Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data processing automation”

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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Expanding Biological Repository Data Available for Sharing and Knowledge Discovery

Biology has developed next-generation data science and alternative analytical approaches with methodologies which require principal investigator (PI) experimental assay data be re-used. This new approach involves mining multiple datasets at once from various hierarchical organizations of biological complexity, while concurrently evaluating how experimental factors affect endpoints of standard assays. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make findable, accessible, interoperable, and reusable (FAIR) all non-human space-relevant biological data. These data include mission metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery, and subject-experienced telemetry (radiation, temperature, humidity, acoustics, vibrations). ALSDA has transformed to bring current biological repository data and all future collected data into this new scientific data mining reality. It has integrated into the ‘NASA Open Science’ group of projects to facilitate a suite of new tools and workflows to improve data accessibility and reusability by implementing data management plans, automating data submission agreements, and adopting the single-point-of-entry data submission portal, originally developed by NASA GeneLab. These systems required ALSDA to develop science assay configurations for the submission portal, capturing essential assay parameters according to established norms in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. ALSDA datasets are curated to maintain rich metadata, accuracy of datasets, data transparency, provenance, and additionally ensure data are machine-readable (e.g., R and Python languages). ALSDA integration with GeneLab and its analysis portals enable higher-order physiological-level datasets be mined in conjunction with -omics datasets. As ALSDA physiological-level datasets are published (micro-computed tomography, histology, intraocular pressure, hormonal assays, immunostaining, ultrasonography), the merging of hierarchical organizations of biological complexity from spaceflight will enable new knowledge discovery approaches.

Ryan T Scott↗
NASA NTRSDate not supplied

Towards Automated Analytics of Research Publications

For readers of scientific publications it remains a big challenge to unambiguously relate the published research with the data used. To a substantial degree it is attributed to authors, journals, editors, and reviewers not prioritizing correct data citation, which impacts traceability, repeatability, and giving credits to published authors and their funding sources. Furthermore, uniform classification of the content of the published research is hampered by journals using journal specific topics and letting authors to assign free text keywords to their papers. We demonstrate automated analytics methods for extracting and relating datasets used and the research application areas by processing 1,300 research papers that referenced the NASA Giovanni service (but probably not the datasets in particular) as supporting their publication process. This presentation was given during the 2022 ESIP January meeting held virtually in January 2022.

Irina Gerasimov↗
NASA NTRSDate not supplied

The TOLNet 2.0 Website: How an API Can Promote Open Science and FAIR Principles

The Tropospheric Ozone Lidar Network (TOLNet) has generated over a decade of ozone vertical profile data products over North America. The science value of the TOLNet data has been demonstrated in numerous peer-reviewed publications on air quality and other ozone relevant research. To support the broad spectrum of data use, the TOLNet team launched a major effort to upgrade the web-based data repository aiming to enhance the data discoverability and to enable machine-to-machine data upload and download processes. Specifically, the TOLNet website included an application programming interface (API), which supports machine-to-machine data search and data download. The API also extracts selected variables from the files, which can be retrieved as JSON objects and used to create data displays without having to download or open the underlying files. The TOLNet science team members can also use the API for automated data upload, including a data file scanning feature to ensure data product integrity. To be presented will include a summary of key features of data repositories, an actual use case of machine-to-machine data access/use, as well as our journey to make TOLNet data more FAIR, i.e., more findable, accessible, interoperable, and (re)usable.

Crystal Gummo↗
NASA NTRS2003

Review of Integrated Noise Model (INM) Equations and Processes

The FAA's Integrated Noise Model (INM) relies on the methods of the SAE AIR-1845 'Procedure for the Calculation of Airplane Noise in the Vicinity of Airports' issued in 1986. Simplifying assumptions for aerodynamics and noise calculation were made in the SAE standard and the INM based on the limited computing power commonly available then. The key objectives of this study are 1) to test some of those assumptions against Boeing source data, and 2) to automate the manufacturer's methods of data development to enable the maintenance of a consistent INM database over time. These new automated tools were used to generate INM database submissions for six airplane types :737-700 (CFM56-7 24K), 767-400ER (CF6-80C2BF), 777-300 (Trent 892), 717-200 (BR7 15), 757-300 (RR535E4B), and the 737-800 (CFM56-7 26K).

Shepherd, Kevin P.↗
NASA NTRS1989

Computational fluid dynamics analysis of space shuttle main propulsion feed line 17-inch disconnect valves

A steady incompressible three-dimensional (3-D) viscous flow analysis was conducted for the Space Shuttle Main Propulsion External Tank (ET)/Orbiter (ORB) propellant feed line quick separable 17-inch disconnect flapper valves for liquid oxygen (LO2) and liquid hydrogen (LH2). The main objectives of the analysis were to predict and correlate the hydrodynamic stability of the flappers and pressure drop with available water test data. Computational Fluid Dynamics (CFD) computer codes were procured at no cost from the public domain, and were modified and extended to carry out the disconnect flow analysis. The grid generator codes SVTGD3D and INGRID were obtained. NASA Ames Research Center supplied the flow solution code INS3D, and the color graphics code PLOT3D. A driver routine was developed to automate the grid generation process. Components such as pipes, elbows, and flappers can be generated with simple commands, and flapper angles can be varied easily. The flow solver INS3D code was modified to treat interior flappers, and other interfacing routines were developed, which include a turbulence model, a force/moment routine, a time-step routine, and initial and boundary conditions. In particular, an under-relaxation scheme was implemented to enhance the solution stability. Major physical assumptions and simplifications made in the analysis include the neglect of linkages, slightly reduced flapper diameter, and smooth solid surfaces. A grid size of 54 x 21 x 25 was employed for both the LO2 and LH2 units. Mixing length theory applied to turbulent shear flow in pipes formed the basis for the simple turbulence model. Results of the analysis are presented for LO2 and LH2 disconnects.

Kandula, Max↗
NASA NTRS1992

Simultaneous real-time data collection methods

This paper describes the development of electronic test equipment which executes, supervises, and reports on various tests. This validation process uses computers to analyze test results and report conclusions. The test equipment consists of an electronics component and the data collection and reporting unit. The PC software, display screens, and real-time data-base are described. Pass-fail procedures and data replay are discussed. The OS2 operating system and Presentation Manager user interface system were used to create a highly interactive automated system. The system outputs are hardcopy printouts and MS DOS format files which may be used as input for other PC programs.

Klincsek, Thomas↗
NASA NTRS1992

Test and analysis results for composite transport fuselage and wing structures

Automated tow placement (ATP) and stitching of dry textile composite preforms followed by resin transfer molding (RTM) are being investigated by researchers at NASA LaRC and Douglas Aircraft Company as cost-effective manufacturing processes for obtaining damage tolerant fuselage and wing structures for transport aircraft. The Douglas work is being performed under a NASA contract entitled 'Innovative Composites Aircraft Primary Structures (ICAPS)'. Data are presented in this paper to assess the damage tolerance of ATP and RTM fuselage elements with stitched-on stiffeners from compression tests of impacted three-J-stiffened panels and from stiffener pull-off tests. Data are also presented to assess the damage tolerance of RTM wing elements which had stitched skin and stiffeners from impacted single stiffener and three blade-stiffened compression tests and stiffener pull-off tests.

Deaton, Jerry W.↗
NASA NTRS2009

Maneuver Automation Software

The Maneuver Automation Software (MAS) automates the process of generating commands for maneuvers to keep the spacecraft of the Cassini-Huygens mission on a predetermined prime mission trajectory. Before MAS became available, a team of approximately 10 members had to work about two weeks to design, test, and implement each maneuver in a process that involved running many maneuver-related application programs and then serially handing off data products to other parts of the team. MAS enables a three-member team to design, test, and implement a maneuver in about one-half hour after Navigation has process-tracking data. MAS accepts more than 60 parameters and 22 files as input directly from users. MAS consists of Practical Extraction and Reporting Language (PERL) scripts that link, sequence, and execute the maneuver- related application programs: "Pushing a single button" on a graphical user interface causes MAS to run navigation programs that design a maneuver; programs that create sequences of commands to execute the maneuver on the spacecraft; and a program that generates predictions about maneuver performance and generates reports and other files that enable users to quickly review and verify the maneuver design. MAS can also generate presentation materials, initiate electronic command request forms, and archive all data products for future reference.

Uffelman, Hal↗
NASA NTRS2008

Intelligent Integrated Health Management for a System of Systems

An intelligent integrated health management system (IIHMS) incorporates major improvements over prior such systems. The particular IIHMS is implemented for any system defined as a hierarchical distributed network of intelligent elements (HDNIE), comprising primarily: (1) an architecture (Figure 1), (2) intelligent elements, (3) a conceptual framework and taxonomy (Figure 2), and (4) and ontology that defines standards and protocols. Some definitions of terms are prerequisite to a further brief description of this innovation: A system-of-systems (SoS) is an engineering system that comprises multiple subsystems (e.g., a system of multiple possibly interacting flow subsystems that include pumps, valves, tanks, ducts, sensors, and the like); 'Intelligent' is used here in the sense of artificial intelligence. An intelligent element may be physical or virtual, it is network enabled, and it is able to manage data, information, and knowledge (DIaK) focused on determining its condition in the context of the entire SoS; As used here, 'health' signifies the functionality and/or structural integrity of an engineering system, subsystem, or process (leading to determination of the health of components); 'Process' can signify either a physical process in the usual sense of the word or an element into which functionally related sensors are grouped; 'Element' can signify a component (e.g., an actuator, a valve), a process, a controller, an actuator, a subsystem, or a system; The term Integrated System Health Management (ISHM) is used to describe a capability that focuses on determining the condition (health) of every element in a complex system (detect anomalies, diagnose causes, prognosis of future anomalies), and provide data, information, and knowledge (DIaK) not just data to control systems for safe and effective operation. A major novel aspect of the present development is the concept of intelligent integration. The purpose of intelligent integration, as defined and implemented in the present IIHMS, is to enable automated analysis of physical phenomena in imitation of human reasoning, including the use of qualitative methods. Intelligent integration is said to occur in a system in which all elements are intelligent and can acquire, maintain, and share knowledge and information. In the HDNIE of the present IIHMS, an SoS is represented as being operationally organized in a hierarchical-distributed format. The elements of the SoS are considered to be intelligent in that they determine their own conditions within an integrated scheme that involves consideration of data, information, knowledge bases, and methods that reside in all elements of the system. The conceptual framework of the HDNIE and the methodologies of implementing it enable the flow of information and knowledge among the elements so as to make possible the determination of the condition of each element. The necessary information and knowledge is made available to each affected element at the desired time, satisfying a need to prevent information overload while providing context-sensitive information at the proper level of detail. Provision of high-quality data is a central goal in designing this or any IIHMS. In pursuit of this goal, functionally related sensors are logically assigned to groups denoted processes. An aggregate of processes is considered to form a system. Alternatively or in addition to what has been said thus far, the HDNIE of this IIHMS can be regarded as consisting of a framework containing object models that encapsulate all elements of the system, their individual and relational knowledge bases, generic methods and procedures based on models of the applicable physics, and communication processes (Figure 2). The framework enables implementation of a paradigm inspired by how expert operators monitor the health of systems with the help of (1) DIaK from various sources, (2) software tools that assist in rapid visualization of the condition of the system, (3) analical software tools that assist in reasoning about the condition, (4) sharing of information via network communication hardware and software, and (5) software tools that aid in making decisions to remedy unacceptable conditions or improve performance.

Smith, Harvey↗
NASA NTRS1991

SHARP: Automated monitoring of spacecraft health and status

Briefly discussed here are the spacecraft and ground systems monitoring process at the Jet Propulsion Laboratory (JPL). Some of the difficulties associated with the existing technology used in mission operations are highlighted. A new automated system based on artificial intelligence technology is described which seeks to overcome many of these limitations. The system, called the Spacecraft Health Automated Reasoning Prototype (SHARP), is designed to automate health and status analysis for multi-mission spacecraft and ground data systems operations. The system has proved to be effective for detecting and analyzing potential spacecraft and ground systems problems by performing real-time analysis of spacecraft and ground data systems engineering telemetry. Telecommunications link analysis of the Voyager 2 spacecraft was the initial focus for evaluation of the system in real-time operations during the Voyager spacecraft encounter with Neptune in August 1989.

Atkinson, David J.↗
NASA NTRS1990

SHARP - Automated monitoring of spacecraft health and status

Briefly discussed here are the spacecraft and ground systems monitoring process at the Jet Propulsion Laboratory (JPL). Some of the difficulties associated with the existing technology used in mission operations are highlighted. A new automated system based on artificial intelligence technology is described which seeks to overcome many of these limitations. The system, called the Spacecraft Health Automated Reasoning Prototype (SHARP), is designed to automate health and status analysis for multi-mission spacecraft and ground data systems operations. The system has proved to be effective for detecting and analyzing potential spacecraft and ground systems problems by performing real-time analysis of spacecraft and ground data systems engineering telemetry. Telecommunications link analysis of the Voyager 2 spacecraft was the initial focus for evaluation of the system in real-time operations during the Voyager spacecraft encounter with Neptune in August 1989.

Atkinson, David J.↗
NASA NTRS1993

KSC management training system project

The stated objectives for the summer of 1993 were: to review the Individual Development Plan Surveys for 1994 in order to automate the analysis of the Needs Assessment effort; and to develop and implement evaluation methodologies to perform ongoing program-wide course-to-course assessment. This includes the following: to propose a methodology to develop and implement objective, performance-based assessment instruments for each training effort; to mechanize course evaluation forms and develop software to facilitate the data gathering, analysis, and reporting processes; and to implement the methodology, forms, and software in at lease one training course or seminar selected among those normally offered in the summer at KSC. Section two of this report addresses the work done in regard to the Individual Development Plan Surveys for 1994. Section three presents the methodology proposed to develop and implement objective, performance-based assessment instruments for each training course offered at KSC.

Sepulveda, Jose A.↗
NASA NTRS1990

SigmaCLIPSE = presentation management + NASA CLI PS + SQL

SigmaCLIPSE provides an expert systems and 'intelligent' data base development program for diverse systems integration environments that require support for automated reasoning and expert systems technology, presentation management, and access to 'intelligent' SQL data bases. The SigmaCLIPSE technology and and its integrated ability to access 4th generation application development and decision support tools through a portable SQL interface, comprises a sophisticated software development environment for solving knowledge engineering and expert systems development problems in information intensive commercial environments -- financial services, health care, and distributed process control -- where the expert system must be extendable -- a major architectural advantage of NASA CLIPS. SigmaCLIPSE is a research effort intended to test the viability of merging SQL data bases with expert systems technology.

Weiss, Bernard P., Jr.↗
NASA NTRS2018

Automated Pneumothorax Diagnosis using Deep Neural Networks

Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical We have Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architecturesinterpretation of a patient medical image is highly operator dependent. certain pathologies., applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of 98.3% for M-mode images and 99.8% with B-mode video frames.

US Army collaboration↗
NASA NTRS2016

Green Propellant Test Capabilities of the Altitude Combustion Stand at the NASA Glenn Research Center

The NASA Glenn Research Center (GRC) is committed to providing simulated altitude rocket test capabilities to NASA programs, other government agencies, private industry partners, and academic partners. A primary facility to support those needs is the Altitude Combustion Stand (ACS). ACS provides the capability to test combustion components at a simulated altitude up to 100,000 ft. (approx.0.2 psia/10 Torr) through a nitrogen-driven ejector system. The facility is equipped with an axial thrust stand, gaseous and cryogenic liquid propellant feed systems, data acquisition system with up to 1000 Hz recording, and automated facility control system. Propellant capabilities include gaseous and liquid hydrogen, gaseous and liquid oxygen, and liquid methane. A water-cooled diffuser, exhaust spray cooling chamber, and multi-stage ejector systems can enable run times up to 180 seconds to 16 minutes. The system can accommodate engines up to 2000-lbf thrust, liquid propellant supply pressures up to 1800 psia, and test at the component level. Engines can also be fired at sea level if needed. The NASA GRC is in the process of modifying ACS capabilities to enable the testing of green propellant (GP) thrusters and components. Green propellants are actively being explored throughout government and industry as a non-toxic replacement to hydrazine monopropellants for applications such as reaction control systems or small spacecraft main propulsion systems. These propellants offer increased performance and cost savings over hydrazine. The modification of ACS is intended to enable testing of a wide range of green propellant engines for research and qualification-like testing applications. Once complete, ACS will have the capability to test green propellant engines up to 880 N in thrust, thermally condition the green propellants, provide test durations up to 60 minutes depending on thrust class, provide high speed control and data acquisition, as well as provide advanced imaging and diagnostics such as infrared (IR) imaging.

Testing↗
NASA NTRS1975

Launch Processing System - A system to support the Space Shuttle

Kennedy Space Center (KSC) is designing and acquiring a Launch Processing System (LPS), an important part of Ground Support Equipment (GSE), to Support launch site operations in a more efficient way than was done on previous programs. LPS will provide (1) automatic control of GSE and Shuttle systems for test and operations, (2) real time data analysis and information display, and (3) efficient recall of test data and engineering files to support Shuttle ground operations. Modern automation techniques, off-the-shelf components, and modular design are being employed to the maximum to achieve these goals. The cost of acquisition, operations, and maintenance of LPS is of great importance and is considered with each engineering trade.

Paul, H. C.↗
NASA NTRS1984

Advanced information processing system

Design and performance details of the advanced information processing system (AIPS) for fault and damage tolerant data processing on aircraft and spacecraft are presented. AIPS comprises several computers distributed throughout the vehicle and linked by a damage tolerant data bus. Most I/O functions are available to all the computers, which run in a TDMA mode. Each computer performs separate specific tasks in normal operation and assumes other tasks in degraded modes. Redundant software assures that all fault monitoring, logging and reporting are automated, together with control functions. Redundant duplex links and damage-spread limitation provide the fault tolerance. Details of an advanced design of a laboratory-scale proof-of-concept system are described, including functional operations.

Lala, J. H.↗
NASA NTRS1987

Shape optimization of three-dimensional stamped and solid automotive components

The shape optimization of realistic, 3-D automotive components is discussed. The integration of the major parts of the total process: modeling, mesh generation, finite element and sensitivity analysis, and optimization are stressed. Stamped components and solid components are treated separately. For stamped parts a highly automated capability was developed. The problem description is based upon a parameterized boundary design element concept for the definition of the geometry. Automatic triangulation and adaptive mesh refinement are used to provide an automated analysis capability which requires only boundary data and takes into account sensitivity of the solution accuracy to boundary shape. For solid components a general extension of the 2-D boundary design element concept has not been achieved. In this case, the parameterized surface shape is provided using a generic modeling concept based upon isoparametric mapping patches which also serves as the mesh generator. Emphasis is placed upon the coupling of optimization with a commercially available finite element program. To do this it is necessary to modularize the program architecture and obtain shape design sensitivities using the material derivative approach so that only boundary solution data is needed.

Botkin, M. E.↗