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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 OSTI2023

Nucleon-Pion Spectroscopy from Sparsened Correlators

Neutrino oscillation experiments require accurate reconstructions of neutrino energies, which depend in part on a theoretical understanding of the axial $N\rightarrow\Delta$ transition form factors. A lattice QCD study of this transition will require construction of all hadronic states with energies up to $m_\Delta$, which at the physical point includes $N\pi$ and $N\pi\pi$. Building interpolating operators from sparse grids at the source and sink is a versatile method that allows construction of a wide range of diagram topologies and has successfully been used in other multi-hadron calculations. We will discuss application of this method to nucleon-pion systems and present preliminary results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Towards Lattice QCD Calculations of Pion Production

Neutrino oscillation experiments require accurate reconstructions of neutrino energies, which depend in part on a theoretical understanding of the axial N→Δ transition form factors. A lattice QCD study of this transition will require construction of all hadronic states with energies up to mΔ, which at the physical point includes Nπ and Nππ. Building interpolating operators from sparse grids at the source and sink is a versatile method that allows construction of a wide range of diagram topologies and has successfully been used in other multi-hadron calculations. We will discuss application of this method to nucleon-pion systems and present preliminary results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1991

Evaporation over global oceans derived from satellite data and AGCM

Evaporation over ocean at 100 km has been estimated using operational satellite data. Recent satellite results are compared to data obtained through atmospheric general circulation models (AGCMs). A large systematic error is found in AGCM data which is considered to be due to overestimation of atmospheric moisture. It is suggested that the discrepancy may be caused by errors in operational infrared sounder data assimilated into the model. The error may also be caused by interpolation of very sparse in situ data in areas of sharp gradients.

Liu, W. T.↗
NASA NTRS2002

Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

Rajkumar, T.↗
NASA NTRS2015

The Parameterization of Top-Hat Particle Sensors with Microchannel-Plate-Based Detection Systems and its Application to the Fast Plasma Investigation on NASA's Magnetospheric MultiScale Mission

The most common instrument for low energy plasmas consists of a top-hat electrostatic analyzer geometry coupled with a microchannel-plate (MCP)-based detection system. While the electrostatic optics for such sensors are readily simulated and parameterized during the laboratory calibration process, the detection system is often less well characterized. Furthermore, due to finite resources, for large sensor suites such as the Fast Plasma Investigation (FPI) on NASA's Magnetospheric Multiscale (MMS) mission, calibration data are increasingly sparse. Measurements must be interpolated and extrapolated to understand instrument behavior for untestable operating modes and yet sensor inter-calibration is critical to mission success. To characterize instruments from a minimal set of parameters we have developed the first comprehensive mathematical description of both sensor electrostatic optics and particle detection systems. We include effects of MCP efficiency, gain, scattering, capacitive crosstalk, and charge cloud spreading at the detector output. Our parameterization enables the interpolation and extrapolation of instrument response to all relevant particle energies, detector high voltage settings, and polar angles from a small set of calibration data. We apply this model to the 32 sensor heads in the Dual Electron Sensor (DES) and 32 sensor heads in the Dual Ion Sensor (DIS) instruments on the 4 MMS observatories and use least squares fitting of calibration data to extract all key instrument parameters. Parameters that will evolve in flight, namely MCP gain, will be determined daily through application of this model to specifically tailored in-flight calibration activities, providing a robust characterization of sensor suite performance throughout mission lifetime. Beyond FPI, our model provides a valuable framework for the simulation and evaluation of future detection system designs and can be used to maximize instrument understanding with minimal calibration resources.

Instrument-model↗
NASA NTRSDate not supplied

Surface Expression of Bed Topography in Greenland and Antarctica

It has long been recognized that major subglacial topographic features beneath the Greenland and Antarctic ice sheets – whether especially prominent or wide – can generate an observable surface expression. Recent advances in digital elevation models (ArcticDEM, REMA) and bed-to-surface transfer theory now permit widespread observation of this phenomena and a more robust interpretation. Hillshading a digital elevation model from the direction of ice flow permits straightforward detection of major surface features. For Greenland, comparison against the growing catalog of airborne radar-sounding data confirms that the large majority of these features are associated with subglacial topography – typically valleys. These observations suggest a better path toward interpolating subglacial topography between sparse radar observations by developing methods that also require fidelity to observed surface relief.

Joe Macgregor↗
NASA NTRS1999

Utilizing Multiple Datasets for Snow Cover Mapping

Snow-cover maps generated from surface data are based on direct measurements, however they are prone to interpolation errors where climate stations are sparsely distributed. Snow cover is clearly discernable using satellite-attained optical data because of the high albedo of snow, yet the surface is often obscured by cloud cover. Passive microwave (PM) data is unaffected by clouds, however, the snow-cover signature is significantly affected by melting snow and the microwaves may be transparent to thin snow (less than 3cm). Both optical and microwave sensors have problems discerning snow beneath forest canopies. This paper describes a method that combines ground and satellite data to produce a Multiple-Dataset Snow-Cover Product (MDSCP). Comparisons with current snow-cover products show that the MDSCP draws together the advantages of each of its component products while minimizing their potential errors. Improved estimates of the snow-covered area are derived through the addition of two snow-cover classes ("thin or patchy" and "high elevation" snow cover) and from the analysis of the climate station data within each class. The compatibility of this method for use with Moderate Resolution Imaging Spectroradiometer (MODIS) data, which will be available in 2000, is also discussed. With the assimilation of these data, the resolution of the MDSCP would be improved both spatially and temporally and the analysis would become completely automated.

Tait, Andrew B.↗
NASA NTRS2010

Air traffic management evaluation tool

Method and system for evaluating and implementing air traffic management tools and approaches for managing and avoiding an air traffic incident before the incident occurs. The invention provides flight plan routing and direct routing or wind optimal routing, using great circle navigation and spherical Earth geometry. The invention provides for aircraft dynamics effects, such as wind effects at each altitude, altitude changes, airspeed changes and aircraft turns to provide predictions of aircraft trajectory (and, optionally, aircraft fuel use). A second system provides several aviation applications using the first system. These applications include conflict detection and resolution, miles-in trail or minutes-in-trail aircraft separation, flight arrival management, flight re-routing, weather prediction and analysis and interpolation of weather variables based upon sparse measurements.

Sridhar, Banavar↗
NASA NTRS2012

Air traffic management evaluation tool

Methods for evaluating and implementing air traffic management tools and approaches for managing and avoiding an air traffic incident before the incident occurs. A first system receives parameters for flight plan configurations (e.g., initial fuel carried, flight route, flight route segments followed, flight altitude for a given flight route segment, aircraft velocity for each flight route segment, flight route ascent rate, flight route descent route, flight departure site, flight departure time, flight arrival time, flight destination site and/or alternate flight destination site), flight plan schedule, expected weather along each flight route segment, aircraft specifics, airspace (altitude) bounds for each flight route segment, navigational aids available. The invention provides flight plan routing and direct routing or wind optimal routing, using great circle navigation and spherical Earth geometry. The invention provides for aircraft dynamics effects, such as wind effects at each altitude, altitude changes, airspeed changes and aircraft turns to provide predictions of aircraft trajectory (and, optionally, aircraft fuel use). A second system provides several aviation applications using the first system. Several classes of potential incidents are analyzed and averted, by appropriate change en route of one or more parameters in the flight plan configuration, as provided by a conflict detection and resolution module and/or traffic flow management modules. These applications include conflict detection and resolution, miles-in trail or minutes-in-trail aircraft separation, flight arrival management, flight re-routing, weather prediction and analysis and interpolation of weather variables based upon sparse measurements. The invention combines these features to provide an aircraft monitoring system and an aircraft user system that interact and negotiate changes with each other.

Sridhar, Banavar↗
DOE OSTI2025

Gaussian Process Regression under Computational and Epistemic Misspecification

Gaussian process regression is a classical kernel method for function estimation and data interpolation. In large data applications, computational costs can be reduced using low-rank or sparse approximations of the kernel. This paper investigates the effect of such kernel approximations on the interpolation error. We introduce a unified framework to analyze Gaussian process regression under important classes of computational misspecification: Karhunen-Loève expansions that result in low-rank kernel approximations, multiscale wavelet expansions that induce sparsity in the covariance matrix, and finite element representations that induce sparsity in the precision matrix. Furthermore, our theory also accounts for epistemic misspecification in the choice of kernel parameters.

Gaussian process regression↗
DOE OSTI2024

Collocation methods for nonlinear differential equations on low-rank manifolds

We introduce new methods for integrating nonlinear differential equations on low-rank manifolds. These methods rely on interpolatory projections onto the tangent space, enabling low-rank time integration of vector fields that can be evaluated entry-wise. A key advantage of our approach is that it does not require the vector field to exhibit low-rank structure, thereby overcoming significant limitations of traditional dynamical low-rank methods based on orthogonal projection. To construct the interpolatory projectors, we develop a sparse tensor sampling algorithm based on the discrete empirical interpolation method (DEIM) that parameterizes tensor train manifolds and their tangent spaces with cross interpolation. Using these projectors, we propose two time integration schemes on low-rank tensor train manifolds. The first scheme integrates the solution at selected interpolation indices and constructs the solution with cross interpolation. The second scheme generalizes the well-known orthogonal projector-splitting integrator to interpolatory projectors. We demonstrate the proposed methods with applications to several tensor differential equations arising from the discretization of partial differential equations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Generation Of Surface Grids From Data Points

Computational procedure generates grids on complicated three-dimensional surfaces from sets of data points that lie on and specify those surfaces. Starting with grouping of possibly sparse surface points into lines and/or patches, procedure involves interpolation within and blending of lines and/or patches and possibly redistribution and reassembly of patches to obtain finished system of zonal patch grids that match at boundaries between them. Procedure semiautomated via computer program that performs all steps except selection of patches and interpolation points, left to discretion of user.

Luh, Raymond Ching-Chung↗
NASA NTRSDate not supplied

Characterizing Spatiotemporal Uncertainty in Interpolated Meteorological Data

Interpolated meteorological data invariably contain errors. These errors have structure in time and space, particularly autocorrelation, which can cause the effects of errors to compound when model outputs are aggregated temporally or spatially. One way to account for this uncertainty is with a probabilistic model from which samples can be drawn that are coherent with respect to underlying spatial and temporal covariance structure. This work describes a probabilistic method for spatial interpolation of point-wise meteorological time series. Observational data from weather stations are generally sparse in space and dense in time (but sometimes missing). The method works by projecting time series onto orthogonal basis vectors and spatially interpolating each resulting component independently. Under suitable assumptions, and data transformations to better satisfy those assumptions, Gaussian process regression provides a complete description of the joint predictive distribution over a Gaussian random field. Spatiotemporally coherent realizations are generated as the sum of conditional (spatial) simulations of each orthogonal (temporal) component. Data-derived and generic orthogonal bases are considered. In addition to spatial interpolation, imputation of missing observational data is examined. The method is applied using near-surface air temperature over the Western United States and validated by comparing theoretical versus actual coverage of predictive distributions and analyzing the degree to which spatial and temporal covariance structure is reproduced. Computational considerations, relating to conditional simulation of random fields, are also addressed.

Conor T Doherty↗
NASA NTRS1984

Multivariate optimum interpolation of surface pressure and winds over oceans

The observations of surface pressure are quite sparse over oceanic areas. An effort to improve the analysis of surface pressure over oceans through the development of a multivariate surface analysis scheme which makes use of surface pressure and wind data is discussed. Although the present research used ship winds, future versions of this analysis scheme could utilize winds from additional sources, such as satellite scatterometer data.

Bloom, S. C.↗
NASA NTRS1993

Implementing abstract multigrid or multilevel methods

Multigrid methods can be formulated as an algorithm for an abstract problem that is independent of the partial differential equation, domain, and discretization method. In such an abstract setting, problems not arising from partial differential equations can be treated. A general theory exists for linear problems. The general theory was motivated by a series of abstract solvers (Madpack). The latest version was motivated by the theory. Madpack now allows for a wide variety of iterative and direct solvers, preconditioners, and interpolation and projection schemes, including user callback ones. It allows for sparse, dense, and stencil matrices. Mildly nonlinear problems can be handled. Also, there is a fast, multigrid Poisson solver (two and three dimensions). The type of solvers and design decisions (including language, data structures, external library support, and callbacks) are discussed. Based on the author's experiences with two versions of Madpack, a better approach is proposed. This is based on a mixed language formulation (C and FORTRAN + preprocessor). Reasons for not using FORTRAN, C, or C++ (individually) are given. Implementing the proposed strategy is not difficult.

Douglas, Craig C.↗
NASA NTRS2019

Fusion of Surface Ceilometer Data and Satellite Cloud Retrievals in 2D Mesh Interpolating Model with Clustering

For accurate cloud ceiling information, a data fusion approach is proposed that utilizes satellite data to extend surface station information to much wider areas. Cloud base height (CBH) retrieved from satellite observations provides for much larger spatial coverage and higher resolution. The direct comparison of GOES-16 CBH with surface station ceiling yields a local bias that has to be corrected for in the initial GOES-16 cloud base information. This sparsely sampled bias correction presents an irregular 2D mesh of control points, which is then interpolated by constructing a continuous smooth field using polyharmonic splines. The influence of remote stations is restricted by grouping the control points into clusters depending on an effective distance. This cluster-based approach allows for constructing separate spline surfaces corresponding to physically different clouds. The obtained continuous bias correction function is then applied to the entire GOES-16 pixel level CBH except for areas far away from surface stations in data sparse regions such as offshore. The described method is currently being tested using daytime-only observations over the central and eastern United States. Overall, this approach has potential to provide more accurate, high spatial resolution cloud ceiling information for the aviation community.

Khlopenkov, Konstantin↗
NASA NTRS1991

The fractional Fourier transform and applications

This paper describes the 'fractional Fourier transform', which admits computation by an algorithm that has complexity proportional to the fast Fourier transform algorithm. Whereas the discrete Fourier transform (DFT) is based on integral roots of unity e exp -2(pi)i/n, the fractional Fourier transform is based on fractional roots of unity e exp -2(pi)i(alpha), where alpha is arbitrary. The fractional Fourier transform and the corresponding fast algorithm are useful for such applications as computing DFTs of sequences with prime lengths, computing DFTs of sparse sequences, analyzing sequences with noninteger periodicities, performing high-resolution trigonometric interpolation, detecting lines in noisy images, and detecting signals with linearly drifting frequencies. In many cases, the resulting algorithms are faster by arbitrarily large factors than conventional techniques.

Bailey, David H.↗