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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Large Eddy Simulation of Convective Heat Transfer in a Random Pebble Bed Using the Spectral Element Method

The development of fluoride-cooled high-temperature reactors has drastically increased the demand for an in-depth understanding of the heat transfer (HT) in packed beds cooled by liquid salts. The complex flow fields and space-dependent porosity found in a pebble bed require a detailed understanding to ensure the proper cooling of the reactor core during normal and accident conditions. As detailed experimental data are complicated to obtain for these configurations, high-fidelity simulation such as large eddy simulation and direct numerical simulation (DNS) can be employed to create a high-resolution heat transfer numerical database that can assist in addressing industrial-driven issues associated with the heat transfer behavior of fluoride-cooled high-temperature reactors. In this paper, we performed a series of large eddy simulation using computational fluid dynamics (CFD) code NekRS to investigate the heat transfer for a bed of 1741 pebbles. Further, the characteristics of the flow, such as average, rms, and time series of velocity and temperature, have been analyzed. Porous media averages have also been performed. The simulation results show a good agreement between non-conjugate heat transfer and conjugate heat transfer. The generated data will be used to benchmark heat transfer modeling methods and local maxima/minima of heat transfer parameters. It will also be used for supporting convective heat transfer quantification for Kairos Power and benchmarking lower fidelity models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Large Eddy Simulation of Random Pebble Bed Using the Spectral Element Method

The high-fidelity simulations are performed with Nek5000/NekRS to show the importance of various phenomena associated with fluid flow and heat transfer through pebble beds. As a result of this work, Kairos Power and NEAMS have a better understanding of the behavior of pebble bed geometries. Kairos Power will leverage the data generated and conclusions reached as part of this investigation to help accelerate the deployment of their reactor design.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1985

Spline methods for approximating quantile functions and generating random samples

Two cubic spline formulations are presented for representing the quantile function (inverse cumulative distribution function) of a random sample of data. Both B-spline and rational spline approximations are compared with analytic representations of the quantile function. It is also shown how these representations can be used to generate random samples for use in simulation studies. Comparisons are made on samples generated from known distributions and a sample of experimental data. The spline representations are more accurate for multimodal and skewed samples and to require much less time to generate samples than the analytic representation.

Schiess, J. R.↗
DOE OSTI2022

Memory row recording for mitigating crosstalk in dynamic random access memory

A method includes adding a set of one or more victim rows to a first probabilistic filter and to a second probabilistic filter, in response to a memory access request, identifying a candidate victim row adjacent to a memory address specified by a memory access request, identifying the candidate victim row as a victim row in the set of victim rows based on performing a lookup of the candidate victim row in a selected filter, where the selected filter includes one of the first probabilistic filter and the second probabilistic filter, in response to identifying the candidate row as the victim row, enabling a row hammering countermeasure, clearing the first probabilistic filter in each of a first set of time periods, and clearing the second probabilistic filter in each of a second set of time periods interleaved with the first set of time periods.

SeyedzadehDelcheh, SeyedMohammad↗
DOE OSTI2019

Estimating physics models and quantifying their uncertainty using optimization with a Bayesian objective function

This paper reports a verification study for a method that fits functions to sets of data from several experiments simultaneously. The method finds a maximum a posteriori probability (MAP) estimate of a function subject to constraints (e. g., convexity in the study), uncertainty about the estimate, and a quantitative characterization of how data from each experiment constrains that uncertainty. While the present work focuses on a model of the Equation Of State (EOS) of gasses produced by detonating a high explosive, the method can be applied to a wide range of physics processes with either parametric or semi-parametric models. As a verification exercise, a reference EOS is used and artificial experimental data sets are created using numerical integration of ordinary differential equations and pseudo-random noise. The method yields an estimate of the EOS that is close to the reference, and identifies how each experiment most constrains the result.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1989

Design guide for predicting nonlinear random response (including snap-through) of buckled plates

This design guide describes a method for predicting the random response of flat and curved plates which is based on theoretical analyses and experimental results. The plate curvature can be due to postbuckling, in-plane mechanical or thermal stresses. Based on a single mode formula, root mean square values of the strain response to broadband excitation are evaluated for different static buckled configurations using the equivalent linearization technique. The effects on the overall strain response due to instability motion of snap-through are included. Panel parameters include clamped and simply-supported boundaries, aspect ratio, thickness and length. Analytical results are compared with experimental results from tests with 12 in. x 15 in. aluminum plates under thermal loading in a progressive wave facility. Comparisons are also made with results from tests with a 2 in. x 15 in. x 0.032 in. aluminum beam under base mechanical excitation. The comparisons help to assess the accuracy of the theory and the conditions under which deviations from the theory due to effects of imperfection and higher modes are significant.

Ng, Chung Fai↗
DOE OSTI2021

Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal–organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. Here, the results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Synthesis, structural and optical properties of Sm{sup 3+} and Nd{sup 3+} doped cadmium sulfide nanocrystals

Graphical abstract: Samarium (Sm{sup 3+}) and neodymium (Nd{sup 3+}) doped cadmium sulfide nanocrystals have been prepared via precipitation method. The structural and the luminescent properties of the as-synthesised nanocrystals have been discussed. - Highlights: • Cubic phase lanthanide ion doped cadmium sulfide nanocrystals were prepared by co-precipitation method. • HRTEM displays randomly aggregated nanoparticles with well-defined lattice fringes. • Energy gap and optical properties were affected by the different doping ions. • Effect of Sm and Nd ion doping on photo-emission of CdS nanocrystals was clarified. - Abstract: Cubic phase samarium (Sm{sup 3+}) and neodymium (Nd{sup 3+}) doped cadmium sulfide nanocrystals were synthesized through the chemical co-precipitation method. The crystallite size computed with high intense (1 1 1) peak using Scherrer formula was ∼3 nm. Morphology was examined with scanning electron microscopy (SEM). The transmission electron microscopy (TEM) images further established the formation of nanoclusters and EDAX spectra confirms the presence of cadmium, sulphide and rare earth elements in the sample. SAED pattern shows the crystallinity of the synthesized sample. Blue shift in the bandgap energy in the reflectance UV spectra, illustrates size quantization effect and dopant ion incorporation into the host lattice. The effect of doping concentrations of Sm{sup 3+} and Nd{sup 3+} on the luminescence spectra of CdS was studied. The emission spectra revealed that the intensity increased considerably in the presence of dopant ions. The variation in the optical properties and the enhancement in the luminescence were discussed for different doping levels.

36 MATERIALS SCIENCE↗
NASA NTRS1982

A data based random number generator for a multivariate distribution (using stochastic interpolation)

Let X be a K-dimensional random variable serving as input for a system with output Y (not necessarily of dimension k). given X, an outcome Y or a distribution of outcomes G(Y/X) may be obtained either explicitly or implicity. The situation is considered in which there is a real world data set X sub j sub = 1 (n) and a means of simulating an outcome Y. A method for empirical random number generation based on the sample of observations of the random variable X without estimating the underlying density is discussed.

Thompson, J. R.↗
DOE OSTI2016

TU-FG-209-11: Validation of a Channelized Hotelling Observer to Optimize Chest Radiography Image Processing for Nodule Detection: A Human Observer Study

Purpose: To validate the use of a Channelized Hotelling Observer (CHO) model for guiding image processing parameter selection and enable improved nodule detection in digital chest radiography. Methods: In a previous study, an anthropomorphic chest phantom was imaged with and without PMMA simulated nodules using a GE Discovery XR656 digital radiography system. The impact of image processing parameters was then explored using a CHO with 10 Laguerre-Gauss channels. In this work, we validate the CHO’s trend in nodule detectability as a function of two processing parameters by conducting a signal-known-exactly, multi-reader-multi-case (MRMC) ROC observer study. Five naive readers scored confidence of nodule visualization in 384 images with 50% nodule prevalence. The image backgrounds were regions-of-interest extracted from 6 normal patient scans, and the digitally inserted simulated nodules were obtained from phantom data in previous work. Each patient image was processed with both a near-optimal and a worst-case parameter combination, as determined by the CHO for nodule detection. The same 192 ROIs were used for each image processing method, with 32 randomly selected lung ROIs per patient image. Finally, the MRMC data was analyzed using the freely available iMRMC software of Gallas et al. Results: The image processing parameters which were optimized for the CHO led to a statistically significant improvement (p=0.049) in human observer AUC from 0.78 to 0.86, relative to the image processing implementation which produced the lowest CHO performance. Conclusion: Differences in user-selectable image processing methods on a commercially available digital radiography system were shown to have a marked impact on performance of human observers in the task of lung nodule detection. Further, the effect of processing on humans was similar to the effect on CHO performance. Future work will expand this study to include a wider range of detection/classification tasks and more observers, including experienced chest radiologists.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1990

Multiple Access Trade Study

The Personal Access Satellite System (PASS) strawman design uses a hybrid Time Division Multiple Access (TDMA)/Frequency Division Multiple Access (FDMA) implementation. TDMA is used for the forward direction (from Suppliers to Users), and FDMA for the return direction (from Users to Suppliers). An alternative architecture is proposed that will require minimal real time coordination and yet provide a fast access method by using random access Code Division Multiple Access (CDMA). The CDMA system issues are addressed such as connecting suppliers and users, both of whom may be located anywhere in the CONUS, when the user terminals are constrained in size and weight; and providing efficient traffic routing under highly variable traffic requirements. It is assumed that bandwidth efficiency is not of paramount importance. CDMA or Spread Spectrum Multiple Access (SSMA) communication is a method in which a group of carriers operate at the same nominal center frequency but are separable from each other by the low cross correlation of the spreading codes used. Interference and multipath rejection capability, ease of selective addressing and message screening, low density power spectra for signal hiding and security, and high resolution ranging are among the benefits of spread spectrum communications.

Motamedi, Masoud↗
DOE OSTI2026

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Chemistry↗
DOE OSTI2025

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Arasteh, Kourosh [Lawrence Livermore National Labo↗
DOE OSTI2015

Bismuth zinc vanadate, BiZn{sub 2}VO{sub 6}: New crystal structure type and electronic structure

We report a combined experimental and computational study of the crystal structure and electronic properties of bismuth zinc vanadate, BiZn{sub 2}VO{sub 6}, known for its visible light photocatalytic activity. The crystal structure has been solved from laboratory powder X-ray diffraction data using the repeated minimisations from random starting values method. BiZn{sub 2}VO{sub 6} adopts a new structure type, based on the following building blocks: corner- and edge-sharing ZnO{sub 4} tetrahedra, ZnO{sub 6} octahedra and VO{sub 4} tetrahedra, and Bi{sub 2}O{sub 12} dimers. It is the only known member of the BiM{sub 2}AO{sub 6} (M=Pb, Ca, Cd, Mn, Zn, Mg, Cu; A=V, P, As) family which does not appear to be structurally closely related to others. The electronic structure of BiZn{sub 2}VO{sub 6}, calculated by DFT methods, shows that it is an indirect gap semiconductor with a calculated band gap of 1.6 eV, which compares favourably to the experimentally measured value of 2.4 eV. - Graphical abstract: The crystal structure of BiZn{sub 2}VO{sub 6}, a new structure type in the BiM{sub 2}AO{sub 6} (M=Mg, Ca, Cd, Cu, Pb, Mn, Zn; A=V, P, As) family. - Highlights: • Structure solution from PXRD data by repeated minimisations from random starting values. • New structure type in the BiM{sub 2}AO{sub 6} (M=Pb, Ca, Cd, Mn, Zn, Mg, Cu; A=V, P, As) family. • Electronic structure calculation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1990

Planetary atmosphere representation for Monte-Carlo simulation of aerospace vehicle trajectories

Methods of simulating large scale random atmospheric variations are examined for use in Monte Carlo simulations of hypervelocity vehicles. Random density, temperature, and pressure variations have a significant impact on the performance of such vehicles. Methods for generating random atmosphere profiles are described and compared in the context of a Monte Carlo simulation of the Aeroassist Flight Experiment (AFE). It is seen that these techniques result in statistically significant differences in key simulation point statistics.

Dutton, K.↗