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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Frequency optimization of repetitive lattice beam-like structures using a continuum model

A new method for obtaining the maximum frequency design of a beam-like repetitive lattice structure is presented. Using existing techniques, the lattice is first modeled as an equivalent anisotropic Timoshenko beam. The computation of the stiffness and inertial properties of the beam, determined by matching the strain and kinetic energies of the beam with those of the lattice, is facilitated by the repetitive nature of the lattice. The optimum design is obtained by maximizing Rayleigh's quotient using methods of variational calculus. For the problem selected, results show excellent agreement with those obtained by traditional finite-element methods. Moreover, unlike FE methods, cpu time is relatively unaffected by the size of the truss.

Reiss, Robert↗
NASA NTRS2010

Statistical Model Selection for TID Hardness Assurance

Radiation Hardness Assurance (RHA) methodologies against Total Ionizing Dose (TID) degradation impose rigorous statistical treatments for data from a part's Radiation Lot Acceptance Test (RLAT) and/or its historical performance. However, no similar methods exist for using "similarity" data - that is, data for similar parts fabricated in the same process as the part under qualification. This is despite the greater difficulty and potential risk in interpreting of similarity data. In this work, we develop methods to disentangle part-to-part, lot-to-lot and part-type-to-part-type variation. The methods we develop apply not just for qualification decisions, but also for quality control and detection of process changes and other "out-of-family" behavior. We begin by discussing the data used in ·the study and the challenges of developing a statistic providing a meaningful measure of degradation across multiple part types, each with its own performance specifications. We then develop analysis techniques and apply them to the different data sets.

Ladbury, R.↗
NASA NTRS1980

Newton's method: A link between continuous and discrete solutions of nonlinear problems

Newton's method for nonlinear mechanics problems replaces the governing nonlinear equations by an iterative sequence of linear equations. When the linear equations are linear differential equations, the equations are usually solved by numerical methods. The iterative sequence in Newton's method can exhibit poor convergence properties when the nonlinear problem has multiple solutions for a fixed set of parameters, unless the iterative sequences are aimed at solving for each solution separately. The theory of the linear differential operators is often a better guide for solution strategies in applying Newton's method than the theory of linear algebra associated with the numerical analogs of the differential operators. In fact, the theory for the differential operators can suggest the choice of numerical linear operators. In this paper the method of variation of parameters from the theory of linear ordinary differential equations is examined in detail in the context of Newton's method to demonstrate how it might be used as a guide for numerical solutions.

Thurston, G. A.↗
NASA NTRS1992

Development of a four-dimensional variational analysis system using the adjoint method at GLA. I - Dynamics

Recent developments in the field of data assimilation have pointed to variational analysis (essentially least-squares fitting of a model solution to observed data) using the adjoint method as a new direction that holds the potential of major improvements over the current optimal interpolation method. This paper describes the initial effort in the development of a 4D variational analysis system. Although the development is based on the Goddard Laboratory for Atmospheres General Circulation Model (GCM), the methods and procedures described in this paper can be applied to any model. The adjoint code that computes the gradients needed in the analysis can be written directly from the GCM code. An easy error-detection technique was devised in the construction of the adjoint model. Also, a method of determining the weights and the preconditioning scales for the cases where model-generated data, which are error free, are used as observation is proposed. Two test experiments show that the dynamics part of the system has been successfully completed.

Chao, Winston C.↗
NASA NTRS2012

Spectroscopic Chemical Analysis Methods and Apparatus

This invention relates to non-contact spectroscopic methods and apparatus for performing chemical analysis and the ideal wavelengths and sources needed for this analysis. It employs deep ultraviolet (200- to 300-nm spectral range) electron-beam-pumped wide bandgap semiconductor lasers, incoherent wide bandgap semiconductor lightemitting devices, and hollow cathode metal ion lasers. Three achieved goals for this innovation are to reduce the size (under 20 L), reduce the weight [under 100 lb (.45 kg)], and reduce the power consumption (under 100 W). This method can be used in microscope or macroscope to provide measurement of Raman and/or native fluorescence emission spectra either by point-by-point measurement, or by global imaging of emissions within specific ultraviolet spectral bands. In other embodiments, the method can be used in analytical instruments such as capillary electrophoresis, capillary electro-chromatography, high-performance liquid chromatography, flow cytometry, and related instruments for detection and identification of unknown analytes using a combination of native fluorescence and/or Raman spectroscopic methods. This design provides an electron-beampumped semiconductor radiation-producing method, or source, that can emit at a wavelength (or wavelengths) below 300 nm, e.g. in the deep ultraviolet between about 200 and 300 nm, and more preferably less than 260 nm. In some variations, the method is to produce incoherent radiation, while in other implementations it produces laser radiation. In some variations, this object is achieved by using an AlGaN emission medium, while in other implementations a diamond emission medium may be used. This instrument irradiates a sample with deep UV radiation, and then uses an improved filter for separating wavelengths to be detected. This provides a multi-stage analysis of the sample. To avoid the difficulties related to producing deep UV semiconductor sources, a pumping approach has been developed that uses ballistic electron beam injection directly into the active region of a wide bandgap semiconductor material.

Hug, William F.↗
NASA NTRS1985

Variational assimilation of VAS data into a mesoscale model Assimilation method and sensitivity experiments

The assimilation of temperature and moisture gradient information from the Visible IR Spin-Scan Radiometer's Atmospheric Sounder (VAS) into a mesoscale model is presently undertaken by means of a variational method and followed by tests of the sensitivity of both diabatic and adiabatic versions of the model to VAS data assimilations for the case of July 20-21, 1981. The synoptic scale effects of the assimilation of VAS data are noted to be negligible; the greatest impact was instead on mesoscale forecasts of convective instability patterns. The additional assimilation of relative humidity gradients did not significantly change the patterns of the forecast instabilities. The greatest improvements from assimilation resulted from the resolution of the strong mesoscale temperature gradients by the asynoptic VAS data.

Cram, J. M.↗
NASA NTRS1988

Assimilation of satellite surface wind speed data using the GLA analysis/forecast system

Research at the Goddard Laboratory for Atmospheres to evaluate methods to assign directions to both real and simulated surface wind speed data is discussed. Surface wind speed measurements are obtained from satellites, including Seasat, Nimbus-7, and Geosat. The methods include the interpolation of modeled forecast winds to the wind speed datum location, the use of surface pressures with a balance relation, and a variational analysis method. It is found that the best estimates may be obtained using a multipass approach to perform a surface analysis incorporating all conventional surface data.

Bloom, S. C.↗
DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗
NASA NTRS1992

Minor/major element variation within and among diogenite and howardite orthopyroxenite groups

Diogenites are orthopyroxenites that may contain chromite and olivine as accessory minerals. Howardite breccias contain orthopyroxenite clasts with similar properties compared to monomict diogenites. We used statistical methods and variation plots of major and minor elements in orthopyroxene and chromite to show whether or not howardite orthopyroxenites are related to monomict diogenites, and to assess their petrogenesis. Our results fail to establish any evidence that monomict diogenites are significantly different from howardite orthopyroxenites in terms of major and minor elements. We also found no differences between Antarctic diogenites and non-Antarctic diogenites. Although element variation plots show compelling evidence that most diogenites originated by igneous fractionation, linear trends among the various diogenites and howardite orthopyroxenite clasts are either non-existent or ill-defined. This militates against an origin from a single magma body, but suggests an origin from multiple magma bodies in the parent planetoid.

Berkley, John L.↗
DOE OSTI2025

Reducing model error using optimized galaxy selection: weak lensing cluster mass estimation

Galaxy clusters are one of the most powerful probes to study extensions of General Relativity and the Standard Cosmological Model. Upcoming surveys like the Vera Rubin Observatory’s Legacy Survey of Space and Time are expected to revolutionise the field, by enabling the analysis of cluster samples of unprecedented size and quality. To reach this era of high-precision cluster cosmology, the mitigation of sources of systematic error is crucial. A particularly important challenge is bias in cluster mass measurements induced by the mismodelling of photometric redshift estimates of source galaxies. This work proposes a method to optimise the source sample selection in cluster weak lensing analyses drawn from wide-field survey lensing catalogs to reduce the bias on reconstructed cluster masses. We use a combinatorial optimisation scheme and methods from variational inference to select galaxies in latent space to produce a probabilistic galaxy source sample catalog for highly accurate cluster mass estimation. We show that our method reduces the critical surface mass density Σ crit relative modelling bias on the 60-70% level, while maintaining up to 90% of galaxies. We highlight that our methodology has applications beyond cluster mass estimation as an approach to jointly combine galaxy selection and model inference under sources of systematics.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1971

Implicit determination of multispectral scanner data variation over extended areas.

Description of a method by which variations in aerial multispectral scanner data can be accounted for when there is insufficient ground truth information. The data are suitably filtered and smoothed along the flight line, and the reduced results are presented in a form such that the investigator can readily determine the preprocessing that should be used and the spatial extent to which it should be applied. An algorithm is developed for the detection of cloud-shadowed areas.

Kriegler, F. J.↗
DOE OSTI2024

Impact of storage and extraction methods on peat soil microbiomes

Recovered microbial community structure is known to be influenced by sample storage conditions and nucleic acid extraction methods, and the impact varies by sample type. Peat soils store a large portion of soil carbon and their microbiomes mediate climate feedbacks. Here, we tested three storage conditions and five extraction protocols on peat soils from three physicochemically distinct habitats in Stordalen Mire, Sweden, revealing significant methodological impacts on microbial (here, meaning bacteria and archaea) community structure. Initial preservation method impacted alpha but not beta diversity, with in-field storage in LifeGuard buffer yielding roughly two-thirds the richness of in-field flash-freezing or transport from the field on ice (all samples were stored at −80 °C after return from the field). Nucleic acid extraction method impacted both alpha and beta diversity; one method (the PowerSoil Total RNA Isolation kit with DNA Elution Accessory kit) diverged from the others (PowerMax Soil DNA Isolation kit-High Humic Acid Protocol, and three variations of a modifiedPowerMax Soil DNA/RNA isolation kit), capturing more diverse microbial taxa, with divergent community structures. Although habitat and sample depth still consistently dominated community variation, method-based biases in microbiome recovery for these climatologically-relevant soils are significant, and underscore the importance of methodological consistency for accurate inter-study comparisons, long-term monitoring, and consistent ecological interpretations.

Science & Technology - Other Topics↗
NASA NTRS1980

An application of extremal field guidance in statistical low thrust guidance analysis

The subject of this paper is the application of extremal field guidance to the covariance analysis of low thrust retargeting guidance. The general procedure for low thrust navigation analysis is briefly reviewed. The basic equations of trajectory optimization and extremal field guidance are presented with emphasis on the low thrust problem. The adopted formulation includes thruster on-off times as control variables and accounts for the effects of model parameter variations. A method of solution of the guidance equations is outlined; and the application of the solution in a covariance analysis, including a procedure for the treatment of model parameter variations as either biases or first order Gauss-Markov stochastic processes, is discussed.

Jacobson, R. A.↗
NASA NTRS1973

Computational methods for studying acoustic propagation in nonuniform waveguides.

A variational technique is used to model sound transmission through a nonuniform duct segment consisting of an axial variation in wall admittance or cross sectional area. The method involves the Ritz minimization of functionals which have the governing equations as stationary conditions. The variational method is verified by application to segments of variables-separable geometry for which eigenfunction expansion techniques offer an alternative solution procedure, and by comparison with the results of stepped duct approximations to the nonuniformity. Quantitative data are presented which indicate the boundary condition matching to be a suitable measure of the accuracy of the transmitted field.

Beckemeyer, R. J.↗
DOE OSTI2023

Unsupervised machine learning discovery of structural units and transformation pathways from imaging data

We show that unsupervised machine learning can be used to learn chemical transformation pathways from observational Scanning Transmission Electron Microscopy (STEM) data. To enable this analysis, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. With only these postulates, we developed a machine learning method leveraging a rotationally invariant variational autoencoder (VAE) that can identify the existing molecular fragments observed within a material. The approach encodes the information contained in STEM image sequences using a small number of latent variables, allowing the exploration of chemical transformation pathways by tracing the evolution of atoms in the latent space of the system. The results suggest that atomically resolved STEM data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods in the physical sciences and will stimulate the development of more sophisticated ways to encode physical constraints in the encoder–decoder architectures and generative physical laws and causal relationships in the latent space of VAEs.

97 MATHEMATICS AND COMPUTING↗