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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An Extreme-Preserving Long-Term Gridded Daily Precipitation Data Set for the Conterminous United States

Extreme daily precipitation contributes to flooding that can cause significant economic damages, and so is important to properly capture in gridded meteorological data sets. This work examines precipitation extremes, the mean precipitation on wet days, and fraction of wet days in two widely used gridded data sets over the conterminous U.S. (CONUS). When compared to the underlying station observations, the gridded data show a 27% reduction in annual 1-day maximum precipitation, 25% increase in wet day fraction, 1.5 to 2.5 day increase in mean wet spell length, 30% low bias in 20-year return values of daily precipitation, and 25% decrease in mean precipitation on wet days. It is shown these changes arise primarily from the time-adjustment applied to put the precipitation gauge observations into a uniform time frame, with the gridding process playing a lesser role. A new daily precipitation data set is developed that omits the time-adjustment (as well as extending the gridded data by 7 years) and is shown to perform significantly better in reproducing extreme precipitation metrics. When the new data set is used to force a land surface model, annually averaged 1-day maximum runoff increases 38% compared to the original data, annual mean runoff increases 17%, evapotranspiration drops 2.3%, and fewer wet days leads to a 3.3% increase in estimated solar insolation. Consequently, these changes are large enough to affect portrayals of flood risk and water balance components important for ecological and climate-change applications across the CONUS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Reservoir Computing as a Tool for Climate Predictability Studies

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate models. In this context, the linear inverse modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that reservoir computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea surface temperature in the North Atlantic in the preindustrial control simulation of a popular earth system model, the Community Earth System Model so that we can compare the performance of the new RC-based approach with the traditional LIM approach both when learning data are plentiful and when such data are more limited. The improved predictive skill of the RC approach over a wide range of conditions—larger number of retained EOF coefficients, extending well into the limited data regime, etc.—suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator—the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory—is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Joint galaxy–galaxy lensing and clustering constraints on galaxy formation

ABSTRACT We compare predictions for galaxy–galaxy lensing profiles and clustering from the Henriques et al. public version of the Munich semi-analytical model (SAM) of galaxy formation and the IllustrisTNG suite, primarily TNG300, with observations from KiDS + GAMA and SDSS-DR7 using four different selection functions for the lenses (stellar mass, stellar mass and group membership, stellar mass and isolation criteria, and stellar mass and colour). We find that this version of the SAM does not agree well with the current data for stellar mass-only lenses with $M_\ast \gt 10^{11}\, \mathrm{ M}_\odot$. By decreasing the merger time for satellite galaxies as well as reducing the radio-mode active galactic nucleus accretion efficiency in the SAM, we obtain better agreement, both for the lensing and the clustering, at the high-mass end. We show that the new model is consistent with the signals for central galaxies presented in Velliscig et al. Turning to the hydrodynamical simulation, TNG300 produces good lensing predictions, both for stellar mass-only (χ2 = 1.81 compared to χ2 = 7.79 for the SAM) and locally brightest galaxy samples (χ2 = 3.80 compared to χ2 = 5.01). With added dust corrections to the colours it matches the SDSS clustering signal well for red low-mass galaxies. We find that both the SAMs and TNG300 predict $\sim 50\, {{\ \rm per\ cent}}$ excessive lensing signals for intermediate-mass red galaxies with 10.2 < log10M*[M⊙] < 11.2 at $r \approx 0.6\, h^{-1}\, \text{Mpc}$, which require further theoretical development.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Commissioning of a Cask Enabling Characterization of Irradiated Nuclear Fuels with Pulsed Neutrons

Irradiation tests are a key component of nuclear fuel development and identifying typical and atypical regions in the irradiated fuel volume relies on very few characterization techniques. The goal of the effort reported here is to provide complementary measurements adding to the available parameter space for post irradiation examination as well as to inform subsequent hot cell PIE examinations by identifying typical and atypical regions with respect to microstructure, tomographic data, or isotope densities. Pulsed neutrons, enabling diffraction as well as energy-resolved neutron imaging and neutron absorption resonance spectroscopy, offer unique capabilities for this purpose. Time-of-flight neutron diffraction has the potential to offer efficient, non-destructive and non-contact microstructural characterization of irradiated fuel pins with spatial resolution of 1 mm 3 to 1 cm 3 while for energy-resolved neutron imaging (and by extension tomography) a resolution of 100 μm was demonstrated. The potential results include crystallinity vs. amorphous volumes and microstructural information such as phase compositions, lattice strains (indicative of residual stresses or chemistry variations) and textures from the diffraction data as well as distances (e.g. pellet to cladding), cracks, and isotopic distributions of minor actinides, fission products as well as fission gas partial pressures e.g. in the plenum from energy resolved neutron imaging.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Progress Report on Mockup Irradiation Capsule Fuel Measurements at LANSCE

Irradiation tests are a key component of nuclear fuel development and identifying typical and atypical regions in the irradiated fuel volume relies on very few characterization techniques. The goal of the effort reported here is to provide complementary measurements adding to the available parameter space for post irradiation examination as well as to inform subsequent hot cell PIE examinations by identifying typical and atypical regions with respect to microstructure, tomographic data, or isotope densities. Pulsed neutrons, enabling diffraction as well as energy-resolved neutron imaging and neutron absorption resonance spectroscopy, offer unique capabilities for this purpose. Time-of-flight neutron diffraction has the potential to offer efficient, non-destructive and non-contact microstructural characterization of irradiated fuel specimen with spatial resolution of 1 mm 3 to 1 cm 3 while for energy-resolved neutron imaging (and by extension tomography) a resolution of 100 μm 3 was demonstrated. The potential results include crystallinity vs. amorphous volumes and microstructural information such as phase compositions, lattice strains (indicative of residual stresses or chemistry variations) and textures from the diffraction data as well as distances (e.g. pellet to cladding), cracks, and isotope densities of minor actinides, fission products as well as fission gas partial pressures e.g. in the plenum from energy resolved neutron imaging.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

WARE 1-9 Well

Computed tomography and core logger data described in Technical Report "Computed Tomography Scanning and Geophysical Measurements of the J. F. WARE Gas Unit 1-9 Well in Smith County, Texas" by Brinza et al. , 2024.

CoreCM↗
DOE OSTI2023

Building a DFT+U machine learning interatomic potential for uranium dioxide

Despite uranium dioxide (UO 2 ) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO 2 . Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO 2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO 2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

AlgoSCR: an algorithm for solar contamination removal from radio interferometric data

Hydrogen intensity mapping is a new field in astronomy that promises to make three-dimensional maps of the matter distribution of the Universe using the redshifted 21cm line of neutral hydrogen gas (HI). Several ongoing and upcoming radio interferometers, such as Tianlai, CHIME, HERA, HIRAX, etc., are using this technique. These instruments are designed to map large swaths of the sky by drift scanning over periods of many months. One of the challenges of the observations is that the daytime data are contaminated by strong radio signals from the Sun. In the case of Tianlai, this results in almost half of the measured data being unusable. We try to address this issue by developing an algorithm for solar contamination removal (AlgoSCR) from the radio data. The algorithm is based on an eigenvalue analysis of the visibility matrix and hence is applicable only to interferometers. We apply AlgoSCR to simulated visibilities, as well as real daytime data from the Tianlai dish array. The algorithm can reduce strong solar contamination by about 95 per cent without seriously affecting other weaker sky signals and thus makes the data usable for certain applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Machine learning at the Spallation Neutron Source accelerator and target

We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.

Accelerator Physics↗
DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 1: Summary of Utah FORGE Wells 16A(78)-32, 56-32, 78B-32 and 16B(78)-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

EXFOR-NSR PDF database: a system for nuclear knowledge preservation and data curation

Current needs of nuclear science and technology include complete, well-documented, and easily verifiable nuclear data. The complete data records require supporting nuclear bibliography, presently stored in dedicated libraries, in addition, to actual data. Additionally, experimental nuclear reaction data (EXFOR) and Nuclear Science References (NSR) databases contain compilations based on primary (journals) and secondary (conference proceedings, theses, preprints, etc.) publications, and data received from authors via private communications. The secondary library materials and private communications often represent a bottleneck for nuclear data verification, compilation, evaluation, and dissemination activities. To address this issue, bibliographic materials were scanned into PDF (Portable Document Format) files and uploaded in a relational database. The traditional scope of nuclear databases that includes meta-data and numbers derived from data in specialized formats was broadened to accommodate the large volumes of original nuclear data publications. The complete PDF publication files were stored in a relational database as Binary Large OBjects (BLOB). This unique collection of nuclear data compilations and supporting publications generate many opportunities for machine learning applications. The Web interfaces for authorized and public access to the EXFOR-NSR nuclear publications database were implemented at the U.S. National Nuclear Data Center, https://www.nndc.bnl.gov/ and IAEA Nuclear Data Section, https://www-nds.iaea.org/ . The current system is complementary to major nuclear libraries and narrowly focused on nuclear data compilation and evaluation procedures. The contents of the PDF database, details of implementation, and Web interface are described. New capabilities for data curation, knowledge preservation, worldwide dissemination, and natural language processing (NLP) applications are given.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Identifying Light-Duty Vehicle Travel from Large-Scale Multimodal Wearable GPS Data with Novelty Detection Algorithms

Identifying travel mode within travel survey data sets, especially light-duty vehicle (LDV) travel, is foundational, though nontrivial, to travel behavior analysis and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. They are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the data requirements. This paper proposes an LDV travel mode detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique-one-class support vector machines (OCSVMs)-and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multimodal prediction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

INDEN Fissile Actinide Issues [Slides]

This presentation addresses the outlook for the International Nuclear Data Evaluation Network (INDEN), specifically regarding general issues that have been solved, including Thermal Neutron Constants (TNC) and Prompt Fission Neutron Spectra (PFNS). Other topics discussed include outstanding issues in fissile evaluations, comments on the RPI quasi-differential experiment, and reaction rate testing of trial evaluations in fast assemblies. The presentation concludes by discussing how INDEN interactions strongly helped to find deficiencies in existing evaluations and highlighted potential solutions to existing challenges, that updated trial evaluations for U-235, U-233, Pu-239 are available for testing, that further work is expected on U-235, Pu-239, and U-233, and that performance was tested on the ICSBEP and/or SINBAD benchmarks, during which significant improvement was demonstrated. The INDEN scheme of international collaboration on nuclear data evaluation is working well.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

The Geology of The Mt. Simon Sandstone Storage Complex at the Wabash #1 Well, Vigo Co., Indiana (Subtask 7.2, Technical Report)

The Wabash CarbonSAFE project drilled the Wabash #1 stratigraphic test well (ID# 168045) at the Wabash Valley Resources (WVR) IGCC facility in Vigo County, Indiana, to characterize and evaluate the basal Cambrian Mt. Simon Sandstone for commercial-scale CO 2 storage near the site. This report presents an extensive geologic characterization of the Mt. Simon storage complex and relevant data collected from the Wabash #1 well, such as lithologic data collected from cuttings and core, geophysical logging, geomechanical analysis of core samples, and well testing and fluid sampling within the Mt. Simon Sandstone. The Mt. Simon storage complex comprises two major sections: the Mt. Simon Sandstone as the potential reservoir and the overlying Eau Claire Formation as its primary seal. Within the report, an extensive depositional, sedimentological, and geochronologic characterization of the Mt. Simon is included with supportive chapters on the regional geology and the geophysical, petrophysical, and petrologic data collected during the project. An overview of 2D seismic reflection data collected from and around the test well is presented. Also presented are chapters on the characterization of the sealing Eau Claire Formation, including a chapter on the capacity of the primary and secondary seals to the Mt. Simon as well as a chapter on geomechanical testing results of the Eau Claire Formation and Mt. Simon Sandstone. Some of the information discussed in this report was used in the development of static and dynamic geologic models of the Mt. Simon Sandstone storage complex. The static and dynamic modeling of CO 2 injection in the Mt. Simon Sandstone are discussed in a separate report (Dessenberger et al., 2022) under the Wabash CarbonSAFE project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Electric Field Data from the North Slope of Alaska

File contains raw as well as calibrated electric field values (V/m) from two CS110 electric field meters. The file also contains raw 2-m U and V winds at the same site. The sampling rate is 1Hz. All files contain raw electric field data, as well as calibrated values to a one week ground-based calibration setup. During this week, simultaneous upward-facing ground based electric field measurements were taken alongside the two downward facing instruments on the pole (CS110-3 at 2m and CS110-2 at 5m). Slopes and intercepts were calculated and applied to the values influenced by the pole to convert the raw data to calibrated values. These calibrated values are absolute electric field measurements. There are no missing data codes. The missong data is not included in the files, and thus any files with missing data will be shorter than the normal 86,400 values. In July 2023, the CS110-3 was replaced with an identical instrument named CS110-8, and the CS110-2 was replaced with an identical instrument called CS110-9. All the variable names remained consistent and the same.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Diagnostic assessment of reservoir response to fracturing: a case study from Hydraulic Fracturing Test Site (HFTS) in Midland Basin

Abstract This paper outlines a data collection and diagnostics case study involving multiple horizontal shale wells. We look at well production profiles using rate transient analysis, differences in near wellbore complexity, geologic variations within the area of interest, as well as compositional differences in the rocks based on cores obtained from within the stimulated reservoir. The Hydraulic Fracturing Test Site is a multi-well experiment involving stimulation of unconventional shale wells in the southeastern Midland portion of the Permian Basin. The targeted formations include both the upper as well as the middle Wolfcamp formations, also referred alternatively as Wolfcamp A and Wolfcamp B. Data integration and analysis shared in this paper help us understand the various geologic controls impacting well productivity, particularly the wide variance observed between the Wolfcamp A and Wolfcamp B formations. Rate transient analysis indicates similar system permeabilities for stimulated wells. However, we observe higher effective fracture half-lengths for upper Wolfcamp wells. Using observations from 3D seismic interpretations (such as pad scale faults) as well as petrophysical and image log data, we highlight the substantial differences in stimulation as we move along the well laterals from the heel toward the toe sections. These differences are further reconciled with observations from zones with high data density at the core locations through stimulated rock, as well as independent data such as microseismic emissions. At the test site, Wolfcamp A was found to be relatively quartz rich with significant heterogeneity whereas Wolfcamp B is richer in clay and organic content. This impacts the geomechanical characteristics of the rock mass with much higher natural fracture density in the shallower interval. Thus, the fracture growth is more uniform in the deeper interval and more heterogeneous with branching likely in upper interval. Increased complexity also leads to consistently better productivity from the wells in the shallower interval as demonstrated from RTA results. This case study is unique because it provides valuable insights from actual sampling of the stimulated zones in hydraulically fractured wells and helps understand impact of various factors that contribute toward variability in well production. The findings from this study provides insights into need for optimization of completion designs in the various Wolfcamp landing zones, such as optimization of cluster or fracture spacing in various Wolfcamp intervals. In addition, it provides a useful template for data collection and research direction in future field test sites of similar nature in unconventional reservoirs.

Energy & Fuels↗
DOE OSTI2022

Evaluating the X2 initial core zero power physics tests with Serpent-Ants

The validation of the Ants nodal neutronics code for VVER applications is started by modelling the zero power physics tests for the initial core of the Khmelnitsky 2 nuclear power plant as described in the X2 benchmark using the Serpent-Ants two step neutronics calculation chain. The Ants prediction compare favorably against an earlier continuous energy Monte-Carlo reference solution as well as the measured data, except for discrepancies in the SCRAM worth between predicted and measured values. Similar discrepancies have been previously reported for VVER-1000 reactors, and the Ants predicted SCRAM worths match well with the Serpent results. Additionally, the assembly power distribution and axial power distribution predicted by Ants for the critical HZP state of the reactor are also compared to a reference Serpent solution with very good results. Lastly, an investigation into the effects of the few-group structure used in the nodal calculations and the use or lack of leakage correction for group constants on the results shows that the best accuracy is reached with seven or eight energy groups without leakage correction, although a reasonable accuracy can also be obtained with two or three energy groups using fundamental mode leakage correction. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗