published its latest determination of $a_\mu$ with a world-leading precision of 0.2\,ppm. In this publication, it surpassed the systematic uncertainty goal defined in the TDR. The analyses of a dataset approximately four times larger than this recent publication is now underway. The principle measurement of the Muon g-2 experiment measures $a_{\mu}$ by taking the ratio of two frequencies; the anomalous precession frequency ($\omega_a$) and the muon-weighted magnetic field of the experiment's storage ring measured from the precession frequency of protons in water using nuclear magnetic resonance (NMR) probes. In all publications to date, $\omega_a$ has been determined using energy deposits in the 24 calorimeters. However, the Fermilab experiment has t wo straw tracker detectors measuring the time and momentum of charged particles which can in principle also be used to to measure $\omega_a$ and such a measurement can provide an invaluable cross-check of the calorimeter result with different, and reduced, systematic uncertainties. This thesis presents the first (blinded) determination of $\omega_a$ using just charged tracks from the straw tracking detectors as opposed to calorimeter energy deposits. This analysis was undertaken using the Run-2/3 dataset which represents approximately 25\% of the final dataset. A total uncertainty of 2.19\,ppm on $\omega_a$ was obtained which is dominated by the statistical uncertainty of 2.16\,ppm. Additionally two new methodologies important to the analysis of the straw tracking data have been developed: one to better determine the track arrival time ($t_0$) and one to determine the level of pileup in the tracking detectors. The new $t_0$ algorithm which incorporates angular information improves t he resolution on the determination of the $t_0$ by a factor of two and results in 19\% more tracks being successfully reconstructed. The data from the trackers is also used to determine the beam profile that weights the magnetic field in the determination of $a_\mu$ and in determining several of the systematic uncertainties in the calorimeter-based $\omega_a$ analysis. A detailed study of the impact of the internal alignment of the tracker, the $t_0$ and pileup on the determination of the beam position was undertaken and propagated through to an uncertainty in the $\omega_a$ determination. These uncertainties were used in the Fermilab Muon g-2 experiment's recent publication in Phys. Rev. Lett.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Plant-specific Model and Data Analysis using Dynamic Security Modeling and Simulation

The requirements for U.S. nuclear power plants to maintain a large on-site physical security force contribute to their high operational costs. The cost of maintaining the current physical security posture is approximately 10% of the overall operation and maintenance budget for commercial nuclear power plants. The goal of the Light Water Reactor Sustainability (LWRS) program’s physical security pathway is to develop tools, methods, and technologies and provide the technical basis for an optimized physical security posture. The conservatisms built into current security postures may be analyzed and minimized in order to reduce security costs while still ensuring adequate security and operational safety. The research performed at Idaho National Laboratory within LWRS program’s physical security pathway has successfully developed a dynamic force-on-force modeling framework using various computer simulation tools and integrating them with the dynamic assessment Event Modeling Risk Assessment using Linked Diagrams (EMRALD) tool. This document provides an update on the progress in applying a dynamic computational framework that links results from a commercially available force-on-force simulation tool, a commercially available thermal-hydraulic tool, and EMRALD to an operating commercial nuclear power plant. This report is only a summary of the progress and does not contain specific modeling results as those contain sensitive security information. This process of including plant procedures and multiple analysis results is being called Modeling and Analysis for Safety Security using Dynamic EMRALD Framework or MASS-DEF. Previous reports described how a user could integrate their plant-specific force-on-force models with the dynamic simulation tool EMRALD, model operator actions, integrate with probabilistic risk assessment tools, such as CAFTA (Computer Aided Fault Tree Analysis System) or SAPHIRE (Systems Analysis Programs for Hands-on Integrated Reliability Evaluations), and with thermal-hydraulic tools, such as RELAP-5. Previous reports applied various combinations of available simulations codes with EMRALD using generic plant models to demonstrate how to perform the analysis. This report documents the results of applying the dynamic computational framework to an actual nuclear facility using their security scenarios and timelines. This report does not contain any plant's sensitive information and/or Safeguards Information. The purpose of this study was to verify that results achieved using generic models are similar to actual plant results and to refine our guidance on the use of the framework. This assessment enables further analysis, such as what-if scenarios and staff-reduction evaluation, thereby optimizing physical security at plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Investigation of CAD-based Geometry Workflows for Multiphysics Fusion Problems Using OpenMC and MOOSE

Fusion system designs are complex and require intricate and accurate meshes to be properly modeled. In this study, we investigate the use of CAD-based geometry workflows in fusion systems multiphysics problems. A simplified tokamak was introduced and modeled in CAD using a multiphysics coupling of OpenMC Monte Carlo transport and MOOSE heat conduction. The meshed geometry was prepared using direct accelerated geometry Monte Carlo (DAGMC) for particle transport, and a volumetric mesh was also prepared to be used in MOOSE and to tally OpenMC results. Cardinal was used to run OpenMC Monte Carlo particle transport within MOOSE framework. The heat source distribution and tritium production were calculated in OpenMC. The data transfer system was used to transfer heat source and temperature distribution between OpenMC and MOOSE. Two computational studies related to mesh refinement were performed: (1) refining the DAGMC and volumetric meshes used for tallying results and solving heat conduction and (2) only refining the DAGMC particle transport mesh. The refinement of the tally mesh has a much larger effect on the runtime compared to the refinement of the DAGMC particle transport surface mesh.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1999

Detection of endogenous alkaline phosphatase activity in intact cells by flow cytometry using the fluorogenic ELF-97 phosphatase substrate

BACKGROUND: The alkaline phosphatase (AP) substrate 2-(5'-chloro-2'-phosphoryloxyphenyl)-6-chloro-4-(3H)-quinazolinone (ELF((R))-97 for enzyme-labeled fluorescence) has been found useful for the histochemical detection of endogenous AP activity and AP-tagged proteins and oligonucleotide probes. In this study, we evaluated its effectiveness at detecting endogenous AP activity by flow cytometry. METHODS: The ELF-97 phosphatase substrate was used to detect endogenous AP activity in UMR-106 rat osteosarcoma cells and primary cultures of chick chondrocytes. Cells were labeled with the ELF-97 reagent and analyzed by flow cytometry using an argon ultraviolet (UV) laser. For comparison purposes, cells were also assayed for AP using a Fast Red Violet LB azo dye assay previously described for use in detecting AP activity by flow cytometry. RESULTS: The ELF-97 phosphatase substrate effectively detected endogenous AP activity in UMR-106 cells, with over 95% of the resulting fluorescent signal resulting from AP-specific activity (as determined by levamisole inhibition of AP activity). In contrast, less than 70% of the fluorescent signal from the Fast Red Violet LB (FRV) assay was AP-dependent, reflecting the high intrinsic fluorescence of the unreacted components. The ELF-97 phosphatase assay was also able to detect very low AP activity in chick chondrocytes that was undetectable by the azo dye method. CONCLUSIONS: The ELF-97 phosphatase assay was able to detect endogenous AP activity in fixed mammalian and avian cells by flow cytometry with superior sensitivity to previously described assays. This work also shows the applicability of ELF-97 to flow cytometry, supplementing its previously demonstrated histochemical applications. Copyright 1999 Wiley-Liss, Inc.

Non-NASA Center↗

DOE OSTI2026

Biorefinery siting and sizing to achieve the US Billion‐Ton Bioeconomy vision: A case study using a gasification–Fischer–Tropsch process

Achieving a secure, abundant, and affordable energy future requires a robust and adaptable energy strategy, with bioenergy playing a pivotal role. Biomass-based energy presents a promising pathway to use domestic resources while fostering economic opportunities in rural areas. Despite the potential to source more than 1 billion dry short tons of biomass annually in the US, significant infrastructure and economic barriers hinder full utilization for energy production. This study used the Biofuel Infrastructure, Logistics, and Transportation (BILT) model to assess biorefinery siting and scale and determine the number and size of facilities required to maximize use of the US biomass potential. A spatially agnostic approach first assessed the effects of facility capacity and transportation constraints on biomass use. Then, a spatially explicit analysis integrated county-level biomass availability from the US Department of Energy's 2023 Billion-Ton Report and technoeconomic assessments to evaluate different biorefinery deployment scenarios. The results indicate that an optimized mix of facility sizes is essential to leverage biomass resources fully across varying regional production densities to maximize use of the US biomass potential. Larger biorefineries or co-located smaller facilities significantly enhance biomass use while reducing costs through economies of scale. These findings underscore the importance of strategically balancing facility capacity and spatial distribution to optimize the bioenergy supply chain. In conclusion, this study provides critical insights for advancing the US bioenergy economy by aligning biorefinery deployment with biomass resource availability and economic viability.

BILT Model↗

DOE OSTI2021

Plant functional types broadly describe water use strategies in the Caatinga, a seasonally dry tropical forest in northeast Brazil

In seasonally dry tropical forests, plant functional type can be classified as deciduous low wood density, deciduous high wood density, or evergreen high wood density species. While deciduousness is often associated with drought-avoidance and low wood density is often associated with tissue water storage, the degree to which these functional types may correspond to diverging and unique water use strategies has not been extensively tested. We examined (a) tolerance to water stress, measured by predawn and mid-day leaf water potential; (b) water use efficiency, measured via foliar δ 13 C; and (c) access to soil water, measured via stem water δ 18 O. We found that deciduous low wood density species maintain high leaf water potential and low water use efficiency. Deciduous high wood density species have lower leaf water potential and variable water use efficiency. Both groups rely on shallow soil water. Evergreen high wood density species have low leaf water potential, higher water use efficiency, and access alternative water sources. These findings indicate that deciduous low wood density species are drought avoiders, with a specialized strategy for storing root and stem water. Deciduous high wood density species are moderately drought tolerant, and evergreen high wood density species are the most drought tolerant group. Our results broadly support the plant functional type framework as a way to understand water use strategies, but also highlight species-level differences.

54 ENVIRONMENTAL SCIENCES↗