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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Probing the galactic disk and halo. 2: Hot interstellar gas toward the inner galaxy star HD 156359

We present Goddard High Resolution Spectrograph intermediate-resolution measurements of the 1233-1256 A spectral region of HD 156396, a halo star at l = 328.7 deg, b = -14.5 deg in the inner Galaxy with a line-of sight distance of 11.1 kpc and a z-distance of -2.8 kpc. The data have a resolution of 18 km/s Full Width at Half Maximum (FWHM) and a signal-to-noise ratio of approximately 50:1. We detect interstellar lines of Mg II, S II, S II, Ge II, and N V and determine log N/(Mg II) = 15.78 +0.25, -0.27, log N(Si II) greater than 13.70, log N(S II) greater than 15.76, log N(Ge II) = 12.20 +0.09,-0.11, and log N(N v) = 14.06 +/- 0.02. Assuming solar reference abundances, the diffuse clouds containing Mg, S, and Ge along the sight line have average logarithmic depletions D(Mg) = -0.6 +/- 0.3 dex, D(S) greater than -0.2 dex, and D(Ge) = -0.2 +/- 0.2 dex. The Mg and Ge depletions are approximately 2 times smaller than is typical of diffuse clouds in the solar vicinity. Galactic rotational modeling of the N v profiles indicates that the highly ionized gas traced by this ion has a scale height of approximately 1 kpc if gas at large z-distances corotates with the underlying disk gas. Rotational modeling of the Si iv and C iv profiles measured by the IUE satellite yields similar scale height estimates. The scale height results contrast with previous studies of highly ionized gas in the outer Milky Way that reveal a more extended gas distribtion with h approximately equals 3-4 kpc. We detect a high-velocity feature in N v and Si II v(sub LSR) approximately equals + 125 km/s) that is probably created in an interface between warm and hot gas.

Sembach, Kenneth R.↗
NASA NTRS2007

The Hottest Horizontal-Branch Stars in Omega Centauri: Late Hot Flasher vs. Helium Enrichment

UV observations of some massive globular clusters uncovered a significant population of very hot stars below the hot end of the horizontal branch (HB), the so-called blue hook stars. This feature might be explained either by the late hot flasher scenario here stars experience the helium flash while on the white dwarf cooling curve or by the helium-rich sub-population recently postulated to exist in some clusters. Spectroscopic analyses of blue hook stars in omega Cen and NGC 2808 support the late hot flasher scenario, but the stars contain much less helium than expected and the predicted C, N enrichment could not be verified from existing data. We want to determine effective temperatures, surface gravities and abundances of He, C, N in blue hook and canonical extreme horizontal branch (EHB) star candidates. Moderately high resolution spectra of stars at the hot end of the blue horizontal branch in the globular cluster omega Cen were analysed for atmospheric parameters (T(sub eff), log g) and abundances using LTE and Non-LTE model atmospheres. In the temperature range 30,000 K to 50,000 K we find that 37% of our stars are helium-poor (log nHe/nH less than -2), 49% have solar helium abundance within a factor of 3 (-1.5 less than or equal to log nHe/nH less than or equal to -0.5) and 14% are helium rich (log nHe/nH greater than -0.4). We also find carbon enrichment in step with helium enrichment, with a maximum carbon enrichment of 3% by mass. At least 30% of the hottest HB stars in omega Centauri show helium abundances well above the predictions from the helium enrichment scenario (Y = 0.42 corresponding to log nHe/nH approximately equal to -0.74). In addition the most helium-rich stars show strong carbon enrichment as predicted by the late hot flasher scenario. We conclude that the helium-rich HB stars in omega Cen cannot be explained solely by the helium-enrichment scenario invoked to explain the blue main sequence.

Moehler, S.↗
NASA NTRS2017

Feeding and Feedback in the Powerful Radio Galaxy 3C 120

We present a spectral analysis of a 200-kilosecond observation of the broad-line radio galaxy 3C 120, performed with the high-energy transmission grating spectrometer on board the Chandra X-Ray Observatory. We find (i) a neutral absorption component intrinsic to the source with a column density of log N (sub H) equals 20.67 plus or minus 0.05 square centimeters; (ii) no evidence for a warm absorber (WA) with an upper limit on the column density of just log N (sub H) less than 19.7 square centimeters, assuming the typical ionization parameter log xi approximately equal to 2.5 ergs per second per centimeter; the WA may instead be replaced by (iii) a hot emitting gas with a temperature kT approximately equal to 0.7 kiloelectronvolts observed as soft X-ray emission from ionized Fe L-shell lines, which may originate from a kiloparsec-scale shocked bubble inflated by the active galactic nucleus (AGN) wind or jet with a shock velocity of about 1000 kilometers per second determined by the emission line width; (iv) a neutral Fe K alpha line and accompanying emission lines indicative of a Compton-thick cold reflector with a low reflection fraction R approximately equal to 0.2, suggesting a large opening angle of the torus; (v) a highly ionized Fe XXV emission feature indicative of photoionized gas with an ionization parameter log xi equal to 3.75 (sup plus 0.38) (sub minus 0.27) ergs per second per centimeter and a column density of log N (sub H) greater than 22 square centimeters localized within approximately 2 pc from the X-ray source; and (vi) possible signatures of a highly ionized disk wind. Together with previous evidence for intense molecular line emission, these results indicate that 3C 120 is likely a late-state merger undergoing strong AGN feedback.

black hole physics – galaxies: active – line: ↗
DOE OSTI2018

National Plan for the Disposal of Existing and Future TRU Wastes - 18616

Now that the Waste Isolation Pilot Plant (WIPP) has re-opened, after being down for three years due to an accident the DOE National TRU Program has re-developed a plan for the disposal of existing and future TRU wastes. The National TRU Waste Management Plan (NTWMP) is an integrated plan to address the needs of DOE sites across the DOE Weapons Complex that create and store TRU waste, and is centered on the capability of WIPP to receive and dispose of TRU waste. The WIPP capability is planned as a weekly shipping rate and is defined by emplacement and receipt rate capabilities since WIPP is not permitted for long-term storage. And, shipping rates may be constrained by new WIPP Waste Acceptance Criteria (WAC) and future plans for repository configurations and disposal package capabilities over the next 30 years. Since WIPP is the only disposal system available to the DOE for TRU waste disposal, DOE has a newly projected life-cycle need for WIPP to remain open and available up to 2050. It is projected that the need for WIPP will continue up to the year 2050 for the disposition of the remaining legacy TRU inventories and for the disposal of TRU waste continuing to be generated at DOE's National Laboratories. In addition, there are several other potential new waste streams in which WIPP is being considered an alternative solution for their disposition. The NTWMP has been updated to reflect the short-term and long-term plans for TRU waste disposal at WIPP. The short-term plan is used by DOE for budget projection and integration purposes to facilitate and inform budget planning, both in DOE's Environmental Management (EM) and National Nuclear Security Administration (NNSA) organizations. Most legacy TRU waste (generated in the past and stored at DOE sites today) is owned and managed by EM. And, EM submits integrated budget requests for DOE sites, including the integration of resources for TRU waste management. NNSA and DOE's Office of Science (OS) funds TRU waste management as part of their mission funding and NNSA should eventually be the primary user of the WIPP resources as EM's legacy TRU waste is cleaned-up. Prior to the unexpected shutdown of WIPP in 2014, there was a small back-log of waste certified for WIPP disposal and shipments of TRU waste to WIPP were 'just-in-time' as waste was certified. After three years of WIPP being down, DOE sites have built significant back-logs of TRU waste to be certified for WIPP. That back-log built up to over 25,000 containers of TRU waste during that three-year period (Table 1). The backlog included both Contact-Handled (CH) TRU waste and Remote-Handled (RH) TRU waste, and even though WIPP re-opened in April of 2017, it is only open for TRU waste containers that can be stacked on disposal room floors. Plans for changing RH waste disposal packages and methods are in-the-works, but are not yet mature enough for significant planning. In addition, the WIPP underground facilities are now partially contaminated with radioactive particulate, which significantly reduces the WIPP operational efficiencies as compared to 2014. With a large back-log of waste ready for WIPP disposal and limited throughput for WIPP disposal rates, the focus of the NTWMP is to prioritize the selection of TRU wastes from the various sites based on WIPP waste emplacement rates and shipping rates to WIPP. Some TRU waste sites have a significant need to be top priority for shipments to WIPP, and that must be balanced with the need for all TRU waste sites to have some shipments to deplete their back-log of TRU wastes. Storage and management of TRU waste is very costly for DOE and many of those resources could be used to optimize the overall disposition of TRU waste and save DOE significant funding. In addition, the plan will show the possible transition of TRU Waste Management from primarily being an EM function to being an NNSA function in the coming years. As legacy TRU waste inventories are removed, and as NNSA mission-related TRU waste inventories are produced, DOE will be faced with deciding where the primary responsibility for TRU waste management should reside. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Evaluation of Drilling Performance at The Geysers with Machine Learning Methods Using Geologic Data

A recent well, GDC-36, was drilled in The Geysers Geothermal Field served in a Department of Energy-industry to demonstrate improved drilling performance with polycrystalline diamond compact (PDC) bits. Both PDC and roller cone drill bits were used to drill this well. Key challenges encountered during drilling included lost circulation in the mud-drilled section, and bit damage interfacial severity in the deeper, air-drilled section. The objective of this study is to evaluate the drilling performance in relation to the local geological characteristics using machine learning methods. By applying K-clustering to the sonic log data, we were able to identify areas correlated with measured lost circulation. Also, the boundaries defined by clustering of the mineralogical and lithological data from the mud logs correlate well with interfacial severity during drilling. A random forest model was employed to build correlation between drilling data and rock strength. The confined compressive strength (CCS) of the rock in the training of the machine learning model was inferred from the dipole sonic log. The R-squared of the testing data is 0.78, and the RMSE (Root Mean Squared Error) is 0.06. The trained model was used to forecast rock strength for the section where sonic log data are not available. CCS could also be inferred from mud logs provided the relationship between mineralogy and rock strength is established through core testing data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Utah FORGE 2439: Well 16B(78)-32 Field-Test Data from Mini-Frac Tests

This submittal includes the field-test data collected during stress tests conducted in the Utah FORGE 16B(78)-32 wellbore to measure/characterize the stresses in the geothermal reservoir. The type of stress test performed is referred to as a mini-frac test or a micro-frac test. The test is a hydraulic fracture test that involves injecting a small volume of water into a short interval of the reservoir that is isolated by a straddle packer to create a fracture. The test provides information about the minimum and maximum horizontal stress at the test depths. A total of seven mini-frac tests were performed within the upper (vertical or nearly vertical) section of the 9-5/8-inch diameter uncased wellbore, between depths of 5,202 ft MD [5,202 ft TVD] and 5,980 ft MD [5966.18 ft TVD]. Two types of data were generated as part of the mini-frac tests. The first type of data is time-series data for each mini-frac test (e.g., injection-rate, test-interval pressure), and are contained in the "Mini-Frac Test Data.zip" file below. The second type is geophysical log data, attached below in the "Geophysical Logs.zip" file. Here, two types of image logs and acoustic logs were obtained before and after the mini-frac tests to examine each test depth for visual evidence of induced fracture(s) and to measure the azimuth (orientation) of any observed fractures. The attached Readme file includes the test depth information and contents of each data file included here. Analysis of the field-test data was conducted and is documented in the attached report below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

A probabilistic perspective on thermodynamic parameter uncertainties: Understanding aqueous speciation of mercury

Speciation plays an important role in determining the fate and transport of metals in terrestrial surface and subsurface systems. Equilibrium speciation modeling in aqueous systems relies on thermodynamic constants (log K values) of complexes, which are subject to uncertainties. In this study, using Monte Carlo (MC) simulations with Latin hypercube sampling (LHS) we systematically analyze the propagation of thermodynamic constant uncertainty through speciation modeling of an inorganic mercury-sulfide-chloride-water system. We find that seemingly small variances of the input log K normal distributions can lead to output species concentrations spanning multiple orders of magnitude, with highly skewed probability distributions. When equilibrium with mineral metacinnabar (β-HgS(s)) is neglected, the relative uncertainty of each output species is strongly positively correlated with the skewness of its concentration probability distribution, i.e., the lowest uncertainty occurs when the species concentration probability distribution is the most negatively skewed as its concentration approaches the total element concentration limit. The highest uncertainty in the identity of dominant species is located around species equivalence points. For cases where the mineral equilibrium is included, we derive analytical probability density functions for the log concentrations of all major species in the system. The mineral log K uncertainty is found to be an important contributor to all output concentration uncertainties. The analysis of combined effects of both pH and total sulfide concentration on output concentration uncertainties shows that high concentration uncertainties occur under highly sulfidic alkaline conditions and low uncertainties at low pH and sulfide concentrations. Finally, an analysis as presented here can distinguish between conditions that require a full uncertainty analysis and those for which the classical deterministic speciation modeling suffices.

58 GEOSCIENCES↗
DOE OSTI2022

Method to account for natural fracture induced elastic anisotropy in geomechanical characterization of shale gas reservoirs

Shale has been usually recognized as a transverse isotropic (TI) medium in conventional geomechanical log interpretation due to its laminated nature. However, when natural fractures exist in the shale rock, additional elastic anisotropy is introduced, converting laminated Shale to an orthorhombic (OB) medium. Previous studies illustrate that neglecting the natural fracture induced anisotropy in shale geomechanical log interpretation could lead to inaccurate evaluations of elastic moduli and in-situ stresses. In this paper, a new method is developed to account for the natural fracture induced anisotropy in geomechanical log interpretation based upon the TI acoustic model developed by the author and a characterization technique of elastic wave anisotropy (Sayers, 1991). The new OB model incorporates the four acoustic log data inputs and five modeling constraints in a nonlinear optimization algorithm to solve for the nine independent stiffness coefficients of an OB rock, and further to solve for the geomechanical properties and in-situ stress profiles in an OB formation. The new method was validated with a Marcellus Gas Shale field case. Both the new OB model and the conventional TI model were applied to interpret the minimum horizontal stress profile for the same formation. By comparing the results, the OB model is more robust than the TI from two aspects. First, the average stress magnitude predicted by the OB model is closer to the one measured by the Diagnostic Fracture Injection Test (DFIT). Second, the OB model predicts a more obvious stress barrier between the lower Marcellus and upper Onondaga Limestone than the TI model does. Finally, the predicted stress barrier is consistent with the observation of the microseismic events of a horizontal well drilled and completed nearby, which reveals that no hydraulic fracture propagates downward through the bottom boundary of Marcellus Shale into the underlying Onondaga Limestone.

03 NATURAL GAS↗
DOE OSTI2022

Forest structure and solar-induced fluorescence across intact and degraded forests in the Amazon

Tropical forest degradation (e.g., anthropogenic disturbances such as selective logging and fires) alters forest structure and function and influences the forest's carbon sink. In this study, we explored structure-function relationships across a variety of degradation levels in the southern Brazilian Amazon by 1) investigating how forest structural properties vary as a function of degradation history using airborne lidar data; 2) assessing the effects of degradation on solar-induced chlorophyll fluorescence (SIF) seasonality using TROPOMI data; and 3) quantifying the contribution of structural variables to SIF using multiple regression models with stepwise selection of lidar metrics. Forest degradation history was obtained through Landsat time-series classification. We found that fire, logging, and time since disturbance were major determinants of forest structure, and that forests affected by fires experienced larger variability in leaf area index (LAI), canopy height and vertical structure relative to logged and intact forests. Moreover, only recently burned forests showed significantly depressed SIF during the dry season compared to intact forests. Canopy height and the vertical distribution of foliage were the best predictors of SIF. Unexpectedly, we found that wet-season SIF was higher in active regenerating forests (~ 4 years after fires or logging) compared with intact forests, despite lower LAI. Furthermore, our findings help to elucidate the mechanisms of carbon accumulation in anthropogenically disturbed tropical forests and indicate that they can capture large amounts of carbon while recovering.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Effects of forest degradation classification on the uncertainty of aboveground carbon estimates in the Amazon

Tropical forests are critical for the global carbon budget, yet they have been threatened by deforestation and forest degradation by fire, selective logging, and fragmentation. Existing uncertainties on land cover classification and in biomass estimates hinder accurate attribution of carbon emissions to specific forest classes. In this study, we used textural metrics derived from PlanetScope images to implement a probabilistic classification framework to identify intact, logged and burned forests in three Amazonian sites. We also estimated biomass for these forest classes using airborne lidar and compared biomass uncertainties using the lidar-derived estimates only to biomass uncertainties considering the forest degradation classification as well. Our classification approach reached overall accuracy of 0.86, with accuracy at individual sites varying from 0.69 to 0.93. Logged forests showed variable biomass changes, while burned forests showed an average carbon loss of 35%. We found that including uncertainty in forest degradation classification significantly increased uncertainty and decreased estimates of mean carbon density in two of the three test sites. Our findings indicate that the attribution of biomass changes to forest degradation classes needs to account for the uncertainty in forest degradation classification. By combining very high-resolution images with lidar data, we could attribute carbon stock changes to specific pathways of forest degradation. This approach also allows quantifying uncertainties of carbon emissions associated with forest degradation through logging and fire. Both the attribution and uncertainty quantification provide critical information for national greenhouse gas inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine Learning Application to Assess Occurrence and Saturations of Methane Hydrate in Marine Deposits Offshore India

Artificial Neural Networks (ANN) were used to assess methane hydrate occurrence and saturation in marine sediments offshore India. The ANN analysis classifies the gas hydrate occurrence into three types: methane hydrate in pore space, methane hydrate in fractures, or no methane hydrate. Further, predicted saturation characterizes the volume of gas hydrate with respect to the available void volume. Log data collected at six wells, which were drilled during the India National Gas Hydrate Program Expedition 02 (NGHP-02), provided a combination of well log measurements that were used as input for machine learning (ML) models. Well log measurements included density, porosity, electrical resistivity, natural gamma radiation, and acoustic wave velocity. Combinations of well logs used in the ML models provide good overall balanced accuracy (0.79 to 0.86) for the prediction of the gas hydrate occurrence and good accuracy (0.68 to 0.92) for methane hydrate saturation prediction in the marine accumulations against reference data. The accuracy scores indicate that the ML models can successfully predict reservoir characteristics for marine methane hydrate deposits. In conclusion, the results indicate that the ML models can either augment physics-driven methods for assessing the occurrence and saturation of methane hydrate deposits or serve as an independent predictive tool for those characteristics.

58 GEOSCIENCES↗
DOE OSTI2016

Subsea ice-bearing permafrost on the U.S. Beaufort Margin: 2. Borehole constraints: SUBSEA PERMAFROST IN U.S. BEAUFORT II

Borehole logging data from legacy wells directly constrain the contemporary distribution of subsea permafrost in the sedimentary section at discrete locations on the U.S. Beaufort Margin and complement recent regional analyses of exploration seismic data to delineate the permafrost's offshore extent. Most usable borehole data were acquired on a ~500 km stretch of the margin and within 30 km of the contemporary coastline from north of Lake Teshekpuk to nearly the U.S.-Canada border. Relying primarily on deep resistivity logs that should be largely unaffected by drilling fluids and hole conditions, the analysis reveals the persistence of several hundred vertical meters of ice-bonded permafrost in nearshore wells near Prudhoe Bay and Foggy Island Bay, with less permafrost detected to the east and west. Permafrost is inferred beneath many barrier islands and in some nearshore and lagoonal (back-barrier) wells. The analysis of borehole logs confirms the offshore pattern of ice-bearing subsea permafrost distribution determined based on regional seismic analyses and reveals that ice content generally diminishes with distance from the coastline. Lacking better well distribution, it is not possible to determine the absolute seaward extent of ice-bearing permafrost, nor the distribution of permafrost beneath the present-day continental shelf at the end of the Pleistocene. However, the recovery of gas hydrate from an outer shelf well (Belcher) and previous delineation of a log signature possibly indicating gas hydrate in an inner shelf well (Hammerhead 2) imply that permafrost may once have extended across much of the shelf offshore Camden Bay.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Double brane holographic model dual to 2d ICFTs

A minimal single brane holographic model can be used as a dual to 2d conformal interfaces (ICFTs) to calculate the transmission coefficient T of energy transported across the defect as well as boundary entropy log g, the additional entanglement entropy for some sub-region that encloses the defect. Both T and log g are uniquely determined by the tension characterizing the brane. In contrast, in field theory defects typically the transmission coefficient can be dialed from 0 to 1 independently for each allowed value of log g. To address this discrepancy, we look at a double brane (3-region bulk) holographic model. Merger of two single brane interfaces creates genuinely new interfaces which indeed allow a range of accessible transmission coefficients for a fixed value of log g. In particular, the T = 0 limit of two completely decoupled BCFTs can be achieved.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Clustered BSRs: Evidence for gas hydrate-bearing turbidite complexes in folded regions, example from the Perdido Fold Belt, northern Gulf of Mexico

We detail previously undocumented but extensive gas hydrate accumulations in the mouth of Perdido Canyon in the northern Gulf of Mexico. The accumulations are located within central parts of structural domes (four-way closures) and are characterized by stacked, high-amplitude bottom simulating reflections (BSRs) that we call clustered BSRs. Seismic data from Perdido Canyon show two clustered BSRs associated with turbidite sequences within two dome folds formed from tectonic folding and salt diapir rise. The northwestern (NW) and southeastern (SE) clustered BSRs have aerial extents of ~25 km2 and 50 km2, respectively. Well log data confirm gas hydrate occurs above the NW clustered BSR, within a 225 m-thick consistently high-resistivity interval that we interpret as gas hydrate in near-vertical fractures and turbidite sands. The SE dome is only drilled at the edge of the BSR; nevertheless, the well log data indicate that a 30 m-thick gas hydrate accumulation is present. Gas chromatographic logs in both domes suggest a gradual transition from predominantly microbial gas below the BSR (500–1000 m below seafloor (mbsf)) to thermogenic gas at 1000–2000 mbsf. Based on the well log data and seismic stratigraphic analysis, we find gas hydrate is concentrated in fractures in marine mud, as well as in the pores of submarine fan turbidities, where saturations reach as high as 75%. An estimate of the total gas hydrate-bound gas volume at standard temperature and pressure is between 0.04 and 0.17 trillion cubic meters (TCM) assuming average hydrate saturation of 5-20% in a ~45 m thick turbidite sand unit above the Perdido Canyon BSR area. Measured BSR extent and gas volume estimates indicate that the NW and SE reservoirs are among the largest gas hydrate occurrences known in the Gulf of Mexico.

58 GEOSCIENCES↗
DOE OSTI2018

Pressure transient analysis during CO 2 push-pull tests into faults for EGS characterization

Here, with the goal of detecting and characterizing faults and fractures in enhanced geothermal systems (EGS), a new technology involving CO 2 push-pull testing, active-source geophysical imaging, and well logging has recently been proposed. This technique takes advantage of (1) the contrasting properties of supercritical CO 2 and water which cause CO 2 to appear distinct from surrounding brine in seismic and other geophysical logging approaches, (2) the non-wetting nature of CO 2 which keeps it localized to the faults and fractures to create contrast potentially sufficient for active seismic and well-logging approaches to image faults and fracture zones at EGS sites. In this study, we evaluate the feasibility of using pressure transient monitoring during CO 2 push-pull tests to complement active seismic and wireline well logging for EGS characterization. For this purpose, we developed a 2D model of a prototypical geothermal site (Desert Peak, NV) that includes a single fault. The fault zone consists of a slip plane, fault gouge, and damage zone, and is bounded by the surrounding matrix of the country rock. Through numerical simulation using iTOUGH2, we found that the pressure transient at the monitoring wells in the fault gouge shows unique traits due to the multiphase flow conditions developed by CO 2 injection, and varies sensitively on the arrival of the CO 2 plume and the degree of CO 2 saturation. A sensitivity analysis shows the pressure transient is most sensitive to the fault gouge permeability, but also depends on multiphase flow parameters and the boundary conditions of the fault. Lastly, an inversion study reveals that the fault gouge permeability can be best estimated with the pressure transient data, whereas additional CO 2 saturation data do not improve the accuracy of the inversion significantly.

58 GEOSCIENCES↗
DOE OSTI2024

Insights into Prismatic Loop Formation in Irradiated Fe–Cr Alloys from Hypothesis-Driven Active Learning and Causal Analysis

Neutron and electron irradiation experimental studies conducted on body-centered cubic Fe and Fe–Cr alloys have established two prismatic dislocation loop populations, which have Burgers vectors of either a/2$\langle$111$\rangle$ or a$\langle$100$\rangle$. Here, the loop formation depends on factors such as dose (D), dose rate (D rt ), temperature (T), chromium content (Cr%), and other alloying elements. Hence, it is important to understand how irradiation-induced dislocation loops evolve conditional upon the loop characteristics, such as loop density (DD), average loop size d̅, and irradiation parameters (D, D rt , T, and irradiation type), which is still an active area of research. To understand these complex structure–property relationships, machine learning (ML) is employed in a three-step approach. This includes imputing missing data with a k-nearest neighbor, generating functionalized features, and assessing feature importance with random forest classification and regression. Physics-based features are incorporated in a hypothesis-driven active learning scheme to overcome data unavailability challenges. Insights obtained from ML models (i) to categorize dislocation loop types, show the highest correlation with d̅; (ii) Log(DD), obtained through mathematical formulations involving D, Cr%, d̅, and T (e.g., Log(DD) ~ D + exp(-Cr%) + 1/d̅ and log(DD) ~ D + exp(-Cr%) + 1/T). Hypothesis-driven active learning is able to predict Log(DD) in which the experimental date is not known. Causal models verify cause–effect relationships for dislocation loop classification and irradiation factors in FeCr alloys.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗