Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “science data processing”

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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Numerical Investigation of Desulfurization Kinetics in Gas-Stirred Ladles by a Quick Modeling Analysis Approach

A quick modeling analysis approach for predicting the slag-steel reaction and desulfurization kinetics in argon gas-stirred ladles has been developed in this study. The model consists of two uncoupled components: (i) a computational fluid dynamics (CFD) model for predicting the fluid flow and the characteristics of slag-steel interface, and (ii) a multicomponent reaction kinetics model for calculating the desulfurization evolution. The steel-slag interfacial area and mass transfer coefficients predicted by the CFD simulation are used as the processing data for the reaction model. Since the desulfurization predictions are uncoupled from the CFD simulation, the computational time of this uncoupled predictive approach is decreased by at least 100 times for each case study when compared with the CFD-reaction kinetics fully coupled model. The uncoupled modeling approach was validated by comparing the evolution of steel and slag compositions with the experimentally measured data during ladle metallurgical furnace (LMF) processing at Nucor Steel Tuscaloosa, Inc. Then, the validated approach was applied to investigate the effects of the initial steel and slag compositions, as well as different types of additions during the refining process on the desulfurization efficiency. The results revealed that the sulfur distribution ratio and the desulfurization reaction can be promoted by making Al and CaO additions during the refining process. It was also shown that by increasing the initial Al content in liquid steel, both Al oxidation and desulfurization rates rapidly increase. In addition, it was found that the variation of the initial Si content in steel has no significant influence on the desulfurization rate. Lastly, if the initial CaO content in slag is increased or the initial Al{sub 2}O{sub 3} content is decreased in the fluid-slag compositional range, the desulfurization rate can be improved significantly during the LMF process.

36 MATERIALS SCIENCE↗
NASA NTRS2020

The Solar Orbiter SPICE Instrument An Extreme UV imaging spectrometer

Aims. The Spectral Imaging of the Coronal Environment (SPICE) instrument is a high-resolution imaging spectrometer operating at extreme ultraviolet wavelengths. In this paper, we present the concept, design, and pre-launch performance of this facility instrument on the ESA/NASA Solar Orbiter mission. Methods. The goal of this paper is to give prospective users a better understanding of the possible types of observations, the data acquisition, and the sources that contribute to the instrument’s signal. Results. The paper discusses the science objectives, with a focus on the SPICE-specific aspects, before presenting the instrument’s design, including optical, mechanical, thermal, and electronics aspects. This is followed by a characterisation and calibration of the instrument’s performance. The paper concludes with descriptions of the operations concept and data processing. Conclusions. The performance measurements of the various instrument parameters meet the requirements derived from the mission’s science objectives. The SPICE instrument is ready to perform measurements that will provide vital contributions to the scientific success of the Solar Orbiter mission.

M. Anderson↗
DOE OSTI2024

Density estimation via measure transport: Outlook for applications in the biological sciences

Abstract One among several advantages of measure transport methods is that they allow or a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure.

gene expression data↗
DOE OSTI2020

Mirostructure Characterization of Friction Consolidated Copper-Nickel using a Machine Learning Approach: Developing Process to Microstructure Associations

Friction consolidation (FC) is a solid phase processing approach where discrete material forms such as powders, chips, nuggets, etc. are densified via shear deformation. The precursors are placed in a billet container and brought in contact with a rotating tool that applying the desirable amount of normal force. Under the combined action of the rotation and normal pressure, the discrete precursor is consolidated through porosity reduction and shear deformation. FC is increasingly being studied as an attractive approach to manufacturing fully dense parts from powder forms owing to its ability to mix, alloy and consolidate difficult-to-process precursors in minimal number of process steps. Material consolidation and deformation in shear consolidation processes have been studied extensively previously for different material combinations previously. However, despite the extensive research in this area, understanding of the mechanistic processes in pore consolidation, deformation-induced mixing and material solubility during FC is still evolving. Material development using solid phase processing approaches such as FC is often performed based on research experience/education, which can be biased. Conventional analysis and simulation tools in this area tend to be successful only when material thermodynamic pathways and microstructural evolution sequences resulting from processing are clearly defined or known. They are not as effective for emerging advanced manufacturing technologies where material evolution pathways are not well established. The ability to predict optimal process parameters based on material chemistry and bulk properties is essential to accelerate materials design and processing, as are an understanding of the relevant structure-processing-property relationships. These structure-processing-property-performance relationships are at the core of materials science research. Microstructure characterization provides the link to these four core areas, often through visualizing material microstructure using imaging techniques. However, linking microstructure image data (i.e., micrographs) to variables of interest (e.g., processing parameters, material chemistry) in a reproducible, generalizable, and quantitative manner is a significant challenge. Typically, quantitatively linking image data to processing history relies on significant domain knowledge and manual or subject matter expert (SME)-heuristic based image analysis. Such an approach to image analysis has the potential to be biased, inefficient, and difficult to replicate.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes

Abstract Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active‐source seismic data, which probe asperity‐scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P‐wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to “learn” about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics‐informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.

58 GEOSCIENCES↗
DOE OSTI2023

Water narratives in local newspapers within the United States

Sustainable use of water resources continues to be a challenge across the globe. This is in part due to the complex set of physical and social behaviors that interact to influence water management from local to global scales. Analyses of water resources have been conducted using a variety of techniques, including qualitative evaluations of media narratives. This study aims to augment these methods by leveraging computational and quantitative techniques from the social sciences focused on text analyses. Specifically, we use natural language processing methods to investigate a large corpus (approx. 1.8M) of newspaper articles spanning approximately 35 years (1982–2017) for insights into human-nature interactions with water. Focusing on local and regional United States publications, our analysis demonstrates important dynamics in water-related dialogue about drinking water and pollution to other critical infrastructures, such as energy, across different parts of the country. Our assessment, which looks at water as a system, also highlights key actors and sentiments surrounding water. Extending these analytical methods could help us further improve our understanding of the complex roles of water in current society that should be considered in emerging activities to mitigate and respond to resource conflicts and climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Development and evaluation of a portable CZT coded aperture gamma-camera

We present the design and the evaluation of a CdZnTe (CZT) based gamma camera using a coded aperture mask. This camera, based on a 8 cm{sup 3} detection module, is small enough to be portable and battery-powered (4 kg weight and 4 W power dissipation). As the detector has spectral capabilities, the gamma camera allows isotope identification and colored imaging, by affecting one color channel to each identified isotope. As all data processing is done at real time, the user can directly observe the outcome of an acquisition and can immediately react to what he sees. We first present the architecture of the system, how the detector works, and its performances. After, we focus on the imaging technique used and its strengths and limitations. Finally, results concerning sensitivity, spatial resolution, field of view and multi-isotope imaging are shown and discussed. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1976

Space station systems analysis study. Part 1, volume 1: Executive study

Potential space station system options were examined for a permanent, manned, orbital space facility and to provide data to NASA program planners and decision makers for their use in future program planning. There were ten space station system objectives identified. These were categorized into five major objectives and five supporting objectives. The major objectives were to support the development of: (1) satellite power systems, (2) nuclear energy plants in space, (3) space processing, (4) earth services, and (5) space cosmological research and development. The five supporting objectives, to define space facilities which would be basic building blocks for future systems, were: (1) a multidiscipline science laboratory, (2) an orbital depot to maintain, fuel, and service orbital transfer vehicles, (3) cluster support systems to provide power and data processing for multiple orbital elements, (4) a sensor development facility, and (5) the facilities necessary to enhance man's living and working in space.

Source record↗
DOE OSTI2020

Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Natural Language Processing for Extracting Rich Disease Data Aligned To Satellite Meteorological Data

Global climate change is redefining our understanding of how diseases spread. In Sri Lanka, vector-borne diseases such as dengue fever, encephalitis, and leptospirosis historically surged during the monsoon seasons when temperatures were high enough for mosquito eggs to hatch. Unfortunately, due to rising temperatures and more erratic rainfall patterns, mosquito eggs can now hatch year-round and are increasingly unpredictable, leading to an alarmingly increasing number of hospitalizations and deaths. More data is needed to adapt our response to these diseases in an increasingly warmer world. In the contemporary landscape, a wealth of disease information is available, yet accessibility remains limited due to unstructured data formats such as PDFs. Therefore, converting unstructured disease reports into structured formats is necessary for effectively leveraging data. This paper introduces a comprehensive framework for collecting unstructured disease reports and transforming them into analyzable formats. By creating separate models tailored to each data format, we can ensure accuracy compared to general models. These straightforward models enhance accessibility and empower other researchers to use our tools. The returned structured data can then be harnessed for analysis, statistical purposes, and informing evidence-based public health interventions, thus facilitating more informed decision-making in healthcare. We deploy this framework to produce geospatial data for Sri Lanka and Brazil for many different conditions and align these data with satellite environmental data, providing for the first time a structured, aligned powerful dataset for disease modeling.

Open Source Open Science↗
NASA NTRSDate not supplied

Revolutionizing Earth Science with Generalized AI Models

Foundation Models (FM) are generalized Artificial Intelligence (AI) models that are designed to replace a task or an application-specific model and can be used for many downstream applications. These FM can be built on any sequence data and are trained utilizing self-supervised approaches. The obstacle of creating a sizable labeled dataset for training is removed by using self-supervised learning. Most FM employ transformer design that takes advantage of the idea of self-attention, allowing the network to represent the impact of distant data points on one another in space and time. The FM models show emergent qualities that are induced from the data. FM can become a valuable tool for Earth science researchers. Due to the size of these models, downstream applications built fine-tuning these FM perform better and exhibit greater accuracy than models created from scratch. FM significantly lowers the entry barrier in terms of both the time and effort required to develop various downstream applications. For some scientific datasets, such as optical remote sensing data, FM can speed up processes like classification, object detection and prediction. By eliminating the training data bottleneck and maximizing the usage of science data, FM can make it simpler to integrate AI into scientific research. Initial results for three different FMs will be presented.

Rahul Ramachandran↗
DOE OSTI2016

Automated Work Package: Conceptual Design and Data Architecture

The automated work package (AWP) is one of the U.S. Department of Energy’s (DOE) Light Water Reactor Sustainability Program efforts to enhance the safety and economics of the nuclear power industry. An AWP is an adaptive and interactive work package that intelligently drives the work process according to the plant condition, resources status, and users progress. The AWP aims to automate several manual tasks of the work process to enhance human performance and reduce human errors. Electronic work packages (eWPs), studied by the Electric Power Research Institute (EPRI), are work packages that rely to various extent on electronic data processing and presentation. AWPs are the future of eWPs. They are envisioned to incorporate the advanced technologies of the future, and thus address the unresolved deficiencies associated with the eWPs in a nuclear power plant. In order to define the AWP, it is necessary to develop an ideal envisioned scenario of the future work process without any current technology restriction. The approach followed to develop this scenario is specific to every stage of the work process execution. The scenario development resulted in fifty advanced functionalities that can be part of the AWP. To rank the importance of these functionalities, a survey was conducted involving several U.S. nuclear utilities. The survey aimed at determining the current need of the nuclear industry with respect to the current work process, i.e. what the industry is satisfied with, and where the industry envisions potential for improvement. The survey evaluated the most promising functionalities resulting from the scenario development. The results demonstrated a significant desire to adopt the majority of these functionalities. The results of the survey are expected to drive the Idaho National Laboratory (INL) AWP research and development (R&D). In order to facilitate this mission, a prototype AWP is needed. Since the vast majority of earlier efforts focused on the frontend aspects of the AWP, the backend data architecture was researched and developed in this effort. The backend design involved data architecture aspects. It was realized through this effort that the key aspects of this design are hierarchy, data configuration and live information, data templates and instances, the flow of work package execution, the introduction of properties, and the means to interface the backend to the frontend. After the backend design was developed, a data structure was built to reflect the developed data architecture. The data structure was developed to accommodate the fifty functionalities identified by the envisioned scenario development. The data structure was evaluated by incorporating an example work order from the nuclear power industry. The implementation resulted in several optimization iterations of the data structure. In addition, the rearrangement of the work order information to fit the data structure highlighted several possibilities for improvement in the current work order design, and significantly reduced the size of the work order.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1987

Computer Sciences and Data Systems, volume 2

Topics addressed include: data storage; information network architecture; VHSIC technology; fiber optics; laser applications; distributed processing; spaceborne optical disk controller; massively parallel processors; and advanced digital SAR processors.

Source record↗
NASA NTRS2004

Multi-Sensor Distributive On-Line Processing, Visualization, and Analysis Infrastructure for an Agricultural Information System at the NASA Goddard Earth Sciences DAAC

The Goddard Space Flight Center Earth Sciences Data and Information Services Center (GES DISC) Distributed Active Center (DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM On-line Visualization and Analysis System precipitation and other satellite data products and services. AIS outputs will be ,integrated into existing operational decision support system for global crop monitoring, such as that of the U.N. World Food Program. The ability to use the raw data stored in the GES DAAC archives is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. To gain this understanding is a time-consuming process and not a productive investment of the user's time. This is an especially difficult challenge when users need to deal with multi-sensor data that usually are of different structures and resolutions. The AIS has taken a major step towards meeting this challenge by incorporating an underlying infrastructure, called the GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni," that integrates various components to support web interfaces that ,allow users to perform interactive analysis on-line without downloading any data. Several instances of the Giovanni-based interface have been or are being created to serve users of TRMM precipitation, MODIS aerosol, and SeaWiFS ocean color data, as well as agricultural applications users. Giovanni-based interfaces are simple to use but powerful. The user selects geophysical ,parameters, area of interest, and time period; and the system generates an output ,on screen in a matter of seconds.

Teng, William↗
NASA NTRS1983

1983 International Geoscience and Remote Sensing Symposium (IGARSS '83), San Francisco, CA, August 31-September 2, 1983, Digest. Volumes 1 & 2

Various papers on remote sensing are presented. The general topics addressed include: remote sensing of the biosphere, geophysical inversion, science and archaeology, image processing, sensing the atmosphere with microwaves, analysis of scattering for remote sensing, advanced sensors and technology, archaeology, data processing, fiber optics, Landsat-4 and TM. Also discussed are: remote sensing and microwave instrumentation, soil moisture and hydrology, measurement techniques for ocean surface waves, geological remote sensing, radar millimeter wave propagation through the atmosphere, and remote sensing in planetary science, geophysics, hydrology, oceanography, and surface vegetation.

Source record↗
NASA NTRS2001

Level 1 Processing of MODIS Direct Broadcast Data at the GSFC DAAC

The GSFC DAAC is working to test and package the MODIS Level 1 Processing software for Aqua Direct Broadcast data. This entails the same code base, but different lookup tables for Aqua and Terra. However, the most significant change is the use of ancillary attitude and ephemeris files instead of orbit/attitude information within the science data stream (as with Terra). In addition, we are working on Linux: ports of the algorithms, which could eventually enable processing on PC clusters. Finally, the GSFC DAAC is also working with the GSFC Direct Readout laboratory to ingest Level 0 data from the GSFC DB antenna into the main DAAC, enabling level 1 production in near real time in support of applications users, such as the Synergy project. The mechanism developed for this could conceivably be extended to other participating stations.

Lynnes, Christopher↗