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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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NASA NTRS1994

D/H ratios and H2O contents of mantle-derived amphibole megacrysts from Dish Hill, California

D/H ratios are, in principle, useful in characterizing reservoirs of mantle hydrogen and as tracers of volatile transfer processes in Earth's interior. In practice, however, interpretation of isotopic measurements on mantle derived H is complicated by surface processes such as contamination and degassing which may alter the primary D/H ratio. Although there are indications that water associated with subduction zones and certain chemically enriched basalts is enriched in D relative to 'typical' upper mantle water, the extent of isotopic heterogeneity of mantle H remains uncertain. Kaersutitic amphibole megacrysts in alkaline basalts are one of the most widespread sources of mantle water and are therefore potentially useful for large-scale regional studies of D/H variation. However, D/H ratios of these amphiboles vary widely (from plus 8 to minus 113 percent), even in samples from the same locality, so that this potential has yet to be realized. In order to investigate the origin of this variability, and to explore the possibility that primary mantle D/H ratios may be deduced from these amphiboles, we analyzed the D/H ratios and chemical compositions of a suite of 17 kaersutitic amphiboles from Dish Hill, California. This work contrasts with previous studies in which sampling is widespread, but representatives from any given locality are few. Samples were collected from a restricted area on the southern flank of the volcanic center and are associated with the basal volcanic breccia. Fourteen of the samples were large single crystals or crystal fragments (megacrysts, 0.4 to 30 grams), believed to derive from pegmatitic veins crystallized from melts in the mantle. Two were coarse-grained intergrowths of amphibole with olivine and spinel, and one was a thin (2 mm) selvage on a peridotite xenolith.

Bell, David R.↗

DOE OSTI2019

Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques

Abstract. Improving predictive understanding of Earth system variability and change requires data–model integration. Efficient data–model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a large-scale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations. In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fast-to-evaluate surrogate system of model outputs over large spatial and temporal domains. We first use singular value decomposition to reduce the output dimensions and then use Bayesian optimization techniques to generate an accurate neural network surrogate model based on limited ESM simulation samples. Our machine-learning-based surrogate methods can build and evaluate a large surrogate system of many variables quickly. Thus, whenever the quantities of interest change, such as a different objective function, a new site, and a longer simulation time, we can simply extract the information of interest from the surrogate system without rebuilding new surrogates, which significantly reduces computational efforts. We apply the proposed method to a regional ecosystem model to approximate the relationship between eight model parameters and 42 660 carbon flux outputs. Results indicate that using only 20 model simulations, we can build an accurate surrogate system of the 42 660 variables, wherein the consistency between the surrogate prediction and actual model simulation is 0.93 and the mean squared error is 0.02. This highly accurate and fast-to-evaluate surrogate system will greatly enhance the computational efficiency of data–model integration to improve predictions and advance our understanding of the Earth system.

58 GEOSCIENCES↗

DOE OSTI2018

Synthesis route attribution of sulfur mustard by multivariate data analysis of chemical signatures

A multivariate model was developed here to attribute samples to a synthetic method used in the production of sulfur mustard (HD). Eleven synthetic methods were used to produce 66 samples for model construction. Three chemists working in both participating laboratories took part in the production, with the aim to introduce variability while reducing the influence of laboratory or chemist specific impurities in multivariate analysis. A gas chromatographic/mass spectrometric data set of peak areas for 103 compounds was subjected to orthogonal partial least squares - discriminant analysis to extract chemical attribution signature profiles and to construct multivariate models for classification of samples. For one- and two-step routes, model quality allowed the classification of an external test set (16/16 samples) according to synthesis conditions in the reaction yielding sulfur mustard. Classification of samples according to first-step methodology was considerably more difficult, given the high purity and uniform quality of the intermediate thiodiglycol produced in the study. Model performance in classification of aged samples was also investigated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Stochastic modeling and statistical calibration with model error and scarce data

This paper introduces a procedure to assess the predictive accuracy of stochastic models subject to model error and sparse data. Model error is introduced as uncertainty on the coefficients of appropriate polynomial chaos expansions (PCE). The error associated with finite sample size allows us to conceive of these coefficients as statistics of the data that we describe as random variables whose influence on output quantities of interest is evaluated through the extended polynomial chaos expansion (EPCE). A Bayesian data assimilation scheme is introduced to update these expansions by considering the resulting nested chaos expansion as a hierarchical probabilistic model. Stochastic models of quantities of interest (QoI) are thus constructed and efficiently evaluated. Here, the Metropolis–Hastings Markov chain Monte Carlo procedure is used to sample the posterior. Two illustrative analytical and numerical problems are used to demonstrate the proposed approach.

Bayesian inference↗

NASA NTRS1971

Technique for measuring high-temperature thermal conductivity of solids by the use of a heat pipe.

A suggested technique for accurately measuring thermal conductivity of solids in the temperature range 800-1500 C is presented. The procedure employs the sample to be tested in series combination with a high-temperature heat pipe and a heat-transfer device, which has a variable thermal conductance. By changing the thermal conductance of the heat-transfer unit and measuring the change in heat-pipe power input to maintain a constant heat-pipe temperature, one can accurately measure the heat flux through the sample in conjunction with the temperature drop across it. This steady-state technique has some inherent advantages over methods currently employed to measure thermal conductivity at elevated temperatures.

Forman, R.↗

NASA NTRS1988

Analysis of long term heart rate variability: methods, 1/f scaling and implications

The use of spectral techniques to quantify short term heart rate fluctuations on the order of seconds to minutes has helped define the autonomic contributions to beat-to-beat control of heart rate. We used similar techniques to quantify the entire spectrum (0.00003-1.0 Hz) of heart rate variability during 24 hour ambulatory ECG monitoring. The ECG from standard Holter monitor recordings from normal subjects was sampled with the use of a phase locked loop, and a heart rate time series was constructed at 3 Hz. Frequency analysis of the heart rate signal was performed after a nonlinear filtering algorithm was used to eliminate artifacts. A power spectrum of the entire 24 hour record revealed power that was inversely proportional to frequency, 1/f, over 4 decades from 0.00003 to 0.1 Hz (period approximately 10 hours to 10 seconds). Displaying consecutive spectra calculated at 5 minute intervals revealed marked variability in the peaks at all frequencies throughout the 24 hours, probably accounting for the lack of distinct peaks in the spectra of the entire records.

Non-NASA Center↗

NASA NTRS1984

Costs of measuring leaf area index of corn

The magnitude of plant-to-plant variability of leaf area of corn plants selected from uniform plots was examined and four representative methods for measuring leaf area index (LAI) were evaluated. The number of plants required and the relative costs for each sampling method were calculated to detect 10, 20, and 50% differences in LAI using 0.05 and 0.01 tests of significance and a 90% probability of success (beta = 0.1). The natural variability of leaf area per corn plant was nearly 10%. Additional variability or experimental error may be introduced by the measurement technique employed and by nonuniformity within the plot. Direct measurement of leaf area with an electronic area meter had the lowest CV, required that the fewest plants be sampled, but required approximately the same amount of time as the leaf area/weight ratio method to detect comparable differences. Indirect methods based on measurements of length and width of leaves required more plants but less total time than the direct method. Unless the coefficients for converting length and width to area are verified frequently, the indirect methods may be biased. When true differences in LAI among treatments exceed 50% of mean, all four methods are equal. The method of choice depends on the resources available, the differences to be detected, and what additional information, such as leaf weight or stalk weight, is also desired.

Daughtry, C. S. T.↗

DOE OSTI2018

Multi-site evaluation of the LN34 pan-lyssavirus real-time RT-PCR assay for post-mortem rabies diagnostics

Rabies is a fatal zoonotic disease that requires fast, accurate diagnosis to prevent disease in an exposed individual. The current gold standard for post-mortem diagnosis of human and animal rabies is the direct fluorescent antibody (DFA) test. While the DFA test has proven sensitive and reliable, it requires high quality antibody conjugates, a skilled technician, a fluorescence microscope and diagnostic specimen of sufficient quality. The LN34 pan-lyssavirus real-time RT-PCR assay represents a strong candidate for rabies post-mortem diagnostics due to its ability to detect RNA across the diverse Lyssavirus genus, its high sensitivity, its potential for use with deteriorated tissues, and its simple, easy to implement design. Here, we present data from a multi-site evaluation of the LN34 assay in 14 laboratories. A total of 2,978 samples (1,049 DFA positive) from Africa, the Americas, Asia, Europe, and the Middle East were tested. The LN34 assay exhibited low variability in repeatability and reproducibility studies and was capable of detecting viral RNA in fresh, frozen, archived, deteriorated and formalin-fixed brain tissue. The LN34 assay displayed high diagnostic specificity (99.68%) and sensitivity (99.90%) when compared to the DFA test, and no DFA positive samples were negative by the LN34 assay. The LN34 assay produced definitive findings for 80 samples that were inconclusive or untestable by DFA; 29 were positive. Five samples were inconclusive by the LN34 assay, and only one sample was inconclusive by both tests. Furthermore, use of the LN34 assay led to the identification of one false negative and 11 false positive DFA results. Together, these results demonstrate the reliability and robustness of the LN34 assay and support a role for the LN34 assay in improving rabies diagnostics and surveillance.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1985

Long term monitoring of methane in the atmosphere by multiplex gas chromatography

Methane is of interest in the study of the Earth's atmosphere because of its implication in the future global warming of the surface. This warming is produced by the absorption of infrared energy by trace gases. It has been estimated that in the next 40 to 50 years, methane could contribute 20 to 25 pct. as much atmospheric warming as that expected from carbon dioxide increases. Studies to examine sources, sinks, and cycles of methane will require analytical methods capable of continuous unattended measurement with temporal resolution of an hour or less for weeks at a time. Gas chromatography (GC) is one of the most practical methods available to conduct the analysis of air, but limitations in this technique still exist which can be alleviated with multiplex GC (MGC). MGC is a technique where many samples are pseudo-randomly introduced to the chromatograph without regard to the length of time required for an analysis. The resulting data must then be reduced using computational methods such as cross correlation. In the technique reported, a tube packed with silver oxide was used at the inlet of the GC column to create concentration pulses of methane in a sample stream of air. By using only one carrier, i.e., ambient air, an effective and accurate method to monitor the variations in concentration of methane in the atmosphere over long periods of time was developed. Methane in ambient air was monitored for an eight day period and an interesting temporal variability was found. This work has shown the utility of a relatively simple MGC for the analysis of a real environmental sample.

Valentin, Jose R.↗

DOE OSTI2022

Variable Active Galactic Nuclei in the Galaxy Evolution Explorer Time Domain Survey

We searched the northern hemisphere fields of the Galaxy Evolution Explorer Time Domain Survey for galaxies with UV variability indicative of active galactic nuclei (AGNs). We identified 48 high-probability candidate AGNs from a parent sample of 1819 galaxies in the NASA Sloan Atlas catalog. We further characterized these systems using optical spectroscopic diagnostics, Wide-field Infrared Survey Explorer IR color selection criteria, and spectral energy distribution modeling. Of the 48 candidates, eight were identified as AGNs from optical emission lines, two were identified by their IR colors, and 28 were identified through spectral energy decomposition. Observational biases of each selection method are discussed in connecting these AGN subsamples to another. By selecting AGNs based on UV variability, we also identified six low-mass AGN candidates, all of which would have been missed by spectroscopic selection.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1993

The concentration and isotopic composition of carbon in basaltic glasses from the Juan de Fuca Ridge, Pacific Ocean

The abundance and C-13/C-12 ratios of carbon were analyzed in basaltic glass from twenty locations along the Juan de Fuca Ridge using a 3-step combustion/extraction technique. Carbon released during the first two combustion steps at 400-500 C and 600-650 C is interpreted to be secondary, and only the carbon recovered during a final combustion step at about 1200 C is thought to be indigenous to the samples. For carbon released at about 1200 C, glasses analyzed as 1-2 mm chips contained 23-146 ppm C with delta C-13 values of -4.8 to -9.3 per mil, whereas samples crushed to 38-63 microns or 63-90 microns yielded 56-103 ppm C with delta C-13 values of -6.1 to -9.2 per mil. The concentrations and isotopic compositions of the primary carbon dissolved in the glasses and present in the vesicles are similar to those previously reported for other ocean-ridge basalts. The Juan de Fuca basaltic magmas were not in equilibrium with respect to carbon when they erupted and quenched on the sea floor. Evidence of disequilibrium includes (1) a large range of carbon contents among glasses collected at similar depths, (2) a highly variable calculated carbon isotopic fractionation between melt and vapor determined by comparing crushed and uncrushed splits of the same sample, and (3) a lack of correlation between vesicle abundance, carbon concentration, and depth of eruption. Variations in carbon concentration and delta C-13 ratios along the ridge do not correlate with major element chemistry. The observed relationship between carbon concentrations and delta C-13 values may be explained by late-stage, variable degrees of open-system (Rayleigh-like) degassing.

Blank, Jennifer G.↗

DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Nisqually River, WA

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of glacial freeze/thaw cycle-induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Nisqually River, WA, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in August 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. Note that the included hydrograph has a separate DOI that should also be cited. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Erpe River, Germany

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of wastewater treatment plant effluent induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Erpe River, Germany. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in September 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Columbia River in Washington, USA

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of the upstream Priest Rapids Dam-induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Columbia River, WA, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in August 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Jordan River, Israel

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of groundwater pumping induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Jordan River, Israel. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in August 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the East Fork Poplar Creek in Tennessee, USA

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of wastewater treatment plant effluent induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the East Fork Poplar Creek, TN, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in August 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. Note that the included hydrograph has a separate DOI that should also be cited. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗

DOE OSTI2021

Identifying Periodic Variable Stars and Eclipsing Binary Systems with Long-term Las Cumbres Observatory Photometric Monitoring of ZTF J0139+5245

We present the results of our search for variable stars using the long-term Las Cumbres Observatory (LCO) monitoring of white dwarf ZTF J0139+5245 with the two 1.0 m telescope nodes located at McDonald Observatory using the Sinistro imaging instrument. In this search, we find 38 variable sources, of which 27 are newly discovered or newly classified (71%) based on comparisons with previously published catalogs, thereby increasing the number of detections in the field of view under consideration by a factor of ≈2.5. We find that the improved photometric precision per exposure due to longer exposure time for LCO images combined with the greater time sampling of LCO photometry enables us to increase the total number of detections in this field of view. Each LCO image covers a field of view of 26' × 26' and observes a region close to the Galactic plane (b = -9.°4) abundant in stars with an average stellar density of ≈8 arcmin{sup -2}. We perform aperture photometry and Fourier analysis on over 2000 stars across 1560 LCO images spanning 537 days to find 28 candidate BY Draconis variables, three candidate eclipsing binaries of type EA, and seven candidate eclipsing binaries of type EW. In assigning preliminary classifications to our detections, we demonstrate the applicability of the Gaia color–magnitude diagram as a powerful classification tool for variable-star studies.

47 OTHER INSTRUMENTATION↗