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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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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A User’s Guide to the PLTEMP/ANL Code (V.4.3)

PLTEMP/ANL V4.3 is a program that obtains a steady-state flow and temperature solution for a nuclear reactor core, or for a single fuel assembly. It is based on an evolutionary sequence of codes originally used for plate temperatures, hence “PLTEMP”, developed at Argonne National Laboratory over several decades. Fueled and non-fueled regions are modeled. Each fuel assembly consists of one or more plates or tubes separated by coolant channels. The fuel plates may have one to five layers of different materials, each with heat generation. The width of a fuel plate may be divided into multiple longitudinal stripes, each with its own axial power shape. Depending upon the heat transfer method selected, the temperature solution is effectively 2-dimensional or 3-dimensional (Appendix XV). It should be noted that for a 3-dimensional solution the axial power shape is modeled without accounting for axial heat conduction. It begins with a solution across all coolant channels and fuel plates or tubes within a given fuel assembly, at the entrance to the assembly. The temperature solution is repeated for each axial node along the length of the fuel assembly. The geometry may be either slab or radial, corresponding to fuel assemblies made of a series of flat (or slightly curved) plates, or of nested tubes. A variety of thermal-hydraulic correlations are available to determine safety margins such as onset-of-nucleate boiling ratio(ONBR), departure from nucleate boiling ratio (DNBR), and onset of flow instability ratio (OFIR). Coolant properties for either light or heavy water are obtained from FORTRAN functions rather than from tables. The code is intended for thermal-hydraulic analysis of research reactor performance in the sub-cooled boiling regime. Both turbulent and laminar flow regimes can be modeled. Options to calculate both forced flow and natural circulation are available. A general search capability is available (Appendix XII) to greatly reduce the reactor analyst’s time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Investigating Local Carrier Dynamics in PERC Patterned CdTe Solar Cells

Remarkable progress has been achieved in CdTe photovoltaics (PVs) to further improve cell performance while reducing manufacturing costs. Researchers optimized the front contact that leads the short-circuit current density (J sc ) over 31mA/cm 2 (e.g., (Zn, Mg)O buffer layer and/or alloying CdTe with Se). Reese et al. reported an open-circuit voltage (V oc ) over 1 V with group-V doped single crystal, showing the feasibility of increasing V oc in CdTe PV that is fixed to less than 900 mV in most cases. Recent studies suggested that the V oc improvement in polycrystalline CdTe PVs requires well-passivated back contact. One possible strategy is to use CdMgTe alloy (E g > 1.8 eV), where the band-gap offset (ΔE CB ≈ 0.2 eV) reflects minority carrier electrons, thereby decreasing surface recombination. Another strategy is to utilize a stable Al 2 O 3 layer as an electron reflector; experimental works confirmed the improved performance with a conformal Al 2 O 3 layer. In this configuration, the precise control of Al 2 O 3 (≈1 nm) over the entire CdTe layer is essential because the J sc greatly depends on the tunneling current. While promising, both CdMgTe and Al 2 O 3 passivation on CdTe introduce unfavorable valence band offset that blocks the hole transport. Kephart et al. proposed a patterned passixc vation layer (≈ 20 nm Al 2 O 3 ) with point contacts (≈ 3 μm in diameter) that extract hole carriers. This configuration is similar to the passive emitter and rear contact (PERC) design that has been extensively studied in Si PVs. While remarkably high carrier lifetime and photoluminescence (> 10 X) were measured, consistent improvement in V oc and efficiency in this CdTe PERC were not yet observed. At this time, it is unclear how the surface potential is distributed in the presence of patterned Al 2 O 3 reflectors and how this additional field impacts the PV performance for Cu-doped and group-V doped CdTe PERC devices. To tackle this challenge, we develop robust nanofabrication and characterization platform to study CdTe PERC devices consisting of a patterned Al 2 O 3 layer on CdSe( 1-x )Te x (Cu-doped, GrV-doped; x = 0 to 1; CST). First Solar supplies high-quality CST materials. The dark and light I-Vs under 1-sun illumination are extracted for a series of each set of devices. We perform quantitative and qualitative PV analysis for a series of Cu-doped and Gr-V doped PERC devices, showing notably different V oc changes for GrV-doped compared to Cu-doped PERC devices. This project attempts to produce individual contacts on single grains and grain boundaries to measure lateral transport. As a model system, we use a CdSe( 1-x )Te x film on glass prepared by a colossal grain growth (CGG) technique at National Renewable Research Laboratories (NREL). Our preliminary results confirm that intergranular transport is hindered by the electrical barrier formed near grain boundaries. The PERC fabrication and the lateral transport measurement platform developed in this project can easily be applied to other types of advanced CdSe( 1-x )Te x architectures to better understand local photocarrier transport, in turn, providing the fundamental knowledge to improve the performance of CdTe PVs.

14 SOLAR ENERGY↗
DOE OSTI2021

ESR1 mediated circ{sub 0}004018 suppresses angiogenesis in hepatocellular carcinoma via recruiting FUS and stabilizing TIMP2 expression

Angiogenesis has been certified to account for tumor pathobiology. Circular RNAs (circRNAs) have been demonstrated to be involved in angiogenesis-related diseases, including hepatocellular carcinoma (HCC). Nevertheless, the regulatory roles of most circRNAs remain obscure. This study aims to uncover the function of hsa{sub c}irc{sub 0}004018 on angiogenesis in HCC. Firstly, quantitative real-time RT-PCR (RT-qPCR) analyzed that circ{sub 0}004018 was definitely down-regulated in HCC. Western blot analysis was conducted to detect the protein level of fused protein in sarcoma (FUS) and TIMP metallopeptidase inhibitor 2 (TIMP2). Functional assays were carried out to assess the impacts of circ{sub 0}004018 on HCC. From the experimental results, we found that overexpression of circ{sub 0}004018 significantly inhibited angiogenesis in HCC. The regulatory mechanism of circ{sub 0}004018 in HCC was determined by chromatin immunoprecipitation (ChIP), luciferase reporter assays and RNA immunoprecipitation (RIP) assay. Therefore, we proved that estrogen receptor 1 (ESR1) mediated circ{sub 0}004018 regulated TIMP2 by recruiting FUS. A series of rescue assays verified that circ{sub 0}004018 participated in angiogenesis in HCC via modulating TIMP2. In summary, this paper disclosed that ESR1 activated circ{sub 0}004018 inhibited angiogenesis in HCC via binding to FUS and stabilizing TIMP2 expression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning

Abstract Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an R 2 score of 0.85 and a root mean square error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real-time EELS/XAS analysis pipeline on mixtures of copper-containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Symbolic pregression: Discovering physical laws from distorted video

In this work, we present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled synthetic video (or, more generally, for discovering and modeling predictable features in time-series data). We first train an autoencoder that maps each video frame into a low-dimensional latent space where the laws of motion are as simple as possible, by minimizing a combination of nonlinearity, acceleration, and prediction error. Differential equations describing the motion are then discovered using Pareto-optimal symbolic regression. We find that our pre-regression (“pregression”) step is able to rediscover Cartesian coordinates of unlabeled moving objects even when the video is distorted by a generalized lens. Using intuition from multidimensional knot theory, we find that the pregression step is facilitated by first adding extra latent space dimensions to avoid topological problems during training and then removing these extra dimensions via principal component analysis. An inertial frame is autodiscovered by minimizing the combined equation complexity for multiple experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Improving 3D reconstruction quality for root phenotyping: assessing the impact of camera calibration and imaging parameters

Arate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracyccu remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding in downstream analysis.

3D reconstruction↗
DOE OSTI2020

A novel two-layer-integrated microfluidic device for high-throughput yeast proteomic dynamics analysis at the single-cell level

Abstract Current microfluidic methods for studying multicell strains (e.g., m-types) with multienvironments (e.g., n-types) require large numbers of inlets/outlets (m*n), a complicated procedure or expensive machinery. Here, we developed a novel two-layer-integrated method to combine different PDMS microchannel layers with different functions into one chip by a PDMS through-hole array, which improved the design of a PDMS-based microfluidic system. Using this method, we succeeded in converting 2 × m × n inlets/outlets into m + n inlets/outlets and reduced the time cost of loading processing (from m × n to m) of the device for studying multicell strains (e.g., m-types) in varied multitemporal environments (i.e., n-types). Using this device, the dynamic behavior of the cell-stress-response proteins was studied when the glucose concentration decreased from 2% to a series of lower concentrations. Our device could also be widely used in high-throughput studies of various stress responses, and the new concept of a multilayer-integrated fabrication method could greatly improve the design of PDMS-based microfluidic systems.

Chen, Kaiyue↗
DOE OSTI2022

Fatty acid capped, metal oxo clusters as the smallest conceivable nanocrystal prototypes

Metal oxo clusters of the type M 6 O 4 (OH) 4 (OOCR) 12 (M = Zr or Hf) are valuable building blocks for materials science. Here, we synthesize a series of zirconium and hafnium oxo clusters with ligands that are typically used to stabilize oxide nanocrystals (fatty acids with long and/or branched chains). The fatty acid capped oxo clusters have a high solubility but do not crystallize, precluding traditional purification and single-crystal XRD analysis. We thus develop alternative purification strategies and we use X-ray total scattering and Pair Distribution Function (PDF) analysis as our main method to elucidate the structure of the cluster core. We identify the correct structure from a series of possible clusters (Zr3, Zr4, Zr6, Zr12, Zr10, and Zr26). Excellent refinements are only obtained when the ligands are part of the structure model. Further evidence for the cluster composition is provided by nuclear magnetic resonance (NMR), infrared spectroscopy (FTIR), thermogravimetry analysis (TGA), and mass spectrometry (MS). We find that hydrogen bonded carboxylic acid is an intrinsic part of the oxo cluster. Using our analytical tools, we elucidate the conversion from a Zr6 monomer to a Zr12 dimer (and vice versa), induced by carboxylate ligand exchange. Finally, we compare the catalytic performance of Zr12-oleate clusters with oleate capped, 5.5 nm zirconium oxide nanocrystals in the esterification of oleic acid with ethanol. The oxo clusters present a five times higher reaction rate, due to their higher surface area. Since the oxo clusters are the lower limit of downscaling oxide nanocrystals, we present them as appealing catalytic materials, and as atomically precise model systems. In addition, the lessons learned regarding PDF analysis are applicable to other areas of cluster science as well, from semiconductor and metal clusters, to polyoxometalates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Development of Integrated Thermal Fluids Modeling Capability for MSRs

The DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program supports a full range of computational thermal fluids analysis capabilities and code developments for a broad class of light-water and advanced reactor concepts. The research and development approach under thermal fluids technical area synergistically combines three length and time scales in a hierarchal multi-scale approach. To demonstrate the feasibility and capabilities of a multi-scale thermal fluids capability using these codes, a key joint effort has been pursued to develop an integrated system-and engineering-scale thermal fluids analysis capability with the MOOSE-based codes, through integration of SAM and Pronghorn, both based on the MOOSE framework. This report summarizes the progress in developing an integrated system- and engineering-scale thermal fluids analysis capability based on SAM and Pronghorn for molten salt reactors (MSRs), which gained significant interest in recently years. Two coupling approaches were studied, i.e. separate domain or domain-segregated coupling approach and the domain-overlapping approach. A series of coupled multi-physics models have been developed for a common reference molten salt fast reactor (MSFR) concept, ranging from standalone SAM system model to integrated SAM-Pronghorn-Griffin models. Both the steady state and the transient simulations are performed to the state-of-the-art simulation capabilities of NEAMS software suite in MSFR system applications. This report also covers further development and testing of the gas transport model in SAM for MSR modeling support. The presence of noncondensable gas in MSR systems would have strong impacts on fission gas removal and transport of noble metals throughout the system. Fission products removed through the off-gas system can also impact reactivity and can act as an additional point of heat removal. To ensure that the gas transport model is adequately tested, the supported modeling capabilities and features of SAM were identified, and a suite of tests were developed to test the model for each identified feature. Validation and UQ testing were also performed for the model and demonstrated that the buoyancy term, which was originally developed using non-salt/helium gas experimental data, can capture the experimental gas bubble velocity and diameter to within experimental and code uncertainty. An existing MSRE model was also modified in this work to include the gas transport model and to demonstrate the gas model behavior for realistic conditions of interest.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection

In this work, two physics-based feature extraction techniques are developed for bulk time-series phasor measurement unit (PMU) datasets collected from the field to train the machine learning model for anomaly detection. Two approaches have been developed to extract useful features for different types of events. An admittance-based feature extraction technique is developed to detect events that involve line outages and system topology variations. The developed algorithm extracts the system equivalent admittance variation. Additionally, Fielder’s Theory is utilized to further reduce the potential computation burden by sectionalizing large-scale grids and datasets into smaller areas. Second, an oscillation-based feature extraction technique is developed to detect low-frequency oscillations in power grids. The dominant oscillation modes in the grids are extracted using energy-sorted Prony analysis. The extracted dominant oscillation modes by the developed work exhibit a high fitting resolution. Finally, the developed techniques have been validated using large-scale and real-world datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Downscaled CMIP5 projections of physical fire risk understate historical trends

Reliable projections of wildfire risk are important for multi-sector impacts analysis. Statistically downscaled and bias-corrected Earth system model ensemble products are routinely used to analyze regional physical wildfire risk, but evaluations of historical observed trends and variability are lacking. Here, we evaluate physical fire risk over the western United States using the Canadian Forest Fire Weather Index (FWI) by comparing model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), statistically downscaled via the Multivariate Adaptive Constructed Analogs (MACA) approach, against the observational target dataset gridMET, a gridded high-resolution surface meteorological product. We analyze multidecadal trends and interannual variability in seasonal average FWI for the historical period and future projections under two emissions scenarios, and we compare MACA-CMIP5 ensemble results with a simple time series model that generates historical and future projections of seasonal FWI based on bootstrapping observed historical trends and variability. Our findings indicate that MACA-CMIP5 accurately captures the magnitude and spatial patterns of seasonally averaged FWI but tends to underestimate historical decadal trends. We show that future increases in fire risk may be underestimated relative to the simple time series model that projects historical variability into the future. We also highlight that model biases in relative humidity contribute significantly to model-data differences. Our results underscore the importance of historical hindcasting exercises for informing broader multi-sector applications.

FWI↗
DOE OSTI2023

A User’s Guide to the PLTEMP/ANL Code

PLTEMP/ANL V4.4 is a program that obtains a steady-state flow and temperature solution, in geometrical extent, for a nuclear reactor core, or a single fuel assembly, including the effect of manufacturing and modelling uncertainties by the means of hot channel factors. It is based on an evolutionary sequence of codes originally used for calculating plate temperatures, hence “PLTEMP”, developed at Argonne National Laboratory over the past 30 years. Fueled and non-fueled regions are modeled. Each fuel assembly consists of one or more plates or tubes separated by coolant channels. The fuel plates may have one to five layers of different materials, each with heat generation. The width of a fuel plate may be divided into multiple longitudinal stripes, each with its own axial power shape. Depending upon the heat transfer method selected, the temperature solution is effectively 2-dimensional or 3-dimensional. The geometry may be either slab or radial, corresponding to fuel assemblies made of a series of flat (or slightly curved) plates, or of nested tubes. A variety of thermal-hydraulic correlations are available to determine safety margins such as onset of nucleate boiling ratio (ONBR), departure from nucleate boiling ratio (DNBR), and onset of flow instability ratio (OFIR). Coolant properties for either light or heavy water are obtained from FORTRAN functions rather than from tables. The code is intended for thermal-hydraulic analysis of research reactor performance in the sub-cooled boiling regime. Both turbulent and laminar flow regimes can be modeled. Options to calculate both forced flow and natural circulation are available. A general search capability for select design and safety parameters is available to greatly reduce the reactor analyst’s time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

HarDWR - Raw Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. In order to hold a water right in the western United States, an entity, (e.g., an individual, corporation, municipality, sovereign government, or non-profit) must register a physical document with the state's water regulatory agency. State water agencies each maintain their own database containing all registered water right documents within the state, along with relevant metadata such as the point of diversion and place of use of the water. All western U.S. states have digitized their individual water rights databases, along with the geospatial data describing the spatial units where water rights are managed. Each state maintains and provides their own water rights data in accordance with individual state regulations and standards. We collected water rights databases from 11 western United States states either by downloading them from publicly accessible web portals, or by contacting state water management representatives; detailed descriptions of where and when the data was collected is provided in the README.txt, as well as Lisk et al.(in review). This collection of data are those raw water rights. Each state formats their data differently, meaning that file types, field availability, and names vary from state to state. Note, the data provided here reflects the state of the water rights databases at the time we collected the data; updates have likely occurred in many states. Some pieces of information are common among all states. These are: priority date, volume or flow of water allowed by the right, stated water use of the right, and some means of identifying the geography and source of the water pertaining to the right - typically the coordinates of the Point of Diversion (PoD) of a waterbody or well. Arizona regulates water in a different way than the other 10 states. Outside of some relatively small critical agricultural areas called Active Management Areas (AMAs), Arizona does not maintain any water rights. However, the state does require registration of surface and groundwater pumping devices, which includes disclosing the mechanical specifics of the devices. We used these records as a proxy for water rights. Each state, and their respective water right authorities, have made their water right records available for non-commercial reference uses. In addition, the states make no guarantees as to the completeness, accuracy, or timeliness of their respective databases, let alone the modifications which we, the authors of this paper, have made to the collected records. None of the states should be held liable for using this data outside of its intended use. In addition, the following states have requested specifically worded disclaimers to be included with their data. Colorado: "The data made available here has been modified for use from its original source, which is the State of Colorado. THE STATE OF COLORADO MAKES NO REPRESENTATIONS OR WARRANTY AS TO THE COMPLETENESS, ACCURACY, TIMELINESS, OR CONTENT OF ANY DATA MADE AVAILABLE THROUGH THIS SITE. THE STATE OF COLORADO EXPRESSLY DISCLAIMS ALL WARRANTIES, WHETHER EXPRESS OR IMPLIED, INCLUDING ANY IMPLIED WARRANTIES OF MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the Web feed is being used at one's own risk." Montana: "The Montana State Library provides this product/service for informational purposes only. The Library did not produce it for, nor is it suitable for legal, engineering, or surveying purposes. Consumers of this information should review or consult the primary data and information sources to ascertain the viability of the information for their purposes. The Library provides these data in good faith but does not represent or warrant its accuracy, adequacy, or completeness. In no event shall the Library be liable for any incorrect results or analysis; any direct, indirect, special, or consequential damages to any party; or any lost profits arising out of or in connection with the use or the inability to use the data or the services provided. The Library makes these data and services available as a convenience to the public, and for no other purpose. The Library reserves the right to change or revise published data and/or services at any time." Oregon: "This product is for informational purposes and may not have been prepared for, or be suitable for legal, engineering, or surveying purposes. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information." The available data is provided as a series of compressed files, which each containing the full data collected from each state. Some of the files have been renamed, to more easily know which state the data belongs to. The file renaming was also required as some files from different states had the same name. In other cases, the data for a state has been placed in a folder indicating which state it belongs to - as the state organized its data by selected subregions. Below is a brief description of the format of the collected data from each state. ArizonaRights_StatementOfClaimants: A folder containing a database of interconnected CSV files. The soc_erd.pdf file contains a visual flowchart of how the various files are connected, beginning with SOC_MAIN.csv in the center of the page. ArizonaRights_SurfaceWaterRightsData: A folder containing a database of a single Shapefile and 10 associated CSVs. SurfaceWater.pdf contains a visual flowchart of how the various files are connected, beginning with ADWR_SW_APPL_REGRY.csv. ArizonaRights_Well55Registry: A folder containing a database of a single Shapefile and 59 associated CSVs. Wells55.pdf contains a visual flowchart of how the various files are connected, beginning with WellRegistry.shp. CaliforniaRights_eWRIMS_directDatabase: A folder containing a collection of four "series" Microsoft Excel files, as either XLS or XLSX. The four "series": byCounty, byEntity (what type of legal entity holds the right), byUse (stated water use), and byWatershed, are various methods by which the California water rights are organized within the state's database. However, it was observed that by only collecting a single series, not all water rights were being provided. So, essentially, the majority of records within each "series" are copies of each other, with each "series" containing some unique records. ColoradoRights_NetAmounts: A folder containing 78 CSV files, with one file per Colorado Water District. IdahoRights_PointOfDiversion: A Shapefile containing the Points of Diversion for the entire state of Idaho. IdahoRights_PlaceOfUse: A Shapefile containing the Place of Use polygons for the entire state of Idaho. MontanaRights_WaterRights: A Geodatabase file containing the Points of Diversion and Places of Use for the entire state of Montana. The name of the Points of Diversion Feature Layer within the Geodatabase is "WRDIV", and the name of the Places of Use Feature Layer is "WRPOU". NevadaRights_POD_Sites: A Shapefile containing the Points of Diversion for the entire state of Nevada. NewMexicoRights_Points_of_Diversion: A Shapefile containing the Points of Diversion for the entire state of New Mexico. OregonRights_state_shp: A folder containing 36 Shapefiles and are split between "pod" (Point of Diversion) and "pou" (Place of Use) for each water management basin within Oregon. In other words, each basin has one "pod" file and one "pou" file. The "pod" files are point shapes, and the "pou" files are polygons. UtahRights_Points_of_Diversion: A Shapefile containing the Points of Diversion for the entire state of Utah. WashingtonRights_WaterDiversions_ECY_NHD: A Geodatabase file containing both the Points of Diversion for the entire state of Washington. The name of the Feature Layer within the Geodatabase is "WaterDiversions_ECY_NHD". WyomingRights: A folder containing four subdirectories, one for each Wyoming Water Division. Each Division directory includes a varying number of subdirectories for each Wyoming Water District. Each District folder contains two copies of the Point of Diversion records for that area, with one copying being in CSV and one copy in Microsoft Excel XLS format.

Lisk, Matthew↗
DOE OSTI2023

Spectroscopic Investigations of Molecular Symmetry Breakdown

This project addressed precise measurements of the spectra of molecules to address fundamental questions in chemical physics in the realm of nuclear spin symmetries and their breakdown in molecules, and parity non-conservation caused by the weak nuclear force. Nuclear spin symmetry is intimately connected to parity in symmetrical molecules and plays a central role in the physical and chemical properties of symmetric molecules, their collisional relaxation, and the establishment of thermodynamic equilibrium in an ensemble. Such effects are present in every molecular system or process, but their small size means they are often only directly observable under special conditions. However, their measurement is of fundamental, and often practical importance, and strongly relates to the themes of coherence in light and matter in the recent Basic Energy Sciences Advisory Committee (BESAC) report “Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science”. We begin by addressing lifetime broadening of spectroscopic lines caused by self-collisions in acetylene, where efficient resonant rotational energy transfer between a pair of molecules separated in energy by rotational quantum number ΔJ = ± 2, can result in a different pressure broadening for ortho- and para- nuclear spin state molecules. This work was prompted by a controversial report [Iwakuni et al. Phys. Rev Letts. 117 (2016) 143902] of the observation of such an effect in the v 1 +v 3 combination band transition of acetylene discussed in the original proposal. After delays caused by the COVID-19 pandemic, we have completed these measurements and a paper was published in the Journal of Chemical Physics describing the work. The measurements pointed out that the use of an approximate model in the analysis of the original work was the cause of the erroneous conclusion that ortho and para acetylene molecules had substantially different collisional lifetime broadening. In the process, much improved broadening measurements have been made that will permit more reliable modeling of the effects in the future. Subsequently, we have completed a new set of measurements of self- and nitrogen-collisional broadening at a series of low and intermediate temperatures to characterize the temperature dependence of the effects in the molecule. This work is currently being analyzed and will be the subject of a paper to be published in the relatively near future. In addition, some very preliminary measurements of CH 4 as described in the proposal, have been completed. If facilities and time permit, these will continue as an undergraduate research project.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

“Mirror” magnet experiment for quench studies, quench analysis techniques development and in support of superconducting magnet modeling

In order to progress on various research points related to superconducting magnets, we suggest performing series of tests with induced and natural quenches on a mirror magnet, probably using a “11 T” coil. The “mirror” is to have voltage taps instrumented on half the strands, on both coil ends. It is to be instrumented with multiple spot-heaters, of the order of ten, in designated pole-turn areas. Instrumentation is to include quench antenna array facing the inner coil surface, as well as acoustic sensors on the inner layer pole. Further, acoustic sensors at the magnet ends and an acoustic calibration transducer are to be installed. Thus, we are to test sensitivity and precision of acoustic and quench antenna (QA) array sensors, both at close proximity to the coil, and we are to test and commission acoustic on-magnet real-time “calibration”. New or extended DAQ will be tested as well. In addition to diagnostic development, the purpose of the magnet experiment is to investigate current redistribution in the coil, both during quenching and in operating conditions; to assess voltage build up across strands and in the coil segments during quenching, to explore voltage signature differentiation between different type of quenches; to improve modeling of processes during quenching and for magnet design purposes. In this experiment, we expect to be able to characterize the processes during a quench in unprecedented details.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Orbital Evolution of Binary Black Holes in Active Galactic Nucleus Disks: A Disk Channel for Binary Black Hole Mergers?

We perform a series of high-resolution 2D hydrodynamical simulations of equal-mass binary black holes (BBHs) embedded in active galactic nucleus (AGN) accretion disks to study whether these binaries can be driven to merger by the surrounding gas. We find that the gravitational softening adopted for the BBH has a profound impact on this result. When the softening is less than 10% of the binary separation, we show that, in agreement with recent simulations of isolated equal-mass binaries, prograde BBHs expand in time rather than contract. Eventually, however, the binary separation becomes large enough that the tidal force of the central AGN disrupts them. Only when the softening is relatively large do we find that prograde BBHs harden. We determine through detailed analysis of the binary torque, that this dichotomy is due to a loss of spiral structure in the circum-single disks orbiting each black hole when the softening is a significant fraction of the binary separation. Properly resolving these spirals—both with high resolution and small softening—results in a significant source of binary angular momentum. Only for retrograde BBHs do we find consistent hardening, regardless of softening, as these BBHs lack the important spiral structure in their circum-single disks. This suggests that the gas-driven inspiral of retrograde binaries can produce a population of compact BBHs in the gravitational-wave-emitting regime in AGN disks, which may contribute a large fraction to the observed BBH mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2025

Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures

Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load—derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.

kilic, Ozgur Ozan [Brookhaven National Laboratory ↗