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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS1980

High-temperature molten salt thermal energy storage systems

The results of comparative screening studies of candidate molten carbonate salts as phase change materials (PCM) for advanced solar thermal energy storage applications at 540 to 870 C (1004 to 1600 F) and steam Rankine electric generation at 400 to 540 C (752 to 1004 F) are presented. Alkali carbonates are attractive as latent heat storage materials because of their relatively high storage capacity and thermal conductivity, low corrosivity, moderate cost, and safe and simple handling requirements. Salts were tested in 0.1 kWhr lab scale modules and evaluated on the basis of discharge heat flux, solidification temperature range, thermal cycling stability, and compatibility with containment materials. The feasibility of using a distributed network of high conductivity material to increase the heat flux through the layer of solidified salt was evaluated. The thermal performance of an 8 kWhr thermal energy storage (TES) module containing LiKCO3 remained very stable throughout 5650 hours and 130 charge/discharge cycles at 480 to 535 C (896 to 995 F). A TES utilization concept of an electrical generation peaking subsystem composed of a multistage condensing steam turbine and a TES subsystem with a separate power conversion loop was defined. Conceptual designs for a 100 MW sub e TES peaking system providing steam at 316 C, 427 C, and 454 C (600 F, 800 F, and 850 F) at 3.79 million Pa (550 psia) were developed and evaluated. Areas requiring further investigation have also been identified.

Petri, R. J.↗
NASA NTRS1994

Interactive archives of scientific data

A focus on qualitative methods of presenting data shows that visualization provides a mechanism for browsing independent of the source of data and is an effective alternative to traditional image-based browsing of image data. To be generally applicable, such visualization methods, however, must be based upon an underlying data model with support for a broad class of data types and structures. Interactive, near-real-time browsing for data sets of interesting size today requires a browse server of considerable power. A symmetric multi-processor with very high internal and external bandwidth demonstrates the feasibility of this concept. Although this technology is likely to be available on the desktop within a few years, the increase in the size and complexity of achieved data will continue to exceed the capacity of 'worksation' systems. Hence, a higher class of performance, especially in bandwidth, will generally be required for on-demand browsing. A few experiments with differing digital compression techniques indicates that a MPEG-1 implementation within the context of a high-performance browse server (i.e., parallized) is a practical method of converting a browse product to a form suitable for network or CD-ROM distribution.

Treinish, Lloyd A.↗
NASA NTRS2000

Object-oriented Approach to High-level Network Monitoring and Management

An absolute prerequisite for the management of large investigating methods to build high-level monitoring computer networks is the ability to measure their systems that are built on top of existing monitoring performance. Unless we monitor a system, we cannot tools. Due to the heterogeneous nature of the hope to manage and control its performance. In this underlying systems at NASA Langley Research Center, paper, we describe a network monitoring system that we use an object-oriented approach for the design, we are currently designing and implementing. Keeping, first, we use UML (Unified Modeling Language) to in mind the complexity of the task and the required model users' requirements. Second, we identify the flexibility for future changes, we use an object-oriented existing capabilities of the underlying monitoring design methodology. The system is built using the system. Third, we try to map the former with the latter. APIs offered by the HP OpenView system.

Mukkamala, Ravi↗
NASA NTRS2021

Molecular Dynamics Investigation of the Structural and Mechanical Properties of Off-Stoichiometric Epoxy Resins

Molecular dynamic (MD) simulations were performed to compute the mechanical properties of off-stoichiometric epoxy resins as a function of hardener/epoxy mixture ratio (r). Properties were characterized in relation to their microscopic structures. Such resins have been used recently for adhesive-free bonding of large-scale composite structures using the co-curing-ply method. In this process, two partially precured composite panels with hardener-poor (HP) off-stoichiometric resins are coupled with ply(ies) of complementary hardener-rich (HR) formulations and then cured simultaneously. This bonding process has the potential to produce reliable and certifiable composite joints without the need for additional fasteners, which are often required for many conventional bonding methods because even small amounts of contamination can cause a weak bond. The reflow and mixing of the HP/HR resin in this bonding process result in a joint with no discernable interface that should not be susceptible to surface contamination. However, incomplete mixing of the two offset resins may result in chemical heterogeneity of the cured polymeric joint. Thus, different r values may be obtained across the joint. Classical MD simulations were performed to compute the Young’s modulus of polymers with different r values and correlate their properties to network structures. High stiffness was associated with molecular packing due to chemical crosslinking, leading to a single network structure. Moreover, the networks became denser as the ratio approached the stoichiometric value r = 1. Thus, the r = 1 systems were single clusters, with high stiffness, high molecular weight, and a high degree of crosslinking. Structural properties such as radius of gyration and mean square displacement were determined to investigate the variation in the stiffness with respect to r. This MD simulation study was validated with experimental measurements.

Chang Woon Jang↗
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;↗
NASA NTRS2010

High-Performance CCSDS Encapsulation Service Implementation in FPGA

The Consultative Committee for Space Data Systems (CCSDS) Encapsulation Service is a convergence layer between lower-layer space data link framing protocols, such as CCSDS Advanced Orbiting System (AOS), and higher-layer networking protocols, such as CFDP (CCSDS File Delivery Protocol) and Internet Protocol Extension (IPE). CCSDS Encapsulation Service is considered part of the data link layer. The CCSDS AOS implementation is described in the preceding article. Recent advancement in RF modem technology has allowed multi-megabit transmission over space links. With this increase in data rate, the CCSDS Encapsulation Service needs to be optimized to both reduce energy consumption and operate at a high rate. CCSDS Encapsulation Service has been implemented as an intellectual property core so that the aforementioned problems are solved by way of operating the CCSDS Encapsulation Service inside an FPGA. The CCSDS En capsula tion Service in FPGA implementation consists of both packetizing and de-packetizing features

Clare, Loren P.↗
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↗
DOE OSTI2026

Mauka Energy FEVER Tool Dataset

Mauka Energy’s dataset, developed under the Forestry Electric Vehicle Energy Routing (FEVER) project and funded by the U.S. Department of Energy’s Small Business Innovation Research program, is a high-resolution geospatial resource designed to support energy modeling for electric log trucks in complex forestry environments. The dataset integrates detailed spatial and road network data to enable accurate simulation of vehicle performance across varied terrain. At its core, the dataset incorporates lidar-derived elevation models, road alignments, and surface classifications from Oregon State University’s McDonald-Dunn Research Forest. These data capture fine-scale variations in slope, curvature, and surface conditions across forest road systems, allowing for vehicle-level analysis of energy consumption and recovery. The dataset also includes data collected on the surrounding public and private road networks in Benton County, Oregon, used in real-world haul routes. These connecting segments provide critical context for modeling transitions between forest operations and regional transportation infrastructure, incorporating attributes such as grade profiles, elevation change, and speed constraints. This combined dataset underpins the development of Mauka Energy’s rolldown tool, which quantifies energy use and regenerative braking potential on downhill and variable-grade segments. By leveraging high-resolution terrain and road data, the FEVER project enables more accurate assessment of electric vehicle feasibility and performance in forestry applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗