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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Abstract Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, which are prohibitively computationally expensive. The application of deep learning techniques to generative modeling is renewing interest in using high dimensional density estimators as computationally inexpensive emulators of fully-fledged simulations. These generative models have the potential to make a dramatic shift in the field of scientific simulations, but for that shift to happen we need to study the performance of such generators in the precision regime needed for science applications. To this end, in this work we apply Generative Adversarial Networks to the problem of generating weak lensing convergence maps. We show that our generator network produces maps that are described by, with high statistical confidence, the same summary statistics as the fully simulated maps.

Mustafa, Mustafa (ORCID:0000000291770283)↗
DOE OSTI2019

BSTC: A Novel Binarized-Soft-Tensor-Core Design for Accelerating Bit-Based Approximated Neural Nets

Binarized neural network (or BNN) promises tremendous performance improvement over traditional DNNs through simplified bit-level computation and significantly reduced memory access/storage cost. In addition, it has other advantages of low-cost, low-energy and high-robustness, showing great utilization potential in resources-constrained, volatile and latency-critical applications, which are critical for future HPC execution. However, the promised significant performance gain of BNN inference has never been fully demonstrated on general-purpose processors, particularly on GPUs, due to: (i) the challenge to extract and leverage sufficient fine-grained bit-level-parallelism to saturate GPU cores when batch is small; (ii) the fundamental design conflict between bit-based BNN algorithm and underlying word-based architecture; (iii) architecture \& performance unfriendly BNN network design. To address (i) and (ii), we propose binarized-soft-tensor-core as a software-hardware codesign approach to construct bit-manipulation capability for modern GPUs to effectively harvest the emerging bit-level-parallelism. To tackle (iii), we propose intra- and inter-layer fusion techniques so that the entire BNN inference can be packed into a single GPU kernel, to avoid high-cost frequent launching. Experiments demonstrate that our design can achieve over 1000x speedup for raw inference latency and 10x for inference throughput over the state-of-the-art full-precision simulated BNN inference for AlexNet on ImageNet.

Li, Ang↗
DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗
NASA NTRS1992

Generalized hypercube structures and hyperswitch communication network

This paper discusses an ongoing study that uses a recent development in communication control technology to implement hybrid hypercube structures. These architectures are similar to binary hypercubes, but they also provide added connectivity between the processors. This added connectivity increases communication reliability while decreasing the latency of interprocessor message passing. Because these factors directly determine the speed that can be obtained by multiprocessor systems, these architectures are attractive for applications such as remote exploration and experimentation, where high performance and ultrareliability are required. This paper describes and enumerates these architectures and discusses how they can be implemented with a modified version of the hyperswitch communication network (HCN). The HCN is analyzed because it has three attractive features that enable these architectures to be effective: speed, fault tolerance, and the ability to pass multiple messages simultaneously through the same hyperswitch controller.

Young, Steven D.↗
NASA NTRS1993

Scalable Optical-Fiber Communication Networks

Scalable arbitrary fiber extension network (SAFEnet) is conceptual fiber-optic communication network passing digital signals among variety of computers and input/output devices at rates from 200 Mb/s to more than 100 Gb/s. Intended for use with very-high-speed computers and other data-processing and communication systems in which message-passing delays must be kept short. Inherent flexibility makes it possible to match performance of network to computers by optimizing configuration of interconnections. In addition, interconnections made redundant to provide tolerance to faults.

Chow, Edward T.↗
NASA NTRS2010

Spacecraft VHF Radio Propagation Analysis in Ocean Environments Including Atmospheric Effects

The Communication Systems Simulation Laboratory (CSSL) at the National Aeronautics and Space Administration (NASA)/Johnson Space Center (JSC) is tasked to perform spacecraft and ground network communication system simulations. The CSSL has developed simulation tools that model spacecraft communication systems and the space/ground environment in which they operate. This paper is to analyze a spacecraft's very high frequency (VHF) radio signal propagation and the impact to performance when landing in an ocean. Very little research work has been done for VHF radio systems in a maritime environment. Rigorous Radio Frequency (RF) modeling/simulation techniques were employed for various environmental effects. The simulation results illustrate the significance of the environmental effects on the VHF radio system performance.

Hwu, Shian↗
DOE OSTI2018

circRNA expression analysis in lung adenocarcinoma: comparison of paired fresh frozen and formalin-fixed paraffin-embedded specimens

Highlights: • First comparison of circRNA expression from paired fresh frozen and FFPE specimens. • Good correlation in circRNAs expression data utilizing fresh frozen or FFPE tissues. • Samples clustered correctly by differentially expressed circRNAs in either group. • Three differentially expressed circRNAs in lung cancers verified by qRT-PCR assay. • circRNA-miRNA-protein coding gene regulation network analyzed by bioinformatics tools. Circular RNA (circRNAs) is a novel class of endogenous non-coding RNAs which is widely involved in carcinogenesis. Archived formalin-fixed paraffin-embedded (FFPE) specimens represent valuable resources for cancer research. Currently there is a lack of systematic analysis on the feasibility of circRNAs expression analysis using FFPE samples. Here, we reported the first comparison study of circRNA expression from paired fresh frozen and FFPE specimens of lung adenocarcinoma. circRNAs expression of paired lung adenocarcinoma and adjacent normal samples, starting from either fresh frozen or FFPE materials, were analyzed via a high-throughput circRNA microarray. Hierarchical clustering was performed on samples. qRT-PCR was used to confirm the differentially expressed circRNAs. The circRNA regulation networks were built by bioinformatics tools for several candidate circRNAs. Our results demonstrated that there was a good correlation in circRNAs expression analysis utilizing fresh frozen or FFPE tissues. Tumors and adjacent normal tissues can be clustered correctly by the differentially expressed circRNAs in fresh frozen or FFPE groups. Furthermore, three differentially expressed circRNAs, hsa{sub c}ircRNA{sub 1}00421, hsa{sub c}ircRNA{sub 1}04329 and hsa{sub c}ircRNA{sub 1}01969 were verified by qRT-PCR assay. Bioinformatics analysis was also applied to predict the circRNA-miRNA-protein coding gene interaction network for each of above circRNAs. To the best of our knowledge, this is the first report demonstrating that the circRNAs microarray analysis, starting from FFPE specimens, is comparable with that based on fresh frozen samples. Therefore FFPE specimen represents a good substitute for fresh frozen tissue, especially when fresh frozen specimen is limited.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1995

High speed polling protocol for multiple node network

The invention is a multiple interconnected network of intelligent message-repeating remote nodes which employs a remote node polling process performed by a master node by transmitting a polling message generically addressed to all remote nodes associated with the master node. Each remote node responds upon receipt of the generically addressed polling message by transmitting a poll-answering informational message and by relaying the polling message to other adjacent remote nodes.

Kirkham, Harold↗
DOE OSTI2021

A machine-learning inverse model framework for rapid forecasting and history matching in unconventional reservoirs

Model-based optimization for real-time forecasting in unconventional reser-voirs requires novel methods and work?ows since the strategies and work?ows used in conventional reservoirs are either inapplicable, or prohibitively expen-sive and time-consuming. Insu?cient site data and computational expense of high-?delity simulations mean that work?ows with high-?delity simulations are not ideal for usage in comprehensive uncertainty quanti?cation stud-ies that require 1000s of forward model runs. We present an alternative, novel work?ow for unconventional reservoirs, based on the interplay between reduced-order models and machine-learning. Our physics-informed machine-learning (PIML) work?ow addresses the challenges to real-time reservoir management in uncoventionals, namely lack of data (the time-frame for which the wells have been producing), and computational expense of high-?delity modeling. We use the machine-learning paradigm of transfer-learning to bind together fast but less accurate reduced-order models with slow, but accurate high-?delity models and circumvent the di?culties inherent in the current state-of-the-art for unconventionals. Such a PIML work?ow, grounded in physics, is a viable candidate for real-time history matching and production forecasting in a fractured shale gas reservoir. The signi?cance of our approach is that while it is developed for a particu-lar well and site in the Marcelus Shale gas reservoir of the Appalachian basin (MSEEL), it is not wedded to it. We expect the same work?ow can be ap-plied to other shale formations (e.g., Woodford, Barnett, Utica, EagleFord) should site-data become available, using the same set of machine-learning techniques from transfer learning. Some ?ne-tuning (or minimal retraining of the neural networks) will be required to transfer knowledge across shale gas sites/formations but it is a clearly superior alternative to developing a new machine-learning model altogether when considering a di?erent site.

Srinivasan, Shriram↗
DOE OSTI2018

Mechanically tunable conductive interpenetrating network hydrogels that mimic the elastic moduli of biological tissue

Conductive and stretchable materials that match the elastic moduli of biological tissue (0.5–500 kPa) are desired for enhanced interfacial and mechanical stability. Compared with inorganic and dry polymeric conductors, hydrogels made with conducting polymers are promising soft electrode materials due to their high water content. Nevertheless, most conducting polymer-based hydrogels sacrifice electronic performance to obtain useful mechanical properties. Here we report a method that overcomes this limitation using two interpenetrating hydrogel networks, one of which is formed by the gelation of the conducting polymer PEDOT:PSS. Due to the connectivity of the PEDOT:PSS network, conductivities up to 23 S m-1 are achieved, a record for stretchable PEDOT:PSS-based hydrogels. Meanwhile, the low concentration of PEDOT:PSS enables orthogonal control over the composite mechanical properties using a secondary polymer network. We demonstrate tunability of the elastic modulus over three biologically relevant orders of magnitude without compromising stretchability ( > 100%) or conductivity ( > 10 S m-1 ).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Adaptive Activation Functions Accelerate Convergence in Deep and Physics-informed Neural Networks

We employ adaptive activation functions for regression in deep and physics-informed neural networks (PINNs) to approximate smooth and discontinuous functions as well as solutions of linear and nonlinear partial differential equations. In particular, we solve the nonlinear Klein-Gordon equation, which has smooth solutions, the nonlinear Burgers equation, which can admit high gradient solutions, and the Helmholtz equation. We introduce a scalable hyper-parameter in the activation function, which can be optimized to achieve best performance of the network as it changes dynamically the topology of the loss function involved in the optimization process. The adaptive activation function has better learning capabilities than the traditional one (fixed activation) as it improves greatly the convergence rate, especially at early training, as well as the solution accuracy. To better understand the learning process, we plot the neural network solution in the frequency domain to examine how the network captures successively different frequency bands present in the solution. We consider both forward problems, where the approximate solutions are obtained, as well as inverse problems, where parameters involved in the governing equation are identified. Our simulation results show that the proposed method is a very simple and effective approach to increase the efficiency, robustness and accuracy of the neural network approximation of nonlinear functions as well as solutions of partial differential equations, especially for forward problems. We theoretically prove that in the proposed method, gradient descent algorithms are not attracted to suboptimal critical points or local minima.

machine leaning, Bad minima, Inverse problems, Phy↗
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↗