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At least 865 records · Page 48

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Automated solar collector installation design including version management

Embodiments may include systems and methods to create and edit a representation of a worksite, to create various data objects, to classify such objects as various types of predefined “features” with attendant properties and layout constraints. As part of or in addition to classification, an embodiment may include systems and methods to create, associate, and edit intrinsic and extrinsic properties to these objects. A design engine may apply of design rules to the features described above to generate one or more solar collectors installation design alternatives, including generation of on-screen and/or paper representations of the physical layout or arrangement of the one or more design alternatives. Some embodiments may provide viewing, creating, and manipulating of multiple versions of a solar collector layout design for a particular installation worksite. The use of versions may allow analysis of alternative layouts, alternative feature classifications, and cost and performance data corresponding to alternative design choices. Version summary information providing a representative comparison between versions across a number of dimensions may be provided.

14 SOLAR ENERGY↗

NASA NTRS2022

Earth System Perspective

We review the most current knowledge pertaining to carbon cycle components in an Earth System model and discuss data assimilation applications to inform and improve process-based representations in these models.

Earth System model↗

NASA NTRS1996

Long-term archiving and data access: modelling and standardization

This paper reports on the multiple difficulties inherent in the long-term archiving of digital data, and in particular on the different possible causes of definitive data loss. It defines the basic principles which must be respected when creating long-term archives. Such principles concern both the archival systems and the data. The archival systems should have two primary qualities: independence of architecture with respect to technological evolution, and generic-ness, i.e., the capability of ensuring identical service for heterogeneous data. These characteristics are implicit in the Reference Model for Archival Services, currently being designed within an ISO-CCSDS framework. A system prototype has been developed at the French Space Agency (CNES) in conformance with these principles, and its main characteristics will be discussed in this paper. Moreover, the data archived should be capable of abstract representation regardless of the technology used, and should, to the extent that it is possible, be organized, structured and described with the help of existing standards. The immediate advantage of standardization is illustrated by several concrete examples. Both the positive facets and the limitations of this approach are analyzed. The advantages of developing an object-oriented data model within this contxt are then examined.

Hoc, Claude↗

DOE OSTI2020

Automated solar collector installation design including version management

Embodiments may include systems and methods to create and edit a representation of a worksite, to create various data objects, to classify such objects as various types of pre-defined “features” with attendant properties and layout constraints. As part of or in addition to classification, an embodiment may include systems and methods to create, associate, and edit intrinsic and extrinsic properties to these objects. A design engine may apply of design rules to the features described above to generate one or more solar collectors installation design alternatives, including generation of on-screen and/or paper representations of the physical layout or arrangement of the one or more design alternatives. Some embodiments may provide viewing, creating, and manipulating of multiple versions of a solar collector layout design for a particular installation worksite. The use of versions may allow analysis of alternative layouts, alternative feature classifications, and cost and performance data corresponding to alternative design choices. Version summary information providing a representative comparison between versions across a number of dimensions may be provided.

14 SOLAR ENERGY↗

DOE OSTI2023

Capturing dynamical correlations using implicit neural representations

Abstract Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S ( Q , ω ), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages ‘neural implicit representations’ that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La 2 NiO 4 , showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Accelerating Structure–Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy

Microscopy combined with local spectroscopy is widely used to correlate nanoscale structure with functional properties in materials, but conventional measurements rely heavily on human-selected sampling locations and predefined targets, limiting data set diversity and the potential for discovery. Here, we present a framework that integrates autonomous microscopy with dual-novelty deep kernel learning (DN-DKL) for adaptive data acquisition and a dual variational autoencoder (VAE) for representation learning. DN-DKL actively guides the microscopy toward structurally and spectroscopically novel regions, enabling efficient collection of large spectral data sets. Dual-VAE embeds local structures and spectroscopic responses into a shared latent manifold that serves as a structure–property relationship map. We applied this framework for the investigation of halide perovskite films by using conductive atomic force microscopy. The results reveal distinct hysteresis behaviors that are linked to specific nanoscale structural motifs, including grain boundary junction points that show hysteresis under different bias conditions and asymmetric grain boundaries that suppress the charge transport. This framework establishes a general strategy that leverages the complementary strengths of self-driving microscopy, machine learning, and human expertise to accelerate scientific discovery in functional materials.

atomic force microscopy↗

NASA NTRS1978

Data processing in infrared astronomy

Infrared astronomy is often carried out with rocket probes or orbiting satellite telescopes in order to escape the effects of atmospheric absorption. The data returned from such missions is a highly abstracted digital representation of measurements made by analog detectors. The ability to extract infrared-emission information from these data streams depends on a thorough understanding of the information flow from the telescope aperture to the computer center. This paper reviews the primary elements of this end-to-end concept and the impact of each of these elements on the data processing algorithms, including the division between onboard and ground processing for scientific measurements.

Pelzmann, R. F., Jr.↗

NASA NTRS1978

Design representation and consistency maintenance needs

Two major issues of data base support for large-scale engineering design are considered. The first deals with the need to support multidisciplinary, hierarchical and interactive design without imposing a priori constraints on the sequence of design decisions. An abstract logical model of the data base capable of such support is outlined. The second issue deals with the role the data base must play in maintaining integrity and consistency among the data representing the emerging design. A tentative model implementing a number of consistency management functions is presented.

Eastman, C. M.↗

NASA NTRS1985

Development and testing of a simple assimilation technique to derive average wind fields from simulated scatterometer data

A simple algorithm is developed and tested to derive a regularly spaced wind field in a limited area from simulated multiorbit scatterometer data. The data are generated by sampling a time-varying known wind field, the 1000-mb FGGE data, from a simulated scatterometer. A simple assimilation technique is used to derive a regularly spaced (100-km grid) wind field representation of two-day averages from the simulated data. Several test cases are considered, and it is noted that the assimilation technique might be applicable to large-scale ocean or atmospheric models.

Legler, D. M.↗

NASA NTRS1987

Development of improved gravity field estimates using existing altimeter data

The formulation of a Fourier series and geographic representation expressions for orbit error were reported. Addition activities include the study of undulation errors in the Lagrangian formulation, and the development and testing of all the necessary software for the above expression. Additionally, the matrix formulations that led to the formation of observed equations and covariance propagation were developed.

Source record↗

NASA NTRS1989

Finite solid representation of robot manipulation in three-dimensional geometric reasoning

Intelligence robotic system design involves sensor allocation and sensor data fusion. In the field of geometric reasoning, a fast and effective representation method is needed to determine the physical dimensions of the manipulator as well as the motion environment in sufficient detail. Due to the capability of current sensor technology, the integrity of the sensor information is location dependent and point-wise, line-wise and plane-wise in nature. In order to maneuver the robot by reasoning the environment, there is a minimum requirement of prior information for integrating the allocated sensor feedback. Some new theoretical developments of how to associate the number and the locations of the sensors with three-dimensional geometry of the robot and the motion environment is presented. The related collision avoidance problem (CAP) will be addressed as well.

Chen, Alexander Y. K.↗

DOE OSTI2021

Excalibur: A Nonparametric, Hierarchical Wavelength Calibration Method for a Precision Spectrograph

Excalibur is a nonparametric, hierarchical framework for precision wavelength calibration of spectrographs. It is designed with the needs of extreme-precision radial-velocity (EPRV) instruments in mind, which require calibration or stabilization to better than 10{sup −4} pixels. Instruments vary along only a few dominant degrees of freedom, especially EPRV instruments that feature highly stabilized optical systems and detectors. Excalibur takes advantage of this property by using all calibration data to construct a low-dimensional representation of all accessible calibration states for an instrument. Excalibur also takes advantage of laser-frequency combs or etalons, which generate a dense set of stable calibration points. This density permits the use of a nonparametric wavelength solution that can adapt to any instrument or detector oddities better than parametric models, such as a polynomial. We demonstrate the success of this method with data from the Extreme Precision Spectrograph (EXPRES), which uses a laser-frequency comb. When wavelengths are assigned to laser comb lines using excalibur, the rms of the residuals is about one-fifth that of wavelengths assigned using polynomial fits to individual exposures. Radial-velocity measurements of HD 34411 show a reduction in rms scatter over a 10 month time baseline from 1.17 to 1.05 m s{sup −1}.

47 OTHER INSTRUMENTATION↗

NASA NTRS2001

Insights into Airframe Aerodynamics and Rotor-on-Wing Interactions from a 0.25-Scale Tiltrotor Wind Tunnel Model

A recent experimental investigation into tiltrotor aerodynamics and acoustics has resulted in the acquisition of a set of data related to tiltrotor airframe aerodynamics and rotor and wing interactional aerodynamics. This work was conducted in the National Full-scale Aerodynamics Complex's (NFAC) 40-by-80 Foot Wind Tunnel, at NASA Ames Research Center, on the Full-Span Tilt Rotor Aeroacoustic Model (TRAM). The full-span TRAM wind tunnel test stand is nominally based on a quarter-scale representation of the V-22 aircraft. The data acquired will enable the refinement of analytical tools for the prediction of tiltrotor aeromechanics and aeroacoustics.

Young, L. A.↗

NASA NTRS2012

Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data

The main point of this study was to use realistic representations of cloudy atmospheres to assess errors in solar flux estimates associated with 1D radiative transfer models. A scene construction algorithm, developed for the EarthCARE satellite mission, was applied to CloudSat, CALIPSO, and MODIS satellite data thus producing 3D cloudy atmospheres measuring 60 km wide by 13,000 km long at 1 km grid-spacing. Broadband solar fluxes and radiances for each (1 km)2 column where then produced by a Monte Carlo photon transfer model run in both full 3D and independent column approximation mode (i.e., a 1D model).

Barker, Howard W.↗

NASA NTRS1975

Spline function approximation techniques for image geometric distortion representation

Least squares approximation techniques were developed for use in computer aided correction of spatial image distortions for registration of multitemporal remote sensor imagery. Polynomials were first used to define image distortion over the entire two dimensional image space. Spline functions were then investigated to determine if the combination of lower order polynomials could approximate a higher order distortion with less computational difficulty. Algorithms for generating approximating functions were developed and applied to the description of image distortion in aircraft multispectral scanner imagery. Other applications of the techniques were suggested for earth resources data processing areas other than geometric distortion representation.

Anuta, P. E.↗

DOE OSTI2020

Pass-efficient methods for compression of high-dimensional turbulent flow data

The future of high-performance computing, specifically on future Exascale computers, will presumably see memory capacity and bandwidth fail to keep pace with data generated, for instance, from massively parallel partial differential equation (PDE) systems. Current strategies proposed to address this bottleneck entail the omission of large fractions of data, as well as the incorporation of in situ compression algorithms to avoid overuse of memory. To ensure that post-processing operations are successful, this must be done in a way that a sufficiently accurate representation of the solution is stored. Moreover, in situations where the input/output system becomes a bottleneck in analysis, visualization, etc., or the execution of the PDE solver is expensive, the number of passes made over the data must be minimized. In the interest of addressing this problem, this work focuses on the utility of pass-efficient, parallelizable, low-rank, matrix decomposition methods in compressing high-dimensional simulation data from turbulent flows. Additionally, a particular emphasis is placed on using coarse representation of the data – compatible with the PDE discretization grid – to accelerate the construction of the low-rank factorization. This includes the presentation of a novel single-pass matrix decomposition algorithm for computing the so-called interpolative decomposition. The methods are described extensively and numerical experiments on two turbulent channel flow data are performed. In the first (unladen) channel flow case, compression factors exceeding 400 are achieved while maintaining accuracy with respect to first- and second-order flow statistics. In the particle-laden case, compression factors of 100 are achieved and the compressed data is used to recover particle velocities. These results show that these compression methods can enable efficient computation of various quantities of interest in both the carrier and disperse phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1993

A study of the influence of satellite data on GLA analyses over the Atlantic Ocean during a period of blocking anticyclone development

This paper presents a statistical diagnosis of the differences between two Goddard Laboratory for Atmospheres analysis representations of temperature and wind speed, one containing satellite data influences (SAT analyses) and one without (NOSAT analyses), for a case of oceanic blocking anticyclone development. Results, obtained using area means and standard deviations of the two fields and correlation coefficients and root-mean-square differences between the two fields, indicate that the inclusion of satellite data can have significant impact on ocean-domain analyses. This is especially evident if one examines higher-order fields that represent gradients in the basic variable fields or contain covariances between two different variables (e.g., advections). More specifically, the inclusion of satellite data resulted in a cold (warm) temperature bias at low (high) levels and weaker temperature gradients, stronger winds, weaker vertical wind shears, and a warm-air advection bias at most levels. The SAT analyses also exhibited larger standard deviations than the NOSAT for wind speed, relative vorticity, vorticity advection, temperature advection, and 500-mb height tendencies, suggesting that for this case the spatial variability of the circulation features were enhanced by the inclusion of satellite data. Even so, since height tendencies forced by vorticity and temperature advection were less sensitive to the addition of satellite data than were the advection quantities themselves, the dynamics of the blocking system was apparently less influenced by the presence of satellite data than was the structure of the system.

Lamberty, Gregory L.↗