Search NASA⌕ Search

SEARCH · Search NASA

Results for “deep data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Office of Tracking and Data Acquisition

The Office of Tracking and Data Acquisition (OTDA) and its two worldwide tracking network facilities, the Spaceflight Tracking and Data Network and the Deep Space Network, are described. Other topics discussed include the NASA communications network, the tracking and data relay satellite system, other OTDA tracking activities, and OTDA milestones.

Source record↗
NASA NTRS1975

Madrid space station

The Madrid space station, operated under bilateral agreements between the governments of the United States and Spain, is described in both Spanish and English. The space station utilizes two tracking and data acquisition networks: the Deep Space Network (DSN) of the National Aeronautics and Space Administration and the Spaceflight Tracking and Data Network (STDN) operated under the direction of the Goddard Space Flight Center. The station, which is staffed by Spanish employees, comprises four facilities: Robledo 1, Cebreros, and Fresnedillas-Navalagamella, all with 26-meter-diameter antennas, and Robledo 2, with a 64-meter antenna.

Fahnestock, R. J.↗
DOE OSTI2019

Big Data Framework with Machine Learning for D and D Applications - 19108

The nuclear industry is experiencing a steady increase in maintenance costs even though plants are maintained under high levels of safety, capability and reliability. Nuclear power plants are expected to run every unit at maximum capacity at all times, efficiently utilizing assets with minimal downtime. Surveillance and maintenance of nuclear decommissioning infrastructure provides lot challenges with respect to maintenance or decommissioning of the buildings. There is a need for a framework to analyze the huge amount of data generated by the sensors on the nuclear reactor components as well as structures, to monitor the conditions of these building over a period of time. Emerging technologies such as big data analytics have become a requirement in the nuclear industry to improve structural health monitoring and diagnostics. FIU will make use of existing mature technologies in the areas of imaging, robotics, big data, and machine learning/deep learning to assess the structural integrity of aging facilities at DOE sites. As these facilities await decommissioning, there is a need to understand the structural health of these structures. Many of these facilities were built over 50 years ago and in some cases these facilities have gone beyond the operational life expectancy. In other cases, the facilities have been placed in a state of 'cold and dark' and they are sitting unused, awaiting decommissioning. Finally, some older facilities are one-of-a-kind operational/production facilities supporting critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may be compromised, so it is imperative that adequate inspections and data collection/analysis be performed on a continuous and ongoing basis. The primary goals of the research include collecting various formats of data such as structured, unstructured and streaming data from the various sensors deployed in buildings, collect videos/pictures from various imaging sources, ingest them using a Hadoop distributed file system and process the data using Spark to perform batch processing and real time analytics. Research and development on various machine learning/deep learning algorithms will also be performed to analyze the heterogeneous data collected from nuclear decommission infrastructure. FIU will design the big data framework to ingest, store and process huge amounts of heterogeneous data collected from many sources and optimize the algorithms to provide insights into the data and predict anomalies observed when compared against baseline conditions. Various modules of this framework will include heterogeneous data sources, message broker, Hadoop distributed file system, Spark for stream and batch processing, machine learning/ deep learning, Cassandra - persistent data store and visualization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. In this work, by defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Summary report of the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis (FDPVA)

The objective of the Fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to artificial intelligence for automatization and improving reliability of control processes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in Laser Powder Bed Fusion

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. The current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques such as X-Ray Computed Tomography (XCT), which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. RTX Technologies Research Center (RTRC) has licensed ORNL’s Peregrine software package – a printer- and camera-agnostic data analytics tool designed specifically for detecting process anomalies using in situ data collected during powder bed printing. The goal of this project was to feed temporally rich, multi-modal sensor data, including visible light, integrated near infrared (NIR), and spatially mapped co-axial melt pool thermal emission data into Peregrine to enable detection of subsurface flaws. XCT data was used as ground truth training data to allow Peregrine’s deep learning algorithms to recognize anomalies in these complex data streams in both test artifacts and industrially relevant geometries. Completion of this program has seen the successful implementation of multi-modal, multi-layer sensor data footprints for training of machine learning models in Peregrine. Flaws detected in XCT data have been successfully detected directly from this in situ data footprint, and initial analyses of the in situ probability-of-detection has been conducted, showing performance levels commensurate with traditional non-destructive evaluation (NDE) methods. The in situ monitoring methodology was then applied to an industrially relevant component that was using post-build NDE, highlighting the utility of the proposed method for hard-to-inspect AM components. As a direct result of this program, two journal manuscripts [1], [2] have been published in Additive Manufacturing, with additional manuscripts planned following program completion.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Enhancement of free tropospheric ozone production by deep convection

It is found from model simulations of trace gas and meteorological data from aircraft campaigns that deep convection may enhance the potential for photochemical ozone production in the middle and upper troposphere by up to a factor of 60. Examination of half a dozen individual convective episodes show that the degree of enhancement is highly variable. Factors affecting enhancement include boundary layer NO(x) mixing ratios, differences in the strength and structure of convective cells, as well as variation in the amount of background pollution already in the free troposphere.

Pickering, Kenneth E.↗
DOE OSTI2017

First low-frequency Einstein@Home all-sky search for continuous gravitational waves in Advanced LIGO data

We report results of a deep all-sky search for periodic gravitational waves from isolated neutron stars in data from the first Advanced LIGO observing run. This search investigates the low frequency range of Advanced LIGO data, between 20 and 100 Hz, much of which was not explored in initial LIGO. The search was made possible by the computing power provided by the volunteers of the Einstein@Home project. We find no significant signal candidate and set the most stringent upper limits to date on the amplitude of gravitational wave signals from the target population, corresponding to a sensitivity depth of 48.7 [1/$\sqrt{Hz}$]. At the frequency of best strain sensitivity, near 100 Hz, we set 90% confidence upper limits of 1.8×10 ₋25 . At the low end of our frequency range, 20 Hz, we achieve upper limits of 3.9×10 ₋24 . At 55 Hz we can exclude sources with ellipticities greater than 10 ₋5 within 100 pc of Earth with fiducial value of the principal moment of inertia of 10 38 kg m 2 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Robust, automated sleep scoring by a compact neural network with distributional shift correction

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

In this paper we discuss recent advances in deep convolutional neural networks (CNN) for sequence learning, which allow identifying long-range, multi-scale phenomena in long sequences, such as those found in fusion plasmas. We point out several benefits of these deep CNN architectures, such as not requiring experts such as physicists to hand-craft input data features, the ability to capture longer range dependencies compared to the more common sequence neural networks (recurrent neural networks like long short-term memory (LSTM) networks), and the comparative computational efficiency. We apply this neural network architecture to the popular problem of disruption prediction in fusion energy tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. Initial results trained on a large ECEi dataset show promise, achieving an F 1 -score of ~91% on individual time-slices using only the ECEi data. This indicates the ECEi diagnostic by itself can be sensitive to a number of pre-disruption markers useful for predicting disruptions on timescales not only for mitigation but also avoidance. Future opportunities for utilizing these deep CNN architectures with fusion data are outlined, including impact of recent upgrades to the ECEi diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗