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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
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 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↗
DOE OSTI2024

Characterization of sub-micrometre-sized voids in fixed human brain tissue using scanning X-ray microdiffraction

Using a 5 µm-diameter X-ray beam, we collected scanning X-ray microdiffraction in both the small-angle (SAXS) and the wide-angle (WAXS) regimes from thin sections of fixed human brain tissue from Alzheimer's subjects. The intensity of scattering in the SAXS regime of these patterns exhibits essentially no correlation with the observed intensity in the WAXS regime, indicating that the structures responsible for these two portions of the diffraction patterns, which reflect different length scales, are distinct. SAXS scattering exhibits a power-law behavior in which the log of intensity decreases linearly with the log of the scattering angle. The slope of the log–log curve is roughly proportional to the intensity in the SAXS regime and, surprisingly, inversely proportional to the intensity in the WAXS regime. We interpret these observations as being due to the presence of sub-micrometre-sized voids formed during dehydration of the fixed tissue. The SAXS intensity is due largely to scattering from these voids, while the WAXS intensity derives from the secondary structures of macromolecular material surrounding the voids. The ability to detect and map the presence of voids within thin sections of fixed tissue has the potential to provide novel information on the degradation of human brain tissue in neurodegenerative diseases.

Chemistry↗
DOE OSTI2026

Translation-Invariant Quantum Algorithms for Ordered Search are Optimal

Ordered search is the task of finding an item in an ordered list using comparison queries. The best exact classical algorithm for this fundamental problem uses [log 2 n] queries for a list of length n. Quantum computers can achieve a constant-factor speedup, but the best possible coefficient of log 2 n for exact quantum algorithms is only known to lie between (ln2)/π ≈ 0.221 and 4/log 2 605 ≈ 0.4333. We consider a special class of translation-invariant algorithms with no workspace, introduced by Farhi, Goldstone, Gutmann, and Sipser, that has been used to find the best known upper bounds. First, we show that any bounded-error, k-query quantum algorithm for ordered search can be implemented by a k-query algorithm in this special class. Second, we use linear programming to show that the best exact 5-query quantum algorithm can search a list of length 7265, giving an ordered search algorithm that asymptotically uses 5 log 7265 n ≈ 0.390 log 2 n quantum queries.

Translation-invariant quantum algorithms↗
DOE OSTI2024

Risk of theft and malicious use of radiation sources in transit

Remote radioactive source applications require frequent transportation of sources from storage locations to remote sites. This introduces risk of theft of a source during the transportation process, with the level of risk proportional to the radioactivity of the source. To that end, theft of smaller sources, such as microcurie-level moisture density gauges, are of minor concern, but larger sources, such as those used for radiography and well logging, present more risk. Radiography sources include 192 Ir, 75 Se, or 60 Co radionuclides with radioactivity amounts at or exceeding IAEA Category 2. Well-logging sources, primarily 241 Am/Be, are used for their neutron-emission properties. 137 Cs is also used in well-logging at lower activities than in radiography but at levels that still present some risk. The vulnerability for malicious use of such sources to cause contamination and associated economic effects is dependent on the elemental chemical and physical properties, especially melting point and bulk modulus. Theft of radiography sources is somewhat common, well-logging sources less so. Theft of sources commonly occurs in concert with theft of the vehicle, with the source subsequently abandoned. There have been some instances where a source appears to have been specifically targeted. There are a variety of security measures and protocols, available and under development, to mitigate the risk of theft and assist in source recovery.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Guiding Principles for Geochemical/Thermodynamic Model Development and Validation in Nuclear Waste Disposal: A Close Examination of Recent Thermodynamic Models for H + —Nd 3+ —NO 3 - (—Oxalate) Systems

Development of a defensible source-term model (STM), usually a thermodynamical model for radionuclide solubility calculations, is critical to a performance assessment (PA) of a geologic repository for nuclear waste disposal. Such a model is generally subjected to rigorous regulatory scrutiny. In this article, we highlight key guiding principles for STM model development and validation in nuclear waste management. We illustrate these principles by closely examining three recently developed thermodynamic models with the Pitzer formulism for aqueous H + —Nd 3+ —NO 3 - (—oxalate) systems in a reverse alphabetical order of the authors: the XW model developed by Xiong and Wang, the OWC model developed by Oakes et al., and the GLC model developed by Guignot et al., among which the XW model deals with trace activity coefficients for Nd(III), while the OWC and GLC models are for concentrated Nd(NO 3 ) 3 electrolyte solutions. The principles highlighted include the following: (1) Principle 1. Validation against independent experimental data: A model should be validated against experimental data or field observations that have not been used in the original model parameterization. We tested the XW model against multiple independent experimental data sets including electromotive force (EMF), solubility, water vapor, and water activity measurements. The results show that the XW model is accurate and valid for its intended use for predicting trace activity coefficients and therefore Nd solubility in repository environments. (2) Principle 2. Testing for relevant and sensitive variables: Solution pH is such a variable for an STM and easily acquirable. All three models are checked for their ability to predict pH conditions in Nd(NO 3 ) 3 electrolyte solutions. The OWC model fails to provide a reasonable estimate for solution pH conditions, thus casting serious doubt on its validity for a source-term calculation. In contrast, both the XW and GLC models predict close-to-neutral pH values, in agreement with experimental measurements. (3) Principle 3. Honoring physical constraints: Upon close examination, it is found that the Nd(III)-NO 3 association schema in the OWC model suffers from two shortcomings. Firstly, its second stepwise stability constant for Nd(NO 3 ) 2+ (log K 2 ) is much higher than the first stepwise stability constant for NdNO 3 2+ (log K 1 ), thus violating the general rule of (log K 2 –log K 1 ) < 0, or $\frac{K1}{K2}$>1. Secondly, the OWC model predicts abnormally high activity coefficients for Nd(NO 3 ) 2 + (up to ~900) as the concentration increases. (4) Principle 4. Minimizing degrees of freedom for model fitting: The OWC model with nine fitted parameters is compared with the GLC model with five fitted parameters, as both models apply to the concentrated region for Nd(NO 3 ) 3 electrolyte solutions. The latter appears superior to the former because the latter can fit osmotic coefficient data equally well with fewer model parameters. The work presented here thus illustrates the salient points of geochemical model development, selection, and validation in nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

BSEC Air Quality: Stationary Black and Brown Carbon Properties and Mass Concentration

Data is 1-min absorption/attenuation of aerosol collected on filter and sampled at seven wavelengths (950, 880, 660, 590, 520, 470, and 370 nm) with a Magee AE-33 dual spot aethalometer. The wavelength value for black carbon, as recommended by the manufacturer, is the 880 nm channel. The derived mass concentrations, using the factory default mass absorption cross-sections, are reported for all seven wavelengths. The absorption at all seven wavelengths are reported by dividing the derived mass concentrations by the factory default mass absorption cross-sections. The estimated mass concentration contribution from biomass burning and fossil fuel black carbon, using factory default values, are reported. The estimated brown carbon mass concentration is derived by finding the difference between the recommended black carbon wavelength (880 nm) and the ultra violet wavelength (370 nm), assuming that brown carbon absorption is strongest at 370 nm and minimal at 880 nm. Finally, the estimated absorption Angstrom exponent for black and brown carbon is derived. For black carbon, the slope between each minute log(AbsXXX), where XXX is the wavelength used versus log(XXX) is used, and the wavelengths are 590, 660, 880, and 950 nm. For brown carbon, the slope between each minute log(AbsXXX), where XXX is the wavelength used versus log(XXX) is used, and the wavelengths are 370, 470, and 520 nm.

aerosol↗
NASA NTRS1980

Cosmological tests of the Hoyle-Narlikar conformal gravity

For the first time the Hoyle-Narlikar theory with creation of matter and a variable gravitational constant G, is subjected to the following cosmological tests: (1) the magnitude versus z relation, (2) the N(m) versus m relation for quasars, (3) the metric angular diameters versus z relation, (4) the isophotal angles versus z relation, (5) the log N-log S radio source count, and finally (6) the 3 K radiation. It is shown that the theory passes all these tests just as well as the standard cosmology, with the additional advantage that the geometry of the universe is uniquely determined, with a curvature parameter equal to zero. It is also interesting to note that the variability of G affects the log N-log S curve in a way similar to the density evolution introduced in standard cosmologies. The agreement with the data is therefore achieved without recourse to an ad hoc density evolution.

Canuto, V. M.↗
NASA NTRS1981

Effects of changes in convective efficiency on the solar radius and luminosity

A sequence of solar models has been constructed in order to investigate the sensitivity of the solar radius and luminosity to small changes in the ratio alpha of the mixing length lambda to the pressure-scale height Hp throughout the solar convective envelope. The basic procedure for determining this sensitivity was to impose a perturbation in alpha within the convective envelope and then to follow the resulting changes in the solar radius delta R and luminosity delta L for the next 10 to the 6th power yrs. These calculations gave the following results. (1) A perturbation in alpha produces immediate changes in the solar radius and luminosity. Initially delta L and delta alpha are related by delta L/L = 0.30 delta alpha/alpha. (2) The value of the ratio w = delta log R/delta log L is strongly time dependent. Its value just after the perturbation in alpha is 6.5 x 10 to the minus 4th power. (3) The ratio H = (delta log L) d delta log R/dt is much less time dependent and is a more suitable means for relating the changes in the solar radius and luminosity. (4) Both of these ratios imply that for any reasonable change in the solar luminosity the corresponding change in the solar radius is negligible.

Sweigart, A. V.↗
NASA NTRS1983

Experimental investigation of the partitioning of phosphorus between metal and silicate phases - Implications for the earth, moon and eucrite parent body

An experimental study is reported of the partitioning of Phosphorus between solid metal and basaltic silicate liquid as a function of temperature and oxygen fugacity and of the implications for the earth, moon and eucrite parent body (EPB). The relationship established between the partition coefficient and the fugacity is given at 1190 C by log D(P) = -1.12 log fO2 - 15.95 and by log D(P) = -1.53 log fO2 17.73 at 1300 C. The partition coefficient D(P) was determined, and it is found to be consistent with a valence state of 5 for P in the molten silicate. Using the determined coefficient the low P/La ratios of the earth, moon, and eucrites relative to C1 chondrites can be explained. The lowering of the P/La ratio in the eucrites relative to Cl chondrite by a factor of 40 can be explained by partitioning P into 20-25 wt% sulfur-bearing metallic liquid corresponding to 5-25% of the total metal plus silicate system. The low P/La and W/La ratios in the moon may be explained by the partitioning of P and W into metal during formation of a small core by separation of liquid metal from silicate at low degrees of partial melting of the silicates. These observations are consistent with independent formation of the moon and the earth.

Newsom, H. E.↗
NASA NTRS1983

Laser optogalvanic spectroscopy of molecules

In laser optogalvanic (LOG) spectroscopy, a tunable laser is used to probe the spectral characteristics of atomic or molecular species within an electrical discharge in a low pressure gas. Optogalvanic signals arise when the impedance of the discharge changes in response to the absorption of laser radiation. The technique may, therefore, be referred to as impedance spectroscopy. This change in impedance may be monitored as a change in the voltage across the discharge tube. LOG spectra are recorded by scanning the wavelength of a chopped CW dye laser while monitoring the discharge voltage with a lock-in amplifier. LOG signals are obtained if the laser wavelength matches a transition in a species present in the discharge (or flame), and if the absorption of energy in the laser beam alters the impedance of the discharge. Infrared LOG spectroscopy of molecules has been demonstrated and may prove to be the most productive application in the field of optogalvanic techniques.

Webster, C. R.↗
NASA NTRS1986

Comparison of various techniques for calibration of AIS data

The Airborne Imaging Spectrometer (AIS) samples a region which is strongly influenced by decreasing solar irradiance at longer wavelengths and strong atmospheric absorptions. Four techniques, the Log Residual, the Least Upper Bound Residual, the Flat Field Correction and calibration using field reflectance measurements were investigated as a means for removing these two features. Of the four techniques field reflectance calibration proved to be superior in terms of noise and normalization. Of the other three techniques, the Log Residual was superior when applied to areas which did not contain one dominant cover type. In heavily vegetated areas, the Log Residual proved to be ineffective. After removing anomalously bright data values, the Least Upper Bound Residual proved to be almost as effective as the Log Residual in sparsely vegetated areas and much more effective in heavily vegetated areas. Of all the techniques, the Flat Field Correction was the noisest.

Dar A. Roberts↗
NASA NTRS1987

Far-infrared properties of cluster galaxies

Far-infrared properties are derived for a sample of over 200 galaxies in seven clusters: A262, Cancer, A1367, A1656 (Coma), A2147, A2151 (Hercules), and Pegasus. The IR-selected sample consists almost entirely of IR normal galaxies, with Log of L(FIR) = 9.79 solar luminosities, Log of L(FIR)/L(B) = 0,79, and Log of S(100 microns)/S(60 microns) = 0.42. None of the sample galaxies has Log of L(FIR) greater than 11.0 solar luminosities, and only one has a FIR-to-blue luminosity ratio greater than 10. No significant differences are found in the FIR properties of HI-deficient and HI-normal cluster galaxies.

Bicay, M. D.↗
NASA NTRS1987

The origin of the diffuse X-ray background

The Einstein Deep Survey fields are used to reexamine the problem of the origin of the extragalactic diffuse X-ray background. An approach based on fluctuation analysis is adopted, and a sophisticated Monte Carlo scheme is used to compare the X-ray images with a variety of models representing point and diffuse source contributions. Results are presented for two classes of models: those which determine the flux cutoff beyond which pointlike sources fail to contribute significantly if the log N-log S slope is fixed at the measured value of -1.5, and those which set limits on the slope of the log N-log S relation below its last measured point at the Deep Survey threshold. In both cases, it is found that the known population of X-ray-emitting quasars and any reasonable extrapolations therefrom do not comprise the entire X-ray background.

Hamilton, T. T.↗
NASA NTRS1987

Far-infrared properties of cluster galaxies

Far-infrared properties are derived for a sample of over 200 galaxies in seven clusters: A262, Cancer, A1367, A1656 (Coma), A2147, A2151 (Hercules), and Pegasus. The IR-selected sample consists almost entirely of IR normal galaxies, with Log of L(FIR) = 9.79 solar luminosities, Log of L(FIR)/L(B) = 0.79, and Log of S(100 microns)/S(60 microns) = 0.42. None of the sample galaxies has Log of L(FIR) greater than 11.0 solar luminosities, and only one has a FIR-to-blue luminosity ratio greater than 10. No significant differences are found in the FIR properties of HI-deficient and HI-normal cluster galaxies.

Bicay, Michael D.↗
NASA NTRS1992

Fractal analysis: A new remote sensing tool for lava flows

Many important quantitative parameters have been developed that relate to the rheology and eruption and emplacement mechanics of lavas. This research centers on developing additional, unique parameters, namely the fractal properties of lava flows, to add to this matrix of properties. There are several methods of calculating the fractal dimension of a lava flow margin. We use the 'structured walk' or 'divider' method. In this method, we measure the length of a given lava flow margin by walking rods of different lengths along the margin. Since smaller rod lengths transverse more smaller-scaled features in the flow margin, the apparent length of the flow outline will increase as the length of the measuring rod decreases. By plotting the apparent length of the flow outline as a function of the length of the measuring rod on a log-log plot, fractal behavior can be determined. A linear trend on a log-log plot indicates that the data are fractal. The fractal dimension can then be calculated from the slope of the linear least squares fit line to the data. We use this 'structured walk' method to calculate the fractal dimension of many lava flows using a wide range of rod lengths, from 1/8 to 16 meters, in field studies of the Hawaiian islands. We also use this method to calculate fractal dimensions from aerial photographs of lava flows, using lengths ranging from 20 meters to over 2 kilometers. Finally, we applied this method to orbital images of extraterrestrial lava flows on Venus, Mars, and the Moon, using rod lengths up to 60 kilometers.

Bruno, B. C.↗
NASA NTRS1992

The spatial distribution of gamma-ray bursts from Pioneer Venus Orbiter

The homogeneity of the burst source population in space with the log N(greater than P sub c)-log P sub c and log (dN/dP sub c)-log P sub c relations is studied on the basis of a sample of 225 observed gamma-ray bursts between September 1978 and July 1988 from the Pioneer Venus Orbiter. The relations N(greater than P sub c) is approximately equal to P sub c exp -1.5 and dN/dP sub c is approximately equal to P sub c exp -2.5 are expected if the gamma bursters are uniformly distributed in space. The Kolmogorov-Smirnov statistic gives a probability of 0.93 that the observed integral distribution N(greater than P sub c) is drawn from the expected uniform distribution P sub c exp -1.5. A least-squares 1 standard deviation fit to the observed differential distribution yields dN/dP sub c of about P sub c -2.42 +/- 0.07. Both are consistent with a uniform distribution in space of the parent population of burst sources.

Chuang, K. W.↗