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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Microstructural refinement of cement paste internally cured by polyacrylamide composite hydrogel particles containing silica fume and nanosilica

Supplementary cementitious materials were incorporated into hydrogel-based internal curing agents to improve the hydration, microstructure, and ultimately strength of internally cured high-performance cement paste. Polyacrylamide composite hydrogel particles containing amorphous silica – either silica fume or nanosilica – and two different polymer network crosslink densities were synthesized and incorporated into cement paste. The presence of silica and low crosslink density increased the absorption capacity of the particles in pore solution. Micrographs of internally cured paste indicated a significant improvement in hydrogel-related void-filling ability and an increase in void size for low crosslink density particles containing silica. Compressive strength and electrical resistivity increased at later ages for paste samples containing particles with higher silica dosage. The relationship between extent of hydration, void size, and void-filling activity was found to strongly influence the paste's long-term strength and is thus an important structure-property relationship to consider when selecting hydrogels for internal curing purposes.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Enhanced electrochemical performance of an electrospun carbon/MoO2 composite nanofiber membrane as self-standing anodes for lithium-ion batteries

Highlights: • Flexible C/MoO{sub 2} NFM was prepared by electrospinning and controlled heat treatment. • C/MoO{sub 2} NFM was used directly as self-standing and binder-free anodes for LIBs. • The electrode exhibits good capacity retention, rate performance and cyclability. • A high reversible capacity (811 mAh g{sup –1}) is obtained at 100 mA g{sup –1} after 100 cycles. • The possible mechanisms of the superior electrochemical performance are discussed. - Abstract: A flexible carbon nanofiber membrane (NFM) containing uniformly dispersed MoO{sub 2} nanoparticles is facilely fabricated via electrospinning and subsequent controlled reduction-carbonization. This NFM is used directly as a binder-free anode in lithium-ion batteries (LIBs). The self-standing carbon/MoO{sub 2} (C/MoO{sub 2}) NFM electrode has a good capacity retention, a satisfactory rate capability and a high reversible capacity of 811 mAh g{sup –1} after 100 cycles at a current density of 100 mA g{sup –1}, which is superior to most other MoO{sub 2}-based anodes. The superior electrochemical properties of the resultant C/MoO{sub 2} NFM are attributed to the cooperative effects from the excellent conductivity of the carbon nanofiber matrix, the high electrochemical performance of MoO{sub 2}, the unique one-dimensional nanofiber structure and the three-dimensional porous network structure. The present C/MoO{sub 2} NFM may be an attractive candidate as an anode material for LIBs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Dominant Controls on Preferential Flow and Their Implications for Future Soil Water Fluxes

Abstract Soil water flow, particularly preferential flow (PF), is a critical control on hydrological and biogeochemical processes, including groundwater recharge, contaminant transport, and carbon cycling. However, it remains challenging to predict PF occurrence across large environmental gradients. Here, we developed a deep learning (DL) model to estimate event‐scale soil water flow velocity and the probability of PF occurrence using high‐frequency soil moisture and precipitation data from 33 sites across the National Ecological Observatory Network. The model demonstrated high skill in predicting the binary occurrence of PF (91% F1‐score; 85% accuracy) but the performance was limited in predicting soil water velocity ( R 2 = 0.31). We found that precipitation characteristics (duration, volume, and intensity) were the most important predictors for soil water velocity. Among the non‐precipitation event variables, sand content showed relatively high predictive skill, though differences among non‐event climate variables were generally modest. Lower sand content was associated with increased predicted soil water velocity, a finding that highlights the role of soil structure in producing more non‐uniform flow, which contrasts with traditional uniform flow models. Projecting a reduced DL model under both moderate and high‐emissions future climate scenarios (2060–2099 Representative Concentration Pathways 4.5 and 8.5), we found ∼7.3% increase under RCP4.5 and ∼15% under RCP8.5 of soil water velocities compared to the historical simulation, while modeled likelihood of PF changed little. These findings suggest climate change is not making PF more frequent, but it is making existing PF pathways more efficient with important consequences for associated nutrient and contaminant transport under climate change. Plain Language Summary Water movement in soil is critical for water quality. While often modeled as a uniform flow process, in reality water moves rapidly through cracks and burrows in what is called “preferential flow” (PF), which limits natural filtration and can transport pollutants. We developed a deep learning model, trained on data from 33 U.S. sites, to predict when and how fast this PF occurs based on precipitation, soil, and climate data. The model showed that precipitation characteristics (duration, intensity, volume) were the most important predictors of PF. Lower soil sand content/higher clay content was associated with faster water flow, likely due to clay soils forming aggregates and cracks that water moves through rather than infiltrating uniformly. Further analyses based on climate projections suggest that the speed at which PF occurs will become more rapid under future climate scenarios compared to historical simulation. This highlights the need to represent PF in soil water models when assessing future water quality. Key Points The effect of precipitation peak intensity on soil water velocities declined with increasing precipitation intensity Antecedent soil moisture failed to predict preferential flow (PF), contrasting the high predictive power of sand content Climate predictions suggest that soil water velocities through PF paths will increase ∼15% by 2099

Li, Bonan↗
DOE OSTI2016

Addition of P3HT-grafted Silica nanoparticles improves bulk-heterojunction morphology in P3HT-PCBM blends

We present molecular dynamics simulations of a ternary blend of P3HT, PCBM and P3HT-grafted silica nanoparticles (SiNP) for applications in polymer-based solar cells. Using coarse-grained models, we study the effect of SiNP on the spatial arrangement of PCBM in P3HT. Our results suggest that addition of SiNP not only alters the morphology of PCBM clusters but also improves the crystallinity of P3HT. We exploit the property of grafted SiNP to self-assemble into a variety of anisotropic structures and the tendency of PCBM to preferentially adhere to SiNP surface, due to favorable interactions, to achieve morphologies with desirable characteristics for the active layer, including domain size, crystallinity of P3HT, and elimination of isolated islands of PCBM. As the concentration of SiNP increases, the number of isolated PCBM molecules decreases, which in turn improves the crystallinity of P3HT domains. We also observe that by tuning the grafting parameters of SiNP, it is possible to achieve structures ranging from cylindrical to sheets to highly interconnected network of strings. The changes brought about by addition of SiNP shows a promising potential to improve the performance of these materials when used as active layers in organic photovoltaics.

42 ENGINEERING↗
DOE OSTI2024

Towards Cross-Facility Workflows Orchestration through Distributed Automation

Modern science relies on end-to-end workflows that incorporate experimental instruments and utilize edge, cloud, or high-performance computing and storage resources. These components are geographically dispersed across various user facilities and interconnected through high-speed networks. In this paper, we present Zambeze, an automated distributed framework designed to facilitate this new class of cross-facility workflows. Utilizing swarm intelligence principles, Zambeze orchestrates science campaigns by managing distributed autonomous agents. These agents can offer a suite of services, including computing, storage, and data management. We demonstrate the feasibility of Zambeze through a real-world application involving electron microscopy, enhanced with Artificial Intelligence capabilities.

Skluzacek, Tyler↗
DOE OSTI2026

Janus v1.0

Janus provides a software framework for lightweight container management and orchestration. It's primary use cases are around deploying containerized services for high-performance data movement needs. Thus, Janus differentiates itself from systems like Kubernetes by tailoring the deployment of containers around network, storage, and host tuning optimizations. Janus uses the concept of profiles to capture repeatable deployment patterns and applies them to container execution across one or more endpoints. A Janus Agent component provides remote host tuning and monitoring capabilities.

Essiari, Abdelilah [Lawrence Berkeley National Lab↗
DOE OSTI2014

An evaluation of MPI message rate on hybrid-core processors

Power and energy concerns are motivating chip manufacturers to consider future hybrid-core processor designs that may combine a small number of traditional cores optimized for single-thread performance with a large number of simpler cores optimized for throughput performance. This trend is likely to impact the way in which compute resources for network protocol processing functions are allocated and managed. In particular, the performance of MPI match processing is critical to achieving high message throughput. In this paper, we analyze the ability of simple and more complex cores to perform MPI matching operations for various scenarios in order to gain insight into how MPI implementations for future hybrid-core processors should be designed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Scalable deep text comprehension for Cancer surveillance on high-performance computing

Background: Deep Learning (DL) has advanced the state-of-the-art capabilities in bioinformatics applications which has resulted in trends of increasingly sophisticated and computationally demanding models trained by larger and larger data sets. This vastly increased computational demand challenges the feasibility of conducting cutting-edge research. One solution is to distribute the vast computational workload across multiple computing cluster nodes with data parallelism algorithms. In this study, we used a High-Performance Computing environment and implemented the Downpour Stochastic Gradient Descent algorithm for data parallelism to train a Convolutional Neural Network (CNN) for the natural language processing task of information extraction from a massive dataset of cancer pathology reports. We evaluated the scalability improvements using data parallelism training and the Titan supercomputer at Oak Ridge Leadership Computing Facility. To evaluate scalability, we used different numbers of worker nodes and performed a set of experiments comparing the effects of different training batch sizes and optimizer functions.Results: We found that Adadelta would consistently converge at a lower validation loss, though requiring over twice as many training epochs as the fastest converging optimizer, RMSProp. The Adam optimizer consistently achieved a close 2nd place minimum validation loss significantly faster; using a batch size of 16 and 32 allowed the network to converge in only 4.5 training epochs.Conclusions: We demonstrated that the networked training process is scalable across multiple compute nodes communicating with message passing interface while achieving higher classification accuracy compared to a traditional machine learning algorithm.

Qiu, John X.↗
DOE OSTI2019

BigData Express: Toward Predictable, Schedulable, and High-Performance Data Transfer [PowerPoint]

Topics include: • BigData Express: a schedulable, predictable, and high-performance data transfer service – A peer-to-peer, scalable, and extensible data transfer model – A visually appealing, easy-to-use web portal – A high-performance data transfer engine – A time-constraint-based scheduler – On-demand provisioning of end-to-end network paths with guaranteed QoS – Robust and flexible error handling – CILogon-based security

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Nanowire mesh solar fuels generator

This disclosure provides systems, methods, and apparatus related to a nanowire mesh solar fuels generator. In one aspect, a nanowire mesh solar fuels generator includes (1) a photoanode configured to perform water oxidation and (2) a photocathode configured to perform water reduction. The photocathode is in electrical contact with the photoanode. The photoanode may include a high surface area network of photoanode nanowires. The photocathode may include a high surface area network of photocathode nanowires. In some embodiments, the nanowire mesh solar fuels generator may include an ion conductive polymer infiltrating the photoanode and the photocathode in the region where the photocathode is in electrical contact with the photoanode.

14 SOLAR ENERGY↗
DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗
DOE OSTI2014

A neural network clustering algorithm for the ATLAS silicon pixel detector

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Intrinsically low thermal conductivity from a quasi-one-dimensional crystal structure and enhanced electrical conductivity network via Pb doping in SbCrSe 3

The development of new routes for the production of thermoelectric materials with low-cost and high-performance characteristics has been one of the long-term strategies for saving and harvesting thermal energy. We report a new approach for improving thermoelectric properties by employing the intrinsically low thermal conductivity of a quasi-one-dimensional (quasi-1D) crystal structure and optimizing the power factor with aliovalent ion doping. As an example, we demonstrated that SbCrSe 3 , in which two parallel chains of CrSe 6 octahedra are linked by antimony atoms, possesses a quasi-1D property that resulted in an ultra-low thermal conductivity of 0.56 W m -1 K -1 at 900 K. After maximizing the power factor by Pb doping, the peak ZT value of the optimized Pb-doped sample reached 0.46 at 900 K, which is an enhancement of 24 times that of the parent SbCrSe 3 structure. The mechanisms that lead to low thermal conductivity derive from anharmonic phonons with the presence of the lone-pair electrons of Sb atoms and weak bonds between the CrSe 6 double chains. Our results shed new light on the design of new and high-performance thermoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Application of deep neural networks to improve diagnostic accuracy of rheumatoid arthritis using diffuse optical tomography

A set of deep neural network models for rheumatoid arthritis (RA) classification using a highway network, a convolutional neural network and a residual network is proposed based on the data of diffuse optical tomography (DOT) utilising near-infrared light, which ensures early diagnosis of pathophysiological changes resulting from inflammation. A numerical model of the finger is used to generate images to overcome the inherent problem of insufficient clinical DOT images available. The proposed deep neural network models are applied to automatically classify simulated DOT images of inflamed and non-inflamed joints and transfer learning is also used to improve the performance of the classification. The results demonstrate that all three deep neural network methods improve the diagnostic accuracy as compared to the widely applied support vector machine (SVM), especially for high inter-subject variability databases. In cases of distinct modelled severity of disease, residual network achieved the highest accuracy (> 99 %), and both of highway and convolutional neural networks reached 99 %, respectively. However, as the severity of the modelled disease is reduced, this accuracy is reduced to 75.2 % for residual networks. The results indicate that transfer learning can improve the performance of deep neural network methods on RA classification from DOT data and highlight their potential as a computer aided tool in DOT diagnostic systems. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗
DOE OSTI2015

3D reconstruction of carbon nanotube networks from neutron scattering experiments

Structure reconstruction from statistical descriptors, such as scattering data obtained using x-rays or neutrons, is essential in understanding various properties of nanocomposites. Scattering based reconstruction can provide a realistic model, over various length scales, that can be used for numerical simulations. In this study, 3D reconstruction of a highly loaded carbon nanotube (CNT)-conducting polymer system based on small and ultra-small angle neutron scattering (SANS and USANS, respectively) data was performed. These light-weight and flexible materials have recently shown great promise for high-performance thermoelectric energy conversion, and their further improvement requires a thorough understanding of their structure-property relationships. The first step in achieving such understanding is to generate models that contain the hierarchy of CNT networks over nano and micron scales. The studied system is a single walled carbon nanotube (SWCNT)/poly (3,4-ethylenedioxythiophene): poly (styrene sulfonate) (PEDOT: PSS). SANS and USANS patterns of the different samples containing 10, 30, and 50 wt% SWCNTs were measured. These curves were then utilized to calculate statistical two-point correlation functions of the nanostructure. These functions along with the geometrical information extracted from SANS data and scanning electron microscopy images were used to reconstruct a representative volume element (RVE) nanostructure. Generated RVEs can be used for simulations of various mechanical and physical properties. This work, therefore, introduces a framework for the reconstruction of 3D RVEs of high volume faction nanocomposites containing high aspect ratio fillers from scattering experiments.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Data-Driven Day-Ahead PV Estimation Using Hybrid Deep Learning

Ongoing smart grid activities and associated automation resulted in rich set of data. Here, these data can be utilized for monitoring and estimation of real time photovoltaic (PV) generation. Inherent variability in PV and related impact on power systems is a challenging problem. Improving the accuracy of PV generation estimation is beneficial for both the PV owners and the grid operators. Recently, deep learning algorithms possible by the availability of data have shown its advantages for time series estimation; however, its application on PV generation estimation is still in the early stage. In this paper, a hybrid estimation model with a combination of long-short-term-memory network (LSTM) and persistence model (PM) is developed to provide day-ahead PV estimation at 15-minute time interval with high accuracy and robustness. Simulation results show the superior performance of the proposed method over existing methods for most of the test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Impact of Domain Knowledge on the Property Prediction of Specialized Machine Learning Models

Developing transferable machine learning models is trending in data-driven materials research. However, how to apply such models to a specific research domain remains unclear. Here, in this work, we choose high-entropy materials as a platform with a specialized data set containing 145,323 DFT-relaxed materials. This data set is used to explore the role of domain-specific knowledge in training effective models. Our tests with three representative graph neural network architectures indicate the model complexity has much smaller influence on performance than the data itself. Specifically, the consideration of low-energy atomic ordering, structures with diverse elemental coverage, and high-order interactions significantly influences the model performance. We also find that domain knowledge-driven sampling can greatly enhance unsupervised learning techniques. This research highlights that developing specialized data sets is more beneficial than further complicating deep learning architectures. Additionally, physics-inspired sampling algorithms are crucially needed for better machine learning models for a specific materials research domain.

36 MATERIALS SCIENCE↗