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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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Emerging Trends in Biological Treatment of Wastewater From Unconventional Oil and Gas Extraction

Unconventional oil and gas exploration generates an enormous quantity of wastewater, commonly referred to as flowback and produced water (FPW). Limited freshwater resources and stringent disposal regulations have provided impetus for FPW reuse. Organic and inorganic compounds released from the shale/brine formation, microbial activity, and residual chemicals added during hydraulic fracturing bestow a unique as well as temporally varying chemical composition to this wastewater. Studies indicate that many of the compounds found in FPW are amenable to biological degradation, indicating biological treatment may be a viable option for FPW processing and reuse. This review discusses commonly characterized contaminants and current knowledge on their biodegradability, including the enzymes and organisms involved. Further, a perspective on recent novel hybrid biological treatments and application of knowledge gained from omics studies in improving these treatments is explored.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Models and Processes to Extract Drug-like Molecules From Natural Language Text

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%—on par with that of non-expert humans—and when applied to CORD’19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team’s targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Pretreatment of Bituminous Coal By-Products for the Hydrometallurgical Extraction of Rare Earth Elements

Low-temperature plasma (LTP) oxidation has been widely used to study the mineralogy of the mineral matter existing in coal sources. The current study investigated the potential of LTP oxidation as a pre-treatment method to improve rare earth element (REE) leachability from coal and its by-products. Representative density-fractionated samples of Baker and Fire Clay coarse refuse seam materials were ground to a top size of 180 µm and subjected to low-temperature plasma oxidation. Subsequently, the treated samples were leached at 1% w/v solids concentration and 75 °C for 5 h using (i) de-ionized (DI) water, (ii) 0.1 mol/L of ammonium sulfate, and (iii) 1.2 mol/L of sulfuric acid. It was determined that LTP treatment improved REE leaching characteristics, especially the leaching of heavy REEs (HREE), existing in the lighter density fractions of the Baker seam coarse refuse material. For instance, the HREE recovery for the 1.6 specific gravity (SG) float fraction increased from 8% to 33% using 0.1 mol/L of ammonium sulfate solution after 32 h of LTP treatment. This finding indicated that HREEs associated with the organic matter were released by the LTP treatment and adsorbed onto the surfaces of highly negative charged mineral matter and was exchanged with ammonium to allow their recovery. Similarly, when using 1.2 mol/L of sulfuric acid, the HREE recovery increased from 23% to 53% for the 1.6 SG float fraction. Interestingly, LTP oxidation did not provide significant improvement in REE recovery from the 2.2 sink density fractions, which was likely due to its lower organic content. No significant benefits were observed when treating the Fire Clay coarse refuse material, which was likely due to the lack of organic affinity and the difficult-to-leach REE minerals associated with the coal source such as monazite, xenotime, and zircon. Conversely, high-temperature oxidation within a temperature range of 600–750 °C significantly improved REE leaching characteristics for both coal sources. Improvement in REE recovery was due to decarbonization of the material, clay dehydroxylation and subsequent conversion of liberated REE-bearing minerals into a more leachable form. However, increasing the temperature above 800 °C decreased REE recovery due to the conversion of meta-kaolinite into mullite, which is chemically stable.

58 GEOSCIENCES↗
DOE OSTI2023

Maximizing Wave Energy Converter Power Extraction by Utilizing a Variable Negative Stiffness Magnetic Spring

Complex conjugate impedance matching is a key concept for wave energy converter design. Matching the impedance of the power take-off (PTO) system to the complex conjugate of the wave energy converter's (WEC) impedance ensures efficient transfer of energy from the WEC body motion to electrical power. In low frequency waves, impedance matching often requires a negative PTO stiffness. In this paper, an adjustable stiffness magnetic torsion spring will be presented and modeled to understand its potential to improve WEC performance. The spring has the ability to provide a negative stiffness, allowing the PTO impedance to more closely match the complex conjugate of the WEC impedance at low frequencies. The spring also supports an adjustable stiffness value, meaning it can be tuned according to the incoming wave conditions. The spring's tunability may put less stress on the rest of the PTO system in wave conditions outside its normal operation zone without sacrificing electrical power output. The adjustable magnetic spring's effects are modeled and explored in this paper by examining the resultant average annual electrical power and capacity factor. The study suggests that the tunable magnetic spring has the potential to significantly improve capacity factor while maintaining a high average electrical power.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation

Abstract Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine learning to circumvent the need for lens and source modeling and develop a neural network to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring across a wide range of apparent source magnitudes. The network is then able to resolve subhalos with masses m ≳ 10 8.5 M ⊙ . Training in this way allows the network to learn the gravitational lensing of light, and, remarkably, it is then able to detect entire populations of substructure, even for locations further away from the Einstein ring than those used in training. Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio. With good accuracy and a low false-positive rate, counting the number of pixels assigned to each subhalo class over multiple images allows for a measurement of the subhalo mass function (SMF). When measured over three mass bins from 10 9 –10 10 M ⊙ the SMF slope is recovered with an error of 36% for 50 images, and this improves to 10% for 1000 images with Hubble Space Telescope-like noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Functional Data Analysis for Extracting the Intrinsic Dimensionality of Spectra: Application to Chemical Homogeneity in the Open Cluster M67

High-resolution spectroscopic surveys of the Milky Way have entered the Big Data regime and have opened avenues for solving outstanding questions in Galactic archeology. However, exploiting their full potential is limited by complex systematics, whose characterization has not received much attention in modern spectroscopic analyses. In this work, we present a novel method to disentangle the component of spectral data space intrinsic to the stars from that due to systematics. Using functional principal component analysis on a sample of 18,933 giant spectra from APOGEE, we find that the intrinsic structure above the level of observational uncertainties requires ≈10 functional principal components (FPCs). Our FPCs can reduce the dimensionality of spectra, remove systematics, and impute masked wavelengths, thereby enabling accurate studies of stellar populations. To demonstrate the applicability of our FPCs, we use them to infer stellar parameters and abundances of 28 giants in the open cluster M67. We employ Sequential Neural Likelihood, a simulation-based Bayesian inference method that learns likelihood functions using neural density estimators, to incorporate non-Gaussian effects in spectral likelihoods. By hierarchically combining the inferred abundances, we limit the spread of the following elements in M67: Fe ≲ 0.02 dex; C ≲ 0.03 dex; O, Mg, Si, Ni ≲ 0.04 dex; Ca ≲ 0.05 dex; N, Al ≲ 0.07 dex (at 68% confidence). Our constraints suggest a lack of self-pollution by core-collapse supernovae in M67, which has promising implications for the future of chemical tagging to understand the star formation history and dynamical evolution of the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Scaling of Turbulent Viscosity and Resistivity: Extracting a Scale-dependent Turbulent Magnetic Prandtl Number

Turbulent viscosity νt and resistivity ηt are perhaps the simplest models for turbulent transport of angular momentum and magnetic fields, respectively. The associated turbulent magnetic Prandtl number Pr t ≡ ν t /η t has been well recognized to determine the final magnetic configuration of accretion disks. Here, we present an approach to determining these "effective transport" coefficients acting at different length scales using coarse-graining and recent results on decoupled kinetic and magnetic energy cascades. By analyzing the kinetic and magnetic energy cascades from a suite of high-resolution simulations, we show that our definitions of ν t , η t , and Pr t have power-law scalings in the "decoupled range." We observe that Pr t ≈ 1–2 at the smallest inertial-inductive scales, increasing to ≈5 at the largest scales. Furthermore, based on physical considerations, our analysis suggests that Pr t has to become scale independent and of order unity in the decoupled range at sufficiently high Reynolds numbers (or grid resolution) and that the power-law scaling exponents of velocity and magnetic spectra become equal. In addition to implications for astrophysical systems, the scale-dependent turbulent transport coefficients offer a guide for large-eddy simulation modeling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The lattice extraction of the TMD soft function using the auxiliary field representation of the Wilson line

The transverse momentum dependent (TMD) soft function can be obtained by formulating the Wilson line in terms of auxiliary 1-dimensional fermion fields on the lattice. In this formulation, the directional vector of the auxiliary field in Euclidean space has the form ˜ 𝑛 = ¹𝑖𝑛0– ®0?– 𝑛3º, where the time component is purely imaginary. The components of these complex directional vectors in the Euclidean space can be mapped directly to the rapidities of the Minkowski space soft function. We present the results of the one-loop calculation of the Euclidean space analog to the soft function using these complex directional vectors. As a result, we show that the calculation is valid only when the directional vectors obey the relation: j𝑟 j = j𝑛3𝑛0j ¡ 1, and that this result corresponds to a computation in Minkowski space with space-like directed Wilson lines. Finally, we show that a lattice calculable object can be constructed that has the desired properties of the soft function.

Francis, Anthony↗
DOE OSTI2025

Factorial growth in perturbation theory, power corrections: precise extraction of quark masses and $\alpha_\text{s}$

These proceedings summarize a newly found connection between the factorial growth of coefficients in perturbative QCD and power corrections to the perturbation series, discussed in refs. [1-4]. The improved convergence is shown for three quantities four which four terms in the series are available: the static energy, the quark pole mass, and the polarized Bjorken sum rule. Prospects for determinations of $\alpha_\text{s}$ with controlled truncation uncertainties are discussed, as was found earlier in quark-mass determinations [3,5].

Kronfeld, Andreas S. [Fermilab; TUM-IAS, Munich] (↗
DOE OSTI2025

Algorithm to extract direction in 2D discrete distributions and a continuous Frobenius norm

In this study, we present a novel algorithm for determining directionality in 2D distributions of discrete data. We compare a reference dataset with a known direction to a measured dataset with an unknown direction by the Frobenius norm of the difference (FND) to find the unknown direction. To generalize this concept, we develop a continuous Frobenius norm of the difference (CFND) as a continuous analog of the FND and derive its analytical expression. By relating fitted and normalized 2D Gaussian distributions, we show that the CFND approximates the FND, and we validate this relationship with computer simulations. We find that a first-order approximation of the CFND between two similar Gaussian distributions takes the form of an absolute sine function, offering a simple analytical form with potential for specialized applications in segmented inverse beta decay (IBD) neutrino detectors, astronomy, machine learning, and more. Although this method may easily extend to 3D scalar fields, our focus here is on 2D real-valued fields as it directly applies to directionality. Our methodology consists of modeling a 2D Gaussian distribution, binning the data into a histogram, and encoding it as a square matrix. Rotating this matrix around its geometric center and comparing it to a measured dataset using the FND gives us rotational data that we fit with an absolute sine function. The location of the minimum of this fit is the angle closest to the true angle of the direction in the measured dataset. We present the derivation and discuss initial applications of the CFND in our novel algorithm, demonstrating its success in approximating directionality in 2D distributions.

Data Analysis, Statistics and Probability (physics↗
DOE OSTI2024

Tensor Extraction of Latent Features (TELF)

Tensor ELF is a user-friendly parallel tensor decomposition Python toolbox that includes a suite of machine learning algorithms for CPU and GPU architectures for the analysis of sparse and dense data including utility tools for pre-processing and post-processing.

Eren, Maksim↗