Battery Data MI Importer Template Quick Start Guide

In order to ensure the persistent availability and reliability of test data generated over the course of the project, the M-SHELLS Project has decided to store acquired test data, as well as associated pedigree information, in the Granta Materials Intelligence (MI) database. To facilitate that effort, an importer template and associated graphical user interface (GUI) software have been developed, with this guide providing the operating instructions for their use. The template and automation software GUI are contained in the BatteryDataImporter.xlsm Excel workbook, and are to be used to import M-SHELLS summary, or pedigree, data and the associated raw test data results into an importer template-based file, formatted in such a way as to be ready for immediate upload to the Test Data: Battery Performance table of the Granta MI database. The provided GUI enables the user to select the appropriate summary data file(s), with each file containing the required information to identify any associated raw test data file(s) to be processed. In addition to describing the setup and operation of the importer template and GUI software, this guide also provides instructions for uploading processed data to the database and for viewing the data following upload

Interactive, Automated Management of Icing Data

IceVal DatAssistant is software (see figure) that provides an automated, interactive solution for the management of data from research on aircraft icing. This software consists primarily of (1) a relational database component used to store ice shape and airfoil coordinates and associated data on operational and environmental test conditions and (2) a graphically oriented database access utility, used to upload, download, process, and/or display data selected by the user. The relational database component consists of a Microsoft Access 2003 database file with nine tables containing data of different types. Included in the database are the data for all publicly releasable ice tracings with complete and verifiable test conditions from experiments conducted to date in the Glenn Research Center Icing Research Tunnel. Ice shapes from computational simulations with the correspond ing conditions performed utilizing the latest version of the LEWICE ice shape prediction code are likewise included, and are linked to the equivalent experimental runs. The database access component includes ten Microsoft Visual Basic 6.0 (VB) form modules and three VB support modules. Together, these modules enable uploading, downloading, processing, and display of all data contained in the database. This component also affords the capability to perform various database maintenance functions for example, compacting the database or creating a new, fully initialized but empty database file

Flight software development for the isothermal dendritic growth experiment

The Isothermal Dendritic Growth Experiment (IDGE) is a microgravity materials science experiment scheduled to fly in the cargo bay of the shuttle on the United States Microgravity Payload (USMP) carrier. The experiment will be operated by real-time control software which will not only monitor and control onboard experiment hardware, but will also communicate, via downlink data and uplink commands, with the Payload Operations Control Center (POCC) at NASA George C. Marshall Space Flight Center (MSFC). The software development approach being used to implement this system began with software functional requirements specification. This was accomplished using the Yourdon/DeMarco methodology as supplemented by the Ward/Mellor real-time extensions. The requirements specification in combination with software prototyping was then used to generate a detailed design consisting of structure charts, module prologues, and Program Design Language (PDL) specifications. This detailed design will next be used to code the software, followed finally by testing against the functional requirements. The result will be a modular real-time control software system with traceability through every phase of the development process

IceVal DatAssistant: An Interactive, Automated Icing Data Management System

As with any scientific endeavor, the foundation of icing research at the NASA Glenn Research Center (GRC) is the data acquired during experimental testing. In the case of the GRC Icing Branch, an important part of this data consists of ice tracings taken following tests carried out in the GRC Icing Research Tunnel (IRT), as well as the associated operational and environmental conditions during those tests. Over the years, the large number of experimental runs completed has served to emphasize the need for a consistent strategy to manage the resulting data. To address this situation, the Icing Branch has recently elected to implement the IceVal DatAssistant automated data management system. With the release of this system, all publicly available IRT-generated experimental ice shapes with complete and verifiable conditions have now been compiled into one electronically-searchable database; and simulation software results for the equivalent conditions, generated using the latest version of the LEWICE ice shape prediction code, are likewise included and linked to the corresponding experimental runs. In addition to this comprehensive database, the IceVal system also includes a graphically-oriented database access utility, which provides reliable and easy access to all data contained in the database. In this paper, the issues surrounding historical icing data management practices are discussed, as well as the anticipated benefits to be achieved as a result of migrating to the new system. A detailed description of the software system features and database content is also provided; and, finally, known issues and plans for future work are presented

Comparison of Aircraft Icing Growth Assessment Software

A research project is underway to produce computer software that can accurately predict ice growth under any meteorological conditions for any aircraft surface. An extensive comparison of the results in a quantifiable manner against the database of ice shapes that have been generated in the NASA Glenn Icing Research Tunnel (IRT) has been performed, including additional data taken to extend the database in the Super-cooled Large Drop (SLD) regime. The project shows the differences in ice shape between LEWICE 3.2.2, GlennICE, and experimental data. The project addresses the validation of the software against a recent set of ice-shape data in the SLD regime. This validation effort mirrors a similar effort undertaken for previous validations of LEWICE. Those reports quantified the ice accretion prediction capabilities of the LEWICE software. Several ice geometry features were proposed for comparing ice shapes in a quantitative manner. The resulting analysis showed that LEWICE compared well to the available experimental data

Comparison of LEWICE and GlennICE in the SLD Regime

A research project is underway at the NASA Glenn Research Center (GRC) to produce computer software that can accurately predict ice growth under any meteorological conditions for any aircraft surface. This report will present results from two different computer programs. The first program, LEWICE version 3.2.2, has been reported on previously. The second program is GlennICE version 0.1. An extensive comparison of the results in a quantifiable manner against the database of ice shapes that have been generated in the GRC Icing Research Tunnel (IRT) has also been performed, including additional data taken to extend the database in the Super-cooled Large Drop (SLD) regime. This paper will show the differences in ice shape between LEWICE 3.2.2, GlennICE, and experimental data. This report will also provide a description of both programs. Comparisons are then made to recent additions to the SLD database and selected previous cases. Quantitative comparisons are shown for horn height, horn angle, icing limit, area, and leading edge thickness. The results show that the predicted results for both programs are within the accuracy limits of the experimental data for the majority of cases

Software Development Processes Applied to Computational Icing Simulation

The development of computational icing simulation methods is making the transition form the research to common place use in design and certification efforts. As such, standards of code management, design validation, and documentation must be adjusted to accommodate the increased expectations of the user community with respect to accuracy, reliability, capability, and usability. This paper discusses these concepts with regard to current and future icing simulation code development efforts as implemented by the Icing Branch of the NASA Lewis Research Center in collaboration with the NASA Lewis Engineering Design and Analysis Division. With the application of the techniques outlined in this paper, the LEWICE ice accretion code has become a more stable and reliable software product

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security