A data-driven method to characterize turbulence-caused uncertainty in wind power generation
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Zhang, Jie↗
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Testing to Evaluate Processes Expected to Occur during MSR Salt Spill Accidents
Obtaining a license for a new nuclear reactor requires the identification and assessment of the potential consequences of specified accident scenarios, which are achieved using accident progression models. Those models need to be parameterized and validated using experimental data, but existing experimental data addressing processes relevant to molten salt reactor accidents are sparse. Specifically, experimental data that quantify the sensitives of important processes to the initial conditions of the spill, the ambient environment, and the containment features are needed to parameterize individual process models. Integrated experiments that simulate accident scenarios are also needed to provide data for model validation, but these experiments will require the use of proven methods to quantify the processes under evaluation. The overarching objectives of this work are to develop the methods for simulating the targeted processes, to determine the effectiveness of the methods in producing the data required for model development, to generate data that can be used to parameterize individual process models, and to provide key insights into the behavior of spilled molten salt that should be considered in models. Experimental methods were designed to quantify aspects of individual processes expected to occur during or after a molten salt spill accident that will affect the fate of spilled molten salt and the radionuclides within. These processes include 1) molten salt spreading and heat transfer, 2) molten salt flowing and freezing in tubing, 3) stainless steel corrosion kinetics in molten salt, and 4) molten salt splashing and aerosol generation. The initial tests described in this report were conducted using eutectic FLiNaK to demonstrate the test methods, the data that are generated, and the analyses of the data to derive values needed for modeling. The primary variables that were tested include initial salt temperature and the presence of volatile surrogate fission products (e.g., cesium and iodine). The developed methods are shown to be effective in quantifying the desired processes and can be applied to study more complex salt compositions of interest to molten salt reactor developers, a wide range of environmental conditions of interest to modelers, and additional variables relevant to salt spill accidents. The developed methods and insights gained from laboratory tests can also be incorporated in future large-scale integrated tests used to simulate molten salt spill accidents.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Single stage experimental evaluation of compressor blading with slots and vortex generators. Part 2 - Data and performance for stage 5 without slots or vortex generators
Comparison performance data of compressor blading without slots or vortex generators
Brent, J. A.↗
High-Resolution South American Wind Resource Data Downscaled with Generative Machine Learning Conditioned on Near-Surface Observations
High-resolution historical wind data was developed for the entirety of South America using the innovative Super-Resolution for Renewable Resource Data (sup3r) machine learning framework. The publicly available Sup3rWind South America dataset represents a significant advancement in wind resource data generation, leveraging generative machine learning conditioned on near-surface observations from the Meteorological Assimilation Data Ingest System (MADIS) to efficiently and accurately downscale coarse reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5). This approach produces fine-scale, spatially and temporally coherent wind and meteorological fields hundreds of times more computationally efficient than traditional numerical weather modeling methods, enabling access to high-fidelity wind information across both continental and offshore regions. Sup3rWind South America builds on the earlier Sup3rWind Ukraine dataset through improvements in model architecture and outputs conditioned on near-surface observation inputs. As with the Ukraine data release, this dataset includes wind speed, wind direction, temperature, relative humidity, and pressure at a horizontal resolution of ~2 km, representing a 15x spatial enhancement relative to the 31 km ERA5 grid. Wind speed and direction are provided at 5-minute resolution, a 12x temporal refinement compared to the hourly ERA5 data, while temperature, relative humidity, and pressure remain at hourly resolution. The data covers all years from 2005 to 2024. Before downscaling, ERA5 inputs were bias-corrected using long-term monthly means and a limited number of quality-controlled observations to align large-scale statistics with regional conditions. The resulting dataset is the first publicly available high-resolution timeseries wind record that provides full spatial coverage of South America. Model validation demonstrates strong agreement with observations across several statistical metrics, consistent with other state-of-the-art high-resolution wind resource datasets. The potential applications of Sup3rWind South America span renewable energy resource assessment, energy system modeling, and grid resilience analysis. The 20-year record and high spatial and temporal resolution support accurate estimation of long-term energy yield and the economic feasibility of potential wind development sites. Continuous coverage across both continental and offshore regions enables comprehensive site prospecting within exclusive economic zones. The 2 km, 5-minute resolution data provide the spatial and temporal variability required for power system simulation, operational planning, and regional risk assessments.
17 WIND ENERGY↗
ORNL Resolved Resonance Covariance Generation for ENDF/B-VII.1
Resonance-parameter covariance matrix (RPCM) evaluations in the resolved resonance regionwere done at the Oak Ridge National Laboratory (ORNL) for the chromium isotopes, titanium isotopes, {sup 19}F, {sup 58}Ni, {sup 60}Ni, {sup 35}Cl, {sup 37}Cl, {sup 39}K, {sup 41}K, {sup 55}Mn, {sup 233}U, {sup 235}U, {sup 238}U, and {sup 239}Pu using the computer code SAMMY. The retroactive approach of the code SAMMY was used to generate the RPCMs for {sup 233}U. For {sup 235}U, the approach used for covariance generation was similar to the retroactive approach with the distinction that real experimental data were used as opposed to data generated from the resonance parameters. RPCMs for {sup 238}U and {sup 239}Pu were generated together with the resonance parameter evaluations. The RPCMs were then converted in the ENDF format using the FILE32 representation. Alternatively, for computer storage reasons, the FILE32 was converted in the FILE33 cross section covariance matrix (CSCM). Both representations were processed using the computer code PUFF-IV. This paper describes the procedures used to generate the RPCM and CSCM in the resonance region for ENDF/B-VII.1. The impact of data uncertainty in nuclear reactor benchmark calculations is also presented.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Computer program generates averaged value data tapes
Computer program generates a magnetic output tape containing time and averaged data values of a specified number of major frames over a specified time interval. A decommutation system is used to acquire the raw data, which is then reformatted and averaged.
Watkins, F. L.↗
The nonequilibrium quantum many-body problem as a paradigm for extreme data science
Generating big data pervades much of physics. But some problems, which we call extreme data problems, are too large to be treated within big data science. The nonequilibrium quantum many-body problem on a lattice is just such a problem, where the Hilbert space grows exponentially with system size and rapidly becomes too large to fit on any computer (and can be effectively thought of as an infinite-sized data set). Nevertheless, much progress has been made with computational methods on this problem, which serve as a paradigm for how one can approach and attack extreme data problems. In addition, viewing these physics problems from a computer-science perspective leads to new approaches that can be tried to solve more accurately and for longer times. In this work, we review a number of these different ideas.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Hyperspectral Aquatic Radiative Transfer Modeling Using a High-Performance Cluster Computing-Based Approach
Abstract For aquatic studies, radiative transfer (RT) modeling can be used to compute hyperspectral above-surface remote sensing reflectance that can be utilized for inverse model development. Inverse models can provide bathymetry and inherent-and bottom-optical property estimation. Because measured oceanic field/organic datasets are often spatio-temporally sparse, synthetic data generation is useful in yielding sufficiently large datasets for inversion model development; however, these forward-modeled data are computationally expensive and time-consuming to generate. This study establishes the magnitude of wall-clock-time savings achieved for performing large, aquatic RT batch-runs using parallel computing versus a sequential approach. Given 2,600 simulations and identical compute-node characteristics, sequential architecture required ~100 hours until termination, whereas a parallel approach required only ~2.5 hours (42 compute nodes) a 40x speed-up. Tools developed for this parallel execution are discussed.
58 GEOSCIENCES↗
Raman Database Considerations for Near-Infrared Systems
For Raman spectroscopy the ability to detect is often limited by the existence and quality of the reference library to which field spectra are compared. Developing such databases is often labor- and resource-intensive; typically the generated data are not transferred to other instruments. Still other considerations may exist for comparing data at visible and ultraviolet excitation wavelengths such as resonance enhancement. However, for the common near-infrared wavelengths of 785, 830, 960, 1047 and 1064 nm where this is normally of a lesser concern, it is logical to consider whether data can be ported from one spectrometer to another so as to obviate the expensive and time-consuming process of generating reference data for each system. The present experiment generated a list of 125 chemical and common substances and formed a database from their corresponding 1064 nm spectra. The same molecules were then measured using a 785 nm system the new spectra were treated as “unknowns” and compared to the 1064 nm database using a commercial search algorithm. We found that at least 108 of the 125 spectra recorded at 785 nm were correctly identified using the search algorithm. For the few that were incorrectly identified, in most cases the spectra were extremely similar or the 785 nm signal was degraded due to fluorescence, as would occur regardless of reference data. Our results indicate that if the spectrometers are properly calibrated on both their wavelength and intensity axes, “foreign” data recorded at a different NIR wavelength can be successfully used as reference libraries
Kunkel, Brenda M↗
Electricity Baseline 2021 Background Data and Log File
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2021 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2021 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2021 model run is also included, which contains the statements at the DEBUG level and above.
Electricity; LCA; LCI; Life Cycle; data inventory↗
Electricity Baseline 2020 Background Data and Log File
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2020 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2020 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2020 model run is also included, which contains the statements at the DEBUG level and above.
Electricity; LCA; LCI; Life Cycle; data inventory↗
Electricity Baseline 2022 Background Data and Log File
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2022 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilized the appdirs Python dependency (https://pypi.org/project/appdirs/). This submission includes the background data used to generate the 2022 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: `python -c "import appdirs; print(appdirs.user_data_dir())"`). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2022 model run is also included, which contains the statements at the DEBUG level and above.
Electricity; LCA; data inventory↗
A New Era for Central Processing and Production in CMS
The goal for CMS computing is to maximise the throughput of simulated event generation while also processing event data generated by the detector as quickly and reliably as possible. To maintain this achievement as the quantity of events increases CMS computing has migrated at the Tier 1 level from its old production framework, ProdAgent, to a new one, WMAgent. The WMAgent framework offers improved processing efficiency and increased resource usage as well as a reduction in operational manpower. In addition to the challenges encountered during the design of the WMAgent framework, several operational issues have arisen during its commissioning. The largest operational challenges were in the usage and monitoring of resources, mainly a result of a change in the way work is allocated. Instead of work being assigned to operators, all work is centrally injected and managed in the Request Manager system and the task of the operators has changed from running individual workflows to monitoring the global workload. In this report we present how we tackled some of the operational challenges, and how we benefitted from the lessons learned in the commissioning of the WMAgent framework at the Tier 2 level in late 2011. As case studies, we will show how the WMAgent system performed during some of the large data reprocessing and Monte Carlo simulation campaigns.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Simulating Secure Data Exchange and Storage for Urban Air Mobility Environments
Urban Air Mobility (UAM) defines an environment for managing operations of vertical takeoff and landing (VTOL) and short takeoff and landing (STOL) vehicles in an urban environment. Within a UAM environment, UAM operators manage fleets of vehicles, relying on Providers of Services for UAM (PSUs) for managing flights in a region of airspace. Flight plan deconfliction is primarily performed by the Discovery and Synchronization Service (DSS), and the Federal Aviation Administration (FAA) maintains control over the UAM space via the FAA-Industry Exchange Protocol (FIDXP). UAM is a federated environment with many different entities owning and operating vehicles, PSUs, and other services. These entities often need to interoperate or access data generated by other organizations. This paper demonstrates the feasibility of using blockchain to facilitate a secure data exchange and storage for this flight information in a UAM environment. In particular, this paper is focused on flight plans and telemetry data. A blockchain network was developed with a set of smart contracts for managing relevant flight data. Hyperledger Fabric was chosen as it is performent, scalable, and allows organizations to reuse existing public key infrastructure (PKI) for identity management. A set of simulated UAM services were also developed. These services propose flight plans and negotiate with other UAM services for airspace access. All interactions between UAM services, as well as vehicle telemetry data, is recorded onto the blockchain. Vehicle telemetry data is generated by a vehicle flight simulation service. This paper successfully demonstrates the feasibility of using blockchain as a secure data exchange and storage mechanism in a UAM environment.
UAM↗
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure–property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure–property relationships from the literature. Finally, models trained on these data sets will improve as they incorporate community feedback.
36 MATERIALS SCIENCE↗
Investigation of criticality safety control infraction data at a nuclear facility
Chemical and metallurgical operations involving plutonium and other nuclear materials account for most activities performed at the LANL's Plutonium Facility (PF-4). Here, the presence of large quantities of fissile materials in numerous forms at PF-4 makes it necessary to maintain an active criticality safety program. The LANL Nuclear Criticality Safety (NCS) Program provides guidance to enable efficient operations while ensuring prevention of criticality accidents in the handling, storing, processing and transportation of fissionable material at PF-4. In order to achieve and sustain lower criticality safety control infraction (CSCI) rates, PF-4 operations are continuously improved, through the use of Lean Manufacturing and Six Sigma (LSS) business practices. Employing LSS, statistically significant variations (trends) can be identified in PF-4 CSCI reports. In this study, trends have been identified in the NCS Program using the NCS Database. An output metric has been developed that measures ADPSM Management progress toward meeting its NCS objectives and goals. Using a Pareto Chart, the primary CSCI attributes have been determined in order of those requiring the most management support. Data generated from analysis of CSCI data help identify and reduce number of corresponding attributes. In-field monitoring of CSCI's contribute to an organization's scientific and technological excellence by providing information that can be used to improve criticality safety operation safety. This increases technical knowledge and augments operational safety.
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
Investigation of injury/illness data at a nuclear facility. Part II
At Los Alamos National Laboratory (LANL), there are several nuclear facilities, accelerator facilities, radiological facilities, explosives sites, moderate- and high-hazard non-nuclear facilities, biosciences laboratory, etc. The Plutonium Science and Manufacturing Directorate (ADPSM) provides special nuclear material research, process development, technology demonstration, and manufacturing capabilities. ADPSM manages the LANL Plutonium Facility. Within the Radiological Control Area at TA-55 (PF-4), chemical and metallurgical operations with plutonium and other hazardous materials are performed. LANL Health and Safety Programs investigate injury and illness data. In this study, statistically significant trends have been identified and compared for LANL, ADPSM, and PF-4 injury/illness cases. A previously described output metric is used to measures LANL management progress towards meeting its operational safety objectives and goals. Timelines are used to determine trends in Injury/Illness types. Pareto Charts are used to prioritize causal factors. The data generated from analysis of Injury/Illness data have helped identify and reduce the number of corresponding causal factors.
97 MATHEMATICS AND COMPUTING↗
MapReduce SVM Game
Despite technological advances making computing devices faster, smaller, and more prevalent in today's age, data generation and collection has outpaced data processing capabilities. Simply having more compute platforms does not provide a means of addressing challenging problems in the big data era. Rather, alternative processing approaches are needed and the application of machine learning to big data is hugely important. The MapReduce programming paradigm is an alternative to conventional supercomputing approaches, and requires less stringent data passing constrained problem decompositions. Rather, MapReduce relies upon defining a means of partitioning the desired problem so that subsets may be computed independently and recom- bined to yield the net desired result. However, not all machine learning algorithms are amenable to such an approach. Game-theoretic algorithms are often innately distributed, consisting of local interactions between players without requiring a central authority and are iterative by nature rather than requiring extensive retraining. Effectively, a game-theoretic approach to machine learning is well suited for the MapReduce paradigm and provides a novel, alternative new perspective to addressing the big data problem. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in a distributed manner, and show an illustrative example of applying this algorithm.
97 MATHEMATICS AND COMPUTING↗