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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRS1972

The composite sequential clustering technique for analysis of multispectral scanner data

The clustering technique consists of two parts: (1) a sequential statistical clustering which is essentially a sequential variance analysis, and (2) a generalized K-means clustering. In this composite clustering technique, the output of (1) is a set of initial clusters which are input to (2) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum likelihood classification techniques. The mathematical algorithms for the composite sequential clustering program and a detailed computer program description with job setup are given.

Su, M. Y.↗
NASA NTRS1998

Analysis of Information Content in High-Spectral Resolution Sounders using Subset Selection Analysis

In this paper, we summarize the results of the sensitivity analysis and data reduction carried out to determine the information content of AIRS and IASI channels. The analysis and data reduction was based on the use of subset selection techniques developed in the linear algebra and statistical community to study linear dependencies in high dimensional data sets. We applied the subset selection method to study dependency among channels by studying the dependency among their weighting functions. Also, we applied the technique to study the information provided by the different levels in which the atmosphere is discretized for retrievals and analysis. Results from the method correlate well with intuition in many respects and point out to possible modifications for band selection in sensor design and number and location of levels in the analysis process.

Velez-Reyes, Miguel↗
DOE OSTI2025

Iterative Reconstruction for Multimodal Neutron Tomography

Here, we describe a unified framework for model-based iterative 3-D reconstruction of multimodal neutron transmission, hydrogen-scatter, and induced-fission images from low resolution data recorded using 14.1-MeV neutrons and the associated-particle imaging (API) technique. The framework, which was developed to facilitate use in challenging field-deployment scenarios, is centered around physics-based system models and a total variation (TV) constrained implementation of the simultaneous iterative reconstruction technique (SIRT). Modified to solve a statistically weighted least squares (WLS) problem, the SIRT algorithm is accelerated using ordered subsets and Nesterov’s momentum for which we derive a near-optimal value of the governing Lipschitz constant. The approach enables the reconstruction of images that are high resolution compared to the acquired data and is robust to both limited statistics and a limited number of projection angles. Moreover, the framework is fast enough to be practical. Example images are provided that demonstrate both the ability to perform fast-neutron imaging of high-atomic-number materials with low radiation dose and the benefit of multimodal neutron imaging to identify key materials.

Hydrogen scatter↗
NASA NTRS1992

Implementation of quality improvement techniques for management and technical processes in the ACRV project

Total Quality Management (TQM) is a cooperative form of doing business that relies on the talents of everyone in an organization to continually improve quality and productivity, using teams and an assortment of statistical and measurement tools. The objective of the activities described in this paper was to implement effective improvement tools and techniques in order to build work processes which support good management and technical decisions and actions which are crucial to the success of the ACRV project. The objectives were met by applications in both the technical and management areas. The management applications involved initiating focused continuous improvement projects with widespread team membership. The technical applications involved applying proven statistical tools and techniques to the technical issues associated with the ACRV Project. Specific activities related to the objective included working with a support contractor team to improve support processes, examining processes involved in international activities, a series of tutorials presented to the New Initiatives Office and support contractors, a briefing to NIO managers, and work with the NIO Q+ Team. On the technical side, work included analyzing data from the large-scale W.A.T.E.R. test, landing mode trade analyses, and targeting probability calculations. The results of these efforts will help to develop a disciplined, ongoing process for producing fundamental decisions and actions that shape and guide the ACRV organization .

Raiman, Laura B.↗
DOE OSTI2014

MEASURING TEMPORAL PHOTON BUNCHING IN BLACKBODY RADIATION

Light from thermal blackbody radiators such as stars exhibits photon bunching behavior at sufficiently short timescales. However, with available detector bandwidths, this bunching signal is difficult to observe directly. We present an experimental technique to increase the photon bunching signal in blackbody radiation via spectral filtering of the light source. Our measurements reveal strong temporal photon bunching from blackbody radiation, including the Sun. This technique allows for an absolute measurement of the photon bunching signature g {sup (2)}(0), and thereby a direct statement on the statistical nature of a light source. Such filtering techniques may help revive the interest in intensity interferometry as a tool in astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

Psychophysical rating of image compression techniques

Image compression schemes abound with little work which compares their bit-rate performance based on subjective fidelity measures. Statistical measures of image fidelity, such as squared error measures, do not necessarily correspond to subjective measures of image fidelity. Most previous comparisons of compression techniques have been based on these statistical measures. A psychophysical method has been used to estimate, for a number of compression techniques, a threshold bit-rate yielding a criterion level of performance in discriminating original and compressed images. The compression techniques studied include block truncation, Laplacian pyramid, block discrete cosine transform, with and without a human visual system scaling, and cortex transform coders.

Stein, Charles S.↗
DOE OSTI2017

Predictive coarse-graining

We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a probabilistic coarse-to-fine map. This corresponds to a directed probabilistic model where the coarse variables play the role of latent generators of the fine scale (all-atom) data. From an information-theoretic perspective, the framework proposed provides an improvement upon the relative entropy method and is capable of quantifying the uncertainty due to the information loss that unavoidably takes place during the coarse-graining process. Furthermore, it can be readily extended to a fully Bayesian model where various sources of uncertainties are reflected in the posterior of the model parameters. The latter can be used to produce not only point estimates of fine-scale reconstructions or macroscopic observables, but more importantly, predictive posterior distributions on these quantities. Predictive posterior distributions reflect the confidence of the model as a function of the amount of data and the level of coarse-graining. The issues of model complexity and model selection are seamlessly addressed by employing a hierarchical prior that favors the discovery of sparse solutions, revealing the most prominent features in the coarse-grained model. A flexible and parallelizable Monte Carlo – Expectation–Maximization (MC-EM) scheme is proposed for carrying out inference and learning tasks. A comparative assessment of the proposed methodology is presented for a lattice spin system and the SPC/E water model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings

In this paper, we present a simple yet effective algorithm to automatically assign reference temperature set-points based on the occupancy information. Both the binary and detailed occupancy estimation cases are considered. In the first case study, we assume the schedule involves only binary states (occupied or not occupied), i.e. the room is invariant. With long-term observations occupancy levels can be estimated using statistical tools. In the second case study, two techniques are introduced. Firstly, we propose an identification-based approaches. More precisely, we identify the models via Expectation Maximization (EM) approach. The statistical state space model is built in linear form for the mapping between the occupancy measurements and real occupancy states with noise considered. Secondly, we introduce a novel finite state automata (FSA) which is successfully reconstructed by general systems problem solver (GSPS). As far as we know, no studies have used the finite state machine or general system theory to estimate occupancy in buildings. All above estimates can be used to adaptively update the temperature set-points for Heating, Ventilation, and Air-Conditioning (HVAC) control strategy.

Djouadi, Seddik M↗
DOE OSTI2015

A STATISTICAL STUDY OF THE SOLAR WIND TURBULENCE AT ION KINETIC SCALES USING THE K-FILTERING TECHNIQUE AND CLUSTER DATA

Plasma turbulence at ion kinetic scales in the solar wind is investigated using the multi-point magnetometer data from the Cluster spacecraft. By applying the k-filtering method, we are able to estimate the full three-dimensional power spectral density P(ω{sub sc}, k) at a certain spacecraft frequency ω{sub sc} in wavevector (k) space. By using the wavevector at the maximum power in P(ω{sub sc}, k) at each sampling frequency ω{sub sc} and the Doppler shifted frequency ω{sub pla} in the solar wind frame, the dispersion plot ω{sub pla} = ω{sub pla}(k) is found. Previous studies have been limited to very few intervals and have been hampered by large errors, which motivates a statistical study of 52 intervals of solar wind. We find that the turbulence is predominantly highly oblique to the magnetic field k >> k {sub ∥}, and propagates slowly in the plasma frame with most points having frequencies smaller than the proton gyrofrequency ω{sub pla} < Ω{sub p}. Weak agreement is found that turbulence at the ion kinetic scales consists of kinetic Alfvén waves and coherent structures advected with plasma bulk velocity plus some minor more compressible components. The results suggest that anti-sunward and sunward propagating magnetic fluctuations are of similar nature in both the fast and slow solar wind at ion kinetic scales. The fast wind has significantly more anti-sunward flux than sunward flux and the slow wind appears to be more balanced.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Review of Onsite Temperature and Solar Forecasting Models to Enable Better Building Design and Operations

Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data. Traditionally, most studies utilize airport weather information as the decision inputs. However, most buildings are in environments that are quite different than those at the airport miles away. Tree cover, adjacent buildings, and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature, humidity, solar irradiance, and wind that are large enough to influence design and operation decisions. In order to overcome this challenge, there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales. Additionally, this paper reviews and complies knowledge on common weather data resources, data processing methodologies and forecasting techniques of weather information. Commonly used statistical, machine learning and physical-based models are discussed and presented as two major categories: deterministic forecasting and probabilistic forecasting. Finally, evaluation metrics for forecasting errors are listed and discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Gas-phase synthesis of naphthalene through an unconventional thermal alkyne–alkene [2 + 2] cycloaddition mechanism

Exotic cycloaddition entrance channels were discovered for the bimolecular gas-phase reactions of the phenylethynyl radical (C 6 H 5 CC, X 2 A 1 ) with ethylene-d 4 (C 2 D 4 ) and propylene (C 3 H 6 ) as explored under single-collision conditions utilizing the crossed molecular beams technique combined with electronic structure and statistical calculations. Connecting the concepts of barrierless entrance channels, excited states, and facile non-photochemically activated cycloadditions, the reaction pathway features an unconventional thermal [2 + 2] cycloaddition forming a four-membered ring collision complex followed by multiple isomerizations prior to unimolecular decomposition via atomic hydrogen loss to (un)substituted naphthalenes—naphthalene-d 4 (C 10 H 4 D 4 ) and 1-/2-methylnaphthalene (C 11 H 10 ). The small energy gap between the singly-occupied a 1 highest occupied molecular orbital (HOMO) with a σ-character and the underlying doubly-occupied b 1 molecular orbital with a π-character allows a facile promotion of an electron. This in turn enables a versatile low-temperature reactivity of phenylethynyl, where the end-on and side-on barrierless approaches of ethylene are due to its interaction with the σ and π orbitals, respectively, thus suggesting this mechanism as a possible method for tuning substituents in polycyclic aromatic hydrocarbon (PAH) formation and highlighting its versatility as a probe of fundamental carbon chemistry via counterintuitive cycloaddition reactions under single-collision conditions.

Goettl, Shane J. [University of Hawaii at Manoa, H↗
DOE OSTI2020

A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons

Reliable, non-intrusive, short-term (of up to 12 hours ahead) prediction of a building's energy demand is a critical component of intelligent energy management applications. A number of such approaches have been proposed over time, utilizing various statistical and, more recently, machine learning techniques, such as decision trees, neural networks and support vector machines. Importantly, all of these works barely outperform simple seasonal auto-regressive integrated moving average models, while their complexity is significantly higher. Here, we propose a novel low-complexity non-intrusive approach that improves the predictive accuracy of the state-of-the-art by up to ~10%. The backbone of our approach is a K-nearest neighbours search method, that exploits the demand pattern of the most similar historical days, and incorporates appropriate time-series pre-processing and easing. In the context of this work, we evaluate our approach against state-of-the-art methods and provide insights on their performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Analyzing the cosmic variance limit of remote dipole measurements of the cosmic microwave background using the large-scale kinetic Sunyaev Zel'dovich effect

Due to cosmic variance we cannot learn any more about large-scale inhomogeneities from the primary cosmic microwave background (CMB) alone. More information on large scales is essential for resolving large angular scale anomalies in the CMB. Here we consider cross correlating the large-scale kinetic Sunyaev Zel'dovich (kSZ) effect and probes of large-scale structure, a technique known as kSZ tomography. The statistically anisotropic component of the cross correlation encodes the CMB dipole as seen by free electrons throughout the observable Universe, providing information about long wavelength inhomogeneities. We compute the large angular scale power asymmetry, constructing the appropriate transfer functions, and estimate the cosmic variance limited signal to noise for a variety of redshift bin configurations. The signal to noise is significant over a large range of power multipoles and numbers of bins. We present a simple mode counting argument indicating that kSZ tomography can be used to estimate more modes than the primary CMB on comparable scales. A basic forecast indicates that a first detection could be made with next-generation CMB experiments and galaxy surveys. This paper motivates a more systematic investigation of how close to the cosmic variance limit it will be possible to get with future observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Lensing substructure quantification in RXJ1131-1231: a 2 keV lower bound on dark matter thermal relic mass

We study the substructure content of the strong gravitational lens RXJ1131-1231 through a forward modelling approach that relies on generating an extensive suite of realistic simulations. We use a semi-analytic merger tree prescription that allows us to stochastically generate substructure populations whose properties depend on the dark matter particle mass. These synthetic halos are then used as lenses to produce realistic mock images that have the same features, e.g. luminous arcs, quasar positions, instrumental noise and PSF, as the data. We then analyse the data and the simulations in the same way with summary statistics that are sensitive to the signal being targeted and are able to constrain models of dark matter statistically using Approximate Bayesian Computing (ABC) techniques. (In this work, we focus on the thermal relic mass estimate and fix the semi-analytic descriptions of the substructure evolution based on recent literature.) We are able, based on the HST data for RXJ1131-1231, to rule out a warm dark matter thermal relic mass below 2 keV at the 2σ confidence level.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Machine learning for single-ended event reconstruction in PROSPECT experiment

The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its 2018 run. Despite challenges with photomultiplier tube base failures affecting some segments, innovative machine learning approaches were employed to perform position and energy reconstruction, and particle classification. This work highlights the effectiveness of convolutional neural networks and graph convolutional networks in enhancing data analysis. By leveraging these techniques, a 3.3% increase in effective statistics was achieved compared to traditional methods, showcasing their potential to improve analysis performance. Furthermore, these machine learning methodologies offer promising applications for other segmented particle detectors, underscoring their versatility and impact.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Lack of clear standards and usable comparisons of downscaled climate projections pose a roadblock for US climate discovery and adaptation

Abstract The release of global climate projections coupled with the demand for local-resolution climate-forced meteorology has prompted many research groups to downscale these projections using various statistical, dynamical, and current machine learning techniques. Such downscaled datasets are being used to plan infrastructure and other community needs over the coming decades. Faced with roughly a dozen available US downscaled datasets, many practitioners ask, ‘What are the relevant differences between datasets?’ This work highlights the difficulty of comparing downscaled datasets and illustrates ways in which datasets differ even when using identical climate model input data. We show that substantial variability in precipitation projections arises from downscaling alone and that the downscaled dataset agreement varies depending on global climate projection. This analysis emphasizes the need for greater coordination and movement toward rigorous benchmarking of downscaling strategies within the downscaling research community, à la the land-modeling community, to better quantify downscaling dataset differences, strengths, and weaknesses for practitioners.

Hartke, Samantha H. (ORCID:0000000202394723)↗
DOE OSTI2025

A kinetic-based regularization method for data science applications

We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy between the discrete and continuum representations of the data, in turn allowing to access more favorable energy landscapes, thus improving the accuracy of the interpolator. Our approach improves performance in both interpolation and regression tasks, even in high-dimensional spaces. Unlike traditional methods, it does not require empirical parameter tuning, making it particularly effective for handling noisy data. We also show that thanks to its local nature, the method offers computational and memory efficiency advantages over Radial Basis Function interpolators, especially for large datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

SDSS-IV MaNGA: drivers of stellar metallicity in nearby galaxies

The distribution of stellar metallicities within and across galaxies is an excellent relic of the chemical evolution across cosmic time. Here we present a detailed analysis of spatially resolved stellar populations based on >2.6 million spatial bins from 7439 nearby galaxies in the Sloan Digital Sky Survey-IV (SDSS-IV) Mapping Nearby Galaxies at APO (MaNGA) survey. To account for accurate inclination corrections, we derive an equation for morphology-dependent determination of galaxy inclinations. Our study goes beyond the well-known global mass–metallicity relation and radial metallicity gradients by providing a statistically sound exploration of local relations between stellar metallicity [Z/H], stellar surface mass density Σ ⋆ , and galactocentric distance in the global mass–morphology plane. We find a significant resolved mass density–metallicity relation rΣ ⋆ ZR for galaxies of all types and masses above 10 9.8 M ⊙ ⁠. Different radial distances make an important contribution to the spread of the relation. Particularly, in low- and intermediate-mass galaxies, we find that at fixed Σ ⋆ metallicity increases with radius independently of morphology. For high masses, this radial dependence is only observed in high Σ ⋆ regions of spiral galaxies. This result calls for a driver of metallicity, in addition to Σ ⋆ that promotes chemical enrichment in the outer parts of galaxies more strongly than in the inner parts. We discuss gas accretion, outflows, recycling, and radial migration as possible scenarios.

79 ASTRONOMY AND ASTROPHYSICS↗