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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Overview of research in progress at the Center of Excellence

The Center of Excellence (COE) was created nine years ago to facilitate active collaboration between the scientists at Ames Research Center and the Stanford Psychology Department. Significant interchange of ideas and personnel continues between Stanford and participating groups at NASA-Ames; the COE serves its function well. This progress report is organized into sections divided by project. Each section contains a list of investigators, a background statement, progress report, and a proposal for work during the coming year. The projects are: Algorithms for development and calibration of visual systems, Visually optimized image compression, Evaluation of advanced piloting displays, Spectral representations of color, Perception of motion in man and machine, Automation and decision making, and Motion information used for navigation and control.

Wandell, Brian A.↗
NASA NTRS2010

Testing Galactic Cosmic Ray Models

Models of the Galactic Cosmic Ray Environment are used for designing and planning space missions. The exising models will be reviewed. Spectral representations from these models will be compared with measurements of galactic cosmic ray spectra made on balloon flights and satellite flights over a period of more than 50 years.

Adams, James H., Jr.↗
NASA NTRS2009

Testing Galactic Cosmic Ray Models

Models of the Galactic Cosmic Ray Environment are used for designing and planning space missions. The existing models will be reviewed. Spectral representations from these models will be compared with measurements of galactic cosmic ray spectra made on balloon flights and satellite flights over a period of more than 50 years.

Adams, James H., Jr.↗
DOE OSTI2024

Hybrid particle-spectral method for kinetic plasma simulations

A hybrid model for numerical solutions of the Vlasov–Poisson equations is presented, which blends spectral and particle approaches. The model splits the distribution function for plasma species into both spectral and particle representations in the velocity space to combine the advantages of each approach. The spectral representation leverages asymmetrically weighted Hermite basis, whereas the particle representation leverages the particle-in-cell method. Configuration phase space is decomposed with the Fourier method, which is well suited for periodic problems. We derive conservation equations for mass, momentum, and energy for the proposed combined method. It is shown that the coupling error between the two methods is absent in the semi-discrete setting (not taking into account time discretization). Finally, numerical test cases are presented simulating a weak electron beam interaction with plasma, leading to beam–plasma instability. The initially localized electron beam evolved into a highly non-equilibrium distribution function in the velocity space. A small growth rate and the resonance nature of instability make it difficult to obtain accurate solutions for purely particle methods due to noise, which falls as ∼1/Np with a number of particles. At the same time, purely spectral methods may require a large number of modes to capture the highly non-equilibrium state of the evolved beam. We show that the hybrid method is well suited for such problems: it reproduces the linear stage as well as nonlinear dynamics with sufficient accuracy using a highly non-equilibrium distribution function.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Hybrid particle-spectral method for kinetic plasma simulations

A hybrid model for numerical solutions of the Vlasov–Poisson equations is presented, which blends spectral and particle approaches. The model splits the distribution function for plasma species into both spectral and particle representations in the velocity space to combine the advantages of each approach. The spectral representation leverages asymmetrically weighted Hermite basis, whereas the particle representation leverages the particle-in-cell method. Configuration phase space is decomposed with the Fourier method, which is well suited for periodic problems. We derive conservation equations for mass, momentum, and energy for the proposed combined method. It is shown that the coupling error between the two methods is absent in the semi-discrete setting (not taking into account time discretization). Finally, numerical test cases are presented simulating a weak electron beam interaction with plasma, leading to beam–plasma instability. The initially localized electron beam evolved into a highly non-equilibrium distribution function in the velocity space. A small growth rate and the resonance nature of instability make it difficult to obtain accurate solutions for purely particle methods due to noise, which falls as ~1/$\sqrt{N_p}$ with a number of particles. At the same time, purely spectral methods may require a large number of modes to capture the highly nonequilibrium state of the evolved beam. We show that the hybrid method is well suited for such problems: it reproduces the linear stage as well as nonlinear dynamics with sufficient accuracy using a highly non-equilibrium distribution function.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Neural chaos: A spectral stochastic neural operator

Building surrogate models for operators with uncertainty quantification capabilities is essential for many engineering applications where randomness–such as variability in material properties, boundary conditions, and initial conditions–is unavoidable. Polynomial Chaos Expansion (PCE) is widely recognized as a go-to method for constructing stochastic surrogates in both intrusive and non-intrusive ways, and it has recently been used in the context of operator learning. However, its application becomes challenging for complex or high-dimensional processes, as achieving accuracy requires higher-order polynomials, which can increase computational demand and/or the risk of overfitting. Furthermore, PCE requires specialized treatments to manage random variables that are not independent, and these treatments may be problem-dependent or may fail with increasing complexity. Here, in this work, we adopt the same formalism as the spectral expansion used in PCE; however, we replace the classical polynomial basis functions with neural network (NN) basis functions to leverage their expressivity. To achieve this, we propose an algorithm that identifies NN-parameterized basis functions in a purely data-driven manner, without any prior assumptions about the joint distribution of the random variables involved, whether independent or dependent, or about their marginal distributions. The proposed algorithm identifies each NN-parameterized basis function sequentially, ensuring they are orthogonal with respect to the data distribution. The basis functions are constructed directly on the joint stochastic variables without requiring a tensor product structure or assuming independence of the random variables. This approach may offer greater flexibility for complex stochastic models, while simplifying implementation compared to the tensor product structures typically used in PCE to handle random vectors. This is particularly advantageous given the current state of open-source packages, where building and training neural networks can be done with just a few lines of code and extensive community support. We demonstrate the effectiveness of the proposed scheme through several numerical examples of varying complexity and provide comparisons with classical PCE.

Polynomial chaos expansion↗
DOE OSTI2021

Machine learning on neutron and x-ray scattering and spectroscopies

Neutron and x-ray scattering represent two classes of state-of-the-art materials characterization techniques that measure materials structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems from catalysts to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and x-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and x-ray techniques, including neutron scattering, x-ray absorption, x-ray scattering, and photoemission. We highlight the integration of machine learning methods into the typical workflow of scattering experiments, focusing on problems that challenge traditional analysis approaches but are addressable through machine learning, including leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials representations, mitigating spectral noise, and others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.

Chen, Zhantao↗
NASA NTRS1977

Energy spectra of cosmic ray nuclei - Z of 4 to 26 and E of 0.3 to 2 GeV/amu

The differential kinetic-energy spectra of cosmic-ray nuclei in the charge range from 4 to 28 are determined using data obtained from a high-altitude balloonborne detector. The data are derived from the response of an acrylic plastic Cerenkov counter and cover the kinetic-energy range from 400 to 2100 MeV/amu. A Cerenkov pulse-height histogram is obtained for each charge, and the kinetic-energy spectra are derived by means of a formal mathematical deconvolution of these histograms. Results of the deconvolution calculation extrapolated to the top of the atmosphere are presented for the most abundant nuclei; it is found that the spectra are not amenable to representation by spectral indices, so the relative spectra of the different elements are compared by evaluating the charge ratios as a function of energy. The results show that secondary/primary ratios decrease with increasing energy, while primary/primary ratios are constant for Z of 6 to 10 and 14 to 26 but vary for Z of 10 to 14. It is concluded that the data seem to indicate more than one cosmic-ray source region.

Maehl, R. C.↗
NASA NTRS1978

Synthetic atmospheric transmittance spectra near 15 and 4.3 microns

Synthetic monochromatic atmospheric transmittance spectra are presented for infrared intervals in the vicinity of the 15 and 4.3 microns CO2 bands. The intervals are nominally 20 kaysers in width, and the spectra partition the intervals 560, 780 kayser and 2180, 2400 kayser, respectively. The spectra are for a vertical atmospheric path. A given spectrum is presented at that pressure for which the mean weighting function is a maximum. In order to indicate the molecular origin of the features in a given spectrum, separate spectra are also presented for those species which make center line contributions to the absorption over the interval. The transmittance model for the calculations is described. In addition to the representation of spectral features for intervals which comprise these two CO2 bands, the spectra afford a utility in the design of high precision satellite temperature radiometers.

Susskind, J.↗
NASA NTRS1983

Quantile Data Analysis of Image Data

Quantile data analysis and functional statistical inference methods are introduced and applied to provide representations of spectral data which may lead to simple statistical discriminators effective for the estimation of ground truth from satellite spectral measurements. To estimate the ground truth of a pixel, the probability of each possible ground truth is estimated, given observed (estimated) quantile theoretic statistical characteristics of the multispectral image data corresponding to the pixel and its neighboring pixels. A strategy for determining which statistical characteristics discriminate best is described. Results are reported of quantile data analysis of an extensive collection of training files of image data.

Parzen, E.↗
NASA NTRS1991

A drag measurement technique for free piston shock tunnels

A new technique is described for measuring drag with 100-microsecond rise time on a nonlifting model in a free piston shock tunnel. The technique involves interpretation of the stress waves propagating within the model and its support. A finite element representation and spectral methods are used to obtain a mean square optimal estimate of the time history of the aerodynamic loading. Thus, drag is measured instantaneously and the previous restriction caused by the mechanical time constant of balances is overcome. The effectiveness of the balance is demonstrated by measuring the drag on cones with 5 and 15 deg semi-vertex angles in nominally Mach 5.6 flow with stagnation enthalpies from 2.6 to 33 MJ/kg.

Sanderson, S. R.↗
NASA NTRS2006

Anisotropic Developments for Homogeneous Shear Flows

The general decomposition of the spectral correlation tensor R(sub ij)(k) by Cambon et al. (J. Fluid Mech., 202, 295; J. Fluid Mech., 337, 303) into directional and polarization components is applied to the representation of R(sub ij)(k) by spherically averaged quantities. The decomposition splits the deviatoric part H(sub ij)(k) of the spherical average of R(sub ij)(k) into directional and polarization components H(sub ij)(sup e)(k) and H(sub ij)(sup z)(k). A self-consistent representation of the spectral tensor in the limit of weak anisotropy is constructed in terms of these spherically averaged quantities. The directional polarization components must be treated independently: models that attempt the same representation of the spectral tensor using the spherical average H(sub ij)(k) alone prove to be inconsistent with Navier-Stokes dynamics. In particular, a spectral tensor consistent with a prescribed Reynolds stress is not unique. The degree of anisotropy permitted by this theory is restricted by realizability requirements. Since these requirements will be less severe in a more accurate theory, a preliminary account is given of how to generalize the formalism of spherical averages to higher expansion of the spectral tensor. Directionality is described by a conventional expansion in spherical harmonics, but polarization requires an expansion in tensorial spherical harmonics generated by irreducible representations of the spatial rotation group SO(exp 3). These expansions are considered in more detail in the special case of axial symmetry.

Cambon, Claude↗
DOE OSTI2025

Dynamic Low-Rank Training with Spectral Regularization: Achieving Robustness in Compressed Representations

Deployment of neural networks on resource-constrained devices demands models that are both compact and robust to adversarial inputs. However, compression and adversarial robustness often conflict. In this work, we introduce a dynamical low-rank training scheme enhanced with a novel spectral regularizer that controls the condition number of the low-rank core in each layer. This approach mitigates the sensitivity of compressed models to adversarial perturbations without sacrificing clean accuracy. The method is model- and data-agnostic, computationally efficient, and supports rank adaptivity to automatically compress the network at hand. Extensive experiments across standard architectures, datasets, and adversarial attacks show the regularized networks can achieve over 94\% compression while recovering or improving adversarial accuracy relative to uncompressed baselines.

Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗
DOE OSTI2023

Quantized Information in Spectral Cyberspace

The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time–frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Spectral-partitioned Kohn-Sham density functional theory

Here we introduce a general, variational scheme for systematic approximation of a given Kohn-Sham free-energy functional by partitioning the density matrix into distinct spectral domains, each of which may be spanned by an independent diagonal representation without requirement of mutual orthogonality. It is shown that by generalizing the entropic contribution to the free energy to allow for independent representations in each spectral domain, the free energy becomes an upper bound to the exact (unpartitioned) Kohn-Sham free energy, attaining this limit as the representations approach Kohn-Sham eigenfunctions. A numerical procedure is devised for calculation of the generalized entropy associated with spectral partitioning of the density matrix. The result is a powerful framework for Kohn-Sham calculations of systems whose occupied subspaces span multiple energy regimes. As a case in point, we apply the proposed framework to warm- and hot-dense matter described by finite-temperature density functional theory, where at high energies the density matrix is represented by that of the free-electron gas, while at low energies it is variationally optimized. We derive expressions for the spectral-partitioned Kohn-Sham Hamiltonian, atomic forces, and macroscopic stresses within the projector-augmented wave (PAW) and the norm-conserving pseudopotential methods. It is demonstrated that at high temperatures, spectral partitioning facilitates accurate calculations at dramatically reduced computational cost. Moreover, as temperature is increased, fewer exact Kohn-Sham states are required for a given accuracy, leading to further reductions in computational cost. Finally, it is shown that standard multiprojector expansions of electronic orbitals within atomic spheres in the PAW method lack sufficient completeness at high temperatures. Spectral partitioning provides a systematic solution for this fundamental problem.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1976

A spectral reflectance estimation technique using multispectral data from the Viking lander camera

A technique is formulated for constructing spectral reflectance curve estimates from multispectral data obtained with the Viking lander camera. The multispectral data are limited to six spectral channels in the wavelength range from 0.4 to 1.1 micrometers and most of these channels exhibit appreciable out-of-band response. The output of each channel is expressed as a linear (integral) function of the (known) solar irradiance, atmospheric transmittance, and camera spectral responsivity and the (unknown) spectral responsivity and the (unknown) spectral reflectance. This produces six equations which are used to determine the coefficients in a representation of the spectral reflectance as a linear combination of known basis functions. Natural cubic spline reflectance estimates are produced for a variety of materials that can be reasonably expected to occur on Mars. In each case the dominant reflectance features are accurately reproduced, but small period features are lost due to the limited number of channels. This technique may be a valuable aid in selecting the number of spectral channels and their responsivity shapes when designing a multispectral imaging system.

Park, S. K.↗
NASA NTRS1976

Cubic spline reflectance estimates using the Viking lander camera multispectral data

A technique was formulated for constructing spectral reflectance estimates from multispectral data obtained with the Viking lander cameras. The output of each channel was expressed as a linear function of the unknown spectral reflectance producing a set of linear equations which were used to determine the coefficients in a representation of the spectral reflectance estimate as a natural cubic spline. The technique was used to produce spectral reflectance estimates for a variety of actual and hypothetical spectral reflectances.

Park, S. K.↗
NASA NTRS1996

On the High-Frequency Asymptotic Evaluation of the Potentials of Elemental Sources on an Anisotropic Impedance Cylinder

In an effort to formulate the high-frequency coupling between antennas located on airframes composed of multilayer imperfectly conducting materials a general model was sought which would embody the electromagnetic properties of the layers and would apply over a broader range of separations of the antennas. Such a model is described here. This work is a generalization of the work of Pearson [1986, 1987a] concerning the high-frequency asymptotic representation of the fields of elemental sources diffracted by a multilayer cylinder. In that work the source and field points were located off the cylinder surface, and they were sufficiently separated to permit the effective use of the residue series representation of the spectral integrals involved. Here the source and field points are located on the cylinder surface and are permitted to be sufficiently close as to render the residue series poorly convergent. To obtain a more effective representation in this situation, the cylinder is modeled by an anisotropic impedance cylinder, and the resulting spectral integrals are evaluated by reduction to forms amenable to multiple applications of techniques described in the literature in connection with treatment of axially uniform sources in this context.

Pogorzelski, Ronald J.↗