Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data diversity”

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 1,207 records · Page 67

Component-specific modeling

The overall objective of this program is to develop and verify a series of interdisciplinary modeling and analysis techniques which have been specialized to address three specific hot section components. These techniques will incorporate data as well as theoretical methods from many diverse areas, including cycle and performance analysis, heat transfer analysis, linear and nonlinear stress analysis, and mission analysis. Building on the proven techniques already available in these fields, the new methods developed through this contract will be integrated to provide an accurate, efficient, and unified approach to analyzing combustor burner liners, hollow air cooled turbine blades, and air cooled turbine vanes. For these components, the methods developed will predict temperature, deformation, stress, and strain histories throughout a complete flight mission.

Roberts, M. L.↗

Component specific modeling

Modern jet engine design imposes extremely high loadings and temperatures on hot section components. A series of interdisciplinary modeling and analysis techniques which were specialized to address three specific components (combustor burner linings, hollow air-cooled turbine blades, and air-cooled turbine vanes) were developed and verified. These techniques will incorporate data as well as theoretical methods from many diverse areas, including cycle and performance analysis, heat transfer analysis, linear and nonlinear stress analysis, and mission analysis. Building on the proven techniques already available in these fields, the new methods developed will be integrated to predict temperature, deformation, stress, and strain histories throughout a complete flight mission.

Roberts, M. L.↗

Component specific modeling

The objective is to develop and verify a series of interdisciplinary modeling and analysis techniques that have been specialized to address three specific hot section components. These techniques will incorporate data as well as theoretical methods from many diverse areas including cycle and performance analysis, heat transfer analysis, linear and nonlinear stress analysis, and mission analysis. The new methods developed will be integrated to provide an accurate, efficient, and unified approach to analyzing combustor burner liners, hollow air-cooled turbine blades, and air-colled turbine vanes. For these components, the methods developed will predict temperature, deformation, stress, and strain histories throughout a complete flight mission.

Mcknight, R. L.↗

Component specific modeling

The overall objective of this program is to develop and verify a series of interdisciplinary modeling and analysis techniques that were specialized to address three specific hot section components. These techniques incorporate data as well as theoretical methods from many diverse areas including cycle and performance analysis, heat transfer analysis, linear and nonlinear stress analysis, and mission analysis. Building on the proven techniques already available in these fields, the new methods developed are integrated to provide an accurate, efficient, and unified approach to analyzing combustor burner liners, hollow air-cooled turbine blades, and air-cooled turbine vanes. For these components, the methods developed predict temperature, deformation, stress, and strain histories throughout a complete flight mission.

Maffeo, R. J.↗

Component specific modeling

The objective was to develop and verify a series of interdisciplinary modeling and analysis techniques specialized to address hot section components. These techniques incorporate data as well as theoretical methods from many diverse areas, including cycle and performance analysis, heat transfer analysis, linear and nonlinear stress analysis, and mission analysis.

Mcknight, R. L.↗

Near infrared reflectance spectra: Applications to problems in asteroid-meteorite relationships

An observing program designed to search for evidence of ordinary chondrite parent bodies near the 3:1 Kirkwood Gap was carried out in 1985 and 1986. Studies by Wisdom (1985), Wetherill (1985), and subsequent work by Milani et al. (1989) indicate that the 3:1 Kirkwood gap is the most probable source region for the majority of ordinary chondrite meteorites. The diversity of the reflectance spectra among this small data set is surprising. Early work by Gaffey and McCord (1978) showed that the inner region of the main asteroid belt is dominated by high albedo objects with mafic silicate surfaces. One would expect to see mostly spectra with 1- and 2-micron absorption bands based on this earlier work. Only 5 (of 12) spectra have these expected features. The distribution of taxonomic types presented by Gradie and Tedesco (1982) is in most cases a useful simplification of the compositional structure of the asteroid belt. The range of spectral characteristics seen with higher resolution in the near-IR has not been previously reported and is not represented in the standard asteroid taxonomy. Near-IR spectra contain valuable mineralogical information which enhances knowledge of the composition and structure of asteroids.

Mcfadden, Lucy A.↗

Application of AI techniques to infer vegetation characteristics from directional reflectance(s)

Traditionally, the remote sensing community has relied totally on spectral knowledge to extract vegetation characteristics. However, there are other knowledge bases (KB's) that can be used to significantly improve the accuracy and robustness of inference techniques. Using AI (artificial intelligence) techniques a KB system (VEG) was developed that integrates input spectral measurements with diverse KB's. These KB's consist of data sets of directional reflectance measurements, knowledge from literature, and knowledge from experts which are combined into an intelligent and efficient system for making vegetation inferences. VEG accepts spectral data of an unknown target as input, determines the best techniques for inferring the desired vegetation characteristic(s), applies the techniques to the target data, and provides a rigorous estimate of the accuracy of the inference. VEG was developed to: infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; infer percent ground cover from any combination of nadir and/or off-nadir view angles; infer unknown view angle(s) from known view angle(s) (known as view angle extension); and discriminate between user defined vegetation classes using spectral and directional reflectance relationships developed from an automated learning algorithm. The errors for these techniques were generally very good ranging between 2 to 15% (proportional root mean square). The system is designed to aid scientists in developing, testing, and applying new inference techniques using directional reflectance data.

Kimes, D. S.↗

Visible to Short Wavelength Infrared Spectroscopy on Rovers: Why We Need it on Mars and What We Need to do on Earth

The next stage of Mars exploration will include the use of rovers to seek out specific mineralogies. Understanding the mineralogical diversity of the locale will be used to determining which targets should be investigated with the full suite of in situ capability on the rover. Visible to Short Wavelength Infrared (VSWIR) spectroscopy is critical in evaluating the mineralogical diversity and to validate the global remote sensing data sets to be collected by Mars Express and the Mars Reconnaissance Orbiter. However, spectroscopy on mobile platforms present challenges in both the design of instruments and in the efficient operation of the instrument and mission. Field-testing and validation on Earth can be used to develop instrument requirements analysis tools needed for used on Mars.

Blaney, D. L.↗

Molecular motors and their functions in plants

Molecular motors that hydrolyze ATP and use the derived energy to generate force are involved in a variety of diverse cellular functions. Genetic, biochemical, and cellular localization data have implicated motors in a variety of functions such as vesicle and organelle transport, cytoskeleton dynamics, morphogenesis, polarized growth, cell movements, spindle formation, chromosome movement, nuclear fusion, and signal transduction. In non-plant systems three families of molecular motors (kinesins, dyneins, and myosins) have been well characterized. These motors use microtubules (in the case of kinesines and dyneins) or actin filaments (in the case of myosins) as tracks to transport cargo materials intracellularly. During the last decade tremendous progress has been made in understanding the structure and function of various motors in animals. These studies are yielding interesting insights into the functions of molecular motors and the origin of different families of motors. Furthermore, the paradigm that motors bind cargo and move along cytoskeletal tracks does not explain the functions of some of the motors. Relatively little is known about the molecular motors and their roles in plants. In recent years, by using biochemical, cell biological, molecular, and genetic approaches a few molecular motors have been isolated and characterized from plants. These studies indicate that some of the motors in plants have novel features and regulatory mechanisms. The role of molecular motors in plant cell division, cell expansion, cytoplasmic streaming, cell-to-cell communication, membrane trafficking, and morphogenesis is beginning to be understood. Analyses of the Arabidopsis genome sequence database (51% of genome) with conserved motor domains of kinesin and myosin families indicates the presence of a large number (about 40) of molecular motors and the functions of many of these motors remain to be discovered. It is likely that many more motors with novel regulatory mechanisms that perform plant-specific functions are yet to be discovered. Although the identification of motors in plants, especially in Arabidopsis, is progressing at a rapid pace because of the ongoing plant genome sequencing projects, only a few plant motors have been characterized in any detail. Elucidation of function and regulation of this multitude of motors in a given species is going to be a challenging and exciting area of research in plant cell biology. Structural features of some plant motors suggest calcium, through calmodulin, is likely to play a key role in regulating the function of both microtubule- and actin-based motors in plants.

Non-NASA Center↗

Meningitis and Climate: From Science to Practice

Meningococcal meningitis is a climate sensitive infectious disease. The regional extent of the Meningitis Belt in Africa, where the majority of epidemics occur, was originally defined by Lapeysonnie in the 1960s. A combination of climatic and environmental conditions and biological and social factors have been associated to the spatial and temporal patterns of epidemics observed since the disease first emerged in West Africa over a century ago. However, there is still a lack of knowledge and data that would allow disentangling the relative effects of the diverse risk factors upon epidemics. The Meningitis Environmental Risk Information Technologies Initiative (MERIT), a collaborative research-to-practice consortium, seeks to inform national and regional prevention and control strategies across the African Meningitis Belt through the provision of new data and tools that better determine risk factors. In particular MERIT seeks to consolidate a body of knowledge that provides evidence of the contribution of climatic and environmental factors to seasonal and year-to-year variations in meningococcal meningitis incidence at both district and national scales. Here we review recent research and practice seeking to provide useful information for the epidemic response strategy of National Ministries of Health in the Meningitis Belt of Africa. In particular the research and derived tools described in this paper have focused at "getting science into policy and practice" by engaging with practitioner communities under the umbrella of MERIT to ensure the relevance of their work to operational decision-making. We limit our focus to that of reactive vaccination for meningococcal meningitis. Important but external to our discussion is the development and implementation of the new conjugate vaccine, which specifically targets meningococcus A

Mineral dust↗

SMAP Mission Status and Plan

The prime mission phase of National Aeronautics Space Administration's (NASA's) Soil Moisture Active Passive (SMAP) project was successfully completed in June 2018. The extended phase has been approved by NASA for operation through 2021 (with option to 2023). SMAP data have been well calibrated and have enabled diverse scientific investigations in water, energy and carbon cycle research, terrestrial ecology and ocean science. This paper will provide the highlights of algorithm updates to radiometric calibration and soil moisture retrieval algorithms. A summary of extended phase activities, in particular the SMAPVEX19 campaign, for product enhancements will be provided.

Water Cycle↗

(abstract) FASTER -- A Tool for DSN Forecasting and Scheduling

FASTER, the Forecasting And Scheduling Tool for Earth-based Resources is a suite of software tools developed at JPL to aid in the process of allocating DSN 70 and 34 meter antennas and equipment to support deep space satellites and ground based astronomy. FASTER has been designed for use by a diverse user community, including mid-level managers, data entry and analysis teams, and project scheduling personnel. The system helps to automate many of the previously labor intensive tasks and ensure proper analysis and consistency throughout all phases of the allocation process. FASTER implements an interactive environment for both forecasting and scheduling. This paper will discuss technical aspects of the FASTER system, including forecasting and scheduling algorithms, issues related to large scale use of a scheduling and forecasting system, implications to the process in which a forecasting and scheduling system is embedded, and lessons learned and implications to similar systems.

forecasting↗

Enhancing Metal Additive Manufacturing Training with the Advanced Vision Language Model: A Pathway to Immersive Augmented Reality Training for Non-Experts

This paper introduces an innovative training system for the Renishaw AM400 metal printer, leveraging the synergy of the advanced Vision Language Model (VLM) with Augmented Reality (AR) within the Digital Twins (DT) framework. Aimed at overcoming the limitations of conventional training methods in metal additive manufacturing (AM), our system integrates AR to provide an immersive learning environment, enhancing the real-world experience with interactive digital overlays. The core of the system lies in its use of VLM, which, pre-trained on diverse datasets, excels in processing multi-modal data, thereby offering nuanced and contextually relevant guidance for trainees. Key experiments demonstrate the system’s effectiveness, particularly highlighting the usage of VLM as an Artificial Intelligence (AI) agent to integrate external tools like YOLO-v7 for valve state classification and CRAFT for control panel text recognition. This approach significantly improves recognition accuracy, operational understanding, and human–machine interaction, especially for non-expert users, making complex metal AM operations more accessible. The research not only showcases the potential of AR and VLM in industrial training but also sets a new standard for smart manufacturing practices, indicating broader applications in various industrial domains.

Metal additive manufacturing↗

NREL Screens Universities for Solar and Battery Storage Potential

In support of the U.S. Department of Energy's SunShot initiative, NREL provided solar photovoltaic (PV) screenings in 2016 and 2017 for universities seeking to go solar. Fifteen universities were selected for screenings based on campus solar and sustainability goals, plans for future solar projects and solar deployment capacity (megawatts), regional diversity, energy costs, and availability of campus energy data for the analysis.

14 SOLAR ENERGY↗

Fostering Remote Visualization: Experiences in Two Different HPC Sites

Visualization of scientific data is crucial for scientific discovery to gain insight into the results of simulations and experiments. Remote visualization is of crucial importance to access infrastructure, data and computational resources and, to avoid data movement from where data is produced and to where data will be analyzed. Remote visualization enables geographically diverse collaboration and enhances user experience through graphical user interfaces. This paper presents two approaches deployed by two different HPC centers: The SC3 - Supercomputación y Cálculo Científico Center in Colombia and the Oak Ridge Leadership Computing Facility in USA. We overview our remote visualization experiences, adopted technologies, use cases, and challenges encountered. Our contribution is to signal the commonality between approaches in terms of the end goal, showing their fitness for their contexts, while not focusing only on attempting to provide a general picture of remote visualization, given the differences between centers in terms of purposes, needs, resources, and national impact.

Gelvez Cortes, Sergio Augusto↗

Machine learning for data-driven discovery in solid Earth geoscience

Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. It is made challenging by the complex, interacting, and multiscale processes needed to understand Earth’s behavior and by the inaccessibility of nearly all of Earth’s subsurface to direct observation. Substantial increases in data availability and in the increasingly realistic character of computer simulations hold promise for accelerating progress, but developing a deeper understanding based on these capabilities is itself challenging. Machine learning will play a key role in this effort. We review the state of the field and make recommendations for how progress might be broadened and accelerated.

Bergen, Karianne J.↗

Revealing Occupancy Patterns in Office Buildings Through the use of Annual Occupancy Sensor Data

Energy simulation programs like DOE-2 and EnergyPlus are tools that have been proven to aid with energy calculations to predict energy use in buildings. Some inputs to energy simulation models are relatively easy to find, including building size, orientation, construction materials, and HVAC system size and type. Others vary with time (e.g. weather and occupancy) and some can be a challenge to estimate in order to create an accurate simulation. In this paper, the analysis of occupancy sensor data for a large commercial, multi-tenant office building is presented. It details occupancy diversity factors for private offices and summarizes the same for open offices, hallways, conference rooms, break rooms, and restrooms in order to better inform energy simulation parameters. Long-term data were collected allowing results to be presented to show variations of occupancy diversity factors in private offices for time of day, day of the week, holidays, and month of the year. The diversity factors presented differ as much as 46% from those currently published in ASHRAE 90.1 2004 energy cost method guidelines, a document referenced by energy modelers regarding occupancy diversity factors for simulations. This may result in misleading simulation results and may introduce inefficiencies in the final equipment and systems design.

47 OTHER INSTRUMENTATION↗

MAVNet: Design of a Reliable Beyond Visual Line of Sight Communication System for Unmanned Vehicles

This paper details the design and system components of the Multi-Modal Autonomous Vehicle Network system (MAVNet). MAVNet was designed to provide reliable and redundant command, control and payload data distribution for unmanned vehicles operating beyond visual line of sight. Current industry solutions do not adequately solve key networking challenges, do not support network diversity and fall short of providing desirable next-generation requirements including data fusion from national air space data sources, multi-pilot and multi-agent control. The MAVNet system and the approach taken to solve these issues will be detailed in this paper.

Stinson, Brad J↗