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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An LSTM Deep Learning Network for ¿Background Radiation Prediction

Determination of appropriate background radiation is important in any measurement application. Environmental radiation monitors and monitors used to assess dose to individuals outside of controlled areas are particularly susceptible to changes in readings due to fluctuations in the environmental conditions. These fluctuations (e.g. radon progeny concentrations) lead to changes in the detector response in the actual radiation environment, and they need to be taken into account when extracting the net operational doses. Work has been ongoing to apply advances in Deep Learning and Artificial Intelligence to account for changes in detector responses based on environmental parameters; in particular, a Long-Short Term Memory (LSTM) Deep Learning architecture has been utilized to incorporate time-series data into a prediction model. In this work, we present the current status of the project to predict radiation measurements based on meteorological conditions and air packet trajectories extracted using the National Oceanic and Atmospheric Administration's (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT4) model.

Degtiarenko, Pavel↗
DOE OSTI2024

Cybersecurity for the Operational Technology Environment (CyOTE) (Final Technical Report)

Electric grids have historically been susceptible to both physical attacks and environmental hazards but the implementation of smart grids, remote management, and self-healing networks, has now made the grid vulnerable to cyber attacks. To address risks introduced by routable connectivity, utilities must establish dynamic solutions to identify, protect, detect, respond to, and recover from cyber security threats and vulnerabilities. In response to the evolving threat landscape U.S. Department of Energy-Office of Cybersecurity, Energy Security, and Emergency Response (DOE CESER) initiated the Cybersecurity for the OT Environment (CyOTE) pilot program, a U.S. Department of Energy (DOE) effort designed to leverage U.S. intelligence capabilities to prevent, detect, or mitigate a cyber attack on utility operational technology (OT) networks. As part of the CyOTE pilot, The Southern Company (Southern Company or Southern) researched, evaluated and deployed emerging Commercial off the Shelf (COTS) technologies and cyber security monitoring architectures to provide previously unrealized network visibility and situational awareness through deep packet inspection and data analytics. This Final Scientific/Technical Report documents the objectives, methodology, lessons learned, and results of Southern Company’s participation in the CyOTE pilot from December 2018 to September 2023.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Hydrogen and Battery Storage Technologies for Low-Cost Energy Decarbonization in Distribution Networks

Deep energy decarbonization cannot be achieved without high penetration of renewables. At higher renewable energy penetrations, the variability and intermittent nature of solar photovoltaic (PV) electricity can cause ramping issues with existing fossil fuel generation, requiring longer term energy storage to increase the reliability of grid operation. Here, a proton exchange membrane electrolyzer can produce H 2 and serves as a utility controllable load. The produced H 2 can then be stored and converted back into electricity, or mixed with natural gas, or used as transportation fuel, or chemical feedstock. This paper considers the perspective of the distribution system operator that operates the distributed energy resources on a standard IEEE 33-node distribution network considering the technical and physical constraints with the goal of minimizing total investment and operation cost. Different case studies, at very high PV penetrations are considered to show the challenges and path to net-zero emission energy production using H 2 energy. Sensitivity of utility PV costs and electrolyzer capital costs on producing H 2 at $\$1$ /kg are presented showing that the distribution network could produce 100% renewable electricity and H 2 could be produced at a cost of $\$1$ /kg by 2050 with conservative cost estimates and by 2030 with accelerated cost declines.

25 ENERGY STORAGE↗
DOE OSTI2022

Deep Reinforcement Learning From Demonstrations to Assist Service Restoration in Islanded Microgrids

Microgrids can be operated in island mode during utility grid outages to support service restoration and improve system resilience. To schedule and dispatch distributed energy resources (DERs) in an islanded microgrid, conventional model-based methods rely on accurate distribution network models and lack generalization and adaptability. Data-driven methods are promising for DER coordination but face practical challenges such as potential hazards to microgrids during online training and insufficient online training opportunities due to low outage rates. This paper presents a novel two-stage learning framework that builds on the deep deterministic policy gradient from demonstrations to identify an optimal restoration strategy. At the pre-training stage, imitation learning is applied to equip the control agent with expert experiences to guarantee acceptable initial performance. At the online training stage, action clipping, reward shaping, and expert demonstrations are leveraged to ensure safe exploration while accelerating the training process. In conclusion, the proposed method is illustrated using the IEEE 123-node system and compared with a representative model-based method and the standard deep deterministic policy gradient method to prove solution accuracy and demonstrate increased computational efficiency.

Du, Yan↗
DOE OSTI2022

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

Abstract Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Wang, Xiaowen↗
DOE OSTI2024

Extraction of Drell-Yan Angular Parameters in $pp$ Collisions with a 120 GeV Beam Energy Using a Deep-Learning Unfolding Algorithm

Dilepton production in pp collisions through the Drell-Yan process provides a crucial tool for studying the internal quark-gluon structure of the nucleon. By precisely measuring the $\cos2\phi$ asymmetry, where $\phi$ represents the azimuthal angle of the $l^{+}l^{-}$ pair in the Collins-Soper frame, we can gain valuable insights into the proton’s structure and the transverse momentum ($q_{T}$) dependence of the $\cos2\phi$ asymmetry. SeaQuest, a fixed-target Drell-Yan experiment at Fermilab, involved an unpolarized proton beam colliding with unpolarized LH$_{2}$ and LD$_{2}$ targets. Measurements obtained from experiments typically require corrections for detector inefficiencies, smearing, and acceptance. Traditionally, these corrections involve “unfolding” the detector-level measurements through matrix operations. However, in higher-dimensional phase space, these conventional methods fail to scale effectively. To overcome these limitations, we employ an unbinned unfolding method that utilizes deep neural networks for unfolding higher-dimensional phase space. In this presentation, we will explain the design of the neural network architecture, our training strategies, and outline our plans to achieve conclusive results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Permeability Prediction Using Vision Transformers

Accurate permeability predictions remain pivotal for understanding fluid flow in porous media, influencing crucial operations across petroleum engineering, hydrogeology, and related fields. Traditional approaches, while robust, often grapple with the inherent heterogeneity of reservoir rocks. With the advent of deep learning, convolutional neural networks (CNNs) have emerged as potent tools in image-based permeability estimation, capitalizing on micro-CT scans and digital rock imagery. This paper introduces a novel paradigm, employing vision transformers (ViTs)—a recent advancement in computer vision—for this crucial task. ViTs, which segment images into fixed-sized patches and process them through transformer architectures, present a promising alternative to CNNs. We present a methodology for implementing ViTs for permeability prediction, its results on diverse rock samples, and a comparison against conventional CNNs. The prediction results suggest that, with adequate training data, ViTs can match or surpass the predictive accuracy of CNNs, especially in rocks exhibiting significant heterogeneity. This study underscores the potential of ViTs as an innovative tool in permeability prediction, paving the way for further research and integration into mainstream reservoir characterization workflows.

58 GEOSCIENCES↗
NASA NTRS1977

Pioneer mission support

Some recent activities within the Deep Space Network in support of the Pioneer project's in-flight spacecraft are reported. The amount of tracking coverage provided by the Network and the current status of operational testing of the Mark III Data Subsystems are presented.

Hadamski, T. P.↗
NASA NTRS1986

Deep space network Mark 4A description

The general system configuration for the Mark 4A Deep Space Network is described. The arrangement and complement of antennas at the communications complexes and subsystem equipment at the signal processing centers are described. A description of the Network Operations Control Center is also presented.

Wallace, R. J.↗
NASA NTRS1992

DSN acquisition of Magellan high-rate telemetry data

The Magellan Project levied the stringent requirement of a 98 percent high-rate telemetry data capture rate on the Deep Space Network (DSN) during the Magellan Prime Mapping Mission. To meet this requirement, the DSN undertook extensive development of the DSN Telemetry System, as well as extensive DSN operation planning and test and training. In actuality, the DSN substantially exceeded the requirement by achieving a Prime Mapping Mission high-rate telemetry data capture rate of 99.14 percent. This article details the DSN telemetry system development, and DSN operations planning and test and training. In addition, the actual high-rate telemetry data outages are comprehensively presented and analyzed.

Berman, A. L.↗
DOE OSTI2023

Galaxy Morphology Classification Using Bayesian Neural Networks for LSST

Within the decade, many new ground and space-based observatories will become operational, generating massive amounts of data on short timescales. New surveys like Rubin Observatory's Legacy Survey of Space and Time (LSST) will be capable of observing objects with greater resolution than ever before, but processing and analyzing these datasets optimally will pose a significant challenge. In an effort to prepare for this, we explore how incorporating Deep Neural Networks can better support future data-intensive Astrophysics tasks such as galaxy morphology classification.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1987

The Telecommunications and Data Acquisition Report

Archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA) are provided. Activities of the Deep Space Network (DSN) in space communications, radio navigation, radio science, and ground-based radio astronomy are reported. Also included are the plans, supporting research and technology, implementation and operations for the Ground Communications Facility (GCF). In geodynamics, the publication reports on the application of radio interferometry at microwave frequencies for geodynamic measurements. In the search for extraterrestrial intelligence (SETI), it reports on implementation and operations for searching the microwave spectrum.

Posner, E. C.↗
NASA NTRS1991

Demonstration of precise estimation of polar motion parameters with the global positioning system: Initial results

Data from the Global Positioning System (GPS) were used to determine precise polar motion estimates. Conservatively calculated formal errors of the GPS least squares solution are approx. 10 cm. The GPS estimates agree with independently determined polar motion values from very long baseline interferometry (VLBI) at the 5 cm level. The data were obtained from a partial constellation of GPS satellites and from a sparse worldwide distribution of ground stations. The accuracy of the GPS estimates should continue to improve as more satellites and ground receivers become operational, and eventually a near real time GPS capability should be available. Because the GPS data are obtained and processed independently from the large radio antennas at the Deep Space Network (DSN), GPS estimation could provide very precise measurements of Earth orientation for calibration of deep space tracking data and could significantly relieve the ever growing burden on the DSN radio telescopes to provide Earth platform calibrations.

Lichten, S. M.↗
NASA NTRS1991

Gamma Ray Observatory (GRO): Emergency support

The Gamma Ray Observatory (GRO) is an Earth orbiting satellite that studies sources of localized, galactic, and extragalactic gamma rays. It will be carried into a near-circular orbit by the Space Shuttle, following which it will be placed in its operational orbit by its on-board hydrazine propulsion system. Formal orbit parameters are 350 km x 450 km x 28.5 degrees with a period of 93 minutes. Deep Space Network coverage will be provided during emergencies that would prevent communications via the normal Tracking and Data Relay Satellite System (TDRSS)-White Sands data link. Emergency support will be provided by the DSN's 26-meter antenna subnetwork. Information is given in tabular form for DSN support, frequency assignments, telemetry, and command.

Schauer, K.↗
NASA NTRS1993

Stability measurements of the radio science system at the 34-m high-efficiency antennas

From 1991 to 1993 the fractional frequency stability of the operational Radio Science System was measured at DSS's 15, 45, and 65. These stations are designed to have the most stable uplink and downlink equipment in the Deep Space Network (DSN). Some measurements were performed when the antenna was moving and the frequency was ramped. The stability, including contributions of all elements in the station except for the antenna and the hydrogen maser, was measured to be 0.3 to 1.3 x 10(exp -15) when the frequency was fixed, and 0.6 to 6.0 x 10(exp -15) when the frequency was ramped (sample interval, 1000 sec). Only one measurement out of fifteen exceeded specification. In all other cases, when previous measurements on the antenna and the hydrogen maser were added, a total system stability requirement of 5.0 x 10(exp -15) as met. In addition, ambient temperature was found to cause phase variation in the measurements at a rate of 5.5 deg of phase per deg C.

Pham, T. T.↗
NASA NTRS2005

A unifying framework for systems modeling, control systems design, and system operation

Current engineering practice in the analysis and design of large-scale multi-disciplinary control systems is typified by some form of decomposition- whether functional or physical or discipline-based-that enables multiple teams to work in parallel and in relative isolation. Too often, the resulting system after integration is an awkward marriage of different control and data mechanisms with poor end-to-end accountability. System of systems engineering, which faces this problem on a large scale, cries out for a unifying framework to guide analysis, design, and operation. This paper describes such a framework based on a state-, model-, and goal-based architecture for semi-autonomous control systems that guides analysis and modeling, shapes control system software design, and directly specifies operational intent. This paper illustrates the key concepts in the context of a large-scale, concurrent, globally distributed system of systems: NASA's proposed Array-based Deep Space Network.

systems modeling↗
NASA NTRS2014

A Geosynchronous Orbit Optical Communications Relay Architecture

NASA is planning to fly a Next Generation Tracking and Data Relay Satellite (TDRS) next decade. While the requirements and architecture for that satellite are unknown at this time, NASA is investing in communications technologies that could be deployed on the satellite to provide new communications services. One of those new technologies is optical communications. The Laser Communications Relay Demonstration (LCRD) project, scheduled for launch in December 2017 as a hosted payload on a commercial communications satellite, is a critical pathfinder towards NASA providing optical communications services on the Next Generation TDRS. While it is obvious that a small to medium sized optical communications terminal could be flown on a GEO satellite to provide support to Near Earth missions, it is also possible to deploy a large terminal on the satellite to support Deep Space missions. Onboard data processing and Delay Tolerant Networking (DTN) are two additional technologies that could be used to optimize optical communications link services and enable additional mission and network operations. This paper provides a possible architecture for the optical communications augmentation of a Next Generation TDRS and touches on the critical technology work currently being done at NASA. It will also describe the impact of clouds on such an architecture and possible mitigation techniques.

optics↗