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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Novel Protection Scheme for Unbalanced Faults in Inverter Dominated Networks: A Computationally Efficient Algorithm for Entry-Level Relays

Microgrids are now a common practice in distribution systems to increase resilience and reliability. However, microgrid protection remains a critical challenge, considering its requirement to operate in both grid connected and islanded, and the variability in fault characteristics under each mode of operation. This paper presents unbalanced power (S unb ) based fault detection algorithm, which considers local voltage and current unbalances to determine faults in the system. S unb is a computationally efficient fault detection algorithm that is suitable for implementation in the programmable logic of entry level protective relays. In addition, the difference in current and voltage unbalance (D n ) is used to determine the fault type. The proposed method demonstrates high sensitivity and selectivity for line-to-ground (LG), line-to-line (LL), and double line-to-ground (LLG) faults, representing the most common faults in distribution systems. It also allows relay coordination with upstream and downstream protection devices in both island and grid connected operation, while preserving grading margins. The same pickup and time multiplier settings of a particular relay for both modes of operation eliminates the need for adaptive settings, which rely on communication networks. Validation was performed with a hardware-in-the-loop (HIL) setup using Typhoon HIL real time simulator interfaced with three entry-level, SEL 751 relays. Results confirmed the algorithm’s ability to discriminate fault conditions, and determine the fault type under both operating modes, maintain fast detection times, and ensure proper protection coordination.

fault classification↗
DOE OSTI2025

Quantum Simulators and Applications on Quantum Framework

Simulating quantum circuits is essential for validating quantum algorithms. However, no single simulator consistently performs best - efficiency depends on circuit structure, entanglement, and depth. In this work, we integrate Qiskit-Aer (state-vector and matrix product state) and QTensor, a tree-tensor-network based simulator, into the Quantum Framework (QFw), a modular platform that supports multiple quantum backends via a unified interface. We also enable distributed quantum approximate optimization algorithm (DQAOA) application compatibility with QFw, allowing sub-problems to be solved in parallel at scale. We then benchmark DQAOA and TFIM (transverse field Ising model) circuits across supported simulators, showing how performance varies significantly with problem type. All simulations are deployed on the Frontier supercomputer using QFw's MPI-based orchestration for distributed, multinode execution. These results underscore the need for simulatoragnostic infrastructure to enable systematic evaluation and highperformance scaling of quantum workloads. QFw provides a practical and extensible path toward reproducible quantum algorithm development across diverse application domains.

Chundury, Srikar [ORNL] (ORCID:0009000183359259)↗
DOE OSTI2023

ITL (InterfaceLimits.jl) [SWR-23-56]

Network flow of electrical power is governed by Kirchhoff's laws which make calculating the power transfer capacity across interfaces connecting groups of nodes complex. This package leverages the capabilities of Sienna\Data to build and solve interface limit calculation problems.

Barrows, Clayton↗
DOE OSTI2021

pnnl/arena

CFA ARENA is a novel programming model with the support of a runtime targeting asynchronous data-centric execution paradigm in a distributed system. All the machine nodes in ARENA are connected by a ring network to bring the specialized computation to the data rather than the reverse to minimize data movement. The programming interfaces are implemented using C++

Tan, Cheng↗
DOE OSTI2026

Point defects in semiconductors: Friends and foes for quantum technologies

Point defects in semiconductors are both a curse and a blessing in microelectronics: they enable the control of electrical conductivity through doping, yet can also act as trapping and recombination centers that degrade device performance. In quantum information science, defects play a similarly dual role. They can be harnessed as spin–photon interfaces enabling the coupling of electronic and nuclear spins to light and the creation of distributed entanglement for quantum networks or used as atomistic scale sensors for quantum sensing. At the same time, defects are a major source of decoherence for superconducting qubits, one of the leading quantum computing platforms. This article discusses how a deeper materials-level understanding of defects can guide the design of improved quantum devices for communication, sensing, and computation.

Zhu, Yizhi [Rice University, Houston, TX (United S↗
DOE OSTI2024

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).

Forestano, Roy T. (ORCID:0000000203552076)↗
DOE OSTI2022

Building a machine learning surrogate model for wildfire activities within a global Earth system model

Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

58 GEOSCIENCES↗
NASA NTRS1979

Orbiter CIU/IUS communications hardware evaluation

Inertial Upper Stage (IUS) and DoD Communication Interface Unit (CIU) communication system design, hardware specifications, and interfaces were evaluated to determine their compatibility with the Orbiter payload communication and data handling equipment and the Orbiter network communication equipment.

Huth, G. K.↗
NASA NTRS1982

Spacecraft radio frequency subsystem

The historical background for the development of the planetary (RFS) is reviewed, and the performance capabilities of the various functional subassemblies are described. The flight radio frequency subsystem is a valid component for the three spacecraft telecommunications functions of tracking, command, and telemetry. It is the radio and the signal processing equipment residing in the spacecraft that interfaces with the control & data subsystem and performs two-way communications with the Earth-based Deep Space Network. The RFS consists of all the elements for RF reception, demodulation, modulation, and transmission, including those for command detection and telemetry modulation.

Tam, M. K.↗
NASA NTRS1988

The Langley Advanced Real-Time Simulation (ARTS) system

This paper is intended as a status report on the ARTS system. It briefly describes the architecture and principal subsystems including: the CAMAC network system (hardware and software), the clocking system, the signal converters, the control consoles, and the minicomputer and microcomputer interfaces. The performance and reliability of the system exceeds expectations and component failure data over an 11-month period are presented. Planned enhancements, including the replacement of the mainframe computers, are discussed.

Crawford, Daniel J.↗
NASA NTRS1989

Neural networks in support of manned space

Many lobbyists in Washington have argued that artificial intelligence (AI) is an alternative to manned space activity. In actuality, this is the opposite of the truth, especially as regards artificial neural networks (ANNs), that form of AI which has the greatest hope of mimicking human abilities in learning, ability to interface with sensors and actuators, flexibility and balanced judgement. ANNs and their relation to expert systems (the more traditional form of AI), and the limitations of both technologies are briefly reviewed. A Few highlights of recent work on ANNs, including an NSF-sponsored workshop on ANNs for control applications are given. Current thinking on ANNs for use in certain key areas (the National Aerospace Plane, teleoperation, the control of large structures, fault diagnostics, and docking) which may be crucial to the long term future of man in space is discussed.

Werbos, Paul J.↗
NASA NTRS1994

Constraint based scheduling for the Goddard Space Flight Center distributed Active Archive Center's data archive and distribution system

The Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) has been operational since October 1, 1993. Its mission is to support the Earth Observing System (EOS) by providing rapid access to EOS data and analysis products, and to test Earth Observing System Data and Information System (EOSDIS) design concepts. One of the challenges is to ensure quick and easy retrieval of any data archived within the DAAC's Data Archive and Distributed System (DADS). Over the 15-year life of EOS project, an estimated several Petabytes (10(exp 15)) of data will be permanently stored. Accessing that amount of information is a formidable task that will require innovative approaches. As a precursor of the full EOS system, the GSFC DAAC with a few Terabits of storage, has implemented a prototype of a constraint-based task and resource scheduler to improve the performance of the DADS. This Honeywell Task and Resource Scheduler (HTRS), developed by Honeywell Technology Center in cooperation the Information Science and Technology Branch/935, the Code X Operations Technology Program, and the GSFC DAAC, makes better use of limited resources, prevents backlog of data, provides information about resources bottlenecks and performance characteristics. The prototype which is developed concurrently with the GSFC Version 0 (V0) DADS, models DADS activities such as ingestion and distribution with priority, precedence, resource requirements (disk and network bandwidth) and temporal constraints. HTRS supports schedule updates, insertions, and retrieval of task information via an Application Program Interface (API). The prototype has demonstrated with a few examples, the substantial advantages of using HTRS over scheduling algorithms such as a First In First Out (FIFO) queue. The kernel scheduling engine for HTRS, called Kronos, has been successfully applied to several other domains such as space shuttle mission scheduling, demand flow manufacturing, and avionics communications scheduling.

Short, Nick, Jr.↗
NASA NTRS1996

An MPA-IO interface to HPSS

This paper describes an implementation of the proposed MPI-IO (Message Passing Interface - Input/Output) standard for parallel I/O. Our system uses third-party transfer to move data over an external network between the processors where it is used and the I/O devices where it resides. Data travels directly from source to destination, without the need for shuffling it among processors or funneling it through a central node. Our distributed server model lets multiple compute nodes share the burden of coordinating data transfers. The system is built on the High Performance Storage System (HPSS), and a prototype version runs on a Meiko CS-2 parallel computer.

Jones, Terry↗
NASA NTRS2012

The Virtual Solar Observatory: What Are We Up To Now?

In the nearly ten years of a functional Virtual Solar Observatory (VSO), http://virtualsolar.org/ we have made it possible to query and access sixty-seven distinct solar data products and several event lists from nine spacecraft and fifteen observatories or observing networks. We have used existing VSO technology, and developed new software, for a distributed network of sites caching and serving SDO HMI and/ or AlA data. We have also developed an application programming interface (API) that has enabled VSO search and data access capabilities in IDL, Python, and Java. We also have quite a bit of work yet to do, including completion of the implementation of access to SDO EVE data, and access to some nineteen other data sets from space- and ground-based observatories. In addition, we have been developing a new graphic user interface that will enable the saving of user interface and search preferences. We solicit advice from the community input prioritizing our task list, and adding to it

Gurman, J. B.↗
DOE OSTI2022

DenKv: Addressing Design Trade-offs of Key-value Stores for Scientific Applications

High-performance computing (HPC) facilities have employed flash-based storage tier near to compute nodes to absorb high I/O demand by HPC applications during periodic system-level checkpoints. To accelerate these checkpoints, proxy-based distributed key-value stores (PD-KVS) gained particular attention for their flexibility to support multiple backends and different network configurations. PD-KVS rely internally on monolithic KVS, such as LevelDB or RocksDB, to exploit the KV interface and query support. However, PD-KVS are unaware of the high redundancy factor in checkpoint data, which can be up to GBs to TBs, and therefore, tend to generate high write and space amplification on these storage layers. In this paper, we propose DenKv which is deduplication-extended node-local LSM-tree-based KVS. DenKv employs asynchronous partially inline dedup (APID) and aims to maintain the performance characteristics of LSM-tree-based KVS while reducing the write and space amplification problems. We implemented DenKv atop BlobDB and showed that our proposed solution maintains performance while reducing write amplification up to 2× and space amplification by 4× on average.

Khan, Awais↗
DOE OSTI2025

Methanol at Water–TiO 2 Interfaces: Free Energies of Water and Methanol Dissociation

Methanol adsorption on TiO 2 surfaces has long been studied due to its role in enhancing photocatalytic hydrogen evolution, yet how it modulates surface chemistry under aqueous conditions remains little understood. Using molecular dynamics with an ab initio-based deep neural network potential, we find that methanol adsorption induces markedly different effects on the aqueous surfaces of anatase and rutile, the two common phases of TiO 2 . In anatase, methanol adsorption significantly enhances water dissociation, which is otherwise rare at the neat water interface. This enhancement arises from an alternative dissociation pathway mediated by surface-bound methoxyl groups. In contrast, methanol adsorption tends to suppress water dissociation on rutile, replacing it with thermodynamically favored methanol dissociation. Overall, methanol adsorption in an aqueous environment alters not only the availability of key reactive intermediates involved in hydrogen evolution but also the hydrogen source, which turns out to be primarily methanol on rutile, whereas both water and methanol are consumed on anatase. These results provide mechanistic insights into the coupled roles of organic adsorbates and water at photocatalytic interfaces, with implications on how methanol enhances the activity of H 2 evolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2013

Space Communication and Navigation SDR Testbed, Overview and Opportunity for Experiments

NASA has developed an experimental flight payload (referred to as the Space Communication and Navigation (SCAN) Test Bed) to investigate software defined radio (SDR) communications, networking, and navigation technologies, operationally in the space environment. The payload consists of three software defined radios each compliant to NASAs Space Telecommunications Radio System Architecture, a common software interface description standard for software defined radios. The software defined radios are new technology developments underway by NASA and industry partners launched in 2012. The payload is externally mounted to the International Space Station truss to conduct experiments representative of future mission capability. Experiment operations include in-flight reconfiguration of the SDR waveform functions and payload networking software. The flight system will communicate with NASAs orbiting satellite relay network, the Tracking and Data Relay Satellite System at both S-band and Ka-band and to any Earth-based compatible S-band ground station. The system is available for experiments by industry, academia, and other government agencies to participate in the SDR technology assessments and standards advancements.

Reinhart, Richard C.↗