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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Cisplatin-Induced Alteration on Membrane Composition of A549 Cells Revealed by ToF-SIMS

Cisplatin has been clinically used for treatment of solid tumors such as non-small-cell lung cancer for decades. However, tumor resistance may be acquired with losing the antitumor activity of cisplatin. As cellular membrane is the first barrier that cisplatin has to overcome before its further action inside the cells, the membrane composition must play a vital role in the cisplatin uptake and excretion which further influences cisplatin sensitivity. In this work, we applied ToF-SIMS surface analysis combined with principle component analysis to distinguish the differences of cell membrane composition between non-small-cell lung cancer cells (A549) and its cisplatin resistant counterpart A549/DDP cells. The decreased phosphatidylcholine content and more abundant cholesterol were observed in the drug resistant cell surfaces, indicating the decreased membrane fluidity of A549/DDP cells. Moreover, we further compared membrane composition of A549 and A549/DDP cells after being treated with different concentrations of cisplatin. A higher composition level of proteins was discovered on all groups of A549/DDP cell membranes. The altered surface chemistry of cellular membranes induced by cisplatin indicate the significance of membrane structures in the drug resistance, which deserves further investigations to this regard.

Zhang, Yanyan↗
DOE OSTI2020

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN

Design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. To tackle this challenge, in this work we propose a novel regressional and conditional generative adversarial network (RCGAN) for inverse design of representative two-dimensional materials, the graphene and boron-nitride (BN) hybrids. RCGAN incorporates a supervised regressor network, thus overcoming the common technical barrier in the traditional unsupervised GANs, which cannot generate data when fed with continuous and quantitative labels. RCGAN can autonomously generate graphene/BN hybrids given any target bandgap values. These structures are distinguished from the ones used for training and exhibit high diversity for a given bandgap. Moreover, they exhibit high fidelity, yielding bandgaps within ~10% MAE F of the desired bandgaps as validated by density functional theory (DFT) calculations. Analysis by the principle component analysis (PCA) and modified locally linear embedding (MLLE) reveals that the generator has successfully generated structures following the statistical distribution of the real structures. It implies the possibility of the RCGAN in recognizing physical rules hidden in the high-dimensional data. The novel strategy for designing regressional GAN architecture together with the successful application to inverse design of materials would inspire further exploration in research fields beyond materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Accelerated version of NUBEAM capabilities in DIII-D using neural networks

A neural network model of the effects of neutral beam injection on DIII-D has been developed. The training and testing data used by the model have been generated by the NUBEAM module of TRANSP for experimental discharges from the 2018 DIII-D campaign. Using a principle component analysis to reduce the dimensionality of profile data, the model has been shown to reproduce the results of the Monte Carlo code NUBEAM with a high level of accuracy and an execution time orders of magnitude faster than the execution time of NUBEAM. Furthermore, this makes the neural network model uniquely suited to applications in model-based scenario planning (off-line) and active control (on-line), where a large number of simulation runs are required by the associated optimization tasks that need to be performed before and during the discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Rotor blade imbalance fault detection for variable-speed marine current turbines via generator power signal analysis

Marine hydrokinetic (MHK) turbines extract renewable energy from oceanic environments. However, due to the harsh conditions that these turbines operate in, system performance naturally degrades over time. Thus, ensuring efficient condition-based maintenance is imperative towards guaranteeing reliable operation and reduced costs for marine hydrokinetic power. This work proposes a novel framework aimed at identifying and classifying the severity of rotor blade pitch imbalance faults experienced by marine current turbines (MCTs). In the framework, a Continuous Morlet Wavelet Transform (CMWT) is first utilized to acquire the wavelet coefficients encompassed within the 1P frequency range of the turbine's rotor shaft. From these coefficients, several statistical indices are tabulated into a six-dimensional feature space. Next, Principle Component Analysis (PCA) is employed on the resulting feature space for dimensionality reduction, and then the application of a K-Nearest Neighbor (KNN) machine learning algorithm is utilized for fault detection and severity classification. The effectiveness of the proposed framework is validated using a high-fidelity MCT numerical simulation platform, where results demonstrate that the presence of a pitch imbalance fault can be accurately detected 100% of the time and correctly classified based upon severity more than 97% of the time.

42 ENGINEERING↗
DOE OSTI2022

Understanding Metal–Organic Framework Nucleation from a Solution with Evolving Graphs

A mechanistic understanding of metal–organic framework (MOF) synthesis and scale-up remains underexplored due to the complex nature of the interactions of their building blocks. In this work, we investigate the collective assembly of building units at the early stages of MOF nucleation, using MIL-101(Cr) as a prototypical example. Using large-scale molecular dynamics simulations, we observe that the choice of solvent (water and N,N-dimethylformamide), the introduction of ions (Na+ and F–) and the relative populations of MIL-101(Cr) half-secondary building unit (half-SBU) isomers have a strong influence on the cluster formation process. Additionally, the shape, size, nucleation and growth rates, crystallinity, and short and long-range order largely vary depending on the synthesis conditions. We evaluate these properties as they naturally emerge when interpreting the self-assembly of MOF nuclei as the time evolution of an undirected graph. Solution-induced conformational complexity and ionic concentration have a dramatic effect on the morphology of clusters emerging during assembly. While pure solvents lead to the rapid formation of a small number of large clusters, the presence of ions in aqueous solutions results in smaller clusters and slower nucleation. This diversity is captured by the key features of the graph representation. Principle component analysis on graph properties reveals that only a small number of molecular descriptors is needed to deconvolute MOF self-assembly. Furthermore, descriptors such as the average coordination number between half-SBUs and fractal dimension are of particular interest as they can be can be followed experimentally by techniques like by time-resolved spectroscopy. Ultimately, graph theory emerges as an approach that can be used to understand complex processes revealing molecular descriptors accessible by both simulation and experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Forensic tracers of exposure to produced water in freshwater mussels: a preliminary assessment of Ba, Sr, and cyclic hydrocarbons

Hydraulic fracturing is often criticized due in part to the potential degradation of ground and surface water quality by high-salinity produced water generated during well stimulation and production. This preliminary study evaluated the response of the freshwater mussel, Elliptio complanata, after exposure to produced water. A limited number of adult mussels were grown over an 8-week period in tanks dosed with produced water collected from a hydraulically fractured well. The fatty tissue and carbonate shells were assessed for accumulation of both inorganic and organic pollutants. Ba, Sr, and cyclic hydrocarbons indicated the potential to accumulate in the soft tissue of freshwater mussels following exposure to diluted oil and gas produced water. Exposed mussels showed accumulation of Ba in the soft tissue several hundred times above background water concentrations and increased concentrations of Sr. Cyclic hydrocarbons were detected in dosed mussels and principle component analysis of gas chromatograph time-of-flight mass spectrometer results could be a novel tool to help identify areas where aquatic organisms are impacted by oil and gas produced water, but larger studies with greater replication are necessary to confirm these results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗
DOE OSTI2023

The Carnegie Supernova Project I: Spectroscopic analysis of stripped-envelope supernovae

An analysis leveraging 170 optical spectra of 35 stripped-envelope (SE) core-collapse supernovae (SNe) observed by the Carnegie Supernova Project I and published in a companion paper is presented. Mean template spectra were constructed for the SNe IIb, Ib, and Ic subtypes, and parent ions associated with designated spectral features are identified with the aid of the spectral synthesis code SYNAPPS. Our modeled mean spectra suggest the ~6150 Å feature in SNe IIb may have an underlying contribution due to silicon, while the same feature in some SNe Ib may have an underlying contribution due to hydrogen. Standard spectral line diagnostics consisting of pseudo-equivalent widths (pEWs) and blue-shifted Doppler velocity were measured for each of the spectral features. Correlation matrices and rolling mean values of both spectral diagnostics were constructed. A principle component analysis (PCA) was applied to various wavelength ranges of the entire dataset and suggests clear separation among the different SE SN subtypes, which follows from trends previously identified in the literature. In addition, our findings reveal the presence of two SNe IIb subtypes, a select number of SNe Ib displaying signatures of weak, high-velocity hydrogen, and a single SN Ic with evidence of weak helium features. Our PCA results can be leveraged to obtain robust subtyping of SE SNe based on a single spectrum taken during the so-called photospheric phase, separating SNe IIb from SNe Ib with ~80% completion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of machine learning

Abstract A computational approach by an implementation of the principle component analysis (PCA) with K -means and Gaussian mixture (GM) clustering methods from machine learning algorithms to identify structural and dynamical heterogeneities of supercooled liquids is developed. In this method, a collection of the average weighted coordination numbers ( W C N s ‾ ) of particles calculated from particles’ positions are used as an order parameter to build a low-dimensional representation of feature (structural) space for K -means clustering to sort the particles in the system into few meso-states using PCA. Nano-domains or aggregated clusters are also formed in configurational (real) space from a direct mapping using associated meso-states’ particle identities with some misclassified interfacial particles. These classification uncertainties can be improved by a co-learning strategy which utilizes the probabilistic GM clustering and the information transfer between the structural space and configurational space iteratively until convergence. A final classification of meso-states in structural space and domains in configurational space are stable over long times and measured to have dynamical heterogeneities. Armed with such a classification protocol, various studies over the thermodynamic and dynamical properties of these domains indicate that the observed heterogeneity is the result of liquid–liquid phase separation after quenching to a supercooled state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Machine learning analysis of perovskite oxides grown by molecular beam epitaxy

Reflection high-energy electron diffraction (RHEED) is a ubiquitous in situ molecular beam epitaxial (MBE) characterization tool. Although RHEED can be a powerful means for crystal surface structure determination, it is often used as a static qualitative surface characterization method at discrete intervals during a growth. A full analysis of RHEED data collected during the entirety of MBE growths is made possible using principle component analysis (PCA) and $\textit{k}$-means clustering to examine significant boundaries that occur in the temporal clusters grouped from RHEED data and identify statistically significant patterns. This process is applied to data from homoepitaxial SrTiO 3 growths, heteroepitaxial SrTiO 3 grown on scandate substrates, BaSnO 3 films grown on SrTiO 3 substrates, and LaNiO 3 films grown on SrTiO 3 substrates. We report this analysis may provide additional insights into the surface evolution and transitions in growth modes at precise times and depths during growth, and that video archival of an entire RHEED image sequence may be able to provide more insight and control overgrowth processes and film quality.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees

Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. Furthermore, the proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor

With the deployment of phasor measurement units (PMU) and wide area measurement system (WAMS), it is feasible to have an insight into the events occurred in power systems based on measured data. Thus, a novel data-driven algorithm based on local outlier factor (LOF) is proposed in this work to detect and locate events in power systems using reduced PMU data. First, the unequal-interval reduction method is presented to reduce the scale of PMU data in sub-stations and reconstruct it in master station of WAMS, which can relieve the burden of communication systems. Then, principle component analysis (PCA)-based similarity search method is proposed to measure the differences of operation state between any two buses. Next, LOF is presented to detect the abnormal events in power systems, and employed to determine the region of the event source. Finally, six cases from the Western electricity coordinating council (WECC) 179-bus power system, a case from the South China power system (SCPS), and a case from the Guangdong power system (GDPS) are utilized to demonstrate the effectiveness of the proposed algorithm. Overall, the results show that proposed algorithm is effective and can be applied to event detection, event location, and online monitoring, which can enhance the situation awareness ability of power system operators.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Phylogenetic diversity of 200+ isolates of the ectomycorrhizal fungus Cenococcum geophilum associated with Populus trichocarpa soils in the Pacific Northwest, USA and comparison to globally distributed representatives

The ectomycorrhizal fungal symbiont Cenococcum geophilum is of high interest as it is globally distributed, associates with many plant species, and has resistance to multiple environmental stressors. C . geophilum is only known from asexual states but is often considered a cryptic species complex, since extreme phylogenetic divergence is often observed within nearly morphologically identical strains. Alternatively, C . geophilum may represent a highly diverse single species, which would suggest cryptic but frequent recombination. Here we describe a new isolate collection of 229 C . geophilum isolates from soils under Populus trichocarpa at 123 collection sites spanning a ~283 mile north-south transect in Western Washington and Oregon, USA (PNW). To further understanding of the phylogenetic relationships within C . geophilum , we performed maximum likelihood and Bayesian phylogenetic analyses to assess divergence within the PNW isolate collection, as well as a global phylogenetic analysis of 789 isolates with publicly available data from the United States, Japan, and European countries. Phylogenetic analyses of the PNW isolates revealed three distinct phylogenetic groups, with 15 clades that strongly resolved at >80% bootstrap support based on a GAPDH phylogeny and one clade segregating strongly in two principle component analyses. The abundance and representation of PNW isolate clades varied greatly across the North-South range, including a monophyletic group of isolates that spanned nearly the entire gradient at ~250 miles. A direct comparison between the GAPDH and ITS rRNA gene region phylogenies, combined with additional analyses revealed stark incongruence between the ITS and GAPDH gene regions, consistent with intra-species recombination between PNW isolates. In the global isolate collection phylogeny, 34 clades were strongly resolved using Maximum Likelihood and Bayesian approaches (at >80% MLBS and >0.90 BPP respectively), with some clades having intra- and intercontinental distributions. Together these data are highly suggestive of divergence within multiple cryptic species, however additional analyses such as higher resolution genotype-by-sequencing approaches are needed to distinguish potential species boundaries and the mode and tempo of recombination patterns.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Constraints on Neutrino Oscillation Parameters from Neutrinos and Antineutrinos with Machine Learning

NOvA is a two detector, long baseline neutrino oscillation experiment measuring the oscillations of muon neutrinos from the \numi neutrino beam over a baseline of \SI{810}{km}. The experiment uses four oscillation channels, $\numu \rightarrow \numu$, $\numubar \rightarrow \numubar$, $\numu \rightarrow \nue$, and $\numubar \rightarrow \nuebar$, with a peak neutrino energy of \SI{1.8}{GeV}. This dissertation describes the analysis of these channels using a dataset of $13.6\times10^{20}$ protons on target neutrino beam mode and $12.5\times10^{20}$ protons on target antineutrino beam mode. The analysis makes use of improvements in the treatment of systematic uncertainties and machine learning techniques to reconstruct neutrino interactions. A technique for decorrelating systematic errors using principle component analysis was utilized to reduce and optimize neutrino cross section and beam related uncertainties. The improved machine learning algorithms make use of convolutional ne ural net works for neutrino event classification, particle classification, and instance segmentation. The selection of neutrino signal events utilizing the neutrino event classifier shows an efficiency of 63\% for the selection of electron neutrinos in neutrino beam mode and 75\% for electron antineutrinos in antineutrino beam mode. Using this algorithm, 82 appearing electron neutrino candidates and 33 appearing electron antineutrino candidates were observed with expected backgrounds of 26.8 and 14.0 respectively. In addition, 211 surviving muon neutrino candidates and 105 muon antineutrino candidates were identified with a purity of more than 96\% using the same neutrino event classifier. Fitting these data to the three flavor neutrino oscillation model, using constraints on \thetaonetwo, \thetaonethree, and \dmsqonetwo from solar and reactor neutrino experiments, the oscillation parameters are measured to be $\sintwothree = 0.57^{+0.04}_{-0.03}$, $\dmsqthreetwo = \SI[parse-numbers= false]{+ 2.41\pm0.07 \times 10^{-3}}{eV^2}$, and $\dcp=0.82^{+0.27}_{-0.87}\pi$ with a preference for the normal neutrino mass hierarchy. Leading systematic uncertainties for these measurements come from detector calibration and neutrino interaction models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1975

The Exploration Ethic: Its Historical-Intellectual Basis. Outlook for Space (1980 - 2000)

Principle components of the exploration ethic are discussed. Attempts were made to justify both the historical and intellectual aspects of the concept. It was noted that intellectual justification is strongly grounded on: (1) the complementarity of objective and normative inquiry as to method, and (2) interdisciplinary alliance of ethics of adaptive systems with contemporary decision sciences, as a theoretical basis. Historical exploration justification was associated with: (1) periods of civilization transition, (2) changes in the process of exploration which cause change in types of rationals used, sponsors involved, and explorers interest, and (3) the incorrectness of proven prior cost/benefit calculations.

Priscoli, J. D.↗
NASA NTRS1981

SPS flexible system control assessment analysis

Active control of the Satellite Power System (SPS0, a large mechanically flexible aerospace structure is addressed. The control algorithm is the principle component in the feedback link from sensors to actuators. An analysis of the interaction of the SPS structure and its active control system is presented.

Balas, M. J.↗
NASA NTRS1983

Study on radiometric consistency of LANDSAT-4 multispectral scanner

Two full frames of radiometrically corrected LANDSAT-4 MSS data were examined to determine a number of radiometric properties. It was found that LANDSAT-4 MSS produces data of good quality with dynamic ranges and target responses qualitatively similar to those of previous MSS sensors. Banding appears to be quite well corrected, with a residual rms error of about 0.3 digital counts being measured; the histogram equalization algorithm appears to be working as advertised. A low level coherent noise effect was found in all bands, appearing in uniform areas as a diagonal striping pattern. The principle component of this noise was found by Fourier analysis to be a highly consistent wavelength of 3.6 pixels along a scan line (28 KHz). The magnitude of this effect ranged from about 0.75 of one count in the worst band (Band 1) to only about 0.25 counts in the best band (Band 4). Preparations were made for establishing a relative radiometric calibration from MSS 4 data with respect to MSS 3.

Malila, W. A.↗
NASA NTRS1985

Preliminary Study of Information Extraction of LANDSAT TM Data for a Suburban/regional Test Site

A substantial amount of spectral information is available from TM (as compared to MSS) data for a 14.25 square km area between Beltsville and Laurel, Maryland. Large buildings and street patterns were resolved in the TM imagery. While there was added information content in TM data for discriminating surburban/regional land cover, characteristics of MSS can improve land cover discrimination over TM when conventional classification procedures are used on digital data. The improved qualitization of TM is likely valuable in situations where there are spectral similarities between classes. The spatial resolution in TM decreased land cover discrimination as a result of increased within class variability. For many general digital evaluations, inclusion of four bands representing the four spectral regions can provide much useful land cover discrimination. Inclusion of TM 6 indicates an improvement in spectral class discrimination. Of primary spectral importance is the discrimination between water, vegetative surfaces, and impervious surfaces due to differences in thermal properties. Results from the principle component transformed data clearly indicates additional information content in TM over MSS.

Toll, D. L.↗