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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

CalderaCast User Manual Version 1.0

CalderaCast is a user-friendly web-based tool for electrical-load forecast, providing stakeholders with a fully customizable decision-support framework that estimates the likely power draw from a possible future electric-vehicle (EV) charging station at a given location on a given day along an alternative fuel corridor (AFC). These EV charging profiles are accurately modeled in CalderaCast using the Caldera software framework developed by Idaho National Laboratories (INL), reflecting the realistic charging levels observed in actual charge events. This tool was developed as part of the National Electric Vehicle Infrastructure (NEVI) program, which is quickly generating substantial interest from would-be charging station operators (CSO), large and small electric utilities, and state transportation planners, some of whom had not seriously considered EV charging previously. All these entities—with or without background in EV infrastructure—must estimate the electricity load that a proposed charging station will generate. This load forecast is critically important for a utility to properly assess the capacity of their distribution network to support the proposed station or properly size grid upgrades for potential load growth due to future EV adoption, vehicle technology improvements, or station growth. This document describes each aspect of the CalderaCast tool and provides guidance to users who are interested in utilizing the tool for their work.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Leveraging Extremal Dependence to Better Characterize the 2021 Pacific Northwest Heatwave

Abstract In late June, 2021, a devastating heatwave affected the US Pacific Northwest and western Canada, breaking numerous all-time temperature records by large margins and directly causing hundreds of fatalities. The observed 2021 daily maximum temperature across much of the U.S. Pacific Northwest exceeded upper bound estimates obtained from single-station temperature records even after accounting for anthropogenic climate change, meaning that the event could not have been predicted under standard univariate extreme value analysis assumptions. In this work, we utilize a flexible spatial extremes model that considers all stations across the Pacific Northwest domain and accounts for the fact that many stations simultaneously experience extreme temperatures. Our analysis incorporates the effects of anthropogenic forcing and natural climate variability in order to better characterize time-varying changes in the distribution of daily temperature extremes. We show that greenhouse gas forcing, drought conditions and large-scale atmospheric modes of variability all have significant impact on summertime maximum temperatures in this region. Our model represents a significant improvement over corresponding single-station analysis, and our posterior medians of the upper bounds are able to anticipate more than 96% of the observed 2021 high station temperatures after properly accounting for extremal dependence. Supplementary materials accompanying this paper appear online.

Zhang, Likun (ORCID:0000000154584556)↗
DOE OSTI2025

Economics of electric vehicle corridor fast charging in the United States

Corridor direct-current fast charging (DCFC) stations enable long-distance electric vehicle travel, yet their economics remain uncertain due to high capital costs, low initial utilization, and exposure to utility demand charges. This study evaluates the long-term economics of corridor DCFC across the United States, incorporating capital and operating expenses-including charging equipment and real-world utility tariffs-alongside modeled station utilization, financial incentives, and ancillary retail revenue. In the Baseline scenario, modeled breakeven costs for corridor DCFC average $\$$0.42/kWh over 20 years, yet fewer than half of stations reach cost parity with gasoline on a per-mile basis. Utilization is the primary driver of cost variation, with low-utilization stations costing roughly six times more per kilowatt-hour than the national average. Excluding stations that fail to reach cost parity reduces National Highway System coverage within 50 miles from 94% to 67%, underscoring the trade-off between market-driven deployment and comprehensive network coverage. These results provide guidance for charging providers, utilities, planners, and policymakers seeking to develop and sustain a financially viable national corridor charging network.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products

Here we assess the accuracy of estimating daily grass reference evapotranspiration (PM-ET o ) using daily shortwave radiation (R s ) and reference evapotranspiration (ET REF ) products provided by the Meteosat Second Generation (MSG) geostationary satellite delivered by the Satellite Applications Facility on Land Surface Analysis (LSA-SAF) framework. The accuracy of using reanalysis ERA5 shortwave radiation data (R s ERA5 ) provided by the European Center for Medium-Range Weather Forecasts (ECMWF) is also evaluated. The assessments were performed using observed weather variables at 37 weather stations distributed across continental Portugal, where climate conditions range from semi-arid to humid, and 12 weather stations located in Azores islands, characterized by humid, windy and often cloudy conditions. This study&#x27;s use of data from a variety of climate conditions contributed to a unique and innovative assessment of the usability of LSA-SAF and ERA5 products for ET o estimation. The first assessment focused on comparing LSA-SAF estimates of R s (R s LSA-SAF ) against ground stations (R s ground ). The results showed a good matching between the two R s data sets for continental Portugal but a tendency for R s LSA-SAF to under-estimate R s ground in the cloudy islands of Azores. ET o values computed using R s LSA-SAF data and observed temperature, humidity and wind speed (ET o LSA-SAF ) were then compared with PM-ET o estimates with ground-based data, which were used as benchmark; input data of temperature and humidity needed for PM-ET o were quality checked for surface aridity effects. It was observed that ET o LSA-SAF is strongly correlated with PM-ET o (R 2 > 0.97) for most locations in continental Portugal, with regression coefficient of a linear regression forced to the origin ranging between 0.95 and 1.05, mean root mean square error (RMSE) of 0.13 mm d –1 , and Nash and Sutcliff efficiency of modeling (EF) above 0.95. For most Azores locations, ET o LSA-SAF over-estimated PM-ET o . This is likely a consequence of the high spatio-temporal heterogeneity of weather conditions that occur in these oceanic islands together with the different footprints of satellite (averaged over the pixel) and station observations. Reanalysis ERA5 shortwave radiation data presented similar behavior to the LSA-SAF products, however with slightly lower accuracy. The daily LSA-SAF ET REF product (ET REF LSA-SAF ) was assessed and results have shown a good accuracy of this product, with acceptable RMSE and high EF values, for continental Portugal but a low accuracy for the Azores islands. A simplified bias correction approach was shown to improve both ET o derived from the LSA-SAF products, namely for Azores stations, which seem to be representative of smaller areas. The use of the FAO-PM temperature approach (PMT) was also assessed using the R s LSA-SAF and R s ERA5 data, which showed a superiority of the LSA-SAF product for ET o estimations (ET o PMT LSA-SAF ). No significant differences (p < 0.05) were observed in terms of the median value of the RMSE when adopting ET o PMT and ET REF LSA-SAF . Differently, results showed that using the R s LSA-SAF in the PMT approach (ET o PMT LSA-SAF ) produces significantly better RMSE results than ET o PMT and ET REF LSA-SAF . Overall, the performed assessment allows concluding that the use of R s LSA-SAF, and to a lesser extent the use of the R s ERA5 , highly improves the accuracy of computation of ET o when R s observations are not available, including when only temperature data are accessible. The use of the ET REF LSA-SAF product is a good alternative when observed weather data are not available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Optimal design of microseismic monitoring network: Synthetic study for the Kimberlina CO2 storage demonstration site

Microseismic monitoring can play a crucial role to ensure safe long-term geological carbon storage. For reliable long-term monitoring for CO2-injection-induced microseismic events, a surface seismic array is desirable in addition to borehole geophone sensors. Optimal design of surface seismic network is of great interest to achieve cost-effective monitoring. We develop a methodology to determine the optimal number of surface seismic stations with a geometrically satisfactory distribution for given monitoring regions. We design an optimal microseismic monitoring network based on widely-accepted guiding principles, and the relationship between the location accuracy of microseismic events and the total number of seismic stations. We determine the optimal number of seismic stations based on the trade-off curve of the event location accuracy vs. the total number of seismic stations. We apply our optimal design method to the Kimberlina carbon storage site in California. We use a synthetic Kimberlina model to show that approximately 20 surface seismic stations with a geometrically satisfactory distribution are preferred for the best trade-off between the cost and the event location accuracy.

58 GEOSCIENCES↗
DOE OSTI2021

Comparison of near-background concentrations of Argon-37 and Xenon-133 in the Atmosphere

Radioisotopes of noble gases xenon and argon can be important indicators of underground nuclear explosions. The Comprehensive Nuclear-Test-Ban Treaty (CTBT) includes monitoring capabilities to identify potential nuclear explosions conducted in violation of the CTBT. This monitoring currently focuses on measurement of the xenon isotopes 133Xe, 135Xe, 131mXe and 133mXe. However, it is predicted that within 100 days of an underground nuclear explosion (UNE) 37Ar would be released to the atmosphere at higher concentrations than xenon isotopes (Haas et al. 2010) and with a higher signal to background ratio, depending on the radioxenon background levels. Therefore, inclusion of 37Ar measurement capabilities at atmospheric International Monitoring System (IMS) stations may represent an improvement in the capability to detect a nuclear explosion. At an IMS station, it could be difficult to determine what constitutes an elevated concentration as a result of an UNE without first understanding the expected range of background concentrations. This work describes our analysis of atmospheric samples for 37Ar to evaluate the range of background concentrations. Samples were collected at multiple locations, with approximately half coming from a sampler co-located with an IMS xenon monitoring station (RN75). The range of 37Ar concentrations measured in atmospheric air samples was relatively narrow; for samples considered detectable, the minimum and maximum measured concentrations were 0.56 and 2.3 mBq/m3, respectively. Comparison of 37Ar and 133Xe concentrations measured at the IMS station indicated some correlation between the measured concentrations. The results presented here demonstrate the capability to detect background concentrations of 37Ar in atmospheric air and provide a basis for potential implementation of 37Ar monitoring at IMS stations.

Fritz, Brad G.↗
DOE OSTI2020

Integral LOCA fragmentation test on high-burnup fuel

Increasing the fuel burnup limit in light water reactors is sought to enhance fuel cycle economics and requires establishing a technical basis. Experimental observations of severe fuel fragmentation under loss-of-coolant-accident conditions at Halden and Studsvik had raised the need for additional considerations during the development of this technical basis. These test data suggested that the burnup threshold for high burnup fuel fragmentation may be influenced by pre-transient power. Additional loss-of-coolant-accident test data is therefore valuable to complement these tests and enhance the current state of understating. Oak Ridge National Lab has developed the Severe Accident Test Station capable of examining the oxidation kinetics and accident response of irradiated fuel and cladding materials for design basis accident and beyond design basis accident scenarios. Severe Accident Test Station provides various temperature profiles, steam, and the thermal shock conditions necessary for integral loss of coolant accident testing, defueled oxidation quench testing, and high-temperature beyond design basis accident testing. Severe Accident Test Station has been successfully installed and demonstrated in the Irradiated Fuels Examination Laboratory at Oak Ridge National Lab. Furthermore, descriptions of the in-cell re-fabrication capabilities and assembly of the loss-of-coolant-accident test train are provided. Installation of the Severe Accident Test Station system and in-cell re-fabrication restores United States capability to examine postulated and extended loss-of-coolant-accident conditions on spent fuel and cladding and provides a platform for evaluating advanced fuel and accident-tolerant fuel cladding concepts. Lastly, three in-cell integral loss-of-coolant-accident test were performed in the Severe Accident Test Station and compared to the Nuclear Regulatory Commission sponsored loss-of-coolant-accident test as well as the original loss-of-coolant-accident test performed at Argonne National Lab. Finally, the results of these tests as well as all publicly available integral loss-of-coolant-accident test were used to validate a threshold for high burnup fuel fragmentation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Future Intensity‐Duration‐Frequency Curves of Extreme Precipitation in the Midwest United States From Convection‐Permitting Modeling

Abstract During the last four decades, global warming has statistically significant intensified extreme precipitation events in the Midwestern United States (defined here as the region covering Illinois, Indiana, Ohio, and Kentucky), leading to increased risks to human life, property, and infrastructure. To enable climate change adaptation and resilience across various economic and social sectors in this region, updated information about future climate changes, specifically at finer spatial scales, is essential. Leveraging a new 150‐year dynamical downscaling data set at convection‐permitting resolution, this study introduces a framework to construct the projected future intensity‐duration‐frequency (IDF) curves of heavy precipitation, which are prominent tools for infrastructure design and water resources management. This framework generates IDF curves at both sub‐daily and multi‐day duration utilizing hourly in situ observations as well as quantile‐based statistical techniques in bias‐correction and return levels selection. The assumption of non‐stationarity in the distribution parameter fitting process is also implemented in this workflow. Compared to historical IDF curves for 1980–2022, future projected IDF curves for 2058–2100 under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios indicate an average intensity increase of approximately 15% and 25%, respectively, across 74 stations, considering both annual and seasonal timescales. Future projections suggest that extreme precipitation events may become more severe across six investigated return periods, with longer return periods showing a greater increase. The frequency of future extreme precipitation events in the Midwest region is also projected to double. Furthermore, current results reveal spatial heterogeneity of future trends across stations owing to the high‐resolution input data set. Plain Language Summary This study investigates the evolving nature of extreme precipitation events in the Midwestern United States under a changing climate. By leveraging a high‐resolution dynamical downscaling data set, we construct projected intensity‐duration‐frequency (IDF) curves for future extreme rainfall events. These curves serve as vital tools for infrastructure planning and water resource management. Our analysis reveals a significant increase in both the intensity and frequency of extreme precipitation events in the region. Future projected IDF curves for the late century indicate an average intensity increase of approximately 15%–25% compared to historical values. Moreover, the frequency of such events is expected to double. Spatial heterogeneity in future trends is observed across different stations within the Midwest, highlighting the importance of high‐resolution modeling in capturing localized climate variability. These findings underscore the urgent need for climate adaptation strategies to mitigate the increasing risks associated with extreme precipitation events in the region. Key Points This study introduces a workflow to construct future intensity‐duration‐frequency (IDF) curves over the Midwest United States using a new convection‐permitting modeling data set The current IDF construction workflow reproduces well the historical observed IDF 30 curves in summer months with median relative errors of 2.4% among 74 stations and 6 investigated durations The projected IDF curves show diverse future trends of extreme precipitation across stations, with intensity increases of approximately 15% and 25% under RCP4.5 and RCP8.5 climate scenarios, respectively, and a doubling of frequency on average

Nguyen, Trung↗
DOE OSTI2026

A Hierarchical Optimization Method for Electric Vertical Takeoff and Landing Aircraft Network Design

Electric vertical takeoff and landing aircraft (eVTOLs) are expected to serve urban air mobility in a station-to-station configuration, which makes the optimal network design of eVTOL stations a critical question to explore. Existing approaches often face limitations, such as the inability to interact station locations with demand or difficulty in finding the optimal solution for large study regions. Here, this paper first proposes a mathematical model to generate optimal eVTOL station locations while considering associated potential eVTOL demand, and then proposes a heuristic algorithm, Hierarchical Optimization MEthod (HOME), to efficiently solve the model. With a case study of Southern California, HOME was compared to 1) directly solving the original integer linear programming-based network design problem, and 2) employing the widely used genetic algorithm. Results suggest that HOME can find optimal solutions with limited computational resources. The proposed framework powered by HOME provides a computationally efficient way to support urban air mobility planning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting

Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.

Lin, Jiun-Ting [Lawrence Livermore National Labora↗
DOE OSTI2022

Specifications of EBR-II Neutron Radiography Method Description and Digitization Approach

All neutron radiography (NRAD) images of fuel pins in Argonne’s collection were originally generated using the NRAD imaging facility established in the Hot Fuel Examination Facility (HFEF) at Idaho National Laboratory (INL). The NRAD reactor facility was built in 1977 and has been operating since. The reactor is a TRIGA-type reactor operating at a power level of 250 kWth to provide a neutron source for radiography imaging. The reactor is equipped with two beam tubes (i.e., east beam tube and north beam tube) to guide the neutron beams to two radiography stations. The east radiography station is directly under the HFEF main cell and is dedicated for specimens already in the HFEF hot cell. The north radiography station is outside of the main HFEF hot cell and allows NRAD imaging of non-irradiated items. The NRAD images of EBR-II irradiated metallic fuel pins were taken in the east radiography station. Thermal neutrons have the capability to transmit through most materials and are ideal for NRAD imaging. However, because of their high thermal neutron absorption cross-section, fissile materials (e.g., highly-enriched nuclear fuels) may not be as transmissible to thermal neutrons. This is also the case for oversize specimens with extraneous thickness. Epithermal neutron imaging is therefore used as a complement to thermal neutron NRAD imaging. At HFEF’s NRAD facility, both thermal and epithermal neutrons can be used for NRAD imaging. Irradiated nuclear fuels emit high levels of γ radiation that can easily darken X-ray films, so direct exposure NRAD cannot be used to image them. Instead, an indirect NRAD imaging method was developed at HFEF’s NRAD facility. In this method, foils made of materials that can be activated by neutrons (i.e., with large neutron absorption cross section) are used to collect transmitted neutron signals. Then the activated foils are then placed against X-ray films and enclosed in a vacuum cassette so that the γ decay from the activated foils can produce images on the X-ray films. Then, general X-ray film processing procedures are used to digitize and store the images. By using different foil materials, different energy neutrons can be used for NRAD imaging. At the HFEF NRAD station, two types of films are commonly used: dysprosium (Dy) foils with thickness of 130 microns are used to capture thermal neutron signal, while indium (In) foils with thickness of 130 microns are used to capture epithermal neutron signal. A cadmium or gadolinium foil is put before the indium foil to work as a thermal neutron filter. The thermal and epithermal NRAD images can be taken simultaneously by using a Dy/Cd/In sandwiched foil combination. The typical NRAD exposure time is approximately 20 minutes. Then the exposed foils are transferred to film vacuum cassettes. The vacuum ensures that there is no gap between the foil and the film. The foil-to-film exposure time is at least three half-lives of the corresponding radioisotopes, which are 3 hours for In and 7.5 hours for Dy, respectively. Exposed films are processed using an automatic film processor to produce completed NRAD images.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Refueling Infrastructure Deployment in Low-Income and Non-Urban Communities

The U.S. National Blueprint for Transportation Decarbonization identifies the need to invest in infrastructure supporting low- and zero-emission vehicles, especially in low-income and overburdened communities, to eliminate nearly all greenhouse gas emissions from the transportation sector by 2050. The alternative fuel vehicle refueling property tax credit (26 U.S. Code § 30C) includes eligibility criteria intended to encourage investment in underserved communities based on the economic characteristics or urban character of the census tract in which the fueling infrastructure is installed. Eligible census tracts are those that qualify for the New Markets Tax Credit or that are not located within urban areas as defined by the U.S. Census Bureau. This study quantifies how many fueling-related amenities are currently located in census tracts that qualify and do not qualify for the 30C tax credit based on IRS Notice 2024-20. For existing electric vehicle charging stations, 51% of Level 2 and 60% of Direct Current Fast Charging public stations are located in eligible census tracts. 73% of natural gas, propane, and hydrogen fueling stations are in qualifying census tracts and 75% of biodiesel and renewable fuel stations are in qualifying census tracts. This compares with 73% of existing gas stations in eligible census tracts. For deploying the refueling infrastructure to satisfy future demand, this study shows that truck stops (94%), commercial truck stops (92%), and Federal Highway alternative fuel corridors (89%) are predominantly located in eligible locations. Additionally, significant percentages of the population (62%), light-duty vehicle registrations (64%), and medium- and heavy-duty vehicle registrations (68%) fall within eligible areas.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Motivation and Design of the OCPP Security Service

Pacific Northwest National Laboratory is conducting in-depth research aimed at exploring how zero trust security principles can be effectively applied to electric vehicle charging infrastructure. This investigation seeks to enhance the resilience and reliability of these systems against cyber threats, ensuring secure and uninterrupted access to charging services for electric vehicle users and electric supply. Zero trust is a security concept centered on the belief that system operators should not automatically trust users or systems based on their location, whether inside or outside the organization, but instead must verify everything trying to connect to their systems before granting access. A key aspect of the project is to demonstrate and validate zero trust approaches targeted to electric vehicle (EV) charging infrastructure. It has been observed that both open-source and commercial solutions often overlook the specific protocols employed in managing EV charging stations and proceeded with a general, protocol-agnostic approach. While these strategies effectively block non-authorized routes to the charging infrastructure, they do not tackle the situations where attackers may exploit legitimate access channels, such as the inattentive operator model posited by the Idaho National Laboratory. To address this gap, this paper proposes and discusses a new security service targeted to the Open Charge Point Protocol (OCPP), which is the de facto protocol for the management of charging stations and serves a critical role in the broader adoption of electric vehicles. The design and architecture of the proposed OCPP security service are discussed in detail, outlining how it aims to safeguard charging station management system (CSMS) functions. The service is particularly important in scenarios where the charging station operator (CSO), responsible for the maintenance and operation of charging stations, and the charging network provider (CNP), which manages the charging network's accessibility and billing, are separate entities. This distinction is crucial because CSOs and CNPs often have different priorities, objectives, and operational responsibilities, which may not always align perfectly. For instance, a CSO might prioritize uptime and customer satisfaction, while a CNP might focus on maximizing revenue and network utilization. Such misalignment can create security vulnerabilities, as each entity might implement different policies and standards, potentially leaving gaps in the overall security posture.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Rock Valley Direct Comparison Relocation Working Group Location Results and Recommendations

Work accomplished: Collected and compared historic data for the 1993 Rock Valley earthquake sequence; Compared preliminary and prior location work from different location algorithms, phase pick sets, station constellations, and velocity models; Selected a common set of stations that could be used across all location methods for consistency; Reviewed 8 different sets of phase picks and converged on a single, reviewed set of picks for all common stations; Evaluated four pre-existing regional velocity models and incorporated new and preliminary results for five new velocity models that provide information on the very shallow (< 2km) structure near station RTPP; Compared location results from different methods while using the common sets of picks, stations, and velocity models

58 GEOSCIENCES↗
DOE OSTI2020

Beowulf v2.5.3 User Guide: Revision 5

This document is a user guide for the Beowulf (rebranding of Watchmen) tool. It describes how operators can access and utilize the application's functionality. Beowulf is a research software application developed by Pacific Northwest National Laboratory (PNNL) that incorporates the scientific and operational expertise for reviewing data from treaty monitoring radionuclide stations. These stations are part of a worldwide network to monitor for nuclear explosions, and the data they produce are critical to make the determination of whether a sample is from a nuclear explosion or some other source (i.e., nuclear reactor or medical isotope production facility). Stations deliver their measurements and system status to the International Monitoring System (IMS), which forwards it via email to all subscribers. Beowulf is capable of processing data from several radioxenon station types and development is in progress on a solution for particulate stations. Screening of data in Beowulf may be done by a number of different users such as radionuclide analysts, evaluators, and data quality experts. This guide is provided to assist those users in navigating the application. The term Beowulf is used generically throughout this document to refer to any of the various components in the software application. The user interface that is viewed with a web browser is the primary focus of this user guide. Other components include a database to store measurements and state of health (SOH) data; and the data loader that monitors incoming emails, parses the data, populates the database, does the initial analysis, and routes data for review.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Bottleneck Detection in Modular Construction Factories Using Computer Vision

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Multiple Indicators of Extreme Changes in Snow-Dominated Streamflow Regimes, Yakima River Basin Region, USA

Snow plays a major role in the hydrological cycle. Variations in snow duration and timing can have a negative impact on water resources. Excluding predicted changes in snowmelt rates and amounts could result in deleterious infrastructure, military mission, and asset impacts at military bases across the US. A change in snowpack can also lead to water shortages, which in turn can affect the availability of irrigation water. We performed trend analyses of air temperature, snow water equivalent (SWE) at 22 SNOTEL stations, and streamflow extremes for selected rivers in the snow-dependent and heavily irrigated Yakima River Basin (YRB) located in the Pacific Northwest US. There was a clear trend of increasing air temperature in this study area over a 30 year period (water years 1991–2020). All stations indicated an increase in average air temperatures for December (0.97 °C/decade) and January (1.12 °C/decade). There was also an upward trend at most stations in February (0.28 °C/decade). In December–February, the average air temperatures were 0.82 °C/decade. From these trends, we estimate that, by 2060, the average air temperatures for December–February at most (82%) stations will be above freezing. Furthermore, analysis of SWE from selected SNOTEL stations indicated a decreasing trend in historical SWE, and a shift to an earlier peak SWE was also assumed to be occurring due of the shorter snow duration. Decreasing trends in snow duration, rain-on-snow, and snowmelt runoff also resulted from snow modeling simulations of the YRB and the nearby area. We also observed a shift in the timing of snowmelt-driven peak streamflow, as well as a statistically significant increase in winter maximum streamflow and decrease in summer maximum and minimum streamflow trends by 2099. From the streamflow trends and complementary GEV analysis, we show that the YRB basin is a system in transition with earlier peak flows, lower snow-driven maximum streamflow, and higher rainfall-driven summer streamflow. This study highlights the importance of looking at changes in snow across multiple indicators to develop future infrastructure and planning tools to better adapt and mitigate changes in extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Hydrogen and Electric Charging Infrastructure for Heavy-Duty Trucks: A Nationally Scalable Megaregion Assessment

Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint for replacing diesel in over-the-road Class 8 freight truck applications with hydrogen fueling solutions. The study focuses on the Texas Triangle Megaregion (I-45, I-35, and I-10), the I-10 corridor between San Antonio, TX, and Los Angeles, CA, and the I-5/CA-99 corridors between Los Angeles, CA, and San Francisco, CA. This area represents a significant portion of U.S. heavy-duty freight movement, carrying ~8.5% of the national freight volume. Using the OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport) modeling framework, the study conducts an advanced assessment of commercial vehicles, road and freight networks, and energy systems. The framework integrates data on freight mobility, traffic, weather, and energy pathways to deliver a region-specific, optimized vehicles powertrain architectures, infrastructure deployment solutions, operational logistics, and energy pathways. By considering all vehicle origin-destination pairs utilizing these corridors and all feasible fueling station location options, the framework's genetic algorithm identifies the minimum number and optimal locations of hydrogen refueling stations, ensuring no vehicle is stranded. It also determines fuel schedules and quantities at each station. A roadmap for station deployment based on multiple adoption trajectories ensures a strategic rollout of hydrogen refueling infrastructure.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗