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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Emergent Behavior at the Calcite–Water Interface during Reactive Transport in a Simple Microfluidic Channel

Geochemical reactive transport processes in natural mineral-fluid systems may produce a wide array of emergent phenomena that are difficult to predict from basic principles and to reproduce in model systems. In this study, we present experimental results obtained from a simple microfluidic system with which we explored the consequences of reacting the calcite (104) cleavage surface with an acidic Pb-bearing solution (pH = 3.5, [Pb] total = 5 mM) as a function of flow rate. This system is relevant to passive remediation systems for Pb-rich acid mine drainage. We observed periodic banding in the amounts of Pb sorption at flow velocities ≥ 926 μm s -1 , where the band spacing was spatially correlated with the amount of calcite dissolution and the development of micropyramidal topography on the calcite (104) surface. The equivalent coverage of Pb deposited in these Pb-rich bands was at least several monolayers per unit cell, yet there was no evidence for precipitation of any secondary Pb phase implying incorporation of Pb within the near-surface calcite lattice. We also observed spatial variations in nucleation and growth of euhedral secondary Pb-carbonate minerals hydrocerusite and cerussite at flow rates ≤ 278 μm s -1 ,. These findings demonstrate potential for exploiting the rich phenomenology afforded by the interplay among transport phenomena and chemical kinetics in experimental systems designed to yield deeper insights into geochemical self-organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Anomalous cluster detection in spatiotemporal meteorological fields

Finding anomalous regions in spatiotemporal climate data is an important problem with a need for greater accuracy. The collective and contextual nature of anomalies (e.g., heat waves) coupled with the real‐valued, seasonal, multimodal, highly correlated, and gridded nature of climate variable observations poses a multitude of challenges. Existing anomaly detection methods have limitations in the specific setting of real‐valued areal spatiotemporal data. In this paper, we develop a method for extreme event detection in meteorological datasets that follows from well known distribution‐based anomaly detection approaches. The method models spatial and temporal correlations explicitly through a piecewise parametric assumption and generalizes the Mahalanobis distance across distributions of different dimensionalities. The result is an effective method to mine contiguous spatiotemporal anomalous regions from meteorological fields which improves upon the current standard approach in climatology. The proposed method has been evaluated on a real global surface temperature dataset and validated using historical records of extreme events.

Ramachandra, Bharathkumar↗
DOE OSTI2024

Descriptors of water aggregation

For this work, we rely on a total of 23 (cluster size, 8 structural, and 14 connectivity) descriptors to investigate structural patterns and connectivity motifs associated with water cluster aggregation. In addition to the cluster size n (number of molecules), the 8 structural descriptors can be further categorized into (i) one-body (intramolecular): covalent OH bond length (r OH ) and HOH bond angle (θ HOH ), (ii) two-body: OO distance (r OO ), OHO angle (θ OHO ), and HOOX dihedral angle ($\phi$ HOOX ), where X lies on the bisector of the HOH angle, (iii) three-body: OOO angle (θ OOO ), and (iv) many-body: modified tetrahedral order parameter (q) to account for two-, three-, four-, five-coordinated molecules (q m , m = 2, 3, 4, 5) and radius of gyration (R g ). The 14 connectivity descriptors are all many-body in nature and consist of the AD, AAD, ADD, AADD, AAAD, AAADD adjacencies [number of hydrogen bonds accepted (A) and donated (D) by each water molecule], Wiener index, Average Shortest Path Length, hydrogen bond saturation (% HB), and number of non-short-circuited three-membered cycles, four-membered cycles, five-membered cycles, six-membered cycles, and seven-membered cycles. We mined a previously reported database of 4 948 959 water cluster minima for (H 2 O) n , n = 3–25 to analyze the evolution and correlation of these descriptors for the clusters within 5 kcal/mol of the putative minima. It was found that r OH and % HB correlated strongly with cluster size n, which was identified as the strongest predictor of energetic stability. Marked changes in the adjacencies and cycle count were observed, lending insight into changes in the hydrogen bond network upon aggregation. A Principal Component Analysis (PCA) was employed to identify descriptor dependencies and group clusters into specific structural patterns across different cluster sizes. The results of this study inform our understanding of how water clusters evolve in size and what appropriate descriptors of their structural and connectivity patterns are with respect to system size, stability, and similarity. The approach described in this study is general and can be easily extended to other hydrogen-bonded systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

A Multi-layer, Data-driven Advanced Reasoning Tool for Intelligent Data Mining and Analysis for Smart Grids

This paper presents the multi-layer, data-driven advanced reasoning tool (M-DART), a proof-of-principle decision support tool for improved power system operation. M-DART will cross-correlate and examine different data sources to assess anomalies, infer root causes, and anneal data into actionable information. By performing higher-level reasoning “triage” of diverse data sources, M-DART focuses on early detection of emerging power system events and identifies highest priority actions for the human decision maker. M-DART represents a significant advancement over today’s grid monitoring technologies that apply offline analyses to derive model-based guidelines for online real-time operations and use isolated data processing mechanisms focusing on individual data domains. The development of the M-DART will bridge these gaps by reasoning about results obtained from multiple data sources that are enabled by the smart grid infrastructure. This hybrid approach integrates a knowledge base that is trained offline but tuned online to capture model-based relationships while revealing complex causal relationships among data from different domains.

data mining, reasoning, smart grid, AMI, power gen↗
DOE OSTI2022

PDFDataExtractor: A Tool for Reading Scientific Text and Interpreting Metadata from the Typeset Literature in the Portable Document Format

The layout of portable document format (PDF) files is constant to any screen, and the metadata therein are latent, compared to mark-up languages such as HTML and XML. No semantic tags are usually provided, and a PDF file is not designed to be edited or its data interpreted by software. However, data held in PDF files need to be extracted in order to comply with opensource data requirements that are now government-regulated. In the chemical domain, related chemical and property data also need to be found, and their correlations need to be exploited to enable data science in areas such as data-driven materials discovery. Such relationships may be realized using text-mining software such as the “chemistry-aware” natural-language-processing tool, ChemDataExtractor; however, this tool has limited data-extraction capabilities from PDF files. This study presents the PDFDataExtractor tool, which can act as a plug-in to ChemDataExtractor. It outperforms other PDF-extraction tools for the chemical literature by coupling its functionalities to the chemical-named entityrecognition capabilities of ChemDataExtractor. The intrinsic PDF-reading abilities of ChemDataExtractor are much improved. The system features a template-based architecture. This enables semantic information to be extracted from the PDF files of scientific articles in order to reconstruct the logical structure of articles. While other existing PDF-extracting tools focus on quantity mining, this template-based system is more focused on quality mining on different layouts. PDFDataExtractor outputs information in JSON and plain text, including the metadata of a PDF file, such as paper title, authors, affiliation, email, abstract, keywords, journal, year, document object identifier (DOI), reference, and issue number. With a self-created evaluation article set, PDFDataExtractor achieved promising precision for all key assessed metadata areas of the document text.

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

The Role of Data Filtering in Open Source Software Ranking and Selection

Faced with more than 100M open source projects, a more manageable small subset is needed for most empirical investigations. More than half of the research papers in leading venues investigated filtering projects by some measure of popularity with explicit or implicit arguments that unpopular projects are not of interest, may not even represent "real" software projects, or that less popular projects are not worthy of study. However, such filtering may have enormous effects on the results of the studies if and precisely because the sought-out response or prediction is in any way related to the filtering criteria.This paper exemplifies the impact of this common practice on research outcomes, specifically how filtering of software projects on GitHub based on inherent characteristics affects the assessment of their popularity. Using a dataset of over 100,000 repositories, we used multiple regression to model the number of stars -a commonly used proxy for popularity- based on factors such as the number of commits, the duration of the project, the number of authors and the number of core developers. Our control model included the entire dataset, while a second filtered model considered only projects with ten or more authors. The results indicated that while certain characteristics of the repository consistently predict popularity, the filtering process significantly alters the relationships between these characteristics and the response. We found that the number of commits exhibited a positive correlation with popularity in the control sample but showed a negative correlation in the filtered sample. These findings highlight the potential biases introduced by data filtering and emphasize the need for careful sample selection in empirical research of mining software repositories. We recommend that empirical work should either analyze complete datasets such as World of Code, or employ stratified random sampling from a complete dataset to ensure that filtering is not biasing the results.

Malviya Thakur, Addi↗
DOE OSTI2017

Understanding the Hierarchy of Dense Subgraphs in Stationary and Temporally Varying Setting

Graphs are widely used to model relationships in a wide variety of domains such as sociology, bioinformatics, infrastructure, the WWW, to name a few. One of the key observations is that while real-world graphs are often globally sparse, they are locally dense. In other words, the average degree is often quite small (say at most 10 in a million vertex graph), but vertex neighborhoods are often dense. Finding dense subgraphs is a critical aspect of graph mining It has been used for finding communities and spam link farms in web graphs, graph visualization, real-time story identification, DNA motif detection in biological networks, finding correlated genes, epilepsy prediction, finding price value motifs in financial data, graph compression, distance query indexing, and increasing the throughput of social networking site servers. However, most standard formulations of this problem (like clique, quasi-clique, k-densest subgraph) are NP-hard. Furthermore, current dense subgraph finding algorithms usually optimize some objective, and only find a few such subgraphs without providing any structural relations, whereas the goal is rarely to find the "true optimum," but to identify many (if not all) dense substructures, understand their distribution in the graph, and ideally determine relationships among them. In this project, we first aim to devise algorithms and provide 3 implementations with nice visualizations to find the hierarchy between dense subgraphs, and then understand the structure of the hierarchy to gain more insight on the hidden patterns in real-world networks. Another important aspects in graph analysis is the temporal nature of networks. Networks evolve over time and in many applications data arrives at a high velocity, and thus it is important to design algorithms that can process data efficiently. We report three main results towards identifying dense structures in large evolving graphs. First, we will show how the hierarchical connectedness structure can be maintained efficiently, where connectedness is defined by increasing levels of connectivity strength. Next, we present dense structure can be identified in bipartite graphs without building projection graphs. And finally, we present a new method for peeling algorithms This new approach avoids sequential nature of peeling algorithms and is amenable to parallelization, which is crucial for processing high velocity data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Data Mining for Parameters Affecting Polymorph Selection in Contorted Hexabenzocoronene Derivatives

The macroscopic properties of molecular materials can be drastically influenced by their solid-state packing arrangements, of which there can be many (e.g., polymorphism). Strategies to controllably and predictively access select polymorphs are thus highly desired, but computationally predicting the conditions necessary to access a given polymorph is challenging with the current state of the art. Using derivatives of contorted hexabenzocoronene, cHBC, we employed data mining, rather than first-principles approaches, to find relationships between the crystallizing molecule, postdeposition solvent-vapor annealing conditions that induce polymorphic transformation, and the resulting polymorphs. This analysis yields a correlative function that can be used to successfully predict the appearance of either one of two polymorphs in thin films of cHBC derivatives. Within the postdeposition processing phase space of cHBC derivatives, we have demonstrated an approach to generate guidelines to select crystallization conditions to bias polymorph access. Finally, we believe this approach can be applied more broadly to accelerate the predictions of processing conditions to access desired molecular polymorphs, making progress toward one of the grand challenges identified by the Materials Genome Initiative.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Materials characterisation with the associated particle technique

Since the last past years, the Nuclear Measurement Laboratory of CEA Cadarache, France, together with Partners from European and National projects, has been studying the application of fast neutron interrogation with the Associated Particle Technique for material identification in different areas of homeland and maritime security, and for the characterisation of the materials constituting radioactive waste. Fast 14 MeV neutrons are produced from the H-3(H-2,n)alpha fusion reaction in a sealed tube neutron generator embedding an alpha detector. The alpha particle is used to tag neutron direction and emission time, thus allowing the electronic selection of neutron-induced gamma spectra in the voxels of interest. Gamma rays emitted by tagged neutron interactions on the present nuclei (C, O, N, Fe, Al, Si, Cl, etc.) are recorded with spectroscopic detectors and analysed to determine elemental proportions, thus allowing material identification. Investigations have been conducted for the detection of explosives, illicit drugs and contraband materials in cargo containers, for the inspection of objects lying on the sea floor suspected to contain explosives like mines, bombs, torpedoes, etc., for the recognition of an improvised chemical device, and for material identification in radioactive waste packages. Recently the detection of special nuclear materials is being investigated using time correlation analysis between induced fission particles, instead of gamma-ray spectroscopy. The paper presents an overview of these studies and last results. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Modern sensitivity analysis - 15651

Sensitivity analysis has been applied to complex models, such as performance assessment models, since at least the advent of computational tools to support such an approach to model evaluation. Initially, sensitivity analysis methods were applied to deterministic models and focused on changing one input variable at a time. Probabilistic modeling opened the door to evaluating changes in all input variables simultaneously. Standardized rank regression methods were primarily used in the last century to evaluate probabilistic models. These methods assume linearity in the rank response of the model to the inputs. At the very least this assumes monotonicity in the response, but for the most part the response needs to be near-linear. This is highly unlikely for performance assessment models that are based on differential equations, multiplication and division of variables that are usually not correlated, and conservatism that is not well understood. There are better statistical methods for evaluating highly non-linear and dynamic probabilistic models. Some of these methods were founded in the data mining industry. This century has seen a rapid development of the internet that led directly to the development of data mining techniques for massive databases. Although the goals were different, these techniques can be adapted to sensitivity analysis for complex probabilistic models, which, also, form large, or even massive, databases. These modern sensitivity analysis procedures assess the importance of every explanatory variable (input parameters) used in a probabilistic model. In practice, this means that for each endpoint of interest, every explanatory variable in the model is associated with a quantitative measure of importance. For a given explanatory variable, the importance value depends on the endpoint of interest. This global approach to sensitivity analysis allows all levels of interactions to be evaluated, so that the variation in the output can be measured at every level of possible interaction. The effects are collected together during sensitivity analysis processing, and then separated out to attribute the overall contribution of each input parameter to the model output. These global sensitivity analysis procedures have the ability to characterize non-linear and non-monotonic relationships between explanatory variables and endpoints of interest. This is critically important because of the need to characterize complex interactions among multiple explanatory variables in PA models that often have non-linear and non-monotonic effects. The options for sensitivity analysis are fully explored, including an evaluation of several methods when applying them to a well-known complex non-linear non-monotonic model. The results clearly demonstrate the relative benefit of using data mining methods over simple statistical approaches such as standardized rank regression. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Deep data mining in a real space: Separation of intertwined electronic responses in a lightly doped BaFe 2 As 2

Electronic interactions present in material compositions close to the superconducting dome play a key role in the manifestation of high-T c superconductivity. In many correlated electron systems, however, the parent or underdoped states exhibit strongly inhomogeneous electronic landscape at the nanoscale that may be associated with competing, coexisting, or intertwined chemical disorder, strain, magnetic, and structural order parameters. Here we demonstrate an approach based on a combination of scanning tunneling microscopy/spectroscopy and advanced statistical learning for an automatic separation and extraction of statistically significant electronic behaviors in the spin density wave regime of a lightly (~1%) gold-doped BaFe 2 As 2 . Lastly, we show that the decomposed STS spectral features have a direct relevance to fundamental physical properties of the system, such as SDW-induced gap, pseudogap-like state, and impurity resonance states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Mining data in a dynamic PRA framework

Computational (also known as Dynamic) Probabilistic Risk Assessment (PRA) methods employs system simulation codes coupled with stochastic analysis tools in order to determine probabilities of certain outcomes such as system failure. In contrast to classical PRA methods (i.e., Event-Tree and Fault-Tree) in which timing and sequencing of events is set by the analyst, accident progression is dictated by the system control logic and its interaction with system temporal evolution. Due to the nature of the problem, Simulation-Based PRA methods require large amount of computationally expensive simulation runs. In addition, these methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). In this paper we investigate and apply several methods and algorithms to analyze these large amounts of time-dependent data. Furthermore, the scope of this article is to present a broad overview of methods and algorithms that can be used to improve data quality and to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Chemical and isotope compositions of shallow groundwater in areas impacted by hydraulic fracturing and surface mining in the Central Appalachian Basin, Eastern United States

Here, hydraulic fracturing of shale deposits has greatly increased the productivity of the natural gas industry by allowing it to exploit previously inaccessible reservoirs. Previous research has demonstrated that this practice has the potential to contaminate shallow aquifers with methane (CH 4 ) from deeper formations. This study compares concentrations and isotopic compositions of CH 4 sampled from domestic groundwater wells in Letcher County, Eastern Kentucky in order to characterize its occurrence and origins in relation to both neighboring hydraulically fractured natural gas wells and surface coal mines. The studied groundwater showed concentrations of CH 4 ranging from 0.05 mg/L to 10 mg/L, thus, no immediate remediation is required. The δ 13 C values of CH 4 ranged from 66‰ to 16‰, and δ 2 H values ranged from –286‰ to –86‰, suggesting an immature thermogenic and mixed biogenic/thermogenic origin. The occurrence of CH 4 was not correlated with proximity to hydraulically fractured natural gas wells. Generally, CH 4 occurrence corresponded with groundwater abundant in Na + , Cl – , and HCO 3 – , and with low concentrations of SO 4 –2 . The CH 4 and SO 4 –2 concentrations were best predicted by the oxidation/reduction potential of the studied groundwater. CH 4 was abundant in more reducing waters, and SO 4 –2 was abundant in more oxidizing waters. Additionally, groundwater in greater proximity to surface mining was more likely to be oxidized. This, in turn, might have increased the likelihood of CH 4 oxidation in shallow groundwater.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Multi-configuration Membrane Distillation Model (MCMD)

Many membrane distillation models have been created to simulate the heat and mass exchange process involved but most of the literature only validates models to a couple of cases with minor configuration changes. Tools are needed that allow tradeoffs between many configurations. The multiconfiguration membrane distillation model handles many configurations. This report introduces membrane distillation, provides theory, and presents the work to verify and validate the model against experimental data from Colorado School of Mines and a lower resolution model created at the National Renewable Energy Laboratory. Though more data analysis and testing are needed, an initial look at the model to experimental comparisons indicates that the model correlates to the data well but that design comparisons are likely to be incorrect across a broad range of configurations. More accurate quantification of heat and mass transfer through computational fluid mechanics is suggested.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Data Mining of Polymer Phase Transitions upon Temperature Changes by Small and Wide-Angle X-ray Scattering Combined with Raman Spectroscopy

The complex physical transformations of polymers upon external thermodynamic changes are related to the molecular length of the polymer and its associated multifaceted energetic balance. The understanding of subtle transitions or multistep phase transformation requires real-time phenomenological studies using a multi-technique approach that covers several length-scales and chemical states. A combination of X-ray scattering techniques with Raman spectroscopy and Differential Scanning Calorimetry was conducted to correlate the structural changes from the conformational chain to the polymer crystal and mesoscale organization. Current research applications and the experimental combination of Raman spectroscopy with simultaneous SAXS/WAXS measurements coupled to a DSC is discussed. In particular, we show that in order to obtain the maximum benefit from simultaneously obtained high-quality data sets from different techniques, one should look beyond traditional analysis techniques and instead apply multivariate analysis. Data mining strategies can be applied to develop methods to control polymer processing in an industrial context. Crystallization studies of a PVDF blend with a fluoroelastomer, known to feature complex phase transitions, were used to validate the combined approach and further analyzed by MVA.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Pahute Mesa-Oasis Valley Hydrostratigraphic Framework Model for Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nye County, Nevada (Rev. 0)

A new, revised three-dimensional (3-D) hydrostratigraphic framework model (HFM) for the Central and Western Pahute Mesa Corrective Action Units 101 and 102 was completed in 2019. The initial (Phase I) model was completed in 2002 and was documented in A Hydrostratigraphic Model and Alternatives for the Groundwater Flow and Contaminant Transport Model of Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nye County, Nevada (BN, 2002). Subsequent flow and transport modeling revealed the need to collect additional data to reduce uncertainties. The Phase II data collection and characterization effort included extensive well drilling and testing activities, and rebuilding of the Phase I 3-D HFM, resulting in the Phase II HFM (NSTec, 2014). Further drilling (Well ER-20-12), data collection, and interpretive work (e.g., Thirsty Canyon Lineament [Wurtz and Day, 2018]) were incorporated into the Pahute Mesa-Oasis Valley (PM-OV) HFM. The PM-OV HFM incorporates the area of interest from the Phase II HFM and adds additional areas, primarily to the north and west with minor additions to the south and east. The new, expanded area matches the PM-OV groundwater basin as defined in Fenelon et al. (2016) and includes Pahute Mesa, a former nuclear testing area at the Nevada National Security Site; and Oasis Valley, a groundwater discharge area downgradient from contaminant source areas on Pahute Mesa. The data and information necessary to build the PM-OV HFM are provided in Navarro (2019). The PM-OV HFM will be used by hydrologic modelers who are tasked with developing a model to determine how contaminants are transported by groundwater flow in an area of complex geology. The model area is large (more than 6,250 square kilometers) and geologically complex, including Paleozoic- to Mesozoic-age structures (e.g., thrust faults, Thirsty Canyon Lineament), at least seven Tertiary-age calderas, several intrusive bodies, and many relatively recent basin-and-range normal faults. Investigators from the U.S. Geological Survey; National Security Technologies, LLC; National Laboratories; and U.S. Department of Energy contractors have organized the more than 300 volcanic units and other rocks in the model area into 77 hydrostratigraphic units (HSUs). The volcanic rocks are subdivided into 42 aquifers, 11 confining units, and 9 composite units (containing both aquifer and confining unit rocks). The underlying pre-Tertiary rocks were divided into 5 HSUs, comprising 2 aquifers and 3 confining units. The model also includes 1 alluvial aquifer unit, 8 intrusive confining units, and 1 granite confining unit. The model depicts the thickness, extent, and geometric relationships of these HSUs (“layers” in the model) along with all the major structural features that control them, including calderas and faults. The most substantive differences between the Phase II HFM and the PM-OVHFM include (1) addition of the new data and resulting revised interpretations from the additional area encompassed by the PM-OV groundwater basin; (2) incorporation of 16 new faults; (3) 12 boreholes from the northern extension; (4) 2 additional caldera-collapse collars; and (5) correlation of the simulated faulting and hydrostratigraphy in the eastern part of the model domain with the delineations represented in the neighboring Rainier Mesa-Shoshone Mountain HFM-Prime and Yucca Flat-Climax Mine HFM. All PM-OV HFM delineations were based on published and unpublished data from geophysical investigations, surface geologic maps, and drill-hole records. Phase II approached model uncertainty using the model-building software to test model configurations and hydrologic consequences using a range of values for parameters such as fault offset amounts and HSU elevations. The PM-OV HFM expands on this approach to encompass the entire PM-OV groundwater basin.

54 ENVIRONMENTAL SCIENCES↗