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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Production of 28 μm zirconium carbide kernels using the internal gelation process and microfluidics

Studies using internal gelation and microfluidics have shown that the production of sintered spheres with diameters below 200 mm is possible. The addition of carbon can be problematic because of slow flow rates (100 mL/min) and long run times (4 h). However, we report a solution of dispersed carbon, Cabot’s TPX-101, was successfully used with solutions of zirconyl nitrate, urea, and hexamethylenetetramine to make zirconium microspheres with carbon. A simple carbothermic reduction using a maximum temperature of 2073 K and ultrahigh purity argon produced zirconium carbide kernels with an average diameter of 28 mm and a standard deviation of 2.3 mm.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Kernel fusion in atomistic spin dynamics simulations on Nvidia GPUs using tensor core

In atomistic spin dynamics simulations, the time cost of constructing the space- and time-displaced pair correlation function in real space increases quadratically as the number of spins N, leading to significant computational effort. The GEMM subroutine can be adopted to accelerate the calculation of the dynamical spin-spin correlation function, but the computational cost of simulating large spin systems (>40000 spins) on CPUs remains expensive. In this work, we perform the simulation on the graphics processing unit (GPU), a hardware solution widely used as an accelerator for scientific computing and deep learning. Here we show that GPUs can accelerate the simulation up to 25-fold compared to multi-core CPUs when using the GEMM subroutine on both. To hide memory latency, we fuse the element-wise operation into the GEMM kernel using CUTLASS that can improve the performance by 26% ~ 33% compared to implementation based on cuBLAS. Furthermore, we perform the on-the-fly calculation in the epilogue of the GEMM subroutine to avoid saving intermediate results on global memory, which makes the large-scale atomistic spin dynamics simulation feasible and affordable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Structural Characterization of Lignin from Maize ( Zea mays L.) Fibers: Evidence for Diferuloylputrescine Incorporated into the Lignin Polymer in Maize Kernels

The structure of the phenolic polymer in maize grain fibers, with 5.5% Klason lignin content, has been studied. For this, the milled wood lignin (MWL) and dioxane lignin (DL) preparations were isolated and analyzed. The data indicated that the lignin in maize fibers was syringyl rich, mostly involved in β-aryl ether, resinol, and phenylcoumaran substructures. 2D NMR and derivatization followed by reductive cleavage (DFRC) also revealed the occurrence of associated ferulates together with trace amounts of p-coumarates acylating the γ-OH of lignin side chains, predominantly on S-lignin units. More interesting was the occurrence of diferuloylputrescine, a ferulic acid amide, which was identified by 2D NMR and comparison with a synthesized standard, that was apparently incorporated into this lignin. A phenylcoumaran structure involving a diferuloylputrescine coupled through 8–5' linkages to another diferuloylputrescine (or to a ferulate or a guaiacyl lignin unit) was found, providing compelling evidence for its participation in radical coupling reactions. The occurrence of diferuloylputrescine in cell walls of maize kernels and other cereal grains appears to have been missed in previous works, perhaps due to the alkaline hydrolysis commonly used for composition studies.

2D NMR↗
DOE OSTI2021

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. Overall, this analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Heat kernel asymptotics for magnetic Schrödinger operators

We explicitly construct parametrices for magnetic Schrödinger operators on R{sup d} and prove that they provide a complete small-t expansion for the corresponding heat kernel, both on and off the diagonal.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Para-hydrogen and helium cluster size distributions in free jet expansions based on Smoluchowski theory with kernel scaling

The size distribution of para-H{sub 2} (pH{sub 2}) clusters produced in free jet expansions at a source temperature of T{sub 0} = 29.5 K and pressures of P{sub 0} = 0.9–1.96 bars is reported and analyzed according to a cluster growth model based on the Smoluchowski theory with kernel scaling. Good overall agreement is found between the measured and predicted, N{sub k} = A k{sup a} e{sup −bk}, shape of the distribution. The fit yields values for A and b for values of a derived from simple collision models. The small remaining deviations between measured abundances and theory imply a (pH{sub 2}){sub k} magic number cluster of k = 13 as has been observed previously by Raman spectroscopy. The predicted linear dependence of b{sup −(a+1)} on source gas pressure was verified and used to determine the value of the basic effective agglomeration reaction rate constant. A comparison of the corresponding effective growth cross sections σ{sub 11} with results from a similar analysis of He cluster size distributions indicates that the latter are much larger by a factor 6-10. An analysis of the three body recombination rates, the geometric sizes and the fact that the He clusters are liquid independent of their size can explain the larger cross sections found for He.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Decomposition of the polynomial kernel of arbitrary higher spin Dirac operators

In a series of recent papers, we have introduced higher spin Dirac operators, which are generalisations of the classical Dirac operator. Whereas the latter acts on spinor-valued functions, the former acts on functions taking values in arbitrary irreducible half-integer highest weight representations for the spin group. In this paper, we describe how the polynomial kernel spaces of such operators decompose in irreducible representations of the spin group. We will hereby make use of results from representation theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Kernel Density Estimation of Reaction Rates in Neutron Transport Simulations of Nuclear Reactors

Kernel density estimators (KDEs) are applied to estimate neutron scalar flux and reaction rate densities in Monte Carlo neutron transport simulations of heterogeneous nuclear reactors in continuous energy. The mean free path (MFP) KDE is introduced in order to handle the issues that arise from estimating the discontinuous reaction rate densities at material interfaces. Results show the MFP KDE is more accurate at estimating reaction rates compared with previous KDE formulations. An approximate MFP (aMFP) KDE is introduced to circumvent several practical issues presented by the MFP KDE. A volume-averaged KDE is derived and used to determine the bias introduced by the aMFP KDE. A KDE is formulated for cylindrical coordinates to better represent the geometry and capture the physics in two-dimensional reactor physics problems. The results here indicate that the cylindrical MFP KDE and cylindrical aMFP KDE are accurate tools for capturing reaction rates in heterogeneous reactor physics problems in continuous energy, with local biases of less than 1%.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

OPTICAL SPECTRAL OBSERVATIONS OF A FLICKERING WHITE-LIGHT KERNEL IN A C1 SOLAR FLARE

We analyze optical spectra of a two-ribbon, long-duration C1.1 flare that occurred on 2011 August 18 within AR 11271 (SOL2011-08-18T15:15). The impulsive phase of the flare was observed with a comprehensive set of space-borne and ground-based instruments, which provide a range of unique diagnostics of the lower flaring atmosphere. Here we report the detection of enhanced continuum emission, observed in low-resolution spectra from 3600 Å to 4550 Å acquired with the Horizontal Spectrograph at the Dunn Solar Telescope. A small, ≤0.''5 (10{sup 15} cm{sup 2}) penumbral/umbral kernel brightens repeatedly in the optical continuum and chromospheric emission lines, similar to the temporal characteristics of the hard X-ray variation as detected by the Gamma-ray Burst Monitor on the Fermi spacecraft. Radiative-hydrodynamic flare models that employ a nonthermal electron beam energy flux high enough to produce the optical contrast in our flare spectra would predict a large Balmer jump in emission, indicative of hydrogen recombination radiation from the upper flare chromosphere. However, we find no evidence of such a Balmer jump in the bluemost spectral region of the continuum excess. Just redward of the expected Balmer jump, we find evidence of a ''blue continuum bump'' in the excess emission which may be indicative of the merging of the higher order Balmer lines. The large number of observational constraints provides a springboard for modeling the blue/optical emission for this particular flare with radiative-hydrodynamic codes, which are necessary to understand the opacity effects for the continuum and emission line radiation at these wavelengths.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Deep kernel methods learn better: from cards to process optimization

Abstract The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators

Dilute combustion using exhaust gas recirculation (EGR) presents a cost-effective method for increasing the efficiency of spark-ignition (SI) engines. However, the maximum amount of EGR that can be used at a given condition is limited by a rapid increment of cycle-to-cycle variability (CCV). This study describes a methodology to design a model-based stochastic optimal controller to adjust the cycle-to-cycle fuel injection quantity in order to reduce CCV and further extend the dilute limit. Given the complexity and chaotic nature of combustion events, the controller was enhanced with online learning in order to identify the statistical properties of combustion efficiency, which are needed to generate predictions for next-cycle events. This study showed that a kernel density estimator (KDE) can be used to learn the combustion properties in real time and can be incorporated into the feedback policy in order to calculate the optimal control command. Experimental results suggested that the dilute limit can be extended from 18.5% to 21% EGR fraction at an operating condition relevant for highway cruising. Additionally, the proposed controller can achieve a large CCV reduction with less fuel enrichment compared to previous methods, overall contributing to an increase in 0.2% indicated fuel conversion efficiency.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Cyber Spoofing Detection for Grid Distributed Synchrophasor Using Dynamic dual-Kernel SVM

Cyber spoofing with distributed synchrophasor adversely affects the decision-making and situational awareness of the power grid. To detect the spoofing trail, this letter proposes a composite signature-based cyber spoofing detection methodology. The intrinsic principal modes are first extracted from the distributed synchrophasor data. Then, multiple signatures of different intrinsic model components are derived to quantify the spoofing. Thereafter, the dynamic dual-kernel support vector machine is proposed to identify cyber spoofing using multiple signatures. Multiple experimental results using six spoofing methods have verified the validity of the methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

SU-F-T-450: The Investigation of Radiotherapy Quality Assurance and Automatic Treatment Planning Based On the Kernel Density Estimation Method

Purpose: To develop a fast automatic algorithm based on the two dimensional kernel density estimation (2D KDE) to predict the dose-volume histogram (DVH) which can be employed for the investigation of radiotherapy quality assurance and automatic treatment planning. Methods: We propose a machine learning method that uses previous treatment plans to predict the DVH. The key to the approach is the framing of DVH in a probabilistic setting. The training consists of estimating, from the patients in the training set, the joint probability distribution of the dose and the predictive features. The joint distribution provides an estimation of the conditional probability of the dose given the values of the predictive features. For the new patient, the prediction consists of estimating the distribution of the predictive features and marginalizing the conditional probability from the training over this. Integrating the resulting probability distribution for the dose yields an estimation of the DVH. The 2D KDE is implemented to predict the joint probability distribution of the training set and the distribution of the predictive features for the new patient. Two variables, including the signed minimal distance from each OAR (organs at risk) voxel to the target boundary and its opening angle with respect to the origin of voxel coordinate, are considered as the predictive features to represent the OAR-target spatial relationship. The feasibility of our method has been demonstrated with the rectum, breast and head-and-neck cancer cases by comparing the predicted DVHs with the planned ones. Results: The consistent result has been found between these two DVHs for each cancer and the average of relative point-wise differences is about 5% within the clinical acceptable extent. Conclusion: According to the result of this study, our method can be used to predict the clinical acceptable DVH and has ability to evaluate the quality and consistency of the treatment planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

A Hybrid Index for Characterizing Drought Based on a Nonparametric Kernel Estimator

This study develops a nonparametric multivariate drought index, namely, the nonparametric multivariate standardized drought index (NMSDI), by considering the variations of both precipitation and streamflow. Building upon previous efforts in constructing nonparametric multivariate drought index, the nonparametric kernel estimator is used to derive the joint distribution of precipitation and streamflow, thus providing additional insights into drought-index development. The proposed NMSDI is applied in the Wei River basin (WRB), on the basis of which the drought-evolution characteristics are investigated. Three main results were found: 1) In general, NMSDI captures drought onset in a way that is similar to that of the standardized precipitation index and captures drought termination and persistence in a way that is similar to that of the standardized streamflow index. We report the drought events identified by NMSDI match well with historical drought records in the WRB. Performance is also consistent with that of an existing multivariate standardized drought index at various time scales, confirming the validity of the newly constructed NMSDI in drought detections. 2) An increasing risk of drought has been detected for past decades and will persist to a certain extent in the future in most areas of the WRB. 3) The identified changepoints of annual NMSDI are mainly concentrated in the early 1970s and mid-1990s, coincident with extensive water use and soil conservation practices. In summary, this study highlights a nonparametric multivariable drought index that can efficiently and comprehensively be used for drought detections and predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

AGR-5/6/7 LEUCO Kernel Fabrication Readiness Review

In preparation for forming low-enriched uranium carbide/oxide (LEUCO) fuel kernels for the Advanced Gas Reactor (AGR) fuel development and qualification program, Idaho National Laboratory conducted an operational readiness review of the Babcock & Wilcox Nuclear Operations Group – Lynchburg (B&W NOG-L) procedures, processes, and equipment from January 14 – January 16, 2015. The readiness review focused on requirements taken from the American Society Mechanical Engineers (ASME) Nuclear Quality Assurance Standard (NQA-1-2008, 1a-2009), a recent occurrence at the B&W NOG-L facility related to preparation of acid-deficient uranyl nitrate solution (ADUN), and a relook at concerns noted in a previous review. Topic areas open for the review were communicated to B&W NOG-L in advance of the on-site visit to facilitate the collection of objective evidences attesting to the state of readiness.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Kernel Density Estimators for SAMPLER Analyses [Slides]

This presentation discusses Sampler, specifically in terms of uncertainty quantification and states that for many simulation tasks nominal input parameters have complicated influence on the uncertainty in the final result. Sampler provides a convenient means to perform stochastic uncertainty quantification, brute force sampling parameter space and tallying changes in outputs, and it can be coupled with any SCALE modules. Perturbed responses can be collected and analyzed with standard statistical measures; however, two problems have been identified. The first problem is that for Monte Carlo simulations the individual response uncertainties are ignored, and the second problem is that features of distributions are easily hidden in histograms. They found that the solution is to add Kernel Density Estimator (KDE) analysis to the Sampler output. In conclusion, Sampler is a unique tool for stochastic uncertainty propagation, and it can be coupled with any module in SCALE. Current functionality is limited to histograms, and it may not adequately account for sample uncertainties and/or not adequately display underlying distribution features. Work is in progress to implement KDE in the Sampler module and no additional flagging is needed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Accelerated Monte Carlo Fission Source Convergence with Fission Matrix and Kernel Density Estimators

Accurate solution of the k-eigenvalue form of the Boltzmann radiation transport equation is required in many application areas, including nuclear reactor analysis and criticality safety. Monte Carlo calculations are free of the discretization errors inherent to deterministic methods and therefore offer the most accurate solutions. However, converging the fission source can be difficult for many problems, resulting in either a substantial computational overhead or potential inaccuracies that negate many of the benefits of using Monte Carlo methods. Difficulty in converging the fission source occurs for two distinct reasons. The first is the existence of a high dominance ratio in the problem (i.e. the largest and second largest eigenvalues have approximately the same magnitude). The second difficulty arises due to undersampling of the fission source because an insufficient number of particle histories are simulated. The first difficulty is shared by deterministic solvers when using power iteration, while the second difficulty is unique to Monte Carlo solvers. In practice, both difficulties may be present in the same problem, compounding the difficulty in achieving a converged distribution. In this paper, we consider a new approach to accelerate the convergence of the fission source based on combining the use of fission matrix methods with kernel density estimators (KDEs). It has been observed that deterministic fission matrix methods show the potential to accelerate the convergence of the fission source in Monte Carlo criticality calculations but may introduce significant additional noise for difficult problems. In this paper, we have demonstrated the use of a KDE approach in combination with the fission matrix acceleration. Although the addition of the KDE initially slows the convergence of the fission matrix acceleration, it decreases the magnitude of the noise while still offering significantly improved convergence relative to analog Monte Carlo. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Development of a transient signal validation technique via a modified kernel regression model - 365

The demand for robust and resilient performance has led to the use of online-monitoring and signal validation techniques to monitor the process parameters and equipment conditions using several empirical models. One of these models is kernel regression (KR). Although KR is being used and performed excellently when applied to steady state, it has limitation in time-varying data that has several repetition of the same data point of the signals, especially if those signals are used to infer the other signals. In addition, many situations are related to variation and fluctuation during normal states, such as transient - start-up and shutdown mode. However, accurate estimation of the process signal can lead to the proper understanding of the equipment behaviours as well as enhance the online monitoring applications. In the sight of foregoing, this paper proposed a modified KR model, especially for transient signal validation to resolve the setback of conventional KR by improving the measure of similarities in the conventional KR. The performance of the proposed model is verified using both experimental data and the plant simulation data, and its results are compared with that of conventional KR. The results show that the proposed model has a greater robustness in particular situations compared to conventional KR, and it can be used to improve process and equipment monitoring applications. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗