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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Biogeochemistry of the Antrim Shale Natural Gas Reservoir

The Antrim Shale, located in the Michigan Basin, United States (U.S.), is a major U.S. shale play having produced over 2.5 Trillion Cubic Feet (Tcf) of unconventional shale natural gas as of 2010. The shallow nature of this formation sets it apart from other, more characterized unconventional shale gas plays. The depth of gas production of the Antrim ranges from approximately 150 to 600 m and it is typically vertically drilled, contrary to deeper, horizontally drilled shales. A thorough understanding of the biogeochemistry and microbiology of this complex system will be advantageous for improving well performance, produced water management, and potential biocidal treatment as microbial community composition can vary substantially even among closely spaced wells. In this study, we analyzed produced water collected from nine different wells in the Antrim Shale by investigating the geochemical and microbial community composition of the produced water to gain greater insight into the overall biogeochemistry of this unique shale system. The majority of the wells from this study had high total dissolved solids (TDS) primarily composed of chloride and sodium, averaging 86,804 mg/L with a maximum 116,223 mg/L; however, three of the wells sampled along the northern margin of the basin exhibited significantly lower TDS ranging from 4932 to 6496 mg/L. Overall, our microbial community analysis revealed relatively low abundance within our samples and high variability of the microbial community among the sampled wells. The majority of bacterial sequences were identified within Proteobacteria, Firmicutes, and Actinobacteria phyla and metagenomic sequencing revealed the low presence of Methanobacteriaceae within each sample. We also investigated potential microbial community drivers and found that TDS, sodium, chloride, iodide, bromide, ammonium, potassium, and strontium were significantly correlated with the observed microbial community. The varying geochemical conditions between wells demonstrate different subsurface environmental niches, potentially driving the heterogeneous microbial communities we observed from well to well. This analysis suggests an important relationship between both well location and geochemistry and the observed microbial community that can persist in the reservoir. Continued studies of the Antrim Shale will improve our understanding of the complex interdependencies of this ecosystem.

03 NATURAL GAS↗
DOE OSTI2017

Risks to Ecological Receptors Posed by Contaminants of Potential Concern in the Lower Three Runs Cooling Ponds and Canals

The upper portion of Lower Three Runs includes several ponds, reservoirs, and canals that were formerly used as a cooling system for nuclear production reactors. This area was divided into nine exposure areas (EAs) for the assessment of environmental contamination resulting from past reactor operations and other industrial processes. A tiered screening process identified several contaminants of potential concern including aluminum, cyanide, lead, manganese, mercury, DDD, DDE, and DDT. Risks posed by these contaminants to ecological receptors (river otter, belted kingfisher, raccoon, and blue heron) were assessed using contaminant exposure models that estimated contaminant intake resulting from ingestion of food, water, and sediment/ soil and compared these intakes with toxicity reference values (TRVs). The contaminant exposure models showed that the TRVs were not exceeded in the otter model, exceeded by aluminum in EA 7 (Pond 2 and associated canals) in the raccoon model, and exceeded by mercury in EAs 2, 3 (Pond B), 6 (Par Pond), and 8 (Ponds 4 and 5 and Canal to Pond C) in both the kingfisher and blue heron models. Hazard quotients (total exposure dose divided by the TRV) were 2.8 for aluminum and 1.7- 3.6 for mercury. The primary route of exposure for aluminum was the ingestion of soil, and the primary route of exposure for mercury was the ingestion of mercury contaminated fish. Elevated levels of mercury in fish were at least partly the result of the aerial deposition of mercury onto Lower Three Runs and its watershed. The atmospheric deposition of mercury creates pervasive contamination in fish throughout the Savannah River basin. Another possible source of mercury was the discharge of mercury contaminated Savannah River water into the Lower Three Runs cooling ponds and canals during previous years of reactor operation. This contamination originated from industries located upstream of the SRS. The aluminum exceedance for the raccoon was likely the result of naturally high aluminum levels in SRS soils rather than SRS operations. Aluminum exceedances have previously been observed in relatively undisturbed background locations as well as areas affected by SRS operations. Aluminum exceedances are more likely with the raccoon than the other receptors because it consumes more soil as a result of its feeding habits. Sensitivity analysis showed that model uncertainty can be reduced by adequate sampling of key variables (e.g., fish and sediments). Although sediment samples were collected from all EAs, fish samples were not collected from three EAs and some analytes (pesticides and cyanide) were not measured in fish. Water-to-fish concentration ratios were used to estimate contaminant levels in fish when direct measurements from fish were unavailable; however, such estimates are potentially less accurate than direct measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Data Driven Temperature Control of Heat Exchangers

The following poster presents a couple of approaches for temperature control at heat exchanger’s exit. One of the approaches is based on using n-step ahead neural network while the other approach utilizes a combination of control + learning module for temperature control. Designing robust controllers for typical heat exchangers is very challenging primarily because of complexities associated with the synthesis of dynamics of industrial heat exchangers. Even if a dynamics model is synthesized, the accuracy of it would be called into question as there can be lot of unmodeled system dynamics and random noise in input parameters. In many situations, the degree of uncertainty in the model of system being controlled limits the utility of optimal control design. Controller can be manually tuned on the field but it is very difficult to determine manual adjustments that result in overall improvement. These practical challenges call for learning based approaches for temperature control that would result in optimal heat exchanger performance. We utilize the existing mathematical model for air-water system synthesized by Underwood and Crawford for testing our algorithms. Underwood and Crawford developed a model of heating coil by fitting a second-order non-linear equations to measurements of air and water temperature and flow rates obtained from the actual coil. The uncertainties in the model include air and water inlet temperatures and air-flow rate. These uncertainties are modeled as random variables that are sampled from a uniform distribution. The control variable is the water flow rate which is controlled for regulating the air-outlet temperature. Neural networks are frequently used in various literatures for learning time-series. We propose a modified version of neural networks known as n-step ahead neural network that take the state variables and output at time t as input to successfully predict output at time t+n. The variable n is a hyper-parameter which can be optimized based on performance constraints. The n-step ahead neural network was then trained on training data generated from experimental model to high training accuracies. This trained model performed appreciably in its objective to regulate outlet temperature of a heat exchanger. The second approach uses a combination of control and learning module for temperature regulation. The control module used is a proportional controller while the learning module used is steady state neural network. The steady state neural network is used to learn the controller output as a function of random variables and temperature set-point. The proportional controller is then used to compensate for transient behavior of linearized system around various operating points. The algorithm works effectively for temperature regulation as neural network captures the non-linear input-output behavior of closed loop system leaving proportional controller to compensate for steady state errors of a linearized plant. The proposed algorithm’s performances were evaluated against the performance of a PI controller. Further, the robustness of the algorithms were also studied in great detail under small perturbations around tuned parameters of the proposed algorithms.

Sundar, Sreenath↗
DOE OSTI2019

Contour forward flux sampling: Sampling rare events along multiple collective variables

Many rare event transitions involve multiple collective variables (CVs), and the most appropriate combination of CVs is generally unknown a priori. As such, we introduce a new method, contour forward flux sampling (cFFS), to study rare events with multiple CVs simultaneously. cFFS places nonlinear interfaces on-the-fly from the collective progress of the simulations, without any prior knowledge of the energy landscape or appropriate combination of CVs. We demonstrate cFFS on analytical potential energy surfaces and a conformational change in alanine dipeptide.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

The GALEX time domain survey. II. Wavelength-dependent variability of active galactic nuclei in the PAN-STARRS1 medium deep survey

We analyze the wavelength-dependent variability of a sample of spectroscopically confirmed active galactic nuclei selected from near-UV (NUV) variable sources in the GALEX Time Domain Survey that have a large amplitude of optical variability (difference-flux S/N > 3) in the Pan-STARRS1 Medium Deep Survey (PS1 MDS). By matching GALEX and PS1 epochs in five bands (NUV, g {sub P1}, r {sub P1}, i {sub P1}, z {sub P1}) in time, and taking their flux difference, we create co-temporal difference-flux spectral energy distributions (ΔfSEDs) using two chosen epochs for each of the 23 objects in our sample, on timescales of about a year. We confirm the “bluer-when-brighter” trend reported in previous studies, and measure a median spectral index of the ΔfSEDs of α{sub λ} = 2.1 that is consistent with an accretion disk spectrum. We further fit the ΔfSEDs of each source with a standard accretion disk model in which the accretion rate changes from one epoch to the other. In our sample, 17 out of 23 (∼74%) sources are described well by this variable accretion-rate disk model, with a median average characteristic disk temperature T-bar ∗ of 1.2×10{sup 5} K that is consistent with the temperatures expected, given the distribution of accretion rates and black hole masses inferred for the sample. Our analysis also shows that the variable accretion rate model is a better fit to the ΔfSEDs than a simple power law.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Grain engineering: How nanoscale inhomogeneities can control charge collection in solar cells

Statistical and correlative analysis are increasingly important in the design and study of new materials, from semiconductors to metals. Non-destructive measurement techniques, with high spatial resolution, capable of correlating composition and/or structure with device properties, are few and far between. For the case of polycrystalline and inhomogeneous materials, the added challenge is that nanoscale resolution is in general not compatible with the large sampling areas necessary to have a statistical representation of the specimen under study. For the study of grain cores and grain boundaries in polycrystalline solar absorbers this is of particular importance since their dissimilar behavior and variability throughout the samples makes it difficult to draw conclusions and ultimately optimize the material. In this study, we present a nanoscale in-operando approach based on the multimodal utilization of synchrotron nano x-ray fluorescence and x-ray beam induced current collected for grain core and grain boundary areas and correlated pixel-by-pixel in fully operational Cu(In(1-x)Gax)Se2Cu(In(1-x)Gax)Se2 solar cells. We observe that low gallium cells have grain boundaries that over perform compared to the grain cores and high gallium cells have boundaries that under perform. These results demonstrate how nanoscale correlative X-ray microscopy can guide research pathways towards grain engineering low cost, high efficiency solar cells.

14 SOLAR ENERGY↗
DOE OSTI2019

The Effect of Internal Variability on Ocean Temperature Adjustment in a Low-Resolution CESM Initial Condition Ensemble

Due to its large heat capacity and circulation, the ocean contributes significantly to global heat uptake, global heat transport, spatial temperature patterns, and variability. Quantifying ocean heat uptake across different temporal and spatial scales is important to quantify Earth's climate response to anthropogenic warming. Here we evaluate ocean adjustment time scales from two different fully coupled climate model ensembles using the Community Earth System Model. Both ensembles use the same model version, anthropogenic and natural forcings, and coupling configurations, but we initialize the ensembles in two different ways: (1) sampling joint internal variability of the ocean–atmosphere system (unique atmosphere and ocean conditions) and (2) sampling the internal variability of the atmosphere only (unique atmosphere, identical ocean conditions). Uncertainty due to internal variability is used as a proxy to quantify the time scales of ocean temperature adjustment at different depths and basins in Community Earth System Model. Time scales of equilibration are longer in the deep ocean than the upper ocean, highlighting the vertical structure of dynamic adjustment. The Atlantic equilibrates on shorter time scales (82 years above 1,000 m, 140 years below 1,000 m) relative to the Pacific (106 years above 1,000 m, 444 years below 1,000 m) in Community Earth System Model due to the large North Atlantic Deep Water formation and strong overturning circulation in the Atlantic. These results have broad implications for analyzing internal climate variability, ocean adjustment, and drift in global coupled model experiments and intercomparisons.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Disentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning

In this work, domain switching pathways in ferroelectric materials visualized by dynamic piezoresponse force microscopy (PFM) are explored via variational autoencoder, which simplifies the elements of the observed domain structure, crucially allowing for rotational invariance, thereby reducing the variability of local polarization distributions to a small number of latent variables. For small sampling window sizes the latent space is degenerate, and variability is observed only in the direction of a single latent variable that can be identified with the presence of domain wall. For larger window sizes, the latent space is 2D, and the disentangled latent variables can be generally interpreted as the degree of switching and complexity of domain structure. Applied to multiple consecutive PFM images acquired while monitoring domain switching, the polarization switching mechanism can thus be visualized in the latent space, providing insight into domain evolution mechanisms and their correlation with the microstructure.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Variability-selected Intermediate-mass Black Hole Candidates in Dwarf Galaxies from ZTF and WISE

While it is difficult to observe the first black hole seeds in the early universe, we can study intermediate-mass black holes (IMBHs) in local dwarf galaxies for clues about their origins. In this paper we present a sample of variability-selected active galactic nuclei (AGN) in dwarf galaxies using optical photometry from the Zwicky Transient Facility (ZTF) and forward-modeled mid-IR photometry of time-resolved Wide-field Infrared Survey Explorer (WISE) co-added images. We found that 44 out of 25,714 dwarf galaxies had optically variable AGN candidates and 148 out of 79,879 dwarf galaxies had mid-IR variable AGN candidates, corresponding to active fractions of 0.17% ± 0.03% and 0.19% ± 0.02%, respectively. We found that spectroscopic approaches to AGN identification would have missed 81% of our ZTF IMBH candidates and 69% of our WISE IMBH candidates. Only nine candidates have been detected previously in radio, X-ray, and variability searches for dwarf galaxy AGN. The ZTF and WISE dwarf galaxy AGN with broad Balmer lines have virial masses of 10 5 M ⊙ < M BH < 10 7 M ⊙ , but for the rest of the sample, BH masses predicted from host galaxy mass range between 10 5.2 M ⊙ < M BH < 10 7.25 M ⊙ . We found that only 5 of 152 previously reported variability-selected AGN candidates from the Palomar Transient Factory in common with our parent sample were variable in ZTF. We also determined a nuclear supernova fraction of 0.05% ± 0.01% yr -1 for dwarf galaxies in ZTF. Our ZTF and WISE IMBH candidates show the promise of variability searches for the discovery of otherwise hidden low-mass AGN.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Learning Planar Ising Models Software

Learning Planar Ising Models is a software package written in Matlab for learning relationships among variable in a dataset using graphical models. The software package implements a generally-applicable algorithm for learning planar Ising models from any multivariate dataset. The code provides an algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. The software includes demonstrations of the algorithm in simulations and for applications on publicly available datasets. Details of the algorithm, demonstration simulations, and applications are given in Johnson, et al; 2016. Reference: Johnson, J. K., Oyen, D., Chertkov, M., and Netrapalli, P. (2016). Learning planar Ising models. Journal of Machine Learning Research.

Oyen, Diane↗
DOE OSTI2017

Real-time phase evolution of Selective Laser Melted (SLM) Inconel 718 with temperature through synchrotron X-rays

Selective laser melting (SLM) is an additive manufacturing process that uses laser scanning to achieve melting and solidification of a metal powder bed. This process, when applied to develop high temperature material systems, holds great promise for more efficient manufacturing of turbine components that withstand extreme temperatures, heat fluxes, and high mechanical stresses associated with engine environments. These extreme operational conditions demand stringent tolerances and an understanding of the material evolution under thermal loading. This work presents a real-time approach to elucidating the evolution of precipitate phases in SLM Inconel 718 (IN718) under high temperatures using high-energy synchrotron x-ray diffraction. Four representative samples (taken along variable build height) were studied in room temperature conditions. Two samples were studied as-processed (samples 1 and 4) and two samples after different thermal treatments (samples 2 and 3). The as-processed samples were found to contain greater amounts of weakening phase, δ. Precipitation hardening of Sample 2 reduced the detectable volume of δ, while also promoting growth of γ00 in the γ matrix. Inversely, solution treatment of Sample 3 produced an overall decrease in precipitate phases. High-temperature, in-situ synchrotron scans during ramp-up, hold, and cool down of two different thermal cycles show the development of precipitate phases. Sample 1 was held at 870°C and subsequently ramped up to 1100°C, during which the high temperature instability of strengthening precipitate, γ00, was seen. γ00 dissolution occurred after 15 minutes at 870°C and was followed by an increase of δ-phase. Sample 4 was held at 800°C and exhibited growth of γ00 after 20 minutes at this temperature. These experiments use in-situ observations to understand the intrinsic thermal effect of the SLM process and the use of heat treatment to manipulate the phase composition of SLM IN718.

Sarley, Brooke A.↗
DOE OSTI2016

Learning planar Ising models

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Chemical compositions of semiregular variable red giants

ABSTRACT A sample of warm, low-level semiregular variables chosen from the General Catalogue of Variable Stars is studied for their chemical compositions by analysing high-resolution optical spectra. The abundance ratios from Na/Fe to Eu/Fe displayed by these and previously analysed semiregular variables are quite similar to ratios displayed by normal red giants across the Galactic thin and thick discs and halo populations in the solar neighbourhood, suggesting from this perspective that the variables may be among the more photometrically active red giants.

Pozhath, Ramya↗
DOE OSTI2023

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables

The typically rugged nature of molecular free energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for data-driven discovery of CVs parameterizing the important large scale motions of the system. A challenge to CV discovery is learning CVs invariant to symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance has proved a persistent challenge in frustrating the data-driven discovery of multi-molecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate Permutation Invariant Vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation, and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand sampling of configurational phase space and obtain converged CVs and free energy landscapes. Here, we demonstrate the Permutationally Invariant Network for Enhanced Sampling (PINES) approach in applications to the self-assembly of a 13-atom Argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C 45 H 92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Comparing Designed Training Sets to Optimize Multivariate Regression Models for Pr, Nd, and Nitric Acid Using Spectrophotometry

Chemometric regression models were developed for the quantification of praseodymium (Pr, 0–1000 µg/mL), neodymium (Nd, 0–1000 µg/mL), and nitric acid (HNO 3 , 0.1–5 M) using spectrophotometry. Designed calibration sets were composed of 20 samples each: 10 model points and 10 lack-of-fit (LOF) points. The D-optimal designs effectively minimized the number of samples required to build models, and each design resulted in similar prediction performance, suggesting that statistical design of experiments can provide a reliable framework for selecting training set samples in three-variable systems. Partial least squares regression (PLSR) models were validated against a one-factor-at-a-time validation set composed of 125 samples (three variables, five levels). The top PLS-1 models resulted in average percent root mean square error of prediction error values of 3.5%, 1.7%, and 1.2% for Pr(III), Nd(III), and HNO 3 , respectively. Power set augmentations of the model and LOF samples were investigated to optimize the number of training set samples. PLSR models built using just required model points (10) had similar predictive capabilities as models including the LOF points (20) but with fewer samples. The number of validation samples was also varied systematically to learn how many samples are needed to validate regression models. This work addresses long-standing questions in the field of chemometrics to help make this approach amenable to the near-real-time quantification of hazardous species in remote settings.

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

Evaluating Grid Strength under Uncertain Renewable Generation

The increasing displacement of synchronous generators with renewable resources such as wind and solar via power electronic interfaces causes a reduction in short-circuit strength and weak grid issues. The variation and uncertainty of renewable energy increase challenges for identifying weak grid conditions. This paper proposes an efficient method to analyze the impact of uncertain renewable energy on grid strength. The proposed method uses the probabilistic collocation method (PCM) to approximate the results of grid strength assessment under uncertain renewable generation, in order to reduce computational burden without compromising result accuracy when compared with traditional Monte Carlo simulation (MCS). To improve the accuracy of the approximation results, the proposed method integrates the K-means clustering technique with PCM to select the approximation samples of input variables. The efficacy of the proposed method is demonstrated by comparison with MCS on the modified IEEE 9-bus system and modified IEEE 39-bus system with multiple renewable generators.

grid strength↗
DOE OSTI2021

Mixture density network estimation of continuous variable maximum likelihood using discrete training samples

Abstract Mixture density networks (MDNs) can be used to generate posterior density functions of model parameters $$\varvec{\theta }$$ θ given a set of observables $${\mathbf {x}}$$ x . In some applications, training data are available only for discrete values of a continuous parameter $$\varvec{\theta }$$ θ . In such situations, a number of performance-limiting issues arise which can result in biased estimates. We demonstrate the usage of MDNs for parameter estimation, discuss the origins of the biases, and propose a corrective method for each issue.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Simple effective conservative treatment of uncertainty from sparse samples of random functions

This paper examines the variability of predicted responses when multiple stress-strain curves (reflecting variability from replicate material tests) are propagated through a finite element model of a ductile steel can being slowly crushed. Over 140 response quantities of interest (including displacements, stresses, strains, and calculated measures of material damage) are tracked in the simulations. Each response quantity’s behavior varies according to the particular stress-strain curves used for the materials in the model. We desire to estimate response variability when only a few stress-strain curve samples are available from material testing. Propagation of just a few samples will usually result in significantly underestimated response uncertainty relative to propagation of a much larger population that adequately samples the presiding random-function source. A simple classical statistical method, Tolerance Intervals, is tested for effectively treating sparse stress-strain curve data. The method is found to perform well on the highly nonlinear input-to-output response mappings and non-standard response distributions in the can-crush problem. The results and discussion in this paper support a proposition that the method will apply similarly well for other sparsely sampled random variable or function data, whether from experiments or models. Finally, the simple Tolerance Interval method is also demonstrated to be very economical.

36 MATERIALS SCIENCE↗