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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Precipitate Characterization in Model Al-Zn-Mg-(Cu) Alloys Using Small-Angle X-ray Scattering

Model 7000 series alloys with and without copper were fabricated into sheets to study precipitation hardening behavior under isothermal aging conditions. Samples of each alloy were subjected to 3 h annealing treatments at various temperatures to produce a range of precipitate size distributions. Hardness, electrical conductivity, and small-angle X-ray scattering (SAXS) were used to characterize the aging behavior of the two alloys. Precipitate size distributions were modeled from the scattering curves for each annealing condition using a maximum entropy method (MEM) and compared to select transmission electron microscopy (TEM) results. The measured average precipitate diameters from TEM were in good agreement with the average precipitate diameters determined from the scattering curves.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Automatic Labeling for Entity Extraction in Cyber Security

Timely analysis of cyber-security information necessitates automated information extraction from unstructured text. While state-of-the-art extraction methods produce extremely accurate results, they require ample training data, which is generally unavailable for specialized applications, such as detecting security related entities; moreover, manual annotation of corpora is very costly and often not a viable solution. In response, we develop a very precise method to automatically label text from several data sources by leveraging related, domain-specific, structured data and provide public access to a corpus annotated with cyber-security entities. Next, we implement a Maximum Entropy Model trained with the average perceptron on a portion of our corpus (~750,000 words) and achieve near perfect precision, recall, and accuracy, with training times under 17 seconds.

Automatic Labeling↗
DOE OSTI2014

Data free inference with processed data products

Here, we consider the context of probabilistic inference of model parameters given error bars or confidence intervals on model output values, when the data is unavailable. We introduce a class of algorithms in a Bayesian framework, relying on maximum entropy arguments and approximate Bayesian computation methods, to generate consistent data with the given summary statistics. Once we obtain consistent data sets, we pool the respective posteriors, to arrive at a single, averaged density on the parameters. This approach allows us to perform accurate forward uncertainty propagation consistent with the reported statistics.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Hybrid Reduced Order Model for N2-N2 Interactions for Application to Dissociation and Energy Transfer Processes

This work presents a general framework for model reduction of non-equilibrium energy transfer and dissociation processes. The multi-group maximum-entropy method is coupled with the quasi-classical trajectory method to directly construct a reduced order model for chemical non-equilibrium. Kinetic data is calculated by applying detailed balance at a microscopic level, overcoming the limitation of calculating recombination kinetic data. This approach enables the construction of a reduced order model for kinetics which bypasses the need to compute state-to-state kinetic data. This physics based reduced order model ensures that an equilibrium distribution is reached given infinite time, while allowing for non-equilibrium distributions during the relaxation and dissociation processes. A proof-of-concept test case demonstrates the applicability of this model by comparison with state-to-state kinetic data for the N2(Chi (sup 1) Sigma ((sub g), (sup plus sign)) minus N ((exp 4) S (sub u)) system with excellent agreement.

Maximum Entropy↗
DOE OSTI2022

Quasar Identification Using Multivariate Probability Density Estimated from Nonparametric Conditional Probabilities

Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE). A novel density estimator recently developed by Farmer and Jacobs offers an alternative high-throughput automated approach to univariate nonparametric density estimation based on maximum entropy and order statistics, improving accuracy over univariate KDE. This article presents an extension of the single variable case to multiple variables. The univariate estimator is used to recursively calculate a product array of one-dimensional conditional probabilities. In combination with interpolation methods, a complete joint probability density estimate is generated for multiple variables. Good accuracy and speed performance in synthetic data are demonstrated by a numerical study using known distributions over a range of sample sizes from 100 to 10 6 for two to six variables. Performance in terms of speed and accuracy is compared to KDE. The multivariate density estimate developed here tends to perform better as the number of samples and/or variables increases. As an example application, measurements are analyzed over five filters of photometric data from the Sloan Digital Sky Survey Data Release 17. The multivariate estimation is used to form the basis for a binary classifier that distinguishes quasars from galaxies and stars with up to 94% accuracy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Bayesian Entropy Neural Networks for physics-aware prediction

This article addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring flexible model behavior to incorporate non-data sample information. We introduce Bayesian Entropy Neural Networks (BENN), a framework grounded in Maximum Entropy (MaxEnt) principles, designed to impose constraints on Bayesian Neural Network (BNN) predictions. BENN is capable of constraining not only the predicted values but also their derivatives and variances, ensuring a more robust and reliable model output. To achieve simultaneous uncertainty quantification and constraint satisfaction, we employ the method of multipliers approach. This allows for the concurrent estimation of neural network parameters and the Lagrangian multipliers associated with the constraints. Our experiments, spanning diverse applications such as beam deflection modeling and microstructure generation, demonstrate the effectiveness of BENN. The results highlight significant improvements over traditional BNNs and showcase competitive performance relative to contemporary constrained deep learning methods.

14 SOLAR ENERGY↗
DOE OSTI2012

ENTROPY PRODUCTION IN COLLISIONLESS SYSTEMS. II. ARBITRARY PHASE-SPACE OCCUPATION NUMBERS

We present an analysis of two thermodynamic techniques for determining equilibria of self-gravitating systems. One is the Lynden-Bell (LB) entropy maximization analysis that introduced violent relaxation. Since we do not use the Stirling approximation, which is invalid at small occupation numbers, our systems have finite mass, unlike LB's isothermal spheres. (Instead of Stirling, we utilize a very accurate smooth approximation for ln x{exclamation_point}.) The second analysis extends entropy production extremization to self-gravitating systems, also without the use of the Stirling approximation. In addition to the LB statistical family characterized by the exclusion principle in phase space, and designed to treat collisionless systems, we also apply the two approaches to the Maxwell-Boltzmann (MB) families, which have no exclusion principle and hence represent collisional systems. We implicitly assume that all of the phase space is equally accessible. We derive entropy production expressions for both families and give the extremum conditions for entropy production. Surprisingly, our analysis indicates that extremizing entropy production rate results in systems that have maximum entropy, in both LB and MB statistics. In other words, both thermodynamic approaches lead to the same equilibrium structures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Adaptive schemes for incomplete quantum process tomography

We propose an iterative algorithm for incomplete quantum process tomography with the help of quantum state estimation. The algorithm, which is based on the combined principles of maximum likelihood and maximum entropy, yields a unique estimator for an unknown quantum process when one has less than a complete set of linearly independent measurement data to specify the quantum process uniquely. We apply this iterative algorithm adaptively in various situations and so optimize the amount of resources required to estimate a quantum process with incomplete data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Multimodal biological analysis using NLP and expression profile

The goal of this project is to gather biological data from different sources and use computational analysis to evaluate it together. Two data sources were used: microarray gene expression data for Arabidopsis thaliana, and gene co-occurrences in scientific literature extracted from bioRxiv using natural language processing (NLP). For analysis, the microarray data was normalized, its dimensionality was reduced using principal component analysis (PCA), and it was grouped into different numbers of clusters using K-means clusters. Then these expression clusters were compared to the co-occurrence pairs in the NLP data, to evaluate the quality of the NLP extractions. This evaluation was done using entropy analysis on the combined data, compared to the maximum entropy in the clustering alone. As a result, the evaluation of the NLP data shows that the results do correspond to the clusters from the microarray data, and may be used for further analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Performance of Canberra TruckScan Waste Assay Measurement System - 16361

After the Fukushima accident, large (approximately cubic meter) super-sacks of potentially contaminated soil and vegetation were stored at various temporary locations throughout the prefecture. Canberra has developed the TruckScan system to perform rapid and accurate assay measurements for these waste sacks loaded on trucks to determine the individual and total sack activities for Cs-137 and Cs-134. The measurement system consists of eight 3x3 inch shielded and collimated NaI(Tl) gamma-ray detectors. Data collected with the eight detectors is analyzed using a maximum entropy fitting algorithm which optimizes and predicts the mean and maximum possible activity concentration present in each of the sacks. The algorithm uses SuperISOCS to generate detection efficiencies for each detector-sack combination in the measurement, which are then used to solve for the distribution of activity concentrations in each sack that yields the best agreement with the count rates observed in each detector. Throughout the summer and fall of 2015, a prototype system was developed and tested at Canberra's Meriden CT facility. In October 2015, the prototype system was installed and demonstrated in Japan. Results of the demonstration measurements are presented along with estimates of the total measurement uncertainty (TMU) budget. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Off-policy deep reinforcement learning with automatic entropy adjustment for adaptive online grid emergency control

Electric overloading conditions and contingencies put modern power systems at risk of voltage collapse and blackouts. Load shedding is crucial to maintain voltage stability for grid emergency control. However, the rule- or model-based schemes rely on accurate dynamic system models and face considerable challenges in adapting to various operating conditions and uncertain event occurrences. Here, to address these issues, this paper proposes a novel deep reinforcement learning (DRL)-based voltage stability control algorithm with automatic entropy adjustment (AEA) for grid emergency control. Various dynamic network components for complex system operations are modeled to construct the DRL environment. An off-policy soft actor-critic architecture is developed to maximize the expected reward and policy entropy simultaneously. The AEA mechanism is proposed to facilitate the policy maximum entropy procedure, and the proposed method can automatically provide effective discrete and continuous actions against various fault scenarios. Our approach accomplishes high sampling efficiency, scalability, and auto-adaptivity of the control policies under high uncertainties. Comparative studies with the existing DRL-based control methods in IEEE benchmarks indicate salient performance improvement of the proposed method for dynamic system emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Local entropy of a nonequilibrium fermion system

The local entropy of a nonequilibrium system of independent fermions is investigated and analyzed in the context of the laws of thermodynamics. It is shown that the local temperature and chemical potential can only be expressed in terms of derivatives of the local entropy for linear deviations from local equilibrium. The first law of thermodynamics is shown to lead to an inequality, not equality, for the change in the local entropy as the nonequilibrium state of the system is changed. The maximum entropy principle (second law of thermodynamics) is proven: a nonequilibrium distribution has a local entropy less than or equal to a local equilibrium distribution satisfying the same constraints. It is shown that the local entropy of the system tends to zero when the local temperature tends to zero, consistent with the third law of thermodynamics

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Investigation of the Structural, Magnetic, Magnetocaloric, Electrical Properties, and Spin-Polarized Tunneling Effect of the La{sub 0.5}Ca{sub 0.3}Te{sub 0.2}MnO{sub 3} System

Structural, magnetic, magnetocaloric, electrical properties, and spin-polarized tunneling effect in La{sub 0.5}Ca{sub 0.3}Te{sub 0.2}MnO{sub 3} polycrystalline were studied in details. The temperature dependence of the inverse of the magnetic susceptibility indicates the presence of ferromagnetic exchange interaction between the nearest neighbors at the Weiss temperature θp = 265.32 ± 0.109. Based on the magnetic field dependence of magnetization, M(H), and employing the thermodynamic Maxwell equation, the magnetic entropy change |ΔS{sub M}| of the studied sample was determined. The maximum entropy change value of the system was around 4.99 J kg{sup −1} K{sup −1} under a magnetic field of 5 T. Electrically, the upturned region of resistivity was observed at low temperature. The recovery of the resistivity was found to be strongly affected by the external magnetic field. The electrical data were analyzed considering various models. Interestingly, the exception of the spin-polarized tunneling (SPT) model is in good agreement with the experimental data among other models. In the current work, the SPT is the dominant mechanism leading to the rise of resistivity, while decreasing the temperature values. Magnetotransport analysis was successfully carried out using percolation model in the temperature range between around 50 K to nearly 300 K under different magnetic fields up to 8 T. The isothermal field dependence of magnetoresistance was recorded fairly well via a phenomenological model based on the spin-polarized tunneling model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Magnetic and magnetocaloric properties of La{sub 0.6}Pr{sub 0.1}Sr{sub 0.3}Mn{sub 1−x}Fe{sub x}O{sub 3} (0≤x≤0.3) manganites

The La{sub 0.6}Pr{sub 0.1}Sr{sub 0.3}Mn{sub 1−x}Fe{sub x}O{sub 3} (x=0, 0.1, 0.2 and 0.3) samples have been elaborated by the solid-state reaction method. X-ray powder diffraction shows that all the samples crystallize in a rhombohedric phase with R3{sup ¯}c space group. The variation of magnetization as a function of temperature and applied magnetic field was carried out. The samples for x=0 and 0.1 exhibit a FM–PM transition at the Curie temperature T{sub C}, however, for x=0.2 and 0.3 exhibit an AFM–PM one at the Neel temperature T{sub N}, when the temperature increases. A magneto-caloric effect has been calculated in terms of isothermal magnetic entropy change. A large magneto-caloric effect has been observed, the maximum entropy change, |ΔS{sub M}{sup max}|, reaches the highest value of 3.28 J/kgK under a magnetic field change of 5 T with an RCP value of 220 J/kg for La{sub 0.6}Pr{sub 0.1}Sr{sub 0.3}MnO{sub 3} composition, which will be an interesting compound for application materials working as magnetic refrigerants near room temperature. - Graphical abstract: Magnetic entropy change versus temperature and applied magnetic field for x=0.1 (a) and RCP versus applied magnetic field for x=0, 0.1 (b). - Highlights: • The La{sub 0.6}Pr{sub 0.1}Sr{sub 0.3}Mn{sub 1−x}Fe{sub x}O{sub 3} (0≤x≤0.3) polycrystalline samples were prepared by the solid state reaction method. • Crystalline and magnetic structures were investigated using DRX and magnetization measurements. • The magnetocaloric (MC) effect was estimated versus magnetic field and temperatures. • Compounds with x=0, 0.1 exhibit great potential for magnetic refrigeration at room temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Probabilistic inference of reaction rate parameters from summary statistics

Here, this investigation tackles the probabilistic parameter estimation problem involving the Arrhenius parameters for the rate coefficient of the chain branching reaction H + O 2 → OH + O. This is achieved in a Bayesian inference framework that uses indirect data from the literature in the form of summary statistics by approximating the maximum entropy solution with the aid of approximate bayesian computation. The summary statistics include nominal values and uncertainty factors of the rate coefficient, obtained from shock-tube experiments performed at various initial temperatures. The Bayesian framework allows for the incorporation of uncertainty in the rate coefficient of a secondary reaction, namely OH + H 2 → H 2 O + H, resulting in a consistent joint probability density on Arrhenius parameters for the two rate coefficients. It also allows for uncertainty quantification in numerical ignition predictions while conforming with the published summary statistics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

Bounding Species Distribution Models

Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS) might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for "clamping" model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART) and maximum entropy (Maxent) models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used [Current Zoology 57 (5): 642-647, 2011].

Stohlgren, Thomas J.↗
DOE OSTI2019

Study and Modeling of the Magnetocaloric Effect in the La{sub 0.67}Ba{sub 0.33}Mn{sub 0.9}Fe{sub 0.1}O{sub 3} Compound

The simulated magnetic entropy change of La{sub 0.67}Ba{sub 0.33}Mn{sub 0.9}Fe{sub 0.1}O{sub 3} manganite was determined using the mean field theory and a scaling method. Based on the Brillouin function, this method allowed us to simulate magnetic isotherms which are crucial for the simulation of the magnetic entropy change. The simulated entropy change is very close to that determined from experimental magnetic isotherms. In particular, the experimental and simulated maximum entropy change and relative cooling power are respectively suitable and in good agreement. The Bean–Rodbell model was used to simulate magnetization versus temperature curve. The obtained Curie temperature value is much well than that determined experimentally. Under low values of applied field, a phenomenological model has been intervened to simulate magnetization, magnetic entropy change, and the specific heat change versus temperature. This scaling method based on mean field theory leads to a direct estimation of the exchange parameter (λ ), the saturation magnetization (M{sub 0}), the total angular momentum (J), and the gyromagnetic factor (g) of our material. These parameters were used for simulating M(T,H) isotherms, which are used for the estimation of the magnetic entropy change (ΔS{sub M}) of La{sub 0.67}Ba{sub 0.33}Mn{sub 0.9}Fe{sub 0.1}O{sub 3}.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Theory and implementation of a very high throughput true random number generator in field programmable gate array

The contribution of this paper is proposing a new entropy extraction mechanism based on sampling phase jitter in ring oscillators to make a high throughput true random number generator in a field programmable gate array (FPGA) practical. Starting from experimental observation and analysis of the entropy source in FPGA, a multi-phase sampling method is exploited to harvest the clock jitter with a maximum entropy and fast sampling speed. This parametrized design is implemented in a Xilinx Artix-7 FPGA, where the carry chains in the FPGA are explored to realize the precise phase shifting. The generator circuit is simple and resource-saving, so that multiple generation channels can run in parallel to scale the output throughput for specific applications. The prototype integrates 64 circuit units in the FPGA to provide a total output throughput of 7.68 Gbps, which meets the requirement of current high-speed quantum key distribution systems. The randomness evaluation, as well as its robustness to ambient temperature, confirms that the new method in a purely digital fashion can provide high-speed high-quality random bit sequences for a variety of embedded applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗