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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Neutrino Physics with Deep Learning on NOvA

The NOvA experiment has made both νμ \nu_\mu disappearance and νe \nu_e appearance measurements in Fermilab's NuMI beam, and is working on cross section measurements using near detector data. At the core of NOvA's measurements is the use of deep learning algorithms for identification and reconstruction of the neutrino flavor and energy. These algorithms, used for the first time on NOvA in 2016, yielded large improvements in selection efficiency, and will be applied to our first anti-neutrino results to be released this year. Presented here is the extension of our deep learning efforts for identification of neutrino signal events, final state identification, single particle tagging, and reconstruction using instance segmentation techniques. We will describe the new implementations of modified Convolutional Neural Networks for anti-neutrino events, single particles and their performance for analysis final states selection, standard candle measurements, and reconstruction.

Psihas, Fernanda [Indiana U.]↗
DOE OSTI2024

Deep-Learning-derived Boundary Layer Height from Meteorological Data over the SGP, GOAMAZON, CACTI

The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Testing convolutional neural network based deep learning systems: a statistical metamorphic approach

Machine learning technology spans many areas and today plays a significant role in addressing a wide range of problems in critical domains,i.e., healthcare, autonomous driving, finance, manufacturing, cybersecurity,etc. Metamorphic testing (MT) is considered a simple but very powerful approach in testing such computationally complex systems for which either an oracle is not available or is available but difficult to apply. Conventional metamorphic testing techniques have certain limitations in verifying deep learning-based models (i.e., convolutional neural networks (CNNs)) that have a stochastic nature (because of randomly initializing the network weights) in their training. In this article, we attempt to address this problem by using a statistical metamorphic testing (SMT) technique that does not require software testers to worry about fixing the random seeds (to get deterministic results) to verify the metamorphic relations (MRs). We propose seven MRs combined with different statistical methods to statistically verify whether the program under test adheres to the relation(s) specified in the MR(s). We further use mutation testing techniques to show the usefulness of the proposed approach in the healthcare space and test two CNN-based deep learning models (used for pneumonia detection among patients). The empirical results show that our proposed approach uncovers 85.71% of the implementation faults in the classifiers under test (CUT). Furthermore, we also propose an MRs minimization algorithm for the CUT, thus saving computational costs and organizational testing resources.

Computer Science↗
NASA NTRS1971

The deep space network, volume 6

Progress on Deep Space Network (DSN) supporting research and technology is presented, together with advanced development and engineering, implementation, and DSN operations of flight projects. The DSN is described. Interplanetary and planetary flight projects and radio science experiments are discussed. Tracking and navigational accuracy analysis, communications systems and elements research, and supporting research are considered. Development of the ground communications and deep space instrumentation facilities is also presented. Network allocation schedules and angle tracking and test development are included.

Source record↗
NASA NTRS1972

The deep space network, volume 7

The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Space Flight Operations Facility are described.

Source record↗
NASA NTRS1972

Comparison of advanced propulsion concepts for deep space exploration

Equations and charts are presented that permit rapid estimation of propulsion-system performance requirements for some typical deep-space missions. A number of advanced propulsion concepts for which performance estimates are available are compared with respect to their capability for flyby, rendezvous, and roundtrip planetary missions. Based on these estimates, the gas-core nuclear fission rocket and the pulsed fusion rocket yield the fastest trip times to the near planets. For round trips to Jupiter and beyond, the controlled fusion rocket shows progressively superior capabilities. Several propulsion concepts based on use of impinging laser beams are found to be noncompetitive with the other advanced concepts for deep space missions. Requirements for attainment of interstellar distances within a human lifetime are found to be some orders of magnitude beyond the capabilities of any propulsion concepts for which performance estimates are now possible.

Moeckel, W. E.↗
NASA NTRS1972

Navigation requirements for advanced deep space missions.

Study of the navigation system requirements and capabilities for potential advanced deep space missions of the 1978 to 1990 era. Following a review of these potential missions, the mission-by-mission assessments of the critical navigation system requirements for a mission set selected are presented. The requirements are related to subsystem research and development implications and are used to formulate recommendations for future developments in navigation systems. Programmatic directions required for deep space navigation are presented.

Friedman, L. D.↗
NASA NTRS1973

The Deep Space Network, volume 17

The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Network Control System are described.

Source record↗
NASA NTRS1972

Comparison of advanced propulsion concepts for deep space exploration.

Equations and charts are presented which permit rapid estimation of propulsion system performance requirements for some typical deep space missions. The simplicity results from use of gravity-free equations of motion, which are shown to yield good approximations to trip times obtained with solar gravity and planetary motion included. The agreement is satisfactory for missions that do not enter or depart from low orbits about the major planets. A number of advanced propulsion concepts for which performance estimates are available are compared with respect to their capability for fly-by, rendezvous, and round-trip planetary missions. Based on these estimates, the gas-core nuclear fission rocket and the pulsed fusion rocket yield the fastest trip times to the near planets. For round trips to Jupiter and beyond, the controlled fusion rocket shows progressively superior capabilities. Several propulsion concepts based on use of impinging laser beams are found to be noncompetitive with the other advanced concepts for deep space missions.

Moeckel, W. E.↗
NASA NTRS1973

The deep space network, volume 18

The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Network Control System are described.

Source record↗
NASA NTRS1973

Explosives and pyrotechnic propellants for use in long term deep space missions

Explosives and pyrotechnic propellant materials which will withstand heat sterilization cycling at 125 C and ten year deep space aging under 10 to the minus 6th power torr and 66 C have been selected. The selection was accomplished through a detailed literature survey and an analytical evaluation of the physicochemical properties of the materials. The chemical components of the electroexplosive devices used in U.S. missiles and spacecraft were categorized into primary explosives, secondary explosives, and propellant ingredients. Kinetic data on such parameters as thermal decomposition and sublimation were obtained for these materials and used as a basis for the ten year life prediction. From these experimental data and some analytical calculations, a listing of candidate materials for deep space missions was made.

Gorzynski, C. S., Jr.↗
NASA NTRS1974

Stress intensity factors for deep cracks emanating from the corner formed by a hole intersecting a plate surface

A technique consisting of a marriage between stress freezing photoelasticity and a numerical method was used to obtain stress intensity factors for natural cracks emanating from the corner at which a hole intersects a plate surface. Geometrics studied were: crack depth to thickness ratios of approximately 0.2, 0.5, and 0.75; crack depth to crack length ratios of approximately 1.0 to 2.0. All final crack geometries were grown under monotonic loading and growth was not self similar with most of the growth occurring through the thickness under remote extension. Stress intensity plate surface K sub s factors were determined at the intersection of the flaw border with the plate surface K sub s and with the edge of the hole K sub h. Results showed that for the relatively shallow flaws K sub h approximately equal to 1.5 K sub s, for the moderately deep flaws K sub h approximately equal to K sub s, and for the deep flaws K sub h approximately equal to 0.5 K sub s, revealing a severe sensitivity of K to flaw geometry.

Mcgowan, J. J.↗
NASA NTRS1974

The Deep Space Network

The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Network Control System are described.

Source record↗
NASA NTRS1974

The Deep Space Network

The objectives, functions, and organization, of the Deep Space Network are summarized. Deep Space stations, ground communications, and network operations control capabilities are described. The network is designed for two-way communications with unmanned spacecraft traveling approximately 1600 km from earth to the farthest planets in the solar system. It has provided tracking and data acquisition support for the following projects: Ranger, Surveyor, Mariner, Pioneer, Apollo, Helios, Viking, and the Lunar Orbiter.

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