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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2023

A Distributed Temperature Profiling System for Vertically and Laterally Dense Acquisition of Soil and Snow Temperature: Supporting Data

This dataset was used to assess the potential of a Distributed Temperature Profiling System developed and presented in the article named "A Distributed Temperature Profiling System for Vertically and Laterally Dense Acquisition of Soil and Snow Temperature" and submitted to Cryosphere. There are three comma-delimited data files (.csv). Each of them contains snow or soil temperature with times (UTC) in the first column and temperature at various vertical distance from the ground surface (positive means in air/snow) or below the ground surface (negative means in soil). The measurements were acquired every 15 minutes. Data collected at the East River, Colorado site with two probes: 1) measuring air/snow temperature from 1.1 m above the ground surface to the ground surface (with 5 or 10 cm spacing between sensors) and 2) measuring soil temperature from the ground surface to 0.7 m depth (with 5 or 10 cm spacing between sensors). Data collected at the Teller road (mile 27) site near Nome, Alaska with one probe: measuring soil temperature from 5 cm above the ground surface to 1.05 m depth (with 5 or 10 cm spacing between sensors). Additional metadata included in *.csv and *.pdf files. NGEE Arctic Project Summary: The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Controllable 3D Manufacturing System

Next generation Small Modular Reactor (SMR) requires new and improved techniques, instrumentations and strategies to deal with the anticipated high radiation and high temperature environment (esp. in reactors). Component manufacturing technologies will be required that take full advantage of the new 3-D printing methods employed by additive manufacturing technologies. These manufacturing methods must be capable of producing components or sub components on a limited production basis and with nuclear quality. Based on our success in developing a variety of award-winning high energy and high power ultrafast fiber lasers and our pioneered work in fs laser 3D manufacturing (US patents, 9643361 and 9770760), PolarOnyx proposes to use tunable fiber lasers to develop smart AM & SM system to make nuclear quality components for SMR. In Phase I, we have achieved critical milestones and experimentally demonstrated the most critical functions of the proposed controllable laser AM: • Developed and investigated a series of NDI sensing technologies. Combination of IR thermal image and high resolution visible camera provides a powerful tool to characterize thermal distribution and defects. • Built an in-line laser induced breakdown spectroscopy (LIBS) to real time detect compositions of powder and parts, and melting temperature of the melting pool. It proves the LIBS is a very powerful tool to precisely guide the process. • Filed a patent (Application number 16378485) for integrating IR and visible images and LIBS for intelligent AM system. • Developed control algorithm and proved it works well for overhang experiment. • Achieved high density (>99%) and excellent mechanical properties. • Succeeded in developing a working and reliable calibration method to extract true temperature distribution during AM process. • Demonstrated spatial and temporal pulse shaping methods, and impact on microstructures. • Filed one utility patent • Laid out a solid path for phase II to work on controllable 3D manufacturing system development and optimization and to make components with nuclear quality. Our proprietary fs fiber laser based AM processing offers an ultimate solution for manufacturing high temperature components with high strength for nuclear systems. The 3D AM system in conjunction with the intelligent sensing and control is energy efficient and cost effective for the growing nuclear industries. Successful development of the proposed AM technology will provide a vital tool to solve existing and potential issues. This technology will be directly applied in the 3D printing area with combined market share of $10(s) of billions. The achievement in this research will have significant and wide social, scientific, and economic impacts by providing affordable low cost intelligent AM/SM systems, laser micromachining systems, improving energy efficiency, and reducing fabrication and operational cost. Having demonstrated the proof of concept in Phase I, we will prototype the controllable AM/SM system and investigate the efficiency and qualification of fabricating various nuclear quality components in Phase II.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Effective HTCondor-based monitoring system for CMS

The CMS experiment at the LHC relies on HTCondor and glideinWMS as its primary batch and pilot-based Grid provisioning systems, respectively. Given the scale of the global queue in CMS, the operators found it increasingly difficult to monitor the pool to find problems and fix them. The operators had to rely on several different web pages, with several different levels of information, and sift tirelessly through log files in order to monitor the pool completely. Therefore, coming up with a suitable monitoring system was one of the crucial items before the beginning of the LHC Run 2 in order to ensure early detection of issues and to give a good overview of the whole pool. Our new monitoring page utilizes the HTCondor ClassAd information to provide a complete picture of the whole submission infrastructure in CMS. The monitoring page includes useful information from HTCondor schedulers, central managers, the glideinWMS frontend, and factories. It also incorporates information about users and tasks making it easy for operators to provide support and debug issues.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2023

Effectiveness and predictability of in-network storage cache for Scientific Workflows

Large scientific collaborations often have multiple scientists accessing the same set of files while doing different analyses, which create repeated accesses to the large amounts of shared data located far away. These data accesses have long latency due to distance and occupy the limited bandwidth available over the wide-area network. To reduce the wide-area network traffic and the data access latency, regional data storage caches have been installed as a new networking service. To study the effectiveness of such a cache system in scientific applications, we examine the Southern California Petabyte Scale Cache for a high-energy physics experiment. By examining about 3TB of operational logs, we show that this cache removed 67.6% of file requests from the wide-area network and reduced the traffic volume on wide-area network by 12. 3TB (or 35.4%) an average day. The reduction in the traffic volume (35.4%) is less than the reduction in file counts (67.6%) because the larger files are less likely to be reused. Due to this difference in data access patterns, the cache system has implemented a policy to avoid evicting smaller files when processing larger files. We also build a machine learning model to study the predictability of the cache behavior. Tests show that this model is able to accurately predict the cache accesses, cache misses, and network throughput, making the model useful for future studies on resource provisioning and planning.

Sim, Caitlin↗
DOE OSTI2020

A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task

A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.

Science & Technology - Other Topics↗
DOE OSTI2021

AGR 5/6/7 Data Qualification Report for ATR Cycles 162B through 168A

This report provides the qualification status of experimental data for the Advanced Gas Reactor (AGR) 5/6/7 fuel irradiation. AGR-5/6/7 was conducted in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) in support of development and qualification of tri-structural isotropic (TRISO) low-enriched fuel for use in high temperature gas-cooled reactors. The objectives of the AGR-5/6/7 experiments are to: (i) irradiate reference-design fuel particles to support fuel qualification, (ii) establish operating margins for the fuel beyond normal operating conditions, and (iii) provide irradiated-fuel performance data and irradiated-fuel samples for post-irradiation examination (PIE) and safety testing. The test train contains five separate capsules that were independently controlled and monitored. Each capsule contains multiple 12.51-mm-long compacts filled with low enriched uranium carbide/oxide (UCO) TRISO fuel particles. The primary objective of the AGR-5/6 test (Capsules 1, 2, 4, and 5) is to verify successful performance of the reference-design fuel under normal operating conditions. The AGR-7 test (Capsule 3) was designed to explore fuel performance at higher temperatures to demonstrate the capability of the fuel to withstand conditions beyond normal operating conditions in support of plant design and licensing. AGR 5/6/7 will also provide irradiated-fuel performance data on fission-gas release from failed particles during irradiation. The AGR-5/6/7 capsules were irradiated in the ATR northeast flux trap location. The experiment began on February 16, 2018 and ended on July 22, 2020, spanning nine ATR cycles over two and a half years. Thus, the AGR-5/6/7 fuel compacts were irradiated for a total of 360.9 effective full power days. The AGR 5/6/7 experiment was able to remain in the reactor core during all three Powered Axial Locator Mechanism (PALM) cycles (163A, 165A, and 167A) without overheating its fuel compacts. This report includes irradiation monitoring data from nine ATR Cycles: 162B, 163A, 164A, 164B, 165A, 166A, 166B, 167A, and 168A, as stored in the Nuclear Data Management and Analysis System (NDMAS). During irradiation, data records consisted of instantaneous measurements recorded every minute and provided by text files automatically every 2 hours. The AGR 5/6/7 data streams addressed in this report include thermocouple (TC) temperatures, sweep gas data (flow rates [capsule inlet, outlet, and downstream at detector], pressure, and moisture content), and Fission Product Monitoring System (FPMS) data (release rates and release to birth rate ratios [R/Bs]) for each of the five capsules. A total of 94,989,908 TC temperature and sweep gas data records were received and processed by NDMAS for AGR 5/6/7 irradiation. Of these records, 41,593,387 (or 43.7% of the total) met data collection and accuracy requirements and are labeled as Qualified. A total of 57,746,693 TC temperature readings were captured from 54 installed TCs. Among them, 10,034,676 TC temperature records (only 17.4%) were Qualified and 47,701,371 TC temperatures (or 82.6%) are Failed due to 48 TC failures (63.5%) and due to missing values (19.1%). To assess performance of the operational TCs, analysis of daily correlations between TCs found no evidence of virtual junction failure for any TCs. Analyses on control charts of TC temperature differences revealed trending in TC readings for TC2, 4, 5, and 13 in Capsule 3, but there is no conclusive indication of TC drift failure that caused those trends. Therefore, TC control charts are not used to disqualify TC data, but only for users’ consideration. For sweep gas flow rates, a total of 31,519,747 gas flow records (84.4%) are Qualified for use for AGR-5/6/7 experiment; 5,723,468 gas flow records (15.4%) are Failed due mostly to missing values; and 74,641 high sweep gas flow rates (0.2 %) are Trend. A large number of Failed missing TC temperature and gas flow values were caused by an error in the data output script that outputted a ‘NULL’ value when values were unchanged. This problem was fixed during the outage of Cycle 166B, which led to a substantially decreased number of missing values during the last three cycles. Nonetheless, a large amount of non-missing data remained because of the high data acquisition frequency (1-minute) and still provided sufficient data to effectively monitor the experiment as designed. For FPMS data, NDMAS received and processed fission product release and R/B data for nine ATR cycles, when ATR core reached full power during AGR 5/6/7 irradiation. These data consist of 110,388 release rate records and 110,388 R/B records for the twelve radionuclides (Kr 85m, Kr 87, Kr 88, Kr 89, Kr 90, Xe 131m, Xe 133, Xe 135, Xe 135m, Xe 137, Xe 138, and Xe 139) for each of the five capsules. Equivalent numbers of uncertainty records associated the release rates and R/B values were provided. To date, qualification status of the FPMS data stored in the NDMAS dat

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Analysis of Space-Conditioning Loads in Commercial Buildings

Space conditioning end-uses, which include heating, cooling, and ventilation, represent a significant fraction of commercial building energy use, with a wide variety of heating and cooling technology options available in the market. In the interest of improving the overall efficiency of heating, ventilation and air-conditioning (HVAC) technologies, governments, utilities and private sector entities have implemented a variety of market transformation policies that aim to influence consumer purchase decisions. To evaluate the costs and benefits of such programs, analysts typically postulate a hypothetical default equipment choice, and compare it to one that provides comparable service with lower energy and/or power use. The corresponding reduced operating cost provides a benefit that offsets the potential higher cost of improved efficiency. Typically, life-cycle cost or cash-flow analyses are used to quantify the net economic benefit. These analyses require the capability to assess how a given equipment design would perform across a broad range of characteristics, both of the building and of the local weather. While these assessments can be performed using customized building simulations, it is generally not practical to develop and validate detailed building simulation code to cover all the potential variations of equipment design and installation. An alternative, and somewhat simpler, approach is to solely use detailed building simulations to generate time series of heating and cooling loads in commercial buildings. These loads can then be used as input to more detailed, stand-alone engineering models that simulate HVAC system performance under different equipment designs. This approach was used to evaluate a range of high-efficiency commercial packaged air conditioner design options for the Department of Energy’s Appliance and Equipment Standards Program (DOE-EERE 2015). While there may be some loss of precision relative to full simulation, the accuracy of this approach is sufficient for practical applications of cost-benefit analysis. This report describes the development of a database of commercial building heating and cooling loads, generated using the EnergyPlus software package, a whole building energy use model supported by the Department of Energy (DOE-EERE 2020a). EnergyPlus takes as input a set of configuration files that describe the building itself (size, zoning, envelope characteristics, etc.) and the various systems within it (HVAC, lighting, water heating, etc.). This analysis uses a publicly available collection of commercial reference buildings (CRB), comprised of sixteen building types and three vintages (DOE-EERE 2020b; Deru et al 2011). Each building is simulated in eighteen different locations, covering a wide range of climatic conditions. The prototype building description files assign the type of HVAC equipment used, and capacities across climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Streaming Large-Scale Microscopy Data to a Supercomputing Facility

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s -1 (87,000 frames s -1 ⁠), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera’s data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

4D-STEM↗
DOE OSTI2025

Multiple RGB ortho-mosaics and digital surface models in 2017 and 2018 across the Lower Montane site in the East River Watershed, Colorado

Aerial imagery was collected at the Lower Montane site (Pumphouse) in the East River Watershed, Colorado during the spring, summer, and fall seasons of 2017 and 2018 to improve the understanding of seasonal vegetation dynamics and their drivers. The datasets include Red-Green-Blue (RGB) ortho-mosaics and digital surface models (DSMs) inferred from the Unoccupied Aerial System (UAS) acquired aerial RGB imagery for June 3, June 19, July 7, and August 14, 2017, and for March 14, April 26, June 1, June 18, July 6, and August 7, 2018. Real-Time Kinematic Global Positioning System (RTK-GPS) surveyed Ground control points (GCPs) were used to increase the reconstruction accuracy. The reconstructed RGB mosaics and DSMs have been trimmed to cover a similar spatial domain. The accuracy of the RGB mosaics is considered high (~10 cm). DSM accuracy is highest (~10 cm) where sufficient GCPS are available, and more difficult to assess elsewhere (see reconstruction reports for uncertainty estimates). The dataset includes a total of 20 GeoTIFF (.tif) files, 10 PDF (.pdf) files, 3 data CSV (.csv) files, and 2 metadata CSV (.csv) files. Feel free to contact the authors with any questions or collaboration interests.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Time-Dependent Complete Active Space Embedded in Polarizable Force Field

Electron correlation and environmental effects play important roles in electron dynamics and spectroscopic observables of chemical systems in condensed phase. In this paper, we present a time-dependent complete active space configuration interaction (TD-CASCI) approach embedded in a polaraizable force filed, MMPol. The present implementation of TD-CASCI/MMPol utilizes a direct matrix-vector contraction, allowing studies of large systems. This scheme is used to study the solvatochromic shift of coumarin 153 in methanol. The TD-CASCI/MMPol approach captures the double excitation character in the excited state wave function and accurately predicts the solvatochromic red-shift of coumarin 153 dye within the experimental range, outperforming linear response time-dependent density functional theory. The effect of using different reference orbitals for the TD-CASCI/MMPol simulation is also investigated, highlighting the need for an unbiased treatment of all electronic states in the energy range of interest.

Liu, Hongbin↗
DOE OSTI2023

Enhancing Monte Carlo Workflows for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Monte Carlo codes are essential components of many reactor physics simulation workflows as high-fidelity continuous-energy neutron transport solvers. Among Monte Carlo radiation transport codes, MCNP is particularly notable due to its diverse simulation capabilities, large user base, and long validation history. Despite being a powerful simulation tool, MCNP provides limited capabilities to allow automated execution, model transformation, or support for user-defined logic and abstractions that limit its compatibility with modern workflows. Here, to better integrate MCNP into a modern scientific workflow, we have developed an intuitive yet full-featured MCNP Application Program Interface (API) in Python, named MCNPy, which provides a specialized set of classes for MCNP input development. Moreover, to guarantee that our reading, writing, and modeling capabilities remain self-consistent (and to render the huge scope of the MCNP API manageable), we have adopted a strategy of model-driven software development in which a generalized model of the MCNP input format has been created. From this generalized model, or “metamodel,” problem-specific implementations such as an engine for input validation or a codebase for programmatic operations may be automatically generated. Since MCNPy primarily acts as a Python front-end to the underlying Java API that directly interfaces with the metamodel, it is intrinsically linked to the metamodel and thus remains maintainable. With MCNPy, users can programmatically read, write, and modify any syntactically valid MCNP input file regardless of its origin. These capabilities allow users to automate complicated tasks like design optimization and model translation for nuclear systems. As examples, this work demonstrates the use of MCNPy to find the critical radius of a plutonium sphere and to translate a 9000+ line MCNP input file into a corresponding OpenMC model.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Situational Awareness of Network System Roles (SANSR)

In a large enterprise it is difficult for cyber security analysts to know what services and roles every machine on the network is performing (e.g., file server, domain name server, email server). Using network flow data, already collected by most enterprises, we developed a proof-of-concept tool that discovers the roles of a system using both clustering and categorization techniques. The tool's role information would allow cyber analysts to detect consequential changes in the network, initiate incident response plans, and optimize their security posture. The results of this proof-of-concept tool proved to be quite accurate on three real data sets. We will present the algorithms used in the tool, describe the results of preliminary testing, provide visualizations of the results, and discuss areas for future work. Without this kind of situational awareness, cyber analysts cannot quickly diagnose an attack or prioritize remedial actions.

cyber security↗
DOE OSTI2020

Bringing heterogeneity to the CMS software framework

The advent of computing resources with co-processors, for example Graphics Processing Units (GPU) or Field-Programmable Gate Arrays (FPGA), for use cases like the CMS High-Level Trigger (HLT) or data processing at leadership-class supercomputers imposes challenges for the current data processing frameworks. These challenges include developing a model for algorithms to offload their computations on the co-processors as well as keeping the traditional CPU busy doing other work. The CMS data processing framework, CMSSW, implements multithreading using the Intel Threading Building Blocks (TBB) library, that utilizes tasks as concurrent units of work. In this paper we will discuss a generic mechanism to interact effectively with non-CPU resources that has been implemented in CMSSW. In addition, configuring such a heterogeneous system is challenging. In CMSSW an application is configured with a configuration file written in the Python language. The algorithm types are part of the configuration. The challenge therefore is to unify the CPU and co-processor settings while allowing their implementations to be separate. We will explain how we solved these challenges while minimizing the necessary changes to the CMSSW framework. We will also discuss on a concrete example how algorithms would offload work to NVIDIA GPUs using directly the CUDA API.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

SU-F-T-65: AutomaticTreatment Planning for High-Dose Rate (HDR) Brachytherapy with a VaginalCylinder Applicator

Purpose: High dose rate (HDR) brachytherapy treatment planning is conventionally performed in a manual fashion. Yet it is highly desirable to perform computerized automated planning to improve treatment planning efficiency, eliminate human errors, and reduce plan quality variation. The goal of this research is to develop an automatic treatment planning tool for HDR brachytherapy with a cylinder applicator for vaginal cancer. Methods: After inserting the cylinder applicator into the patient, a CT scan was acquired and was loaded to an in-house developed treatment planning software. The cylinder applicator was automatically segmented using image-processing techniques. CTV was generated based on user-specified treatment depth and length. Locations of relevant points (apex point, prescription point, and vaginal surface point), central applicator channel coordinates, and dwell positions were determined according to their geometric relations with the applicator. Dwell time was computed through an inverse optimization process. The planning information was written into DICOM-RT plan and structure files to transfer the automatically generated plan to a commercial treatment planning system for plan verification and delivery. Results: We have tested the system retrospectively in nine patients treated with vaginal cylinder applicator. These cases were selected with different treatment prescriptions, lengths, depths, and cylinder diameters to represent a large patient population. Our system was able to generate treatment plans for these cases with clinically acceptable quality. Computation time varied from 3–6 min. Conclusion: We have developed a system to perform automated treatment planning for HDR brachytherapy with a cylinder applicator. Such a novel system has greatly improved treatment planning efficiency and reduced plan quality variation. It also served as a testbed to demonstrate the feasibility of automatic HDR treatment planning for more complicated cases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-CAMPUS-JeP3-02: Automated Dose Accumulation and Dose Accuracy Assessment for Online Or Offline Adaptive Replanning

Purpose: With introduction of high-quality treatment imaging during radiation therapy (RT) delivery, e.g., MR-Linac, adaptive replanning of either online or offline becomes appealing. Dose accumulation of delivered fractions, a prerequisite for the adaptive replanning, can be cumbersome and inaccurate. The purpose of this work is to develop an automated process to accumulate daily doses and to assess the dose accumulation accuracy voxel-by-voxel for adaptive replanning. Methods: The process includes the following main steps: 1) reconstructing daily dose for each delivered fraction with a treatment planning system (Monaco, Elekta) based on the daily images using machine delivery log file and considering patient repositioning if applicable, 2) overlaying the daily dose to the planning image based on deformable image registering (DIR) (ADMIRE, Elekta), 3) assessing voxel dose deformation accuracy based on deformation field using predetermined criteria, and 4) outputting accumulated dose and dose-accuracy volume histograms and parameters. Daily CTs acquired using a CT-on-rails during routine CT-guided RT for sample patients with head and neck and prostate cancers were used to test the process. Results: Daily and accumulated doses (dose-volume histograms, etc) along with their accuracies (dose-accuracy volume histogram) can be robustly generated using the proposed process. The test data for a head and neck cancer case shows that the gross tumor volume decreased by 20% towards the end of treatment course, and the parotid gland mean dose increased by 10%. Such information would trigger adaptive replanning for the subsequent fractions. The voxel-based accuracy in the accumulated dose showed that errors in accumulated dose near rigid structures were small. Conclusion: A procedure as well as necessary tools to automatically accumulate daily dose and assess dose accumulation accuracy is developed and is useful for adaptive replanning. Partially supported by Elekta, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Streaming Data from Experimental Facilities to Supercomputers for Real-Time Data Processing

In this paper we demonstrate direct data streaming from instruments and detectors at a large-scale experimental facility to a supercomputer for real-time data processing and feedback. Streaming data to supercomputers introduces the potential for novel scientific applications and workflow models, including the ability to provide real-time feedback from very large datasets during an experiment and the integration of real-time ML training and inference at scale. We discuss a successful demonstration for real-time processing of data from the Advanced Photon Source (APS) on the Polaris supercomputer using an EPICS-based streaming framework. We describe the capabilities of the streaming framework itself, and outline the architecture that allows us to process experimentally derived data on a supercomputer without file-based data transfers. We present throughput measurements that are indicative of system performance capable of sustaining the expected data production rates of the facility, as well as discuss some outstanding challenges and our future directions.

real-time processing↗