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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Sequential Monte Carlo for Cut-Bayesian Posterior Computation

We propose a sequential Monte Carlo (SMC) method to efficiently and accurately compute cut-Bayesian posterior quantities of interest, variations of standard Bayesian approaches constructed primarily to account for model misspecification. We prove finite sample concentration bounds for estimators derived from the proposed method along with a linear tempering extension and apply these results to a realistic setting where a computer model is misspecified. We then illustrate the SMC method for inference in a modular chemical reactor example that includes submodels for reaction kinetics, turbulence, mass transfer, and diffusion. The samples obtained are commensurate with a direct-sampling approach that consists of running multiple Markov chains, with computational efficiency gains using the SMC method. Overall, the SMC method presented yields a novel, rigorous approach to computing with cut-Bayesian posterior distributions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1971

Computational requirements for a discrete Kalman filter.

Computational requirements - i.e., computing time per cycle (iteration) and required storage - which determine minimum sampling rates and computer memory size, were obtained as functions of the dimensions of the important system matrices for a discrete Kalman filter. Two types of measurement processing are discussed: simultaneous and sequential. It is shown that it is often better to process statistically independent measurements in more than one batch and then use sequential processing than to process them together via simultaneous processing.

Mendel, J. M.↗
NASA NTRS1979

Orbit/attitude estimation with Landsat-1 and -2 landmark data

The accuracy of spacecraft orbit and attitude determination is investigated using Landsat-1 and -2 picture data. The data consists of ground control points, of known geodetic coordinates, which are identified on a series of Landsat pictures. Both sequential (extended Kalman filter) and batch (nonlinear Bayesian weighted least-squares) estimation techniques have been implemented in two software systems. Discussion of these systems includes a system overview, mathematical formulation, and sample numerical results. Particular emphasis is given to the application of a spectral analysis technique to derive spacecraft attitude models.

Hall, D. L.↗
DOE OSTI2017

Acquisition of STEM Images by Adaptive Compressive Sensing

Compressive Sensing (CS) allows a signal to be sparsely measured first and accurately recovered later in software [1]. In scanning transmission electron microscopy (STEM), it is possible to compress an image spatially by reducing the number of measured pixels, which decreases electron dose and increases sensing speed [2,3,4]. The two requirements for CS to work are: (1) sparsity of basis coefficients and (2) incoherence of the sensing system and the representation system. However, when pixels are missing from the image, it is difficult to have an incoherent sensing matrix. Nevertheless, dictionary learning techniques such as Beta-Process Factor Analysis (BPFA) [5] are able to simultaneously discover a basis and the sparse coefficients in the case of missing pixels. On top of CS, we would like to apply active learning [6,7] to further reduce the proportion of pixels being measured, while maintaining image reconstruction quality. Suppose we initially sample 10% of random pixels. We wish to select the next 1% of pixels that are most useful in recovering the image. Now, we have 11% of pixels, and we want to decide the next 1% of “most informative” pixels. Active learning methods are online and sequential in nature. Our goal is to adaptively discover the best sensing mask during acquisition using feedback about the structures in the image. In the end, we hope to recover a high quality reconstruction with a dose reduction relative to the non-adaptive (random) sensing scheme. In doing this, we try three metrics applied to the partial reconstructions for selecting the new set of pixels: (1) variance, (2) Kullback-Leibler (KL) divergence using a Radial Basis Function (RBF) kernel, and (3) entropy. Figs. 1 and 2 display the comparison of Peak Signal-to-Noise (PSNR) using these three different active learning methods at different percentages of sampled pixels. At 20% level, all the three active learning methods underperform the original CS without active learning. However, they all beat the original CS as more of the “most informative” pixels are sampled. One can also argue that CS equipped with active learning requires less sampled pixels to achieve the same value of PSNR than CS with pixels randomly sampled, since all the three PSNR curves with active learning grow at a faster pace than that without active learning. For this particular STEM image, by observing the reconstructed images and the sensing masks, we find that while the method based on RBF kernel acquires samples more uniformly, the one on entropy samples more areas of significant change, thus less uniformly. The KL-divergence method performs the best in terms of reconstruction error (PSNR) for this example [8].

Xie, Weiyi↗
DOE OSTI2019

TiO{sub 2} Photocatalyzed Oxidation of Drugs Studied by Laser Ablation Electrospray Ionization Mass Spectrometry

In drug discovery, it is important to identify phase I metabolic modifications as early as possible to screen for inactivation of drugs and/or activation of prodrugs. As the major class of reactions in phase I metabolism is oxidation reactions, oxidation of drugs with TiO{sub 2} photocatalysis can be used as a simple non-biological method to initially eliminate (pro)drug candidates with an undesired phase I oxidation metabolism. Analysis of reaction products is commonly achieved with mass spectrometry coupled to chromatography. However, sample throughput can be substantially increased by eliminating pretreatment steps and exploiting the potential of ambient ionization mass spectrometry (MS). Furthermore, online monitoring of reactions in a time-resolved way would identify sequential modification steps. Here, we introduce a novel (time-resolved) TiO{sub 2}-photocatalysis laser ablation electrospray ionization (LAESI) MS method for the analysis of drug candidates. This method was proven to be compatible with both TiO{sub 2}-coated glass slides as well as solutions containing suspended TiO{sub 2} nanoparticles, and the results were in excellent agreement with studies on biological oxidation of verapamil, buspirone, testosterone, andarine, and ostarine. Finally, a time-resolved LAESI MS setup was developed and initial results for verapamil showed excellent analytical stability for online photocatalyzed oxidation reactions within the set-up up to at least 1 h. .

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

High Thermoelectric Performance in Copper Telluride

Recently, Cu 2-δ S and Cu 2-δ Se were reported to have an ultralow thermal conductivity and high thermoelectric figure of merit zT. Thus, as a member of the copper chalcogenide group, Cu 2-δ Te is expected to possess superior zTs because Te is less ionic and heavy. However, the zT value is low in the Cu 2 Te sintered using spark plasma sintering, which is typically used to fabricate high-density bulk samples. In addition, the extra sintering processes may change the samples’ compositions as well as their physical properties, especially for Cu 2 Te, which has many stable and meta-stable phases as well as weaker ionic bonding between Cu and Te as compared with Cu 2 S and Cu 2 Se. In this study, high-density Cu 2 Te samples were obtained using direct annealing without a sintering process. In the absence of sintering processes, the samples’ compositions could be well controlled, leading to substantially reduced carrier concentrations that are close to the optimal value. The electrical transports were optimized, and the thermal conductivity was considerably reduced. The zT values were significantly improved—to 1.1 at 1000 K—which is nearly 100% improvement. Furthermore, this method saves substantial time and cost during the sample’s growth. The study demonstrates that Cu 2-δ X (X=S, Se and Te) is the only existing system to show high zTs in the series of compounds composed of three sequential primary group elements.

14 SOLAR ENERGY↗
NASA NTRS1997

Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Maghami, Peiman G.↗
NASA NTRS1998

Design of Neural Networks for Fast Convergence and Accuracy

A novel procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed to provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component spacecraft design changes and measures of its performance. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The design algorithm attempts to avoid the local minima phenomenon that hampers the traditional network training. A numerical example is performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Maghami, Peiman G.↗
NASA NTRSDate not supplied

Ignition of Metals in Heated Supersonic Particle Impact with Inert Particulate

The conventional understanding of particle impact ignition characteristic elements relies on the following principles: 1) flammable particulate (except in the case of titanium and aluminum target materials); 2) gas velocity greater than 100 ft/s; 3) Impact point ranging from 45° to perpendicular to the path of the particle [1]; 4) flammable target material. To test assumptions about particulate flammability being necessary for particle impact ignition of less reactive target materials, such as Inconel 718, testing was performed with inert particulate. Supersonic particle impact testing was conducted with heated gas and 1500-µm sapphire particulate on various target materials: 304 stainless steel, wrought and selective laser melted Inconel 718, and Monel 400. Posttest characterization of impact craters was performed on each type of metallic target sample, including surface characterization performed by light optical microscopy and scanning electron microscopy with energy dispersive spectroscopy. Full consumption of the stainless steel and wrought Inconel 718 targets were noted to occur with sequential impacts. The results of testing will be discussed in detail, as well as recommendations for updating the understanding of the particle impact ignition mechanism.

Particle Impact↗
DOE OSTI2017

Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

Subsurface applications, including geothermal, geological carbon sequestration, and oil and gas, typically involve maximizing either the extraction of energy or the storage of fluids. Fractures form the main pathways for flow in these systems, and locating these fractures is critical for predicting flow. However, fracture characterization is a highly uncertain process, and data from multiple sources, such as flow and geophysical are needed to reduce this uncertainty. We present a nonintrusive, sequential inversion framework for integrating data from geophysical and flow sources to constrain fracture networks in the subsurface. In this framework, we first estimate bounds on the statistics for the fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained using flow data. In conclusion, the efficacy of this inversion is demonstrated through a representative example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Reconstruction Techniques for Sparse Multistatic Linear Array Microwave Imaging

Sequentially-switched linear arrays are an enabling technology for a number of near-field microwave imaging applications. Electronically sequencing along the array axis followed by mechanical scanning along an orthogonal axis allows dense sampling of a two-dimensional aperture in near real-time. In this paper, a sparse multi-static array technique will be described along with associated Fourier-Transform-based and back-projection-based image reconstruction algorithms. Simulated and measured imaging results are presented that show the effectiveness of the sparse array technique along with the merits and weaknesses of each image reconstruction approach.

concealed-weapon detection↗
NASA NTRS1979

Chemical processes involved in the initiation of hot corrosion of B-1900 and NASA-TRW VIA

Sodium surface-induced hot corrosion of B-1900 and NASA-TRW VIA alloys at 900 C has been studied, with special attention to the chemical reactions during and immediately after the induction period. Thermogravimetric tests were run and data were obtained by chemical analysis of water soluble metal salts and of residual sulfate. Surface analyses of hot corroded samples were obtained by spectroscopic techniques (ESCA). A chemical mechanism for elucidating Na2SO4-induced hot corrosion is proposed indicating that hot corrosion is initiated by basic fluxing of the protective Al2O3 scale. The sequential, catastrophic corrosion results from molybdenum content. The self-sustaining feature is a consequence of the cyclic nature of the acidic fluxing. It is believed that the mechanism is applicable not only to laboratory results, but also to the practical problem of hot corrosion encountered in gas turbine engines.

Fryburg, G. C.↗
DOE OSTI2020

Application of end-of-shift respirable crystalline silica monitoring to construction

A pilot project was conducted to determine the effect of common construction dusts as interferences in a new portable end-of-shift (EoS), direct-on-filter (DoF) sampling and analysis method for respirable crystalline silica (RCS), in this case, quartz. Construction dusts were prepared from plaster, drywall, cement and brick by grinding, aerosolizing, and collecting respirable dust with high flow rate cyclones. Filters were loaded with different levels of commercial α-quartz powder Min-u-Sil 5, and different levels of interfering dusts, singly and in combination. Samples were analyzed by Fourier Transform Infrared Spectroscopy (FTIR). Good correlations were found between nominal quartz loading (0 µg, 25 µg, 50 µg, and 100 µg) adjusted for quartz in the interfering dust and FTIR absorbance alone and in the presence of all interfering dusts. The slopes of the correlations were similar whether the loading was quartz without interference, or with plaster, drywall, and cement dusts, regardless of quantity. The results show that (a) plaster and drywall dusts do not interfere substantially; (b) cement does not interfere, but a change in the intercept suggests an effect on the background absorbance of the filter; and (c) in addition to having a substantial quartz content, brick dust contains an additional material, probably a silicate mineral, which interferes with the quartz peak. Thus, the presence of cement leads to lower quartz values and brick leads to higher values, but overall, 83% of the quartz contents predicted from the calibration data agreed within 50% of the adjusted nominal loadings within the range 20–110 µg. This result is encouraging given the high levels of interfering dusts. Nine samples loaded with smaller amounts of all four dusts together gave results within 25% of the adjusted nominal loadings. A single mixture addition of the dusts to the filter gave tighter variance in results than sequential additions. Unexpectedly, the two Certified Reference Materials (CRMs) 1878a and 1878b, gave different results when used to calibrate XRD analysis of Min-u-Sil 5.

42 ENGINEERING↗
DOE OSTI2021

Core-flood Effluent and Shale Surface Chemistries in Predicting Interaction between Shale, Brine, and Reactive Fluid

We report field and laboratory observations to date indicate that the efficiency of hydraulic fracturing, as it relates to hydrocarbon recovery, depends significantly on geochemical alterations to rock surfaces that diminish accessibility by partial or total plugging of the pore and fracture networks. This is caused by mineral scale deposition such as coating of fracture surfaces with precipitates, particle migration, and/or crack closure due to dissolution under stress. In reactive flow-through experiments, mineral reactions in response to acidic fluid injection significantly reduced system porosity and core permeability. The present study focuses on changes to fluid chemistry and shale surfaces (inlet and fracture walls) resulting from shale-fluid interactions and integrating these findings for an improved estimate of transport-related consequences. The reacted shale surfaces were examined by spatially-resolved scanning electron microscopy - energy dispersive spectroscopy (SEM-EDS) analysis. Importantly, inductively coupled plasma - mass spectrometry/optical emission spectroscopy (ICP-MS/OES) was utilized to probe the chemical evolution of the core-flood effluents. The three study cores selected from the Marcellus formation represent different mineralogies and structural features. In flow-through experiments, lab-generated brine and HCl-based fracture fluid (pH=2) were injected sequentially under effective stress (up to 500 psi) at reservoir temperature (80°C). SEM-EDS results confirmed by the ICP concentration trends showed significant Fe hydroxide precipitates in clay- and pyrite-rich outcrop samples due to partial oxidation of Fe-bearing phases in the case of intrusion of low salinity water-based fluids. Porosity reduction in the MSEEL (Marcellus Shale Energy and Environmental Laboratory) carbonate-rich sample is related to compaction of cores under stress due to matrix softening with substantial dissolution, and pore-filling by hydroxides, as well as barite and salts. Despite the same fluid compositions and experimental conditions used for both MSEEL samples, barite precipitation was much more intense in the MSEEL clay-rich sample due to its greater sorption capacity and additional sulfate source as well as fissile nature with multiple lengthwise cracks. ICP tests revealed time-resolved concentration trends in produced brine and reactive fluids that in turn complemented the pre-/post-reaction SEM-EDS observations.

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

A Note on the Effects of Linear Topology Preservation in Monte Carlo Simulations of Knotted Proteins

Monte Carlo simulations are a powerful technique and are widely used in different fields. When applied to complex molecular systems with long chains, such as those in synthetic polymers and proteins, they have the advantage of providing a fast and computationally efficient way to sample equilibrium ensembles and calculate thermodynamic and structural properties under desired conditions. Conformational Monte Carlo techniques employ a move set to perform the transitions in the simulation Markov chain. While accepted conformations must preserve the sequential bonding of the protein chain model and excluded volume among its units, the moves themselves may take the chain across itself. We call this a break in linear topology preservation. In this manuscript, we show, using simple protein models, that there is no difference in equilibrium properties calculated with a move set that preserves linear topology and one that does not. However, for complex structures, such as those of deeply knotted proteins, the preservation of linear topology provides correct equilibrium results but only after long relaxation. In any case, to analyze folding pathways, knotting mechanisms and folding kinetics, the preservation of linear topology may be an unavoidable requirement.

Especial, João N. C. (ORCID:0000000254765370)↗
NASA NTRS2009

Photometric Studies of Orbital Debris at GEO

Orbital debris represents a significant and increasing risk to operational spacecraft. Here we report on photometric observations made in standard BVRI filters at the Cerro Tololo Inter-American Observatory (CTIO) in an effort to determine the physical characteristics of optically faint debris at geosynchronous Earth orbit (GEO). Our sample is taken from GEO objects discovered in a survey with the University of Michigan s 0.6-m Curtis-Schmidt telescope (known as MODEST, for Michigan Orbital DEbris Survey Telescope), and then followed up in real-time with the CTIO/SMARTS 0.9-m for orbits and photometry. For a sample of 50 objects, calibrated sequences in RB- V-I-R filters have been obtained with the CTIO/SMARTS 0.9-m. For objects that do not show large brightness variations, the colors are largely redder than solar in both B-R and R-I. The width of the color distribution may be intrinsic to the nature of the surfaces, but also could imply that we are seeing irregularly shaped objects and measuring the colors at different times with just one telescope. For irregularly shaped objects tumbling at unknown orientations and rates, such sequential filter measurements using one telescope are subject to large errors for interpretation. If all observations in all filters in a particular sequence are of the same surface at the same solar and viewing angles, then the colors are meaningful. Where this is not the case, interpretation of the observed colors is impossible. For a smaller sample of objects we have observed with synchronized CCD cameras on the two telescopes. The CTIO/SMARTS 0.9-m observes in B, and the Schmidt in R. The CCD cameras are electronically linked together so that the start time and duration of observations are both the same to better than 50 milliseconds. Now the observed B-R color is a true measure of the scattered illuminated area of the debris piece for that observation.

Seitzer, Patrick↗
DOE OSTI2020

Robust Online Sequential RVFLNs for Data Modeling of Dynamic Time-Varying Systems with Application of an Ironmaking Blast Furnace

In a world where the increasing complexity of modern industrial processes brings difficulties for accurate mathematical modeling, taking advantage of data has become an efficient solution to complex dynamic process modeling issue. In this paper, we develop a novel robust online sequential version of random vector functional-link networks (RVFLNs) for data-driven modeling of dynamic time-varying system and applied it in a blast furnace (BF) ironmaking process. First, to overcome the time-varying dynamics of process and to enable the RVFLNs to learn online with avoiding data saturation, an improved online sequential version of RVFLNs (OS-RFVLNs) is first presented by online sequential learning with forgetting factor. This improved OS-RVFLNs algorithm is not only suitable for the real-time and large data transfer situation, but also can adjust the sensitivity of the algorithm to different samples with the help of the introduced forgetting factor. Second, since the output weights of the improved OS-RVFLNs as well as other RVFLNs algorithms are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with various outliers. To solve this problem, a Cauchy distribution weighted M-estimator is introduced to improve the robustness of the improved OS- RVFLNs. For this proposed robust OS-RVFLNs (R-OS- RVFLNs), since the weights of different outlier data are properly determined by the Cauchy distribution function, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Experiments using actual industrial data of BF ironmaking process and comparative studies have demonstrated that the proposed method produces a better estimation accuracy and stronger robustness than other methods.

Blast furnace (BF), Modelling, Dynamic systems↗
DOE OSTI2025

Partitioned Quantum Subspace Expansion

We present an iterative generalisation of the quantum subspace expansion algorithm used with a Krylov basis. The iterative construction connects a sequence of subspaces via their lowest energy states. Diagonalising a Hamiltonian in a given Krylov subspace requires the same quantum resources in both the single step and sequential cases. We propose a variance-based criterion for determining a good iterative sequence and provide numerical evidence that these good sequences display improved numerical stability over a single step in the presence of finite sampling noise. Implementing the generalisation requires additional classical processing with a polynomial overhead in the subspace dimension. By exchanging quantum circuit depth for additional measurements the quantum subspace expansion algorithm appears to be an approach suited to near term or early error-corrected quantum hardware. Our work suggests that the numerical instability limiting the accuracy of this approach can be substantially alleviated in a parameter-free way.

97 MATHEMATICS AND COMPUTING↗