SEARCH · Search NASA

Results for “management efforts”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22

All sources NASA NTRS DOE OSTI

NASA NTRS1979

The space science data service: A study of its efficiencies and costs

Factors affecting the overall advantages and disadvantages of a centralized facility for both the data base and processing capability for NASA's Office of Space Science programs are examined in an effort to determine the best approach to data management in the light of the increasing number of data bits collected annually. Selected issues considered relate to software and storage savings, security precautions, and the phase-in plan. Information on the current mode of processing and on the potential impact of changes resulting from a conversion to a space science data base service was obtained from five user groups and is presented as an aid in determining the dollar benefits and advantages of a centralized system.

Vette, J. I.↗

NASA NTRS1981

Yield Model Development (YMD) implementation plan for fiscal years 1981 and 1982

A plan is described for supporting USDA crop production forecasting and estimation by (1) testing, evaluating, and selecting crop yield models for application testing; (2) identifying areas of feasible research for improvement of models; and (3) conducting research to modify existing models and to develop new crop yield assessment methods. Tasks to be performed for each of these efforts are described as well as for project management and support. The responsibilities of USDA, USDC, USDI, and NASA are delineated as well as problem areas to be addressed.

Ambroziak, R. A.↗

NASA NTRS1981

Research analysis of Space Shuttle Orbiter entry aerothermodynamic flight data at the NASA Langley Research Center

The methods used to analyze the aerothermodynamic data gathered from the Shuttle protoflights are reviewed. Trajectory and atmospheric reconstruction allow an accurate definition of the freestream environment through which the Shuttle has flown. The reconstructions, combined with aerodynamic coefficient and convective heating rate determinations, provide predictive capability for comparison with future flights. Data are acquired by operational instrumentation (12,000 parameters), development flight instrumentation (4500 measurements), and Orbiter experiments instrumentation; tracking and meteorological data are also included in the analyses. The aerothermodynamic and aerodynamic technology base for large, winged, lifting body entry vehicles is being expanded by the analysis efforts. Sensor locations on the Shuttle and data management techniques are described.

Throckmorton, D. A.↗

NASA NTRS1984

Results of an irrigated lands assessment for water management in California

Periodic assessment of existing and future demands for water within California is one responsibility of the California Department of Water Resources (CDWR). The California Irrigated Lands Assessment for Water Management Project represented a 5-year joint research effort between the NASA and the CDWR with technical support from the University of California (UC) at Berkeley and at Santa Barbara. The objectives were: (1) to develop and demonstrate procedures for providing highly precise, timely, estimates of irrigated area on a statewide basis using Landsat sensor data, and (2) to develop, through research with small demonstration sites, a procedure for the inventory and mapping of crop groups on a regional basis. Both manual and computer-assisted analyses were investigated. This paper highlights the statewide irrigated lands inventory where a procedure for statewide estimation of irrigated land using full frame Landsat MSS imagery and sampled ground data was successfully demonstrated. The statewide estimate of 3 990 112 hectares was within + or - 1.32 percent relative standard error at the 95-percent Confidence Interval, well within the design goal. This procedure represents a new capability for obtaining near-real time data on changes in agricultural water use throughout the state.

Bauer, E. H.↗

NASA NTRS1986

Pilot land data system

The Pilot Land Data System (PLDS) is a multi-institutional effort directed towards solving the data access and management needs of scientists studying the land surface. Some of the hardware and software, which are now available, are reviewed.

Star, J. L.↗

NASA NTRS1988

In-space research, technology and engineering experiments and Space Station

The NASA Space Station will serve as a technology research laboratory, a payload-servicing facility, and a large structure fabrication and assembly facility. Space structures research will encompass advanced structural concepts and their dynamics, advanced control concepts, sensors, and actuators. Experiments dealing with fluid management will gather data on such fundamentals as multiphase flow phenomena. As requirements for power systems and thermal management grow, experiments quantifying the performance of energy systems and thermal management concepts will be undertaken, together with expanded efforts in the fields of information systems, automation, and robotics.

Tyson, Richard↗

NASA NTRS1991

Microgravity strategic planning exercise

The Center for Space and Advanced Technology supported a planning exercise for the Microgravity Program management at the Marshall Space Flight Center. The effort focused on the status of microgravity work at MSFC and elsewhere with the objective of preparing a goal-oriented strategic planning document which could be used for informational/brochure purposes. The effort entailed numerous interactions and presentations with Field Center programmatic components and Headquarters personnel. Appropriate material was consolidated in a draft format for a MSFC Strategic Plan.

Halpern, Richard↗

NASA NTRS1993

Radiation hardened microprocessor for small payloads

The RH-3000 program is developing a rad-hard space qualified 32-bit MIPS R-3000 RISC processor under the Naval Research Lab sponsorship. In addition, under IR&D Harris is developing RHC-3000 for embedded control applications where low cost and radiation tolerance are primary concerns. The development program leverages heavily from commercial development of the MIPS R-3000. The commercial R-3000 has a large installed user base and several foundry partners are currently producing a wide variety of R-3000 derivative products. One of the MIPS derivative products, the LR33000 from LSI Logic, was used as the basis for the design of the RH-3000 chipset. The RH-3000 chipset consists of three core chips and two support chips. The core chips include the CPU, which is the R-3000 integer unit and the FPA/MD chip pair, which performs the R-3010 floating point functions. The two support whips contain all the support functions required for fault tolerance support, real-time support, memory management, timers, and other functions. The Harris development effort had first passed silicon success in June, 1992 with the first rad-hard 32-bit RH-3000 CPU chip. The CPU device is 30 kgates, has a 508 mil by 503 mil die size and is fabricated at Harris Semiconductor on the rad-hard CMOS Silicon on Sapphire (SOS) process. The CPU device successfully passed tesing against 600,000 test vectors derived directly on the LSI/MIPS test suite and has been operational as a single board computer running C code for the past year. In addition, the RH-3000 program has developed the methodology for converting commercially developed designs utilizing logic synthesis techniques based on a combination of VHDK and schematic data bases.

Shah, Ravi↗

NASA NTRS2010

Atrial Arrhythmia Summit: Post Summit Report

The Atrial Arrhythmia Summit brought together nationally and internationally recognized experts in cardiology, electrophysiology, exercise physiology, and space medicine in an effort to elucidate the mechanisms, risk factors, and management of atrial arrhythmias in the unique occupational cohort of the U.S. astronaut corps.

Barr, Yael↗

NASA NTRS2012

Rotary-Wing Relevant Compressor Aero Research and Technology Development Activities at Glenn Research Center

Technical challenges of compressors for future rotorcraft engines are driven by engine-level and component-level requirements. Cycle analyses are used to highlight the engine-level challenges for 3000, 7500, and 12000 SHP-class engines, which include retention of performance and stability margin at low corrected flows, and matching compressor type, axial-flow or centrifugal, to the low corrected flows and high temperatures in the aft stages. At the component level: power-to-weight and efficiency requirements impel designs with lower inherent aerodynamic stability margin; and, optimum engine overall pressure ratios lead to small blade heights and the associated challenges of scale, particularly increased clearance-to-span ratios. The technical challenges associated with the aerodynamics of low corrected flows and stability management impel the compressor aero research and development efforts reviewed herein. These activities include development of simple models for clearance sensitivities to improve cycle calculations, full-annulus, unsteady Navier-Stokes simulations used to elucidate stall, its inception, and the physics of stall control by discrete tip-injection, development of an actuator-duct-based model for rapid simulation of nonaxisymmetric flow fields (e.g., due inlet circumferential distortion), advanced centrifugal compressor stage development and experimentation, and application of stall control in a T700 engine.

Welch, Gerard E.↗

NASA NTRS2016

Cradle Enhanced UI Development

This summer I have been working in the EDI (Exploration, Development, and Integration) office. The primary goal of my office is to facilitate the integration, cooperation, and communication between programs, projects and departments throughout the agency. The majority of my efforts has been focused on Cradle, a requirements management and systems engineering tool. This tool is utilized by teams throughout NASA to plan and track the development of a variety of ongoing projects.

Jentsch, Samuel↗

NASA NTRS2015

Risk Informed Philosophy: Using What We Fear to Lose to Guide Us to Success

When an organization sets out on a new effort, program, or project, one of the first steps undertaken is to understand the related requirements. Most often, these requirements come in the form of standards, directives, or other organizational processes, and are derived to meet a broad range of efforts, programs, and projects. The net result is that organizations or teams are forced to start with a maximum set of requirements and expend time and energy tailoring the set of requirements down to what is more appropriate for the effort, program, or project at hand. This presentation will present an alternate method for the application of requirements or best practices that is based upon the actual risks to the safety, performance, or success of the effort. This additive route for applying best practices and requirements also encourages an early exploration of the risks, thus yielding a better understanding of where resources should be applied. This presentation also extends this concept of risk-based application of best practices and requirements to the selection of organization-wide improvement or standardization efforts, such as Lean, Six Sigma, and Quality Management Systems.

Darpel, Scott↗

NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term. The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗

NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission TRMM Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗

NASA NTRSDate not supplied

Building a Real-Time Flood Prediction Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the local government of Howard County, Maryland, to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a statistical model capable of hindcasting the two severe flash flood events that devastated Ellicott City and transitioned to a ‘Long Short-Term Memory’ based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using Nash-Sutcliffe Efficiency. The final product, the Sequentially Trained Real-time EstimAted Model (STREAM) predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

NASA DEVELOP↗

NASA NTRS2020

Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗

NASA NTRS2020

Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

Erika Munshi↗

NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗