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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dataset of ultralow temperature refrigeration for COVID 19 vaccine distribution solution

Abstract Most COVID-19 vaccines require temperature control for transportation and storage. Two types of vaccine have been developed by manufacturers (Pfizer and Moderna). Both vaccines are based on mRNA and lipid nanoparticles requiring low temperature storage. The Pfizer vaccine requires ultra-low temperature storage (−80 °C to −60 °C), while the Moderna vaccine requires −30 °C storage. However, the last stage of distribution is quite challenging, especially for rural or suburban areas, where local towns, pharmacy chains and hospitals may not have the infrastructure required to store the vaccine at the required temperature. In addition, there is limited data available to address ancillary challenges of the distribution framework for both transportation and storage stages, including safety concerns due to human exposure to large amounts of CO 2 from dry-ice sublimation, issues due to the pressure increase caused by dry-ice sublimation, and the potential issue caused by non-uniform cryogenic temperatures. As such, there is a need for test dataset to assist the development of a quick, effective, secure, and safe solution to mitigate the challenges faced by vaccine distribution logistics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Forming mechanism of equilibrium and non-equilibrium metallurgical phases in dissimilar aluminum/steel (Al–Fe) joints

Abstract Forming metallurgical phases has a critical impact on the performance of dissimilar materials joints. Here, we shed light on the forming mechanism of equilibrium and non-equilibrium intermetallic compounds (IMCs) in dissimilar aluminum/steel joints with respect to processing history (e.g., the pressure and temperature profiles) and chemical composition, where the knowledge of free energy and atomic diffusion in the Al–Fe system was taken from first-principles phonon calculations and data available in the literature. We found that the metastable and ductile (judged by the presently predicted elastic constants) Al 6 Fe is a pressure ( P ) favored IMC observed in processes involving high pressures. The MoSi 2 -type Al 2 Fe is brittle and a strong P -favored IMC observed at high pressures. The stable, brittle η-Al 5 Fe 2 is the most observed IMC (followed by θ-Al 13 Fe 4 ) in almost all processes, such as fusion/solid-state welding and additive manufacturing (AM), since η-Al 5 Fe 2 is temperature-favored, possessing high thermodynamic driving force of formation and the fastest atomic diffusivity among all Al–Fe IMCs. Notably, the ductile AlFe 3 , the less ductile AlFe, and most of the other IMCs can be formed during AM, making AM a superior process to achieve desired IMCs in dissimilar materials. In addition, the unknown configurations of Al 2 Fe and Al 5 Fe 2 were also examined by machine learning based datamining together with first-principles verifications and structure predictions. All the IMCs that are not P- favored can be identified using the conventional equilibrium phase diagram and the Scheil-Gulliver non-equilibrium simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter (β) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Vickers hardness prediction from machine learning methods

Abstract The search for new superhard materials is of great interest for extreme industrial applications. However, the theoretical prediction of hardness is still a challenge for the scientific community, given the difficulty of modeling plastic behavior of solids. Different hardness models have been proposed over the years. Still, they are either too complicated to use, inaccurate when extrapolating to a wide variety of solids or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) as input variables. The model was trained with an experimental Vickers hardness database of 143 materials, assuring various kinds of compounds. The input properties were calculated from the theoretical elastic tensor. The Materials Project’s database was explored to search for new superhard materials, and our results are in good agreement with the experimental data available. Other alternative models to compute hardness from mechanical properties are also discussed in this work. Our results are available in a free-access easy to use online application to be further used in future studies of new materials at www.hardnesscalculator.com .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Structural evolution of two-dimensional silicates using a “bond-switching” algorithm

Silicates are the most abundant materials in the earth's crust. In recent years, two-dimensional (2D) versions of them grown on metal supports (known as bilayer silicates) have allowed their study in detail down to the atomic scale. These structures are self-containing. Furthermore, they are not covalently bound to the metal support but interact with it through van der Waals forces. Like their three-dimensional counterparts, the 2D-silicates can form both crystalline and vitreous structures. Furthermore, the interconversion between vitreous to crystalline structures has been experimentally observed at the nanoscale. While theoretical work has been carried out to try to understand these transformations, a limitation for ab initio methods, and even molecular dynamics methods, is the computational cost of studying large systems and long timescales. In this work, we present a simple and computationally inexpensive approach, that can be used to represent the evolution of bilayer silicates using a bond-switching algorithm. This approach allows reaching equilibrium ring size distributions as a function of a parameter that can be related to the ratio between temperature and the energy required for the bond-switching event. The ring size distributions are compared to experimental data available in the literature.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Description of quantum interference using mixed quantum/classical theory of inelastic scattering

Manifestation of the quantum interference effect in the oscillation of scattering cross section is explored using the N 2 + O system as a case study. Calculations are carried out for two electronic PESs of the system, for various initial rotational states of N 2 , in a broad range of N 2 + O collision energies and using three theoretical methods: two versions of the approximate mixed quantum/classical theory (MQCT and AT-MQCT) and the accurate full-quantum coupled-channel method (implemented in MOLSCAT). A good agreement between different methods is observed, especially at high energies. Elastic scattering cross-sections oscillate as a function of collision energy, which is the result of quantum interference. The effects of initial rotational excitation and of the PES properties are studied in detail. For the final (thermally averaged) cross sections, both MOLSCAT and MQCT calculations predict a rather regular pattern of quantum oscillations that persist through a broad range of collision energies and expand into the low-energy regime where quantum scattering resonances are common. The difference between cross sections predicted by MQCT and MOLSCAT decreases from ~8% at low energies to ~2% at high energies. Finally, experimental data available at high collision energies are well reproduced.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
DOE OSTI2020

Synthetic residential load models for smart city energy management simulations

The ability to control tens of thousands of residential electricity customers in a coordinated manner has the potential to enact system-wide electric load changes, such as reduce congestion and peak demand, among other benefits. To quantify the potential benefits of demand-side management and other power system simulation studies (e.g. home energy management, large-scale residential demand response), synthetic load datasets that accurately characterize the system load are required. This study designs a combined top-down and bottom-up approach for modelling individual residential customers and their individual electric assets, each possessing their own characteristics, using time-varying queueing models. The aggregation of all customer loads created by the queueing models represents a known city-sized load curve to be used in simulation studies. The three presented residential queueing load models use only publicly available data. An open-source Python tool to allow researchers to generate residential load data for their studies is also provided. The simulation results presented consider the ComEd region (utility company from Chicago, IL) and demonstrate the characteristics of the three proposed residential queueing load models, the impact of the choice of model parameters, and scalability performance of the Python tool.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Ensemble voting-based fault classification and location identification for a distribution system with microgrids using smart meter measurements

This study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)-based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event-driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k-nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE-123 bus system modified with added PV-based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real-time simulation-based testbed using a state-of-the-art digital real-time simulator, industry standard DNP3 communication protocol and a cpu-based control centre running the FL algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

The Chandra view of the relation between X-ray and UV emission in quasars

Here, we present a study of the relation between X-rays and ultraviolet emission in quasars for a sample of broad-line, radio-quiet objects obtained from the cross-match of the Sloan Digital Sky Survey DR14 with the latest Chandra Source Catalog 2.0 (2332 quasars) and the Chandra COSMOS Legacy survey (273 quasars). The non-linear relation between the ultraviolet (at 2500 Å, L UV ) and the X-ray (at 2 keV, L X ) emission in quasars has been proved to be characterised by a smaller intrinsic dispersion than the observed one, as long as a homogeneous selection, aimed at preventing the inclusion of contaminants in the sample, is fulfilled. By leveraging on the low background of Chandra, we performed a complete spectral analysis of all the data available for the SDSS-CSC2.0 quasar sample (i.e. 3430 X-ray observations), with the main goal of reducing the uncertainties on the source properties (e.g. flux, spectral slope). We analysed whether any evolution of the L X – L UV relation exists by dividing the sample in narrow redshift intervals across the redshift range spanned by our sample, z ≃ 0.5–4. We find that the slope of the relation does not evolve with redshift and it is consistent with the literature value of 0.6 over the explored redshift range, implying that the mechanism underlying the coupling of the accretion disc and hot corona is the same at the different cosmic epochs. We also find that the dispersion decreases when examining the highest redshifts, where only pointed observations are available. These results further confirm that quasars are ‘standardisable candles’, that is we can reliably measure cosmological distances at high redshifts where very few cosmological probes are available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Astronomical image time series classification using CONVolutional attENTION (ConvEntion)

Aims: The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys following up on transient objects are in progress or under construction, such as the Vera C. Rubin Observatory Legacy Survey for Space and Time (LSST), which is poised to produce huge amounts of these time series. The associated scientific topics are extensive, ranging from the study of objects in our galaxy to the observation of the most distant supernovae for measuring the expansion of the universe. With such a large amount of data available, the need for robust automatic tools to detect and classify celestial objects is growing steadily. Methods: This study is based on the assumption that astronomical images contain more information than light curves. In this paper, we propose a novel approach based on deep learning for classifying different types of space objects directly using images. We named our approach ConvEntion, which stands for CONVolutional attENTION. It is based on convolutions and transformers, which are new approaches for the treatment of astronomical image time series. Our solution integrates spatio-temporal features and can be applied to various types of image datasets with any number of bands. Results: In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time series, and a 12% increase, compared to approaches that use light curves.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A UNIONS view of the brightest central galaxies of candidate fossil groups

The formation process of fossil groups (FGs) is still under debate, and, because of their relative rarity, large samples of such objects are still missing. The aim of the present paper is to increase the sample of known FGs, to analyse the properties of their brightest group galaxies (BGGs), and to compare them with a control sample of non-FG BGGs. We extracted a sample of 87 FG and 100 non-FG candidates from a large spectroscopic catalogue of haloes and galaxies. For all the objects with data available in UNIONS (initially the Canada France Imaging Survey, CFIS) in the u and r bands, and/or in an extra r-band processed to preserve all low-surface-brightness features (rLSB), we performed a 2D photometric fit of the BGG with GALFIT with one or two Sérsic components. We also analysed how the subtraction of the intracluster light (ICL) contribution modifies the BGG properties. From the SDSS spectra available for the BGGs of 65 FGs and 82 non-FGs, we extracted the properties of their stellar populations with Firefly. To complement our study, and in order to provide a detailed illustration of the possible origin of emission lines in the FG BGGs, involving the presence or absence of an AGN, we investigated the origin of the emission lines in a nearby FG that is dominated by the NGC 4104 galaxy. Morphologically, a single Sérsic profile can fit most objects in the u band, while two Sérsics are needed in the r and rLSB bands, both for FGs and non-FGs. Non-FG BGGs cover a larger range of Sérsic index n. FG BGGs follow the Kormendy relation (mean surface brightness versus effective radius) previously derived for almost 1000 brightest cluster galaxies (BCGs), while the majority of non-FGs BGGs are located below this relation, with fainter mean surface brightnesses. This suggests that FG BGGs have evolved similarly to BCGs, and non-FG BGGs have evolved differently from both FG BGGs and BCGs. All the above properties can be strongly modified by the subtraction of the ICL contribution. Based on spectral fitting, the stellar populations of FG and non-FG BGGs do not differ significantly. FG and non-FG BGGs differ from one another in terms of their morphological properties and Kormendy relation, suggesting they have had different formation histories. However, it is not possible to trace differences in their stellar populations or in their large-scale distributions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Templates of expected measurement uncertainties for (n, xn) cross sections

A template is provided for evaluating experimental uncertainties for neutron elastic and inelastic scattering cross sections and γ -ray production cross sections from (n, xn) measurements at laboratories with monoenergetic or white neutron sources. A typical range of uncertainties is presented for experiments detecting the scattered neutrons or the resulting de-excitation γ rays based on a survey of available data and input from many experimentalists and theorists with extensive knowledge in the field. Models commonly used to evaluate the resulting cross-sections are also discussed. Suggestions are made regarding what experimental and uncertainty information is needed for data evaluations and should be included when reporting experimental (n, xn) cross sections. Uncertainty values and correlations are recommended if these values cannot be estimated for past data from the literature.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Machine learning control for disruption and tearing mode avoidance

In this work, real-time feedback control based on machine learning algorithms (MLA) was successfully developed and tested on DIII-D plasmas to avoid tearing modes and disruptions while maximizing the plasma performance, which is measured by normalized plasma beta. Control uses MLAs that were trained with ensemble learning methods using only the data available to the real-time Plasma Control System (PCS) from several thousand DIII-D discharges. A `tearability' metric that quantifies the likelihood of the onset of 2/1 tearing modes (TM) in a given time window, and a 'disruptivity' metric that quantifies the likelihood of the onset of plasma disruptions were first tested o -line then implemented on the PCS. A real-time control system based on these MLAs was successfully tested on DIII-D discharges, using feedback algorithms to maximize β Ν while avoiding tearing modes and to dynamically adjust ramp down to avoid high-current disruptions in ramp down.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Hydrodynamic simulations of shock-driven chemistry in polyimide

Here, the development for equations of state both for polyimide and its reaction products is presented along with hydrodynamic simulations linking the two EOS through an Arrhenius rate law. The equations of state compare favorably to available data, and the hydrodynamic simulations are able to qualitatively reproduce many of the features seen in both legacy shock data and more modern embedded gauge data. However, quantitative agreement is not achieved, suggesting that new rate laws are needed to fully capture the effect of the reaction over a wide range of conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Utilization of machine learning to accelerate colloidal synthesis and discovery

Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work presents a closed-loop machine learning-based strategy for colloidal synthesis of nanoparticles, assuming no prior knowledge of the synthetic process, in order to show that synthetic discovery can be accelerated despite limited data availability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Collective states and charge density waves in the group IV transition metal trichalcogenides

It has been nearly a century since the original mechanism for charge density wave (CDW) formation was suggested by Peierls. Since then, the term has come to describe several related concepts in condensed matter physics, having their origin in either the electron–phonon or electron–electron interaction. The vast majority of CDW literature deals with systems that are metallic, where discussions of mechanisms related to the Fermi surface are valid. Recently, it has been suggested that semiconducting systems such as TiS 3 and TiSe 2 exhibit behavior related to CDWs. In such cases, the origin of the behavior is more subtle and intimately tied to electron–electron interactions. We introduce the different classifications of CDW systems that have been proposed and discuss work on the group IV transition metal trichalcogenides (TMTs) (ZrTe 3 , HfTe 3 , TiTe 3 , and TiS 3 ), which are an exciting and emergent material system whose members exhibit quasi-one-dimensional properties. TMTs are van der Waals materials and can be readily studied in the few-layer limit, opening new avenues to manipulating collective states. We emphasize the semiconducting compound TiS 3 and suggest how it can be classified based on available data. Although we can conjecture on the origin of the CDW in TiS 3 , further measurements are required to properly characterize it.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗