Search NASA⌕ Search

SEARCH · Search NASA

Results for “Process settings”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Impacts of chemical gradients on microbial community structure

Succession of redox processes is sometimes assumed to define a basic microbial community structure for ecosystems with oxygen gradients. In this paradigm, aerobic respiration, denitrification, fermentation and sulfate reduction proceed in a thermodynamically determined order, known as the ‘redox tower’. Here, we investigated whether redox sorting of microbial processes explains microbial community structure at low-oxygen concentrations. We subjected a diverse microbial community sampled from a coastal marine sediment to 100 days of tidal cycling in a laboratory chemostat. Oxygen gradients (both in space and time) led to the assembly of a microbial community dominated by populations that each performed aerobic and anaerobic metabolism in parallel. This was shown by metagenomics, transcriptomics, proteomics and stable isotope incubations. Effective oxygen consumption combined with the formation of microaggregates sustained the activity of oxygen-sensitive anaerobic enzymes, leading to braiding of unsorted redox processes, within and between populations. Analyses of available metagenomic data sets indicated that the same ecological strategies might also be successful in some natural ecosystems.

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

Improvement of cryo-EM maps by density modification

A density-modification procedure for improving maps from single-particle electron cryogenic microscopy (cryo-EM) is presented here. The theoretical basis of the method is identical to that of maximum-likelihood density modification, previously used to improve maps from macromolecular X-ray crystallography. Key differences from applications in crystallography are that the errors in Fourier coefficients are largely in the phases in crystallography but in both phases and amplitudes in cryo-EM, and that half-maps with independent errors are available in cryo-EM. These differences lead to a distinct approach for combination of information from starting maps with information obtained in the density-modification process. The density-modification procedure was applied to a set of 104 datasets and improved map-model correlation and increased the visibility of details in many of the maps. The procedure requires two unmasked half-maps and a sequence file or other source of information on the volume of the macromolecule that has been imaged.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

In situ plant materials hyperspectral imaging by multimodal scattering near-field optical microscopy

Plant cells are elaborate three-dimensional polymer nano-constructs with complex chemistry. The bulk response of plants to light, in the far-field, is ultimately encoded by optical scattering from these nano-constructs. Their chemical and physical properties may be acquired through their interaction with a modulated nano-tip using scattering scanning near-field optical microscopy. Here, using this technique, we present 20 nm spatial resolution mechanical, spectral and optical mappings of plant cell walls. We first address the problem of plant polymers tracking through pretreatment and processing. Specifically, cellulose and lignin footprints are traced within a set of delignified specimen, establishing the factors hindering complete removal of lignin, an important industrial polymer. Furthermore, we determine the frequency dependent dielectric function ${\epsilon }(\omega)={(n+ik)}^{2}$ of plant material in the range 28 ≤ ω ≤ 58 THz, and show how the environmental chemical variation is imprinted in the nanoscale variability of n and k . This nanometrology is a promise for further progress in the development of plant-based (meta-)materials.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Magnetisation reversal in anisotropy graded Co/Pd multilayers

We demonstrate high precision controllability of the magnetization reversal nucleation process in [Co/Pd]{sub 8} multilayer films consisting of two sets of bilayers with high and low perpendicular anisotropy, respectively. The anisotropy of the entire film is set by the degree of Co/Pd interfacial mixing during deposition which provides fine control of the anisotropy of an individual bilayer in the multilayer stack. The relative number of each type of bilayer is used to select the magnetisation reversal behavior such that changing one bilayer changes the properties of the entire multilayer through anisotropy averaging. A simple extension to the sputtering protocol would provide multilayer films with fully graded anisotropy, while maintaining a constant saturation magnetization opening new possibilities for the creation of highly engineered multilayer structures for spin torque devices and future magnetic recording media.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Mechanical strength of [HA/Bioplastic/Sericin] composite part printed by bioprinter

The aim of this research was to determine the effect of hydroxyapatite (HA) content in printed biocomposite to its mechanical strength. The biocomposite paste was prepared by composing HA, bioplastic and sericin with various ratios of [HA/Bioplastic]: 40/60, 50/50, 60,40 and 70/30. Sericin of 0.3% weight was added to the biocomposite. Mechanical test was conducted to observe tensile (ASTM D 638 type 4) and flexural strength (ASTM D 790). Both type of specimens were fabricated using 3D Printer. Printing process parameter (infill speed, print speed and layer height) were set up as 60 mm/s, 10 mm/s, 0.35 mm, respectively. Results showed that biocomposite with [HA/Biplastic]. weight ratio of 60/40(w/w) has an optimum tensile (3.89 ± 1.26 MPa) and flexural strength (2.51 ± 0.45 MPa). Scanning electron microscope observation indicated that microstructure of specimen was influenced by the percentage of the hydroxyapatite. There was no agglomeration of HA particle within the composite.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Numerical parameter optimizations of the Ignition and Growth model for a HMX plastic bonded explosive

We present here a novel method for optimizing parameters of the Ignition and Growth (I&G) reactive flow model for high explosives. The I&G model predicts the shock initiation response of explosives subjected to dynamic loading. However, calibrating the model is a time-consuming process because it has several parameters, typically requires multiple data sets, and there are no direct correlations between parameters and data. In this study, we couple a scalable optimization algorithm to simulations of shock initiation experiments in the multi-physics code ALE3D. We develop four I&G model parameter sets for the HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane)-based explosive LX-07 [90 wt. % HMX, 10 wt. % Viton A] based on minimizing the difference between calculations and measurements at the embedded pressure gauges in 1D gun shock experiments. Our study shows that a cost function based on both shock time of arrival and pressure pulse shape demonstrates the best agreement with experimental data.

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

An extended trajectory-mechanics approach for calculating two-phase flow paths

A technique originating in quantum dynamics is used to derive a trajectory-based, semi-analytical solution for two-phase flow. The partial differential equation governing the evolution of the aqueous phase is equivalent to a family of ordinary differential equations defined along a path through the porous medium. The trajectories may be found by solving the differential equations directly or by post-processing the output of a numerical solution to the full set of governing equations. The trajectories, which differ from conventional streamlines, are found to bend downward in response to gravitational forces. The curvature is more pronounced as the dip of the porous layer containing the flow increases. Subtle changes in the relative permeability curve can lead to significant variations in the trajectories. The ordinary differential equation for the trajectory provides an expression for the travel time along the path. The expression produces a semi-analytical approximation to the model parameter sensitivities, the partial derivatives of the travel times with respect to changes in the permeability model. The semi-analytical trajectory-based sensitivities generally agree with those computed using a numerical reservoir simulator and a perturbation approach. The sensitivities are useful in tomographic imaging algorithms designed to estimate the spatial variation in permeability within a porous medium using multiphase observations.

58 GEOSCIENCES↗
DOE OSTI2023

TeraChem protocol buffers ( TCPB ): Accelerating QM and QM/MM simulations with a client–server model

The routine use of electronic structures in many chemical simulation applications calls for efficient and easy ways to access electronic structure programs. Here, we describe how the graphics processing unit (GPU) accelerated electronic structure program TeraChem can be set up as an electronic structure server, to be easily accessed by third-party client programs. We exploit Google’s protocol buffer framework for data serialization and communication. The client interface, called TeraChem protocol buffers (TCPB), has been designed for ease of use and compatibility with multiple programming languages, such as C++, Fortran, and Python. To demonstrate the ease of coupling third-party programs with electronic structures using TCPB, we have incorporated the TCPB client into Amber for quantum mechanics/molecular mechanics (QM/MM) simulations. The TCPB interface saves time with GPU initialization and I/O operations, achieving a speedup of more than 2× compared to a prior file-based implementation for a QM region with ~250 basis functions. We demonstrate the practical application of TCPB by computing the free energy profile of p-hydroxybenzylidene-2,3-dimethylimidazolinone (p-HBDI - )—a model chromophore in green fluorescent proteins—on the first excited singlet state using Hamiltonian replica exchange for enhanced sampling. All calculations in this work have been performed with the non-commercial freely-available version of TeraChem, which is sufficient for many QM region sizes in common use.

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

Uncertainty Analysis in 3D Equilibrium Reconstruction

Reconstruction is an inverse process where a parameter space is searched to locate a set of parameters with the highest probability of describing experimental observations. Due to systematic errors and uncertainty in experimental measurements, this optimal set of parameters will contain some associated uncertainty. This uncertainty in the optimal parameters leads to uncertainty in models derived using those parameters. V3FIT is a three-dimensional (3D) equilibrium reconstruction code that propagates uncertainty from the input signals, to the reconstructed parameters, and to the final model. Here in this paper, we describe the methods used to propagate uncertainty in V3FIT. Using the results of whole shot 3D equilibrium reconstruction of the Compact Toroidal Hybrid, this propagated uncertainty is validated against the random variation in the resulting parameters. Two different model parameterizations demonstrate how the uncertainty propagation can indicate the quality of a reconstruction. As a proxy for random sampling, the whole shot reconstruction results in a time interval that will be used to validate the propagated uncertainty from a single time slice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Dynamic mode decomposition for gyrokinetic eigenmode analysis

Dynamic mode decomposition (DMD) is a post-processing approach to decompose a complex time series into a set of modes via spectral analysis. DMD provides a new and powerful method to recover gyrokinetic drift-wave eigenfrequencies and eigenfunctions based only on the solution of the gyrokinetic-Maxwell initial value problem with almost no added cost to the initial value solver. In the present paper, DMD is applied to the CGYRO gyrokinetic code using a newly-developed CGYRO-DMD post-processor. CGYRO-DMD is numerically efficient, even on a single CPU. It does not set any restrictions on the plasma shape, beta (ratio of the plasma pressure to the magnetic field pressure), collisionality or number of species, and allows one to resolve numerous eigenmodes, even of comparable growth rates. In addition, DMD is not limited to unstable modes, but rather can capture stable and unstable branches simultaneously. In this work, we illustrate the accuracy of DMD through gyrokinetic analysis of mode transition for electromagnetic drift wave instabilities.

drift-wave eigenmodes↗
DOE OSTI2025

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗
DOE OSTI2026

Simultaneous global and local clustering in multiplex networks with covariate information

Understanding both global and layer-specific group structures is useful for uncovering complex patterns in networks with multiple interaction types. In this work, we introduce a new model, the hierarchical multiplex stochastic blockmodel, which simultaneously detects communities within individual layers of a multiplex network while inferring a global node clustering across the layers. A stochastic blockmodel is assumed in each layer, with probabilities of layer-level group memberships determined by a node’s global group assignment. Our model uses a Bayesian framework, employing a probit stick-breaking process to construct node-specific mixing proportions over a set of shared Griffiths–Engen–McCloseky distributions. These proportions determine layer-level community assignment, allowing for an unknown and varying number of groups across layers, while incorporating nodal covariate information to inform the global clustering. We propose a scalable variational inference procedure with parallelisable updates for application to large networks. Extensive simulation studies demonstrate our model’s ability to accurately recover both global and layer-level clusters in complicated settings, and applications to real data showcase the model’s effectiveness in uncovering interesting latent network structure.

community detection↗
DOE OSTI2022

Model-independent constraints on Ωm and H ( z ) from the link between geometry and growth

ABSTRACT We constrain the expansion history of the Universe and the cosmological matter density fraction in a model-independent way by exclusively making use of the relationship between background and perturbations under a minimal set of assumptions. We do so by employing a Gaussian process to model the expansion history of the Universe from present time to the recombination era. The expansion history and the cosmological matter density are then constrained using recent measurements from cosmic chronometers, Type-Ia supernovae, baryon acoustic oscillations, and redshift-space distortion data. Our results show that the evolution in the reconstructed expansion history is compatible with the Planck 2018 prediction at all redshifts. The current data considered in this study can constrain a Gaussian process on H(z) to an average $9.4 {{\ \rm per\ cent}}$ precision across redshift. We find Ωm = 0.224 ± 0.066, lower but statistically compatible with the Planck 2018 cosmology. Finally, the combination of future DESI measurements with the CMB measurement considered in this work holds the promise of $8 {{\ \rm per\ cent}}$ average constraints on a model-independent expansion history as well as a five-fold tighter Ωm constraint using the methodology developed in this work.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Discovery of a quasar with double-peaked broad balmer emission lines

ABSTRACT Most massive galaxies contain a supermassive black hole (SMBH) at their centre. When galaxies merge, their SMBHs sink to the centre of the new galaxy, where they are thought to eventually merge. During this process, an SMBH binary is formed. The presence of two sets of broad emission lines in the optical spectrum of an active galactic nucleus (AGN) has been interpreted as evidence for two broad-line regions (BLRs), one surrounding each SMBH in a binary. We modelled the broad Balmer emission lines in the SDSS spectra of 373 extreme variability AGNs using one broad and several narrow Gaussian components. We report on the discovery of SDSS J021647.53 − 011341.5 (hereafter J0216) as a double-peaked broad emission line source. Among the 373 AGNs, there were five sources that are known as double-peaked emission line sources. Three of these have been reported as candidate SMBH binaries in previous studies. We present all six objects and their double-peaked broad Balmer emission lines, and discuss the implications for a tidal disruption event (TDE) interpretation of the extreme variability, assuming the double-peaked sources are SMBH binaries.

Terwel, Jacco H. (ORCID:0000000198343439)↗
DOE OSTI2026

Detector alignment for X-ray crystallography using Millepede-II

I describe a method for accurately refining the geometrical parameters of segmented X-ray area detectors on the basis of serial crystallography data, using 'Millepede' – an algorithm created for a very similar problem in high-energy physics. The Millepede method for serial crystallography builds on the approach of Brewster et al. [Acta Cryst. (2018), D74, 877–894], in which the detector parameters are refined simultaneously with the parameters for each individual crystal. This accounts for the mutual dependency between the parameters and thereby avoids the bias and slow convergence problems that have afflicted older approaches in which the deviations between observed and calculated Bragg peak positions were taken directly as the updates for the detector panel positions. The Millepede method uses the special structure of the least-squares normal equations to reduce them to a much smaller form that can be solved very quickly, even compared with the sparse matrix methods used previously. This makes it practical to refine the detector geometry frequently and thereby maintain accurate calibration without specialized alignment campaigns. Tilts of detector panels out of the plane can be reliably refined, as can the overall distance of the detector in the beam direction. With a simulated test case, the new method produced panel shifts within 7% of the correct values with only one iteration, and produced almost exactly correct shifts after a second iteration. A simulated out-of-plane panel rotation was correctly determined to within 0.001°. Applied to experimental data from an X-ray free-electron laser, the method increased the indexable fraction of frames from 30% to 91% in a single iteration, and to 96% after two further iterations. Computing the geometry updates on the basis of 2060 crystals took only 0.819 s on desktop computing hardware, including the time taken to read the required data from disk. The scaling was found to be very close to linear for up to 100 980 sets of crystal parameters, which took only 78.2 s to process under the same conditions. The method has been applied as part of a real-time feedback system at a synchrotron radiation beamline, in which an out-of-plane detector tilt of 0.04° was detected and corrected. Possible further applications are also described here.

Millepede-II↗
DOE OSTI2018

A convolutional neural network-based screening tool for X-ray serial crystallography

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Autonomous Wireless Technology Detection in Seamless IoT Applications

The ever-increasing use of Internet of Things (IoT) devices results in the implementation of multiple wireless technologies that would not only cater their data rate requirements but also support various applications. To optimize the energy efficiency and security of the wireless transmission, it is imperative to identify the wireless technologies in various IoT implementations. Many of the existing approaches are based on measuring only the receiving signal strength indicator (RSSI). However, such approaches may not work well because of transmit power control and complex channel variations among different wireless technologies. In this article, we propose an autonomous wireless detection scheme that considers multiple distinguishable physical (PHY)-layer settings for real-time identification of wireless technologies for real-time applications. Specifically, the proposed scheme relies on the PHY-layer measurements of the targeted spectrum. Transmission settings, such as bandwidth, carrier frequency, and RSSI are estimated from the raw in-phase and quadrature-phase (I/Q) measurements. In addition, a symbol-level extraction scheme is implemented to extract unique features of modulation settings. These aforementioned features are applied to a machine learning process to identify the received wireless technologies. Compared with raw I/Q measurements, the extracted features are much simplified and, thus, the machine learning classifier can be designed with a simple structure for fast processing on IoT nodes. Finally, the proposed schemes are primarily evaluated theoretically, followed by implementing them on a USRP software-defined radio (SDR)-based hardware testbed. The evaluation results demonstrate high accuracy in the real-time detection of different wireless technologies for seamless IoT applications.

42 ENGINEERING↗
DOE OSTI2023

Immunity Study: Port Impedance Measurement of PMU and PCI testing under EMP

With the increased requirements of real-time grid monitoring, disturbance location, and situation awareness, Phasor Measurement Units (PMUs) have become more critical for the Wide Area Measurement System (WAMS). However, the vulnerability of PMUs has not been well studied, especially under electromagnetic pulse (EMP) scenarios. The stable operation of the power system will be affected directly once EMP damages them. Therefore, studying their immunity to EMP events is urgent and necessary. In this paper, the effective impedance measurement scheme and pulsed current injection (PCI) testing are proposed for the port impedance measurement and immunity levels of PMUs. The equivalent non-uniform transmission line model is established to eliminate the impact of the fixture in the de-embedding process. Then, the circuit of the pulsed current generator is set to generate a damping sinusoid, and the double exponential wave is applied to the port. Finally, using measured impedance as a generator load, the voltage and current responses of different ports are calculated in the PCI testing simulation. In conclusion, results reveal the characteristics of port impedance, waveforms of voltage and current, and distribution of accumulative energy. The relation between port impedance and the waveforms is discussed.

42 ENGINEERING↗