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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 sources NASA NTRS DOE OSTI

NASA NTRS1988

Translocation of radiolabeled indole-3-acetic acid and indole-3-acetyl-myo-inositol from kernel to shoot of Zea mays L

Either 5-[3H]indole-3-acetic acid (IAA) or 5-[3H]indole-3-acetyl-myo-inositol was applied to the endosperm of kernels of dark-grown Zea mays seedlings. The distribution of total radioactivity, radiolabeled indole-3-acetic acid, and radiolabeled ester conjugated indole-3-acetic acid, in the shoots was then determined. Differences were found in the distribution and chemical form of the radiolabeled indole-3-acetic acid in the shoot depending upon whether 5-[3H]indole-3-acetic acid or 5-[3H]indole-3-acetyl-myo-inositol was applied to the endosperm. We demonstrated that indole-3-acetyl-myo-inositol applied to the endosperm provides both free and ester conjugated indole-3-acetic acid to the mesocotyl and coleoptile. Free indole-3-acetic acid applied to the endosperm supplies some of the indole-3-acetic acid in the mesocotyl but essentially no indole-3-acetic acid to the coleoptile or primary leaves. It is concluded that free IAA from the endosperm is not a source of IAA for the coleoptile. Neither radioactive indole-3-acetyl-myo-inositol nor IAA accumulates in the tip of the coleoptile or the mesocotyl node and thus these studies do not explain how the coleoptile tip controls the amount of IAA in the shoot.

Non-NASA Center↗

NASA NTRS2004

A Kernel-Free Particle-Finite Element Method for Hypervelocity Impact Simulation

An improved hybrid particle-finite element method has been developed for the simulation of hypervelocity impact problems. Unlike alternative methods, the revised formulation computes the density without reference to any kernel or interpolation functions, for either the density or the rate of dilatation. This simplifies the state space model and leads to a significant reduction in computational cost. The improved method introduces internal energy variables as generalized coordinates in a new formulation of the thermomechanical Lagrange equations. Example problems show good agreement with exact solutions in one dimension and good agreement with experimental data in a three dimensional simulation.

Park, Young-Keun↗

NASA NTRS2003

Kernel PLS-SVC for Linear and Nonlinear Discrimination

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

Rosipal, Roman↗

NASA NTRS2001

Support vector machines and kernel fisher discriminants: a case study using electronic nose data

Tradeoffs between the different kernel methods and performance relative to more traditional methods are discussed.

support vector machines classification↗

NASA NTRS1995

Retrofitting Decision Tree Classifiers Using Kernel Density Estimation

A novel method for combining decision trees and kernel density estimators is proposed. Standard.

Tree Classifiers↗

NASA NTRS2008

A Computational Procedure for Identifying Bilinear Representations of Nonlinear Systems Using Volterra Kernels

A computational procedure for identifying the state-space matrices corresponding to discrete bilinear representations of nonlinear systems is presented. A key feature of the method is the use of first- and second-order Volterra kernels (first- and second-order pulse responses) to characterize the system. The present method is based on an extension of a continuous-time bilinear system identification procedure given in a 1971 paper by Bruni, di Pillo, and Koch. The analytical and computational considerations that underlie the original procedure and its extension to the title problem are presented and described, pertinent numerical considerations associated with the process are discussed, and results obtained from the application of the method to a variety of nonlinear problems from the literature are presented. The results of these exploratory numerical studies are decidedly promising and provide sufficient credibility for further examination of the applicability of the method.

Kvaternik, Raymond G.↗

NASA NTRS2009

Numerical Evaluation of the "Dual-Kernel Counter-flow" Matric Convolution Integral that Arises in Discrete/Continuous (D/C) Control Theory

Discrete/Continuous (D/C) control theory is a new generalized theory of discrete-time control that expands the concept of conventional (exact) discrete-time control to create a framework for design and implementation of discretetime control systems that include a continuous-time command function generator so that actuator commands need not be constant between control decisions, but can be more generally defined and implemented as functions that vary with time across sample period. Because the plant/control system construct contains two linear subsystems arranged in tandem, a novel dual-kernel counter-flow convolution integral appears in the formulation. As part of the D/C system design and implementation process, numerical evaluation of that integral over the sample period is required. Three fundamentally different evaluation methods and associated algorithms are derived for the constant-coefficient case. Numerical results are matched against three available examples that have closed-form solutions.

Nixon, Douglas D.↗

NASA NTRS2010

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods

Das, Santanu↗

NASA NTRS2016

Observation-Based Cloud Radiative Kernels from A-Train

We develop a method to empirically derive broadband and spectral cloud radiative kernels by cloud type from pixel-scale collocated A-Train observations and reanalysis, which does not require additional cloudy radiative transfer calculations nor cloud properties. This method is able to estimate the cloud feedback by maintaining the consistency between CRKs and cloud responses.

Yue, Qing↗

NASA NTRS2025

Robust Trajectory Optimization for NRHO Rendezvous Using SPICE Kernel Relative Motion

In this paper, robust optimization is performed on trajectory correction maneuvers during the lunar lander return phase of an Artemis mission, treating the trajectory from one hour after low lunar orbit departure to arrival in the vicinity of the lunar Gateway as a relative motion problem. To enable rapid stochastic optimization techniques requiring many candidate trajectories, SPICE kernel relative motion as implemented by the Quadratic Interpolated State Transition (QIST) system is used as the underlying dynamics propagation. The optimization is performed with a genetic optimizer using linear covariance (LinCov) software in a simplified operational context, taking into account the availability of navigation sensors with varying measurement models, ranges, and accuracies. No numerical integration is used, since the relative motion around Gateway is fully characterized with the a priori computation of the QIST coefficients. Maneuver placements are computed to optimize the minimum 3σ delta-v of the trajectory, the position dispersion at a target point, and a convex combination of these two metrics. An order of magnitude runtime improvement is provided over legacy methods with less than 10% error introduced. All QIST results are shown to be in-family with legacy methods. The tradespace for optimal delta-v design is found to range from 77.0 to 93.9 m/s, while the range of optimal dispersion is between 1.4 and 11.7 km.

Relative Motion↗

DOE OSTI2022

Multi-Kernel Support Vector Machine based Predictive Maintenance of Circulating Water Pumps in Nuclear Power Plants

Multi-Kernel Support Vector Machine (MK-SVM) is a machine learning classification algorithm that can assist in the development of predictive maintenance strategies for nuclear power plant systems. Predictive maintenance can alleviate maintenance costs and enhance reliability of plant systems. In this work, MK-SVM is utilized for determining the health of the circulating water system (CWS) in a nuclear power plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Kernel Buffer Volume Fraction Margin of the AGR Designed Fuel Particle

Modeling results used to assess the fuel performance of the TRISO-coated fuel particles as a function of kernel/buffer volume fraction include SiC tangential stress, formation of the buffer/IPyC gap, particle temperature profile, internal particle pressure, fission gas released from the kernel, probability of fuel particle failure, and fission product diffusion. These results were evaluated at two burnup levels and irradiation temperatures to bound expected steady-state irradiation conditions. In general, increasing the kernel/buffer volume fraction increases the SiC stress and subsequently the failure probability of a fuel particle when compared to the AGR designed particle. There was little impact on the fission product diffusion through the particle as the kernel/buffer volume fraction increased.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Robust Multi-fidelity Bayesian Optimization with Deep Kernel and Partition

Multi-fidelity Bayesian optimization (MFBO) is a powerful approach that utilizes lowfidelity, cost-effective sources to expedite the exploration and exploitation of a high-fidelity objective function. Existing MFBO methods with theoretical foundations either lack justification for performance improvements over single-fidelity optimization or rely on strong assumptions about the relationships between fidelity sources to construct surrogate models and direct queries to low-fidelity sources. To mitigate the dependency on cross-fidelity assumptions while maintaining the advantages of low-fidelity queries, we introduce a random sampling and partition-based MFBO framework with deep kernel learning. This framework is robust to cross-fidelity model misspecification and explicitly illustrates the benefits of low-fidelity queries. Our results demonstrate that the proposed algorithm effectively manages complex cross-fidelity relationships and efficiently optimizes the target fidelity function.

Zhang, Fengxue [University of Chicago, Illinois, U↗

DOE OSTI2025

Post-irradiation 1600°C Heating Test of AGR-1 UCO Fuel Kernels

The Advanced Gas Reactor Fuel Development and Qualification (AGR) Program was established to perform research and development on tristructural isotropic (TRISO) coated-particle fuel to support deployment of high temperature gas-cooled reactors (HTGRs) . This talk will discuss the results from a post-irradiation heating test of five individual fuel kernels extracted from four TRISO fuel particles that were deconsolidated from an irradiated AGR-1 fuel compact.

heating test↗

DOE OSTI2024