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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
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↗
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↗
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↗
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↗
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↗
DOE OSTI2022

Randomized probe imaging through deep k-learning

Randomized probe imaging (RPI) is a single-frame diffractive imaging method that uses highly randomized light to reconstruct the spatial features of a scattering object. The reconstruction process, known as phase retrieval, aims to recover a unique solution for the object without measuring the far-field phase information. Typically, reconstruction is done via time-consuming iterative algorithms. In this work, we propose a fast and efficient deep learning based method to reconstruct phase objects from RPI data. The method, which we call deep k-learning, applies the physical propagation operator to generate an approximation of the object as an input to the neural network. This way, the network no longer needs to parametrize the far-field diffraction physics, dramatically improving the results. Deep k-learning is shown to be computationally efficient and robust to Poisson noise. The advantages provided by our method may enable the analysis of far larger datasets in photon starved conditions, with important applications to the study of dynamic phenomena in physical science and biological engineering.

Guo, Zhen (ORCID:0000000213473451)↗
DOE OSTI2019

Intrinsic ductility of random substitutional alloys from nonlinear elasticity theory

A method suitable for computing the ideal strength of random substitutional alloys is introduced. The method relies on nonlinear continuum elasticity theory and allows for the high-throughput computation of ideal strength. The method also allows for the high-throughput computation of an intrinsic ductility parameter defined for a given applied stress state as the ratio of the strain associated with the cleavage instability to the strain associated with the first shear instability. The intrinsic ductility parameter is shown to correlate well with the measured elongations to failure for elemental body-centered-cubic and hexagonal close-packed metals. Application to four high-entropy alloys indicates that the intrinsic ductility parameter describes their experimental compressions to failure well. The method is used to argue that the brittle refractory high-entropy alloy Ta-Nb-V-W-Mo could be made much more ductile through replacement of Mo with Nb. Finally, the potential for the high-throughput optimization of high entropy and chemically complex alloys is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Triaxial quadrupole deformation dynamics in sd-shell nuclei around {sup 26}Mg

Large-amplitude dynamics of axial and triaxial quadrupole deformation in {sup 24,26}Mg, {sup 24}Ne, and {sup 28}Si is investigated on the basis of the quadrupole collective Hamiltonian constructed with the use of the constrained Hartree-Fock-Bogoliubov plus the local quasiparticle random-phase approximation method. The calculation reproduces well properties of the ground rotational bands, and {beta} and {gamma} vibrations in {sup 24}Mg and {sup 28}Si. The {gamma} softness in the collective states of {sup 26}Mg and {sup 24}Ne are discussed. Contributions of the neutrons and protons to the transition properties are also analyzed in connection with the large-amplitude quadrupole dynamics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Randomized prospective phase III trial of 68 Ga-PSMA-11 PET/CT molecular imaging for prostate cancer salvage radiotherapy planning [PSMA-SRT]

Salvage radiotherapy (SRT) for prostate cancer (PCa) recurrence after prostatectomy offers long-term biochemical control in about 50–60% of patients. SRT is commonly initiated in patients with serum PSA levels < 1 ng/mL, a threshold at which standard-of-care imaging is insensitive for detecting recurrence. As such, SRT target volumes are usually drawn in the absence of radiographically visible disease. 68 Ga-PSMA-11 (PSMA) PET/CT molecular imaging is highly sensitive and may offer anatomic localization of PCa biochemical recurrence. However, it is unclear if incorporation of PSMA PET/CT imaging into the planning of SRT could improve its likelihood of success. The purpose of this trial is to evaluate the success rate of SRT for recurrence of PCa after prostatectomy with and without planning based on PSMA PET/CT. Methods We will randomize 193 patients to proceed with standard SRT (control arm 1, n = 90) or undergo a PSMA PET/CT scan (free of charge for patients) prior to SRT planning (investigational arm 2, n = 103). The primary endpoint is the success rate of SRT measured as biochemical progression-free survival (BPFS) after initiation of SRT. Biochemical progression is defined by PSA ≥ 0.2 ng/mL and rising. The randomization ratio of 1:1.13 is based on the assumption that approximately 13% of subjects randomized to Arm 2 will not be treated with SRT because of PSMA-positive extra-pelvic metastases. These patients will not be included in the primary endpoint analysis but will still be followed. The choice of treating the prostate bed alone vs prostate bed and pelvic lymph nodes, with or without androgen deprivation therapy (ADT), is selected by the treating radiation oncologist. The radiation oncologist may change the radiation plan depending on the findings of the PSMA PET/CT scan. Any other imaging is allowed for SRT planning in both arms if done per routine care. Patients will be followed until either one of the following conditions occur: 5 years after the date of initiation of randomization, biochemical progression, diagnosis of metastatic disease, initiation of any additional salvage therapy, death. Discussion This is the first randomized phase 3 prospective trial designed to determine whether PSMA PET/CT molecular imaging can improve outcomes in patients with PCa early BCR following radical prostatectomy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

A note on the logistic equation subject to uncertainties in parameters

This paper discusses the logistic equation subject to uncertainties in the intrinsic growth rate, α, in the initial population density, N{sub 0}, and in the environmental carrying capacity, K. These parameters are treated as independent random variables. The random variable transformation method is applied to compute the first probability density function of the time–population density, N(t), and of its inflection point, t{sup ∗}. Results for the density functions of N(t), for a fixed t>0, and t{sup ∗} are also provided for α, N{sub 0} and K uniformly distributed. Finally, numerical experiments illustrate the proposed theoretical results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Self-oscillating AB diblock copolymer developed by post modification strategy

We prepared AB diblock copolymer composed of hydrophilic poly(ethylene oxide) segment and self-oscillating polymer segment. In the latter segment, ruthenium tris(2,2′-bipyridine) (Ru(bpy){sub 3}), a catalyst of the Belousov-Zhabotinsky reaction, is introduced into the polymer architecture based on N-isopropylacrylamide (NIPAAm). The Ru(bpy){sub 3} was introduced into the polymer segment by two methods; (i) direct random copolymerization (DP) of NIPAAm and Ru(bpy){sub 3} vinyl monomer and (ii) post modification (PM) of Ru(bpy){sub 3} with random copolymer of NIPAAm and N-3-aminopropylmethacrylamide. For both the diblock copolymers, a bistable temperature region (the temperature range; ΔT{sub m}), where the block copolymer self-assembles into micelle at reduced Ru(bpy){sub 3}{sup 2+} state whereas it breaks-up into individual polymer chain at oxidized Ru(bpy){sub 3}{sup 3+} state, monotonically extends as the composition of the Ru(bpy){sub 3} increases. The ΔT{sub m} of the block copolymer prepared by PM is larger than that by DP. The difference in ΔT{sub m} is rationalized from the statistical analysis of the arrangement of the Ru(bpy){sub 3} moiety along the self-oscillating segments. By using the PM method, the well-defined AB diblock copolymer having ΔT{sub m} (ca. 25 °C) large enough to cause stable self-oscillation can be prepared. The periodic structural transition of the diblock copolymer in a dilute solution ([Polymer] = 0.1 wt. %) is closely investigated in terms of the time-resolved dynamic light scattering technique at constant temperature in the bistable region. A macroscopic viscosity oscillation of a concentrated polymer solution (15 wt. %) coupled with the periodic microphase separation is also demonstrated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗
DOE OSTI2020

Efficient 3-D velocity model building using joint inline and crossline plane-wave wave-equation migration velocity analyses

SUMMARY Wave-equation migration velocity analysis (WEMVA) is an image-domain inversion method for velocity model building. Automatic plane-wave WEMVA (PWEMVA) calculates the moveouts of plane-wave common-image gathers (CIGs) by searching a best-fitting parabola with semblance analysis and backprojects residual CIG moveouts into wavefield wave paths with a reflection tomographic kernel. However, 3-D PWEMVA is very computationally expensive because 3-D reflection tomographic inversion requires at least five 3-D reverse-time migrations per iteration and stores two types of source wavefields at model boundaries. We develop a joint inline and crossline PWEMVA method for efficient 3-D velocity model building. We alternatively implement the inline and crossline PWEMVAs with a constraint for each other, in which we iteratively construct the 3-D velocity model update through 1-D spline interpolation of 2-D gradients. The inline and crossline joint inversion is practical since PWEMVA only inverts for low-wavenumber velocity perturbations along wave paths, and the method can take less than 1 per cent of the computational cost of full 3-D PWEMVA. To construct unaliased plane waves for our joint inline and crossline PWEMVA, we develop a 3-D data interpolation method in the frequency–wavenumber (FK) domain to recover regularly and randomly missing traces. The method minimizes the misfit on sufficiently localized data subsets with iterative optimal step lengths and a gradient preconditioner that iteratively selects dominant dips along different azimuths. In numerical experiments, we use a 3-D synthetic seismic data set and a land 3-D field seismic data set acquired at the Farnsworth CO2-EOR (enhanced oil recovery) field to demonstrate the efficacy of our velocity model building and data interpolation methods.

Liu, Xuejian↗