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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Modelling the Value of Networked, Small-Hydro Generators for Storage and Dispatch Capability on the Grid

Natel Energy, Inc. (Natel), Alameda, California, has developed a new type of fish-friendly, low-head hydropower turbine, the hydroEngine (R), that is designed to provide reliable, dispatchable power and ancillary services at significantly reduced civil works costs. For the U.S. Department of Energy's (DOE) Small Business Voucher for Hydropower, Third Round, Natel and NREL will model and evaluate the potential value streams of a portfolio of networked small hydropower generators. The project is designed to advance the understanding of the value of small hydro generation to the grid of the future. It will help support dialog with potential customers and stakeholders regarding the value of the Natel's technologies and inform future R&D efforts in this area.

13 HYDRO ENERGY↗
DOE OSTI2024

Scaling neural simulations in STACS

Abstract As modern neuroscience tools acquire more details about the brain, the need to move towards biological-scale neural simulations continues to grow. However, effective simulations at scale remain a challenge. Beyond just the tooling required to enable parallel execution, there is also the unique structure of the synaptic interconnectivity, which is globally sparse but has relatively high connection density and non-local interactions per neuron. There are also various practicalities to consider in high performance computing applications, such as the need for serializing neural networks to support potentially long-running simulations that require checkpoint-restart. Although acceleration on neuromorphic hardware is also a possibility, development in this space can be difficult as hardware support tends to vary between platforms and software support for larger scale models also tends to be limited. In this paper, we focus our attention on Simulation Tool for Asynchronous Cortical Streams (STACS), a spiking neural network simulator that leverages the Charm++ parallel programming framework, with the goal of supporting biological-scale simulations as well as interoperability between platforms. Central to these goals is the implementation of scalable data structures suitable for efficiently distributing a network across parallel partitions. Here, we discuss a straightforward extension of a parallel data format with a history of use in graph partitioners, which also serves as a portable intermediate representation for different neuromorphic backends. We perform scaling studies on the Summit supercomputer, examining the capabilities of STACS in terms of network build and storage, partitioning, and execution. We highlight how a suitably partitioned, spatially dependent synaptic structure introduces a communication workload well-suited to the multicast communication supported by Charm++. We evaluate the strong and weak scaling behavior for networks on the order of millions of neurons and billions of synapses, and show that STACS achieves competitive levels of parallel efficiency.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1983

Jet stream related observations by MST radars

An overview of the jet stream and its observation by MST radar is presented. The climatology and synoptic and mesoscale structure of jet streams is briefly reviewed. MST radar observations of jet stream winds, and associated waves and turbulence are then considered. The possibility of using a network of ST radars to track jet stream winds in near real time is explored.

Gage, K. S.↗
NASA NTRS1992

Heating coronal holes and accelerating the solar wind

The special energy requirements of a coronal hole combined with current knowledge of the limited dissipation of Alfven and fast mode MHD waves in the solar corona suggest a unique source of heat for the coronal hole. The near coronal hole requires approximately 3 - 4 x 10 exp 5 ergs/sq cm s, which can come only from the fluid jets, fast particles, and short period MHD waves from the network activity. The high speed streams of solar wind from coronal holes show that there is substantial heating, of 1 - 2 x 10 exp 5 ergs/sq cm s, beyond the sonic point in the wind, which can come only from the dissipation by thermal conduction of long period (approximately equal or greater than 100 sec) MHD waves from subphotospheric convection. Although the Alfven wave flux from the photosphere is generally taken for granted in the literature, we point out that it is a crucial phenomenon that has yet to be established on either a theoretical or observational scientific basis.

Parker, E. N.↗
NASA NTRS2014

Heterogeneous Wireless Mesh Network Technology Evaluation for Space Proximity and Surface Applications

NASA has identified standardized wireless mesh networking as a key technology for future human and robotic space exploration. Wireless mesh networks enable rapid deployment, provide coverage in undeveloped regions. Mesh networks are also self-healing, resilient, and extensible, qualities not found in traditional infrastructure-based networks. Mesh networks can offer lower size, weight, and power (SWaP) than overlapped infrastructure-perapplication. To better understand the maturity, characteristics and capability of the technology, we developed an 802.11 mesh network consisting of a combination of heterogeneous commercial off-the-shelf devices and opensource firmware and software packages. Various streaming applications were operated over the mesh network, including voice and video, and performance measurements were made under different operating scenarios. During the testing several issues with the currently implemented mesh network technology were identified and outlined for future work.

DeCristofaro, Michael A.↗
DOE OSTI2023

Vis-SAGA: Visual Analytics for Situational Awareness of Grid Anomalies: Preprint

We describe supporting near real-time situational awareness of the electric distribution system by visualizing novel data from voltage sensors deployed on existing broadband cable television network equipment. Our scalable web-based visual analytics platform supports interactive geospatial exploration, time-series analysis, and summarization of grid behavior during potentially anomalous events. The broadband cable television sensor network provides observability of the electrical distribution system at a higher local spatial resolution than is typically available to most utilities, revealing the operational state of the network and aiding in the detection of abnormal behaviors or deviations from expected patterns, particularly across electric utility service areas. We outline the design and development of interactive geospatial and time-series visualization components and the scalable data services that supply metadata, historical, and real-time streams of sensor data across the network. We evaluate our platform during periods of extreme weather, demonstrating its ability to assist in detecting patterns of operation that affect power availability, quality, resiliency, and service restoration.

cable television↗
NASA NTRS1989

Fiber optic voice/data network

An asynchronous, high-speed, fiber optic local area network originally developed for tactical environments with additional benefits for other environments such as spacecraft, and the like. The network supports ordinary data packet traffic simultaneously with synchronous T1 voice traffic over a common token ring channel; however, the techniques and apparatus of this invention can be applied to any deterministic class of packet data networks, including multitier backbones, that must transport stream data (e.g., video, SAR, sensors) as well as data. A voice interface module parses, buffers, and resynchronizes the voice data to the packet network employing elastic buffers on both the sending and receiving ends. Voice call setup and switching functions are performed external to the network with ordinary PABX equipment. Clock information is passed across network boundaries in a token passing ring by preceeding the token with an idle period of non-transmission which allows the token to be used to re-establish a clock synchronized to the data. Provision is made to monitor and compensate the elastic receiving buffers so as to prevent them from overflowing or going empty.

Bergman, Larry A.↗
DOE OSTI2025

eCounter: Inline Per-IP Network Monitoring at Millisecond Resolution via eBPF

Scientific data acquisition (SciDAQ) systems are shifting from archive-based workflows to streaming paradigms, where real-time, fine-grained network monitoring becomes essential. While P4-enabled devices offer per-packet in-band observability, they require specialized switches and routers. Host-side tools like Prometheus exporters lack sufficient temporal granularity. To bridge this gap, we present eCounter, a lightweight, hardware-agnostic, inline telemetry agent built on extended Berkeley Packet Filter (eBPF). eCounter captures per-interface ingress and egress traffic, categorized by IP address and protocol, at millisecond to sub-millisecond resolution. In a 100 Gbps environment, it continuously exports up to 3,257 time-series bins per second with only 4% CPU utilization at a 35¿KiB/s data rate. We evaluate eCounter across diverse NIC MTU settings, hook types, CPU architectures and operating systems, and observed negligible impact on concurrent high-throughput streaming applications. Complexity analysis confirms that it can be readily scaled to distributed SciDAQ deployments.

Mei, Xinxin [Computational Sciences and Technology↗
DOE OSTI2022

Real-time GIS Programming and Geocomputation

Streaming data generated continuously from sensor networks, mobile devices, social media platforms and other edge devices have posed significant challenges to existing computing platforms for achieving both high throughput and low latency data processing in addition to scalable computing. This entry introduces a real-time computing and programming platform for time-critical GIS (Geographic Information System) applications. In this platform, advanced streaming data processing software, such as Apache Kafka and Spark Streaming, are integrated to enable data analytics in real-time. This computing platform can also be extended to integrate GeoAI (Geospatial Artificial Intelligence) based machine learning models to leverage both historical and streaming data to achieve real-time prediction and intelligent geospatial analytics. Two real-time geospatial applications in terms of flood simulation and climate data visualization are introduced to demonstrate how real-time programming and computing can help tackle real-world problems with important societal impacts.

Liu, Yan↗
DOE OSTI2020

Performance and usability enhancements for continuous subgraph matching queries on graph-structured data

A query graph, which includes vertices and edges, represents a query on graph-structured data. The query graph is decomposed into query subgraphs. A network analysis tool performs continuous subgraph matching queries to facilitate analysis of computer network traffic, social media events, or other streams of data represented as a dynamic data graph (graph-structured data). This can help identify emerging trends in the data. Some features of the network analysis tool enhance performance by effectively utilizing distributed computing resources (including processing cores and memory at different nodes of a cluster) to speed up the process of updating the dynamic data graph and detecting matches of query subgraphs. Features of a query graph building tool enhance usability by providing intuitive ways to specify query graphs and their subgraphs. Features of a results visualization tool enhance usability by providing an intuitive way to present the results of continuous subgraph matching queries.

Choudhury, Sutanay↗
DOE OSTI2024

Factorized visual representations in the primate visual system and deep neural networks

Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether (‘invariance’), represented in non-interfering subspaces of population activity (‘factorization’) or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters – lighting, background, camera viewpoint, and object pose – in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Topological Relationship-Based Flow Direction Modeling: Stream Burning and Depression Filling

Flow direction modeling consists of (a) an accurate representation of the river network and (b) digital elevation model (DEM) processing to preserve characteristics with hydrological significance. In part 1 of our study, we presented a mesh-independent approach to representing river networks on different types of meshes. This follow-up part 2 study presents a novel DEM processing approach for flow direction modeling. This approach consists of (a) a topological relationship-based hybrid breaching-filling method to conduct stream burning for the river network and (b) a modified depression removal method for rivers and hillslopes. Our methods reduce modifications to surface elevations and provide a robust two-step procedure to remove local depressions in DEM. They are mesh-independent and can be applied to both structured and unstructured meshes. We applied our new methods with different model configurations to the Susquehanna River Basin. The results show that topological relationship-based stream burning, and depression-filling methods can reproduce the correct river networks, providing high-quality flow direction and other characteristics for hydrologic and Earth system models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

DNET: A communications facility for distributed heterogeneous computing

This document describes DNET, a heterogeneous data communications networking facility. DNET allows programs operating on hosts on dissimilar networks to communicate with one another without concern for computer hardware, network protocol, or operating system differences. The overall DNET network is defined as the collection of host machines/networks on which the DNET software is operating. Each underlying network is considered a DNET 'domain'. Data communications service is provided between any two processes on any two hosts on any of the networks (domains) that may be reached via DNET. DNET provides protocol transparent, reliable, streaming data transmission between hosts (restricted, initially to DECnet and TCP/IP networks). DNET also provides variable length datagram service with optional return receipts.

Tole, John↗
DOE OSTI2020

Data Federation Challenges in Remote Near-Real-Time Fusion Experiment Data Processing

Fusion energy experiments and simulations provide critical information needed to plan future fusion reactors. As next-generation devices like ITER move toward long-pulse experiments, analyses, including AI and ML, should be performed in a wide range of time and computing constraints, from near-real-time constraints, between-shot analysis, and to campaign-wide long-term analysis. However, the data volume, velocity, and variety make it extremely challenging for analyses using only local computational resources. Researchers need the ability to compose and execute workflows spanning edge resources to large-scale high-performance computing facilities.We present Delta, a system to address data analysis challenges, including AI/ML, in fusion science, by leveraging the ADIOS I/O library and middleware, to support executing science workflows over the wide area network for near-real-time streaming. We discuss the data federation challenges in performing remote workflows, focusing on on-going research work in (1) managing, reducing, and streaming data to minimize I/O and data movement overheads, (2) decompressing and reorganizing data for analysis, and (3) executing workflows for automated data analysis. We introduce examples for deep-learning based data analysis for the fusion domain and demonstrate how we use Delta to construct end-to-end workflows for a fusion device in Korea, connecting a remote DOE facility in the USA. The capability demonstrated by this project is the basis for improving the state of the art for near-real-time data federation amongst remote facilities.

Choi, Jong Youl↗
DOE OSTI2021

Pyomo.GDP: an ecosystem for logic based modeling and optimization development

We present three core principles for engineering-oriented integrated modeling and optimization tool sets—intuitive modeling contexts, systematic computer-aided reformulations, and flexible solution strategies—and describe how new developments in Pyomo.GDP for Generalized Disjunctive Programming (GDP) advance this vision. We describe a new logical expression system implementation for Pyomo.GDP allowing for a more intuitive description of logical propositions. The logical expression system supports automated reformulation of these logical constraints to linear constraints. We also describe two new logic-based global optimization solver implementations built on Pyomo.GDP that exploit logical structure to avoid “zero-flow” numerical difficulties that arise in nonlinear network design problems when nodes or streams disappear. These new solvers also demonstrate the capability to link to external libraries for expanded functionality within an integrated implementation. We present these new solvers in the context of a flexible array of solution paths available to GDP models. Finally, we present results on a new library of GDP models demonstrating the value of multiple solution approaches.

42 ENGINEERING↗
DOE OSTI2020

Molecular Lignin Solubility and Structure in Organic Solvents

Lignin, a polymer found in the secondary plant cell wall of terrestrial plants, is the single largest source of renewable aromatics and has attracted considerable attention as a feedstock for potential industrial use. However, the secondary plant cell wall is a crowded environment, and lignin in its native form interacts with other biomass components within a larger network. Application of some organic solvents is known to liberate lignin from this network and creates lignin-rich streams suitable for conversion into target products. Through molecular-scale lignin simulation, we analyze how diverse lignin polymers change their structure in response to varying organic solvent environments. We quantify the relationship between solvent polarity and lignin polymer extension, observing maximum polymer expansion and solvation for solvents with polarity near those of dimethyl sulfoxide. From our observations at the nanoscale, increasing syringyl content within lignin polymers reduces the expansion of the polymer in organic solvent environments and decreases the free energy difference compared to aqueous solvent environments, thereby reducing solubility for high syringyl lignin polymers. The conformational transition rates between lignin polymer shapes increased through a combination of the solvent diffusion constant and polymer extension. The molecular simulations indicate that there is likely no single optimal organic solvent for lignin. Different solvent mixtures have optimal or near-optimal properties in solubilizing lignin polymers, thereby disrupting interactions with other biopolymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning

Spiking Neural Networks (SNNs) are computational models inspired by the event-driven communication and connectivity patterns of biological neural circuits. They enable high energy efficiency and natural support for diverse architectures ranging from layered networks to small-world and graphstructured topologies. In this work, we introduce NeuroCoreX, an open-source, FPGA-based spiking neural network emulator that provides real-time, on-chip learning and flexible network organization. NeuroCoreX supports both feedforward sensory inputs streamed directly from sensors or PCs via UART and recurrent on-chip connectivity, enabling simultaneous processing and learning from external stimuli and internal network dynamics-capabilities rarely available in existing FPGA SNN platforms. The system implements a Leaky Integrate-and-Fire (LIF) neuron model with current-based synapses and supports pair-based STDP learning on both feedforward and recurrent synapses. A lightweight Python interface enables interactive configuration, live monitoring, weight read-back, and experiment control. Importantly, NeuroCoreX is tightly integrated with the SuperNeuroMAT simulator, allowing SNN models to be transferred seamlessly from software to hardware for hardware-in-the-loop development. By combining real-time plasticity, flexible connectivity, and an open-source VHDL implementation, NeuroCoreX provides an extensible and accessible platform for neuromorphic research, algorithm-hardware co-design, and energy-efficient edge intelligence.

Gautam, Ashish [ORNL]↗
DOE OSTI2023

Disentangling Beam Losses in The Fermilab Main Injector Enclosure Using Real-Time Edge AI

The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler Ring. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Deciphering the origin of any of the 260 BLM readings is often difficult. The (Accelerator) Real-time Edge AI for Distributed Systems project, or READS, has developed an AI/ML model, and implemented it on fast FPGA hardware, that disentangles mixed beam losses and attributes probabilities to each BLM as to which machine(s) the loss originated from in real-time. The model inferences are then streamed to the Fermilab accelerator controls network (ACNET) where they are available for operators and experts alike to aid in tuning the machines.

43 PARTICLE ACCELERATORS↗