Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Topic Models”

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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

CERES Clouds and Radiative Swath (CRS) data in HDF. (CER_CRS_Terra-FM2-MODIS_Edition2A

The Clouds and Radiative Swath (CRS) product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The CRS contains all of the CERES SSF product data. For each CERES footprint on the SSF the CRS also contains vertical flux profiles evaluated at four levels in the atmosphere: the surface, 500-, 70-, and 1-hPa. The CRS fluxes and cloud parameters are adjusted for consistency with a radiative transfer model and adjusted fluxes are evaluated at the four atmospheric levels for both clear-sky and total-sky. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=2001-10-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 hour; Temporal_Resolution_Range=Hourly - < Daily].

CLOUD LAYER AREA↗
DOE OSTI2023

CHQ- SocioEmo: Identifying Social and Emotional Support Needs in Consumer-Health Questions

General public, often called consumers, are increasingly seeking health information online. To be satisfactory, answers to health-related questions often have to go beyond informational needs. Automated approaches to consumer health question answering should be able to recognize the need for social and emotional support. Recently, large scale datasets have addressed the issue of medical question answering and highlighted the challenges associated with question classification from the standpoint of informational needs. However, there is a lack of annotated datasets for the non-informational needs. We introduce a new dataset for non-informational support needs, called CHQ-SocioEmo. The Dataset of Consumer Health Questions was collected from a community question answering forum and annotated with basic emotions and social support needs. This is the first publicly available resource for understanding non-informational support needs in consumer health-related questions online. We benchmark the corpus against multiple state-of-the-art classification models to demonstrate the dataset’s effectiveness.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Improving deep learning model performance under parametric constraints for materials informatics applications

Abstract Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric constraints. Here, we present a general deep-learning framework based on branched residual learning (BRNet) with fully connected layers that can work with any numerical vector-based representation as input to build accurate models to predict materials properties. We perform model training for materials properties using numerical vectors representing different composition-based attributes of the respective materials and compare the performance of the proposed models against traditional ML and existing DL architectures. We find that the proposed models are significantly more accurate than the ML/DL models for all data sizes by using different composition-based attributes as input. Further, branched learning requires fewer parameters and results in faster model training due to better convergence during the training phase than existing neural networks, thereby efficiently building accurate models for predicting materials properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A perspective on iron (Fe) in the atmosphere: air quality, climate, and the ocean

As scientists engage in research motivated by climate change and the impacts of pollution on air, water, and human health, we increasingly recognize the need for the scientific community to improve communication and knowledge exchange across disciplines to address pressing and outstanding research questions holistically. Our professional paths have crossed because our research activities focus on the chemical reactivity of Fe-containing minerals in air and water, and at the air-sea interface. (Photo)chemical reactions driven by Fe can take place at the surface of the particles/droplets or within the condensed phase. The extent and rates of these reactions are influenced by water content and biogeochemical activity ubiquitous in these systems. One of these reactions is the production of reactive oxygen species (ROS) that cause damage to respiratory organs. Another is that the reactivity of Fe and organics in aerosol particles alter surficial physicochemical properties that impact aerosol-radiation and aerosol–cloud interactions. Also, upon deposition, aerosol particles influence ocean biogeochemical processes because micronutrients such as Fe or toxic elements such as copper become bioavailable. We provide a perspective on these topics and future research directions on the reactivity of Fe in atmospheric aerosol systems, from sources to short- and long-term impacts at the sinks with emphasis on needs to enhance the predictive power of atmospheric and ocean models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The glass-ceiling convective regime and the origin and diversity of coronae on Venus

Venus and Earth are rocky planets of roughly the same size and bulk density, yet their surface volcanic and tectonic features appear substantially different. On Venus, the coexistence of large volcanic highlands—interpreted as the surface expression of long-lived mantle plumes—alongside coronae, smaller features thought to be caused by transient thermal diapirs, remains enigmatic. Using two-dimensional numerical models of mantle convection with sharp and broad mineral phase transitions for pyrolite, we show that both scales of upwellings can be generated in a stagnant lid planet with an interior temperature 250 to 400 K warmer than Earth’s. The smaller plumes originate from a ~600 km deep internal layer that exists as a consequence of the different sequence of mineral phase transitions that occur in warmer mantles less processed and differentiated by partial melting and volcanism. Future models that include melting will provide further tests of our hypothesis.

Science & Technology - Other Topics↗
DOE OSTI2020

Using Machine Learning Tools to Predict Compressor Stall

Clean energy has become an increasingly important consideration in today’s power systems. As the push for clean energy continues, many coal-fired power plants are being decommissioned in favor of renewable power sources such as wind and solar. However, the intermittent nature of renewables means that dynamic load following traditional power systems is crucial to grid stability. With high flexibility and fast response at a wide range of operating conditions, gas turbine systems are poised to become the main load following component in the power grid. Yet, rapid changes in load can lead to fluid flow instabilities in gas turbine power systems. These instabilities often lead to compressor surge and stall, which are some of the most critical problems facing the safe and efficient operation of compressors in turbomachinery today. Although the topic of compressor surge and stall has been extensively researched, no methods for early prediction have been proven effective. This study explores the utilization of machine learning tools to predict compressor stall. The long short-term memory (LSTM) model, a form of recurrent neural network (RNN), was trained using real compressor stall datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. Two variations of the LSTM model, classification and regression, were tested to determine optimal performance. The regression scheme was determined to be the most accurate approach, and a tool for predicting compressor stall was developed using this configuration. Overall, results show that the tool is capable of predicting stalls 5–20 ms before they occur. With a high-speed controller capable of 5 ms time-steps, mitigating action could be taken to prevent compressor stall before it occurs.

42 ENGINEERING↗
DOE OSTI2022

Provably efficient machine learning for quantum many-body problems

Classical machine learning (ML) provides a potentially powerful approach to solving challenging quantum many-body problems in physics and chemistry. However, the advantages of ML over traditional methods have not been firmly established. In this work, we prove that classical ML algorithms can efficiently predict ground-state properties of gapped Hamiltonians after learning from other Hamiltonians in the same quantum phase of matter. By contrast, under a widely accepted conjecture, classical algorithms that do not learn from data cannot achieve the same guarantee. We also prove that classical ML algorithms can efficiently classify a wide range of quantum phases. Extensive numerical experiments corroborate our theoretical results in a variety of scenarios, including Rydberg atom systems, two-dimensional random Heisenberg models, symmetry-protected topological phases, and topologically ordered phases.

Science & Technology - Other Topics↗
NASA NTRS1975

Some aspects of control of a large-scale dynamic system

Techniques of predicting and/or controlling the dynamic behavior of large scale systems are discussed in terms of decentralized decision making. Topics discussed include: (1) control of large scale systems by dynamic team with delayed information sharing; (2) dynamic resource allocation problems by a team (hierarchical structure with a coordinator); and (3) some problems related to the construction of a model of reduced dimension.

Aoki, M.↗
NASA NTRS1978

Shuttle being tested at Marshall Center

Ground vibration tests of the space shuttle vehicle performed to evaluate the structural dynamics and their effect on the control system of the shuttle are described. Test results are used to verify the system design and mathematical models that predict how the shuttle's control system will react to the much more severe vibrations expected during launch and flight into orbit. The test configurations, the test facility, and the dynamic test suspension system are among the topics discussed.

Source record↗
NASA NTRS1981

Symposium /International/ on Combustion, 18th, University of Waterloo, Waterloo, Ontario, Canada, August 17-22, 1980, Proceedings

Problems related to combustion generated pollution are explored, taking into account the mechanism of NO formation from nitrogen compounds in hydrogen flames studied by laser fluorescence, the structure and similarity of nitric oxide production in turbulent diffusion flames, the effect of steam addition on NO formation, and the formation of NO2 by laminar flames. Other topics considered are concerned with propellant combustion, fluidized bed combustion, the combustion of droplets and sprays, premixed flame studies, fire studies, and flame stabilization. Attention is also given to coal flammability, chemical kinetics, turbulent combustion, soot, coal combustion, the modeling of combustion processes, combustion diagnostics, detonations and explosions, ignition, internal combustion engines, combustion studies, and furnaces.

Source record↗
NASA NTRS1983

Human Factors Considerations in System Design

Human factors considerations in systems design was examined. Human factors in automated command and control, in the efficiency of the human computer interface and system effectiveness are outlined. The following topics are discussed: human factors aspects of control room design; design of interactive systems; human computer dialogue, interaction tasks and techniques; guidelines on ergonomic aspects of control rooms and highly automated environments; system engineering for control by humans; conceptual models of information processing; information display and interaction in real time environments.

Christine M Mitchell↗
NASA NTRS1984

Proceedings of the Ninth Annual Software Engineering Workshop

Experiences in measurement, utilization, and evaluation of software methodologies, models, and tools are discussed. NASA's involvement in ever larger and more complex systems, like the space station project, provides a motive for the support of software engineering research and the exchange of ideas in such forums. The topics of current SEL research are software error studies, experiments with software development, and software tools.

Source record↗
NASA NTRS1988

Research Reports: 1988 NASA/ASEE Summer Faculty Fellowship Program

The basic objectives are to further the professional knowledge of qualified engineering and science faculty members; to stimulate an exchange of ideas between participants and NASA: to enrich and refresh the research and teaching activities of the participants' institutions; and to contribute to the research objectives of the NASA centers. Topics addressed include: cryogenics; thunderstorm simulation; computer techniques; computer assisted instruction; system analysis weather forecasting; rocket engine design; crystal growth; control systems design; turbine pumps for the Space Shuttle Main engine; electron mobility; heat transfer predictions; rotor dynamics; mathematical models; computational fluid dynamics; and structural analysis.

Freeman, L. Michael↗
NASA NTRS1992

On the estimation of risk associated with an attenuation prediction

Viewgraphs from a presentation on the estimation of risk associated with an attenuation prediction is presented. Topics covered include: link failure - attenuation exceeding a specified threshold for a specified time interval or intervals; risk - the probability of one or more failures during the lifetime of the link or during a specified accounting interval; the problem - modeling the probability of attenuation by rainfall to provide a prediction of the attenuation threshold for a specified risk; and an accounting for the inadequacy of a model or models.

Crane, R. K.↗
NASA NTRS1993

Nuclear Engine System Simulation (NESS) version 2.0

The topics are presented in viewgraph form and include the following; nuclear thermal propulsion (NTP) engine system analysis program development; nuclear thermal propulsion engine analysis capability requirements; team resources used to support NESS development; expanded liquid engine simulations (ELES) computer model; ELES verification examples; NESS program development evolution; past NTP ELES analysis code modifications and verifications; general NTP engine system features modeled by NESS; representative NTP expander, gas generator, and bleed engine system cycles modeled by NESS; NESS program overview; NESS program flow logic; enabler (NERVA type) nuclear thermal rocket engine; prismatic fuel elements and supports; reactor fuel and support element parameters; reactor parameters as a function of thrust level; internal shield sizing; and reactor thermal model.

Pelaccio, Dennis G.↗
NASA NTRS1998

Joint Institute for Advancement of Flight Sciences

The program objectives were defined in the original proposal entitled "Program of Research in Flight Dynamics in the JIAFS at NASA Langley Research Center" which was originated March 20, 1975, and in yearly renewals of the research program dated December 1, 1979 to December 1, 1998. The program included three major topics: 1) Improvement of existing methods and development of new methods for flight and wind tunnel data analysis based on system identification methodology. 2) Application of these methods to flight and wind tunnel data obtained from advanced aircraft. 3) Modeling and control of aircraft, space structures and spacecraft. The principal investigator of the program was Dr. Vladislav Klein, Professor at The George Washington University, Washington, D.C.. Thirty-seven Graduate Research Scholar Assistants, two of them doctoral students, also participated in the program. The results of the research conducted during nineteen years of the total co-operative period were published in 23 NASA technical reports, 2 D.Sc. Dissertations, 14 M.S. Theses and 33 papers. The list of these publications is included. The results were also reported in more than 30 seminar lectures presented at various research establishments world-wide. For contributions to the research supported by the co-operative agreement, three NASA Awards were received: 1) NASA LARC Group Achievement Award, May 30, 1990, to Dr. V. Klein as a member of the X-29 Drop Model Team. 2) NASA Medal for Exceptional Engineering Achievement, March 27, 1992, to Dr. V. Klein for innovative contributions in the development of advanced techniques and computer programs in the field of system identification. 3) NASA LaRC Team Excellence Award, May 7, 1994, to Dr. V. Klein as a member of the X-31 Drop Model Team.

Source record↗
NASA NTRS2002

Program of Research in Flight Dynamics in The George Washington University at NASA Langley Research Center, Hampton, Virginia

The program objectives were defined in the original proposal entitled 'Program of Research in Flight Dynamics in the JIAFS at NASA Langley Research Center' which was originated March 20, 1975, and yearly renewals of the research program dated December 1, 1998 to December 31, 2002. The program included three major topics: 1) Improvement of existing methods and development of new methods for flight and wind tunnel data analysis based on system identification methodology; 2) Application of these methods to flight and wind tunnel data obtained from advanced aircraft; 3) Modeling and control of aircraft. The principal investigator of the program was Dr. Vladislav Klein, Professor Emeritus at The George Washington University, DC. Seven Graduate Research Scholar Assistants (GRSA) participated in the program. The results of the research conducted during four years of the total co-operative period were published in 2 NASA Technical Reports, 3 thesis and 3 papers. The list of these publications is included.

Klein, Vladislav↗
NASA NTRS2012

Flight Dynamics and Controls Discipline Overview

This presentation will touch topics, including but not limited to, the objectives and challenges of flight dynamics and controls that deal with the pilot and the cockpit's technology, the flight dynamics and controls discipline tasks, and the full envelope of flight dynamics modeling. In addition, the LCTR 7x10-ft wind tunnel test will also be included along with the optimal trajectories for noise abatement and its investigations on handling quality. Furthermore, previous experiments and their complying results will also be discussed.

Theodore, Colin R.↗