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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Determining Availability Characteristics of DSN Data Systems Using Discrepancy Report Data

A reasonably economical way was developed to determine availability characteristics of Deep Space Network (DSN) data systems, subsystems, and assemblies using the DSN discrepancy report (DR) data base and DSN operating schedule and history data bases. Operating mean time between failures (OMTBF), operating mean time to restore service (OMTTRS), and operating functional availability (OFA) can be computed by year, by system, by subsystem, by assembly, and by station. The effort required to produce the desired reports is described, specific data on the telemetry, command, and tracking systems are presented, and major contributors to system outages are identified. Future improvements in preparing and analyzing DR data are also outlined to enhance their use in correcting conditions that lead to outages.

Ruskin, A. M.↗
NASA NTRS1986

An assessment of the long-term drift in SBUV total ozone data, based on comparison with the Dobson network

Six years of total ozone data derived from the Nimbus 7 Solar Backscatter Ultraviolet (SBUV) experiment are compared with results from 41 Dobson stations to determine year-to-year changes in addition to the monthly bias change for individual stations and for the network. The SBUV measurements are found to drift against the Dobson stations with a linear trend of .38 + or - .13 percent/yr. The cause of the drift could not be separated between residual uncorrected drift in the SBUV instrument, limited sensitivity of the SBUV to increases in tropospheric ozone, and the effect of local increases in pollution level on individual Dobson stations.

Fleig, Albert J.↗
DOE OSTI2021

Online Monitoring System for Concrete Structures Affected by Alkali-Silica Reaction

This report presents a comprehensive study of developing ultrasonic wave and acoustic emission (AE) techniques for long-term monitoring of alkali-silica reaction (ASR) development in concrete. Small, medium, and full-scale concrete specimens were cast, conditioned, and monitored for various periods, from at least one year to 2.4 years. The reactive concrete specimens contain coarse or fine reactive aggregates to study the effects of different types of reactive aggregates. Confinements were also designed to simulate the 2-dimensional confinement effects of reinforcement in the shield building of nuclear power plants. The concrete specimens were stored in a environmental chamber with high humidity and high temperature to accelerate the ASR development. The ultrasonic monitoring data shows high sensitivity to ASR development and could detect cracking initiation well before visible surface cracks occurred. However, the linear ultrasonic analysis based on wave velocity is strongly affected by temperature variation. Therefore, a nonlinear ultrasonic method was proposed to measure thermally induced nonlinear acoustic responses of concrete (thermal modulation of ultrasonic wave). The measured nonlinear acoustic parameter shows a high correlation with ASR expansion across specimens with different reactive aggregates and confinement conditions. The same conclusion was obtained from nonlinear resonance tests on small concrete prisms. Compared to the linear acoustic methods, the nonlinear tests show high sensitivities to ASR damage from internal microcracking initiation at the early stage to visible cracks at the late stage of ASR. The attributes of the thermal modulation of nonlinear ultrasonic method include high sensitivity, immunity to temperature effects, and strong correlation with ASR expansion, which demonstrate great potentials of the nonlinear ultrasonic method for diagnosis of ASR damage and prediction of concrete deterioration process. Acoustic emission is a passive sensing technique for damage assessment, and access to only one surface is needed even for thick and heavily reinforced elements such as the walls utilized for nuclear shield building. Additionally, relatively few sensors are required to monitor the progression of the damage process. Results indicate that the AE data can be related to the damage rating index, which is a petrography-based means of assessing damage due to ASR in reinforced concrete. Furthermore, AE is capable of detecting ASR damage long before surface cracking is visually noticeable. Boundary conditions play in important role in the progression of ASR damage and differences in boundary conditions are reflected in the AE data. Entropy based data assessment methods provide a means to assess the damage state, and convolutional neural network based data assessment provided a means to assess the damage state in real-time. Results indicate that artificial neural network models may be used as a means to predict volumetric expansion based on AE data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Deep learning enables nanoscale X-ray 3D imaging with limited data

Deep neural network can greatly improve tomography reconstruction with limited data. A recent effort of combining ptycho-tomography model with the 3D U-net demonstrated a significant reduction in both the number of projections and computation time, and showed its potential for integrated circuit imaging that requires high-resolution and fast measurement speed.

47 OTHER INSTRUMENTATION↗
NASA NTRS2012

Next Generation NASA Initiative for Space Geodesy

Space geodesy measurement requirements have become more and more stringent as our understanding of the physical processes and our modeling techniques have improved. In addition, current and future spacecraft will have ever-increasing measurement capability and will lead to increasingly sophisticated models of changes in the Earth system. Ground-based space geodesy networks with enhanced measurement capability will be essential to meeting these oncoming requirements and properly interpreting the sate1!ite data. These networks must be globally distributed and built for longevity, to provide the robust data necessary to generate improved models for proper interpretation ofthe observed geophysical signals. These requirements have been articulated by the Global Geodetic Observing System (GGOS). The NASA Space Geodesy Project (SGP) is developing a prototype core site as the basis for a next generation Space Geodetic Network (SGN) that would be NASA's contribution to a global network designed to produce the higher quality data required to maintain the Terrestrial Reference Frame and provide information essential for fully realizing the measurement potential of the current and coming generation of Earth Observing spacecraft. Each of the sites in the SGN would include co-located, state of-the-art systems from all four space geodetic observing techniques (GNSS, SLR, VLBI, and DORIS). The prototype core site is being developed at NASA's Geophysical and Astronomical Observatory at Goddard Space Flight Center. The project commenced in 2011 and is scheduled for completion in late 2013. In January 2012, two multiconstellation GNSS receivers, GODS and GODN, were established at the prototype site as part of the local geodetic network. Development and testing are also underway on the next generation SLR and VLBI systems along with a modern DORIS station. An automated survey system is being developed to measure inter-technique vector ties, and network design studies are being performed to define the appropriate number and distribution of these next generation space geodetic core sites that are required to achieve the driving ITRF requirements. We present the status of this prototype next generation space geodetic core site, results from the analysis of data from the established geodetic stations, and results from the ongoing network design studies.

Merkowitz, S. M.↗
DOE OSTI2023

A Power Application Developer’s Guide to the Common Information Model: An Introduction for Power Systems Engineers and Application Developers – CIM17v40

A key issue in creating the next generation of energy management system (EMS) and advanced distribution management system (ADMS) platforms will be the ability to represent and exchange power system network model data in a consistent manner. To this end, the Common Information Model (CIM) stands out as the only standardized vocabulary (or ontology) for defining power system network models and asset data in a comprehensive, consistent manner across the generation-transmission-distribution boundary. The CIM is freely available to use and extend. The CIM is maintained by the UCAiug (informally known as the CIM User’s Group) under an Apache 2.0 license. The CIM Users Group collaborates with the IEC and other standards communities for the development of technical and informative specifications. Although portions of the information model are referred to by the corresponding IEC standards naming, it is not necessary to purchase any of the IEC standards to use the CIM information model. This document provides a roadmap for power system engineers and application developers not familiar with semantic modeling to start using the CIM for modeling, simulation, optimization, and development of advanced power applications. The key classes needed for defining power system topology and equipment are explained systematically. Key focus areas include modeling of lines, transformers, generators, switching equipment, loads, and distributed energy resources (DERs).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Issues in packet radio network design

The physical aspects of a packet radio network design, the automated management of the network, and the interface of the network to the users are examined. The networks provide data communications to users located over a broad geographic region where direct radio or wire connection between the source and destination user is not practical; the network consists of a radio, antenna, and digital controller. The physical connectivity, bandwidth-time-space management, channel access, and data link control of the network are analyzed. Consideration is given to link determination and control, routing and packet forwarding, congestion and flow control, and supported users management. The operation and management of a packet radio network, in particular network deployment and maintenance, network access methods, and its effect on the radio spectrum, are discussed. The performance and cost of a packet radio network are evaluated.

Leiner, Barry M.↗
DOE OSTI2025

INTEGRATE – Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project developed a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. Training data was obtained from improved turbulence and transition models for RANS and hybrid RANS/LES solvers with machine-learned physics-based data-augmented corrections and then using the resulting neural-network(s) augmented RANS model to run thousands of 2-D and 3-D CFD simulations.

17 WIND ENERGY↗
NASA NTRS1969

Communications for space research.

Tracking and data acquisition networks and communications systems for support of space missions, studying data processing, unified S Band System and Data Relay Satellite System

Truszynski, G.↗
NASA NTRSDate not supplied

Pre-Tropical Cyclone Squall Lines and the Connection to the Diurnal Cycle during Hurricane Laura (2020)

Previous studies on the tropical cyclone (TC) diurnal cycle have shown in observations and modeling the presence of outward propagating squall lines. These squall lines were observed in several TCs during the 2020 Atlantic hurricane season. Of these squall lines, one observed in Hurricane Laura made landfall over 14 hours prior to the hurricane’s eye. This squall was responsible for numerous tornado warnings, which disrupted preparations and evacuations ahead of the storm’s landfall. This study seeks to characterize these squall lines and their environmental and thermodynamic characteristics, determine whether they behave more like midlatitude squall lines or tropical cyclone rain bands, and assess the performance of the convection-allowing High Resolution Rapid Refresh model in forecasting them over the ocean and over land. By using a combination of operational radar data, a network of surface weather observations from land and sea, satellite data, and HRRR model output, this case will undergo an extensive evaluation based on criteria from past literature in order to better understand these features as a whole and in relation to the diurnal cycle.

Vivian L Brasfield↗
NASA NTRSDate not supplied

Pre-Tropical Cyclone Squall Lines and the Connection to the Diurnal Cycle in Hurricane Laura (2020)

Previous studies on the tropical cyclone (TC) diurnal cycle have shown in observations and modeling the presence of outward propagating squall lines. These squall lines were observed in several TCs during the 2020 Atlantic hurricane season. Of these squall lines, one observed in Hurricane Laura made landfall over 13 hours prior to the hurricane’s eye. This squall was responsible for numerous tornado warnings, which disrupted preparations and evacuations ahead of the storm’s landfall. This study seeks to characterize these squall lines and their environmental and thermodynamic characteristics, determine whether they behave more like midlatitude squall lines or tropical cyclone rain bands, and assess the performance of the convection-allowing High Resolution Rapid Refresh model in forecasting them over the ocean and over land. By using a combination of operational radar data, a network of surface weather observations from land and sea, satellite data, and HRRR model output, this case will undergo an extensive evaluation based on criteria from past literature in order to better understand these features as a whole and in relation to the diurnal cycle.

Vivian L Brasfield↗
NASA NTRSDate not supplied

Observational Analysis of Atlantic Basin Tropical Cyclone Squall Lines and Relationship to the Diurnal Cycle

Previous studies on the tropical cyclone (TC) diurnal cycle have shown in observations and modeling the presence of outward propagating squall lines. These squall lines were observed in several TCs during recent Atlantic hurricane seasons. Of these squall lines, one observed in Hurricane Laura made landfall over 14 hours prior to the hurricane’s eye. This squall was responsible for 40 tornado warnings, which disrupted preparations and evacuations ahead of the storm’s landfall. This study seeks to characterize these squall lines and their environmental and thermodynamic characteristics, determine whether they behave more like midlatitude squall lines or tropical cyclone rain bands, and assess the performance of the convection-allowing High Resolution Rapid Refresh model in forecasting them over the ocean and over land. By using a combination of operational radar data, a network of surface weather observations from land and sea, satellite data, and HRRR model output, this case will undergo an extensive evaluation based on criteria from past literature in order to better understand these features as a whole and in relation to the diurnal cycle.

Vivian Brasfield↗
DOE OSTI2021

Lightweight LSTM for CAN Signal Decoding

This paper describes an approach to identify undecoded Controller Area Network (CAN) data from one vehicle, based on the data similarity to previously decoded CAN data from another vehicle. Modern vehicles communicate data and signals from on-board sensors and controllers through the CAN bus. Networked sensors contain information such as wheel speeds, fuel gauges, turn signals, and radar signals. In the effort to use this information and make cars safer through human-in-the-loop CPS, signals on the CAN bus such as wheel speed and radar can be used to support the driver. However, data from the CAN bus are encoded and in some cases compressed, and different car manufacturers use different encoding schemes to represent data on the CAN bus. With hundreds of messages and thousands of possible encoding schemes to consider, it is laborious to identify the unique bits and encoding schemes that represent signals on each vehicle. In this study, we propose a method for training a Long Short-Term Memory (LSTM) neural network on known radar signals from one vehicle manufacturer, a Toyota, and successfully apply the network to identify the encoding for radar signals on a different vehicle, a Honda. By augmenting the training dataset with varied encoding bit boundaries, a small and lightweight LSTM network can learn to recognize radar data across different encoding schemes. The results are an improvement on exhaustive-search algorithms and other methods previously used in the search for such signals.

Ngo, Paul↗
DOE OSTI2025

Solving high-dimensional inverse problems using amortized likelihood-free inference with noisy and incomplete data

Here, we present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observations into a fixed-size vector of summary features, while the inference network generates samples of the approximate posterior distribution of the model parameters based on these summary features. The posterior samples are produced in a deep generative fashion by sampling from a latent Gaussian distribution and passing these samples through an invertible transformation. We construct this invertible transformation by sequentially alternating conditional invertible neural network and conditional neural spline flow layers. The summary and inference networks are trained simultaneously. We apply the proposed method to an inversion problem in groundwater hydrology to estimate the posterior distribution of the log-conductivity field conditioned on spatially sparse time-series observations of the system’s hydraulic head responses. The conductivity field is represented with 706 degrees of freedom in the considered problem. Comparison with the likelihood-based iterative ensemble smoother PEST-IES method demonstrates that the proposed method accurately estimates the parameter posterior distribution and the observations’ predictive posterior distribution at a fraction of the inference time of PEST-IES.

conditional invertible neural network↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information

Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

60 APPLIED LIFE SCIENCES↗