Organizational Knowledge Capture
Summary of capstone project collaboration between Virginia Tech Center for Business Analytics and LaRC OCIO Data Science team.
Edward Lingham McLarney↗
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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.
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Business Intelligence and Data Analytics at the Jet Propulsion Laboratory
No abstract available
Ko, Nina↗
Supply Chain Research and Analysis: Illuminating Risks in Complex Systems
Framing the strategic challenge posed by the interconnected, multitiered supply chains of mission projects managed by the NASA Goddard Space Flight Center. Discussing conditions and risks which can disrupt or threaten the availability, production and delivery of products and services for complex space systems. Utilizing a holistic analytical framework, business intelligence techniques, an integrated information system platform and analytics to produce insight into suppliers / supply chains and associated risks. Advancing supply chain risk management capabilities to support the successful performance of space missions.
supply chain risk management↗
Advanced Noise Abatement Procedures for a Supersonic Business Jet
Supersonic civil aircraft present a unique noise certification challenge. High specific thrust required for supersonic cruise results in high engine exhaust velocity and high levels of jet noise during takeoff. Aerodynamics of thin, low-aspect-ratio wings equipped with relatively simple flap systems deepen the challenge. Advanced noise abatement procedures have been proposed for supersonic aircraft. These procedures promise to reduce airport noise, but they may require departures from normal reference procedures defined in noise regulations. The subject of this report is a takeoff performance and noise assessment of a notional supersonic business jet. Analytical models of an airframe and a supersonic engine derived from a contemporary subsonic turbofan core are developed. These models are used to predict takeoff trajectories and noise. Results indicate advanced noise abatement takeoff procedures are helpful in reducing noise along lateral sidelines.
Berton, Jeffrey J.↗
Noise transmission and attenuation for business aircraft
This paper describes analytical studies applicable for estimating the effects of noise transmission into light aircraft and commuter type aircraft. The propeller noise and turbulent boundary layer noise are considered. The analytical model described uses modal methods and incorporates flat stiffened panels for flat sided sidewalls and curved stiffened panels for cylindrical enclosures. The numerical results include noise attenuation with add-on treatments and the sensitivity of the transmitted noise to the discrete stiffening of the sidewall panels.
Vaicaitis, R.↗
Space shuttle/payload interface analysis. Volume 4: Business Risk and Value of Operations in Space (BRAVO). Part 1: Summary
Background information is provided which emphasizes the philosophy behind analytical techniques used in the business risk and value of operations in space (BRAVO) study. The focus of the summary is on the general approach, operation of the procedures, and the status of the study. For Vol. 1, see N74-12493; for Vol. 2, see N74-14530.
Source record↗
Deriving Earth Science Data Analytics Requirements
Data Analytics applications have made successful strides in the business world where co-analyzing extremely large sets of independent variables have proven profitable. Today, most data analytics tools and techniques, sometimes applicable to Earth science, have targeted the business industry. In fact, the literature is nearly absent of discussion about Earth science data analytics. Earth science data analytics (ESDA) is the process of examining large amounts of data from a variety of sources to uncover hidden patterns, unknown correlations, and other useful information. ESDA is most often applied to data preparation, data reduction, and data analysis. Co-analysis of increasing number and volume of Earth science data has become more prevalent ushered by the plethora of Earth science data sources generated by US programs, international programs, field experiments, ground stations, and citizen scientists.Through work associated with the Earth Science Information Partners (ESIP) Federation, ESDA types have been defined in terms of data analytics end goals. Goals of which are very different than those in business, requiring different tools and techniques. A sampling of use cases have been collected and analyzed in terms of data analytics end goal types, volume, specialized processing, and other attributes. The goal of collecting these use cases is to be able to better understand and specify requirements for data analytics tools and techniques yet to be implemented. This presentation will describe the attributes and preliminary findings of ESDA use cases, as well as provide early analysis of data analytics toolstechniques requirements that would support specific ESDA type goals. Representative existing data analytics toolstechniques relevant to ESDA will also be addressed.
data analytics↗
Supplier Research & Analysis Approach
"The implementation of NASA GSFC's portfolio of mission projects relies upon inter-connected, multi-tiered supply chains of organizations operating under direct and indirect contracts and other agreements throughout the U.S. and around the world. These supply chains are subject to an inter-related array of technical/production, business, market and security risks that are amplified by the ongoing globalization of industry and technology and which can disrupt or threaten the production and delivery of products and services when needed and in conformance with requirements. In recognition of such risks and associated challenges, GSFC's SMA directorate launched an innovative Supplier Research and Analysis (SRA) capability three years ago to gain greater insight into the operating environment, performance, capabilities and viability of current and prospective suppliers for GSFC projects and proposals. The capability uses business intelligence techniques and primarily open source information resources as part of a cost-effective, non-intrusive methodology to produce several types of research and analysis reports. The reports are based on a holistic analytical framework encompassing key technical/production, business enterprise management, market and security factors, and feature in-depth information, summary information profiles, SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis, and candidate risk concerns in order to pro-actively support SMA and project management needs. The SRA capability, which is designed to complement and support ongoing SMA/project management activities and practices, has produced over 95 reports since its start-up in early 2015. This presentation addresses the approach, methodology and performance of the Supplier Research and Analysis (SRA) capability and its value in assuring the success of NASA mission projects. In doing so, the presentation provides lessons-learned, best practices, case examples and address how it fits into the development of an enterprise-level Supply Chain Risk Management capability."
supplier research and analysis↗
Supply Chain Research and Analysis for Space Systems
The implementation of NASA GSFC's portfolio of mission projects relies upon inter-connected, multi-tiered supply chains of organizations operating under direct and indirect contracts and other agreements throughout the U.S. and around the world. These supply chains are subject to an inter-related array of technical/production, business, market and security risks that are amplified by the ongoing globalization of industry and technology and which can disrupt or threaten the production and delivery of products and services when needed and in conformance with requirements. In recognition of such risks and associated challenges, GSFC's SMA directorate launched an innovative Supply Chain Research and Analysis capability three years ago to gain greater insight into the operating environment, performance, capabilities and viability of current and prospective suppliers for GSFC projects and proposals. The capability uses business intelligence techniques and primarily open source information resources as part of a cost-effective, non-intrusive methodology to produce several types of research and analysis reports. The reports are based on a holistic analytical framework encompassing key technical/production, business enterprise management, market and security factors, and feature in-depth information, summary information profiles, SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis, and candidate risk concerns in order to pro-actively support SMA and project management needs. The SRA capability, which is designed to complement and support ongoing SMA/project management activities and practices, has produced over 105 reports since its start-up in early 2015.This presentation addresses the approach, methodology and performance of the Supply Chain Research and Analysiscapability and its value in assuring the success of NASA mission projects. In doing so, the presentation provides lessons-learned, best practices, case examples and address how it fits into the development of an enterprise-level Supply Chain Risk Management capability.
supply chain risk management↗
Preparing Earth Data Scientists for 'The Sexiest Job of the 21st Century'
What Exactly do Earth Data Scientists do, and What do They Need to Know, to do It? There is not one simple answer, but there are many complex answers. Data Science, and data analytics, are new and nebulas, and takes on different characteristics depending on: The subject matter being analyzed, the maturity of the research, and whether the employed subject specific analytics is descriptive, diagnostic, discoveritive, predictive, or prescriptive, in nature. In addition, in a, thus far, business driven paradigm shift, university curriculums teaching data analytics pertaining to Earth science have, as a whole, lagged behind, andor have varied in approach.This presentation attempts to breakdown and identify the many activities that Earth Data Scientists, as a profession, encounter, as well as provide case studies of specific Earth Data Scientist and data analytics efforts. I will also address the educational preparation, that best equips future Earth Data Scientists, needed to further Earth science heterogeneous data research and applications analysis. The goal of this presentation is to describe the actual need for Earth Data Scientists and the practical skills to perform Earth science data analytics, thus hoping to initiate discussion addressing a baseline set of needed expertise for educating future Earth Data Scientists.
data analytics↗
A general model for memory interference in a multiprocessor system with memory hierarchy
The problem of memory interference in a multiprocessor system with a hierarchy of shared buses and memories is addressed. The behavior of the processors is represented by a sequence of memory requests with each followed by a determined amount of processing time. A statistical queuing network model for determining the extent of memory interference in multiprocessor systems with clusters of memory hierarchies is presented. The performance of the system is measured by the expected number of busy memory clusters. The results of the analytic model are compared with simulation results, and the correlation between them is found to be very high.
Taha, Badie A.↗
A Tale of Two Small Business Grants: The Best of Times, the Worst of Times from the NASA Ames Small Business Innovative Research (SBIR) Program
The purposes of the SBIR Program are to: stimulate technological innovation in the private sector; strengthen the role of Small Business Concerns (SBCs) in meeting Federal research and development needs; increase the commercial application of these research results; and encourage participation of socially and economically disadvantaged persons and women-owned small businesses. The process can be highly rewarding, providing the small business with resources to pursue research and development with a focus on providing NASA with new and advanced capabilities. We present two examples of how the NASA Ames SBIR Program has addressed these purposes, nurturing innovative ideas from small, businesses into commercially viable products that also address analytical needs in space research. These examples, from the Science Instruments for Conducting Solar System Exploration Subtopic, describe the journey from innovative concept to analytical instrument, one successful and one hampered by numerous roadblocks (including some international intrigue}.
Kojiro, Daniel R.↗
High Resolution Nature Runs and the Big Data Challenge
NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center is undertaking a series of very computationally intensive Nature Runs and a downscaled reanalysis. The nature runs use the GEOS-5 as an Atmospheric General Circulation Model (AGCM) while the reanalysis uses the GEOS-5 in Data Assimilation mode. This paper will present computational challenges from three runs, two of which are AGCM and one is downscaled reanalysis using the full DAS. The nature runs will be completed at two surface grid resolutions, 7 and 3 kilometers and 72 vertical levels. The 7 km run spanned 2 years (2005-2006) and produced 4 PB of data while the 3 km run will span one year and generate 4 BP of data. The downscaled reanalysis (MERRA-II Modern-Era Reanalysis for Research and Applications) will cover 15 years and generate 1 PB of data. Our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS), a specialization of the concept of business process-as-a-service that is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. In this presentation, we will describe two projects that demonstrate this shift. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS. MERRA/AS enables MapReduce analytics over MERRA reanalysis data collection by bringing together the high-performance computing, scalable data management, and a domain-specific climate data services API. NASA's High-Performance Science Cloud (HPSC) is an example of the type of compute-storage fabric required to support CAaaS. The HPSC comprises a high speed Infinib and network, high performance file systems and object storage, and a virtual system environments specific for data intensive, science applications. These technologies are providing a new tier in the data and analytic services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. In our experience, CAaaS lowers the barriers and risk to organizational change, fosters innovation and experimentation, and provides the agility required to meet our customers' increasing and changing needs
big data analysis↗
Machine Learning-Based Predictive Analytics for Aircraft Engine Conceptual Design
Big data and artificial intelligence/machine learning are transforming the global business environment. Data is now the most valuable asset for enterprises in every industry. Companies are using data-driven insights for competitive advantage. With that, the adoption of machine learning-based data analytics is rapidly taking hold across various industries, producing autonomous systems that support human decision-making. This work explored the application of machine learning to aircraft engine conceptual design. Supervised machine-learning algorithms for regression and classification were employed to study patterns in an existing, open-source database of production and research turbofan engines, and resulting in predictive analytics for use in predicting performance of new turbofan designs. Specifically, the author developed machine learning-based analytics to predict cruise thrust specific fuel consumption (TSFC) and core sizes of high-efficiency turbofan engines, using engine design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks application program interface (API) written in Python, with Google’s TensorFlow (an open source library for numerical computation) serving as the backend engine. The promising results of the predictive analytics show that machine-learning techniques merit further exploration for application in aircraft engine conceptual design.
deep-learning↗
NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 62: The Influence of Knowledge Diffusion on Aeronautics Innovation: The Research, Development, and Production of Large Commercial Aircraft in France, Germany, and the United Kingdom
This paper focuses on how European public policies-individually and collectively - influence the diffusion of knowledge and technology. It begins with an overview of the roles played historically and currently by European governments in the research, development and production (RD&P) of large commercial aircraft (LCA). The analytical framework brings together literature from global political economy, comparative politics, business management, and science and technology policy studies. It distinguishes between the production of knowledge, on the one hand, and the dissemination of knowledge, on the other. France, Germany, and the United Kingdom serve as the analytical cases. The paper concludes with a call for additional research in this area, some tentative lessons learned, and a discussion of the consequences of national strategies and policies for the diffusion of knowledge and technology in an era of globalization.
Pinelli, Thomas E.↗
NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 62: The Influence of Knowledge Diffusion on Aeronautics Innovation: The Research, Development, and Production of Large Commercial Aircraft in France, Germany, and the United Kingdom
This paper focuses on how European public policies-individually and collectively - influence the diffusion of knowledge and technology. It begins with an overview of the roles played historically and currently by European governments in the Research, Development and Production (RD&P) of Large Commercial Aircraft (LCA). The analytical framework brings together literature from global political economy, comparative politics, business management, and science and technology policy studies. It distinguishes between the production of knowledge, on the one hand, and the dissemination of knowledge, on the other. France, Germany, and the United Kingdom serve as the analytical cases. The paper concludes with a call for additional research in this area, some tentative lessons learned, and a discussion of the consequences of national strategies and policies for the diffusion of knowledge and technology in an era of globalizaton.
Golich, Vicki L.↗
Intelligent Agent Technology
Open Sesame! is the first commercial software product that learns user's behavior, and offers automation and coaching suggestions to the user. The neural learning module looks for repetitive patterns that have not been automated; when it finds one, it creates an observation and, upon approval, automates the task. The manufacturer, Charles River Analytics, credits Langley Research Center and Johnson Space Center Small Business Innovation Research grants and the time the president and vice president spent at the two centers in the 1970s as being essential to the development of their product line.
Source record↗
Dynamic IT Security Database and Analytics for Launch Control Systems Software
During the Summer 2020 session, I worked with intern Destani S. Van Arsdalen of EGS Software. Together, we co-created a tool to aid the dynamic investigation, updated over time,of the security compliance of LCS COTS and open source software. We originally planned touse spreadsheet software for management and analysis, but through this exploratoryproject, chose to use Python and JSON after receiving feedback on our project’s current anddesired capabilities at that time.At first, the project was solely designed to help on-board new COTS software, based on aquestionnaire that could be filled out for each software package. This, combined with usingthe spreadsheet application’s web-query capabilities to fetch information from the NVD,allowed presentation and analytics cells to automatically populate as elements of themanually-filled questionnaire changed. While this system was promising, we decided tochange technologies for a few reasons. In the spreadsheet, single cells could not hold complexdata like arrays and objects. The automatic population of cells and dynamic updates made itdifficult to manage and add new features. And finally, it had limited extensibility sinceadding new software required significant understanding of how both the spreadsheet wasconstructed, and the more obscure, proprietary scripting languages packaged with it.The pivot to a standard computer science database language of JSON, aided by thescripting capabilities of Python, greatly helped to improve the project’s functionality. First,and most importantly, the script’s import and analysis of database data is easilyreproducible. Additional data analysis can be modularly added without requiringmodification of the script and is capable of routine scheduling. The revised process can besplit into three parts. First, the conversion of LCS asset and software documentation into theJSON hierarchical database format. Second, the merging of this database with the NVD,forming a new data structure, using CPEs of the CVE object as a linking element betweenthem. And third, the automatically performed analytics and analysis of the combined data,in a modular and extensible format, to produce better informed business decisions. The outputted graphs, for example, are automatically generated by the Python script inconnection with the combined database. This allows updated graphs and any analytics to be re-rendered automatically following updates to the LCS’s initial asset documentation. Afinal report can then be programmatically and easily constructed from these sources to allow fully reproducible metrics for heavily evidenced risk management decisions.
it↗