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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Improving Project Management with Simulation and Completion Distribution Functions

Despite the critical importance of project completion timeliness, management practices in place today remain inadequate for addressing the persistent problem of project completion tardiness. A major culprit in late projects is uncertainty, which most, if not all, projects are inherently subject to. This uncertainty resides in the estimates for activity durations, the occurrence of unplanned and unforeseen events, and the availability of critical resources. In response to this problem, this research developed a comprehensive simulation based methodology for conducting quantitative project completion time risk analysis. It is called the Project Assessment by Simulation Technique (PAST). This new tool enables project stakeholders to visualize uncertainty or risk, i.e. the likelihood of their project completing late and the magnitude of the lateness, by providing them with a completion time distribution function of their projects. Discrete event simulation is used within PAST to determine the completion distribution function for the project of interest. The simulation is populated with both deterministic and stochastic elements. The deterministic inputs include planned project activities, precedence requirements, and resource requirements. The stochastic inputs include activity duration growth distributions, probabilities for events that can impact the project, and other dynamic constraints that may be placed upon project activities and milestones. These stochastic inputs are based upon past data from similar projects. The time for an entity to complete the simulation network, subject to both the deterministic and stochastic factors, represents the time to complete the project. Repeating the simulation hundreds or thousands of times allows one to create the project completion distribution function. The Project Assessment by Simulation Technique was demonstrated to be effective for the on-going NASA project to assemble the International Space Station. Approximately $500 million per month is being spent on this project, which is scheduled to complete by 2010. NASA project stakeholders participated in determining and managing completion distribution functions produced from PAST. The first result was that project stakeholders improved project completion risk awareness. Secondly, using PAST, mitigation options were analyzed to improve project completion performance and reduce total project cost.

Cates, Grant R.↗
NASA NTRS1990

Space Projects: Improvements Needed in Selecting Future Projects for Private Financing

The Office of Management and Budget (OMB) and NASA jointly selected seven projects for commercialization to reduce NASA's fiscal year 1990 budget request and to help achieve the goal of increasing private sector involvement in space. However, the efforts to privately finance these seven projects did not increase the commercial sector's involvement in space to the extent desired. The General Accounting Office (GAO) determined that the projects selected were not a fair test of the potential of increasing commercial investment in space at an acceptable cost to the government, primarily because the projects were not properly screened. That is, neither their suitability for commercialization nor the economic consequences of seeking private financing for them were adequately evaluated before selection. Evaluations and market tests done after selection showed that most of the projects were not viable candidates for private financing. GAO concluded that projects should not be removed from NASA's budget for commercial development until after careful screening has been done to determine whether adequate commercial demand exists, development risks are commercially acceptable and private financing is found or judged to be highly likely, and the cost effectiveness of such a decision is acceptable. Premature removal of projects from NASA's budget ultimately can cause project delays and increased costs when unsuccessful commercialization candidates must be returned to the budget. NASA also needs to ensure appropriate comparisons of government and private financing options for future commercialization projects.

Source record↗
NASA NTRS1989

Elementary and middle school science improvement project

The Alabama A and M University Elementary and Middle School Science Improvement Project (Project SIP) was instituted to improve the science knowledge of elementary and middle school teachers using the experimental or hands-on approach. Summer workshops were conducted during the summers of 1986, 1987, and 1988 in the areas of biology, chemistry, physics, and electricity, and magnetism. Additionally, a manual containing 43 lessons which included background information, experiments and activities for classroom and home use was provided to each teacher. During the course of the project activities, the teachers interacted with various university faculty members, scientists, and NASA staff. The administrative aspects of the program, the delivery of the services to participating teachers, and the project outcome are addressed.

Mcguire, Saundra Y.↗
NASA NTRS2012

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and Pilot Studies

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Analyses of the agricultural impacts of climate variability and change require a transdisciplinary effort to consistently link state-of-the-art climate scenarios to crop and economic models. Crop model outputs are aggregated as inputs to regional and global economic models to determine regional vulnerabilities, changes in comparative advantage, price effects, and potential adaptation strategies in the agricultural sector. Climate, Crop Modeling, Economics, and Information Technology Team Protocols are presented to guide coordinated climate, crop modeling, economics, and information technology research activities around the world, along with AgMIP Cross-Cutting Themes that address uncertainty, aggregation and scaling, and the development of Representative Agricultural Pathways (RAPs) to enable testing of climate change adaptations in the context of other regional and global trends. The organization of research activities by geographic region and specific crops is described, along with project milestones. Pilot results demonstrate AgMIP's role in assessing climate impacts with explicit representation of uncertainties in climate scenarios and simulations using crop and economic models. An intercomparison of wheat model simulations near Obregón, Mexico reveals inter-model differences in yield sensitivity to [CO2] with model uncertainty holding approximately steady as concentrations rise, while uncertainty related to choice of crop model increases with rising temperatures. Wheat model simulations with midcentury climate scenarios project a slight decline in absolute yields that is more sensitive to selection of crop model than to global climate model, emissions scenario, or climate scenario downscaling method. A comparison of regional and national-scale economic simulations finds a large sensitivity of projected yield changes to the simulations' resolved scales. Finally, a global economic model intercomparison example demonstrates that improvements in the understanding of agriculture futures arise from integration of the range of uncertainty in crop, climate, and economic modeling results in multi-model assessments.

Rosenzweig, C.↗
NASA NTRS1987

Elementary and middle school science improvement project

The Alabama A & M University Elementary and Middle School Science Improvement Project (Project SIP) was instituted in response to a need to improve the ability of North Alabama teachers to teach science effectively using the experimental or hands-on approach. The major component of the project was a two-week workshop. Follow-up visits were made to the classrooms of many of the participating teachers to obtain information on how the program was being implemented in the classroom. The administrative aspects of the program, the delivery of the services to participating teachers, and the project outcomes are addressed.

Mcguire, Saundra Yancy↗
NASA NTRS2007

Solutions Network Formulation Report. The Potential Contribution of the Landsat Data Continuity Mission to the Mississippi Coastal Improvements Project

The 2005 hurricane season - especially Hurricane Katrina - left the Mississippi Gulf Coast devastated. In its efforts to assist in coastal re-building efforts, the U.S. Congress passed the Defense Appropriation Act of 2006. This legislation directed the Secretary of the Army, via the USACE, to generate reports discussing possible projects that would improve the State of Mississippi?s coastal region. These projects are referred to as the MsCIP. Data collected via the LDCM OLI could be used to evaluate the success of projects that result from the MsCIP by monitoring regional coastal changes associated with such projects. This Candidate Solution is in alignment with the Coastal Management, Water Management, and Disaster Management National Applications and will benefit society by improving the post-Hurricane Katrina Mississippi Gulf Coast.

Hilbert, Kent↗
NASA NTRS2015

The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science

The Agricultural Model Intercomparison and Improvement Project (AgMIP) was founded in 2010. Its mission is to improve substantially the characterization of world food security as affected by climate variability and change, and to enhance adaptation capacity in both developing and developed countries. The objectives of AgMIP are to: Incorporate state-of-the-art climate, crop/livestock, and agricultural economic model improvements into coordinated multi-model regional and global assessments of future climate impacts and adaptation and other key aspects of the food system. Utilize multiple models, scenarios, locations, crops/livestock, and participants to explore uncertainty and the impact of data and methodological choices. Collaborate with regional experts in agronomy, animal sciences, economics, and climate to build a strong basis for model applications, addressing key climate related questions and sustainable intensification farming systems. Improve scientific and adaptive capacity in modeling for major agricultural regions in the developing and developed world, with a focus on vulnerable regions. Improve agricultural data and enhance data-sharing based on their intercomparison and evaluation using best scientific practices. Develop modeling frameworks to identify and evaluate promising adaptation technologies and policies and to prioritize strategies.

farm crops↗
NASA NTRS2015

Introduction The Role of the Agricultural Model Intercomparison and Improvement Project

Climate impacts on agriculture are of increasing concern in both the scientific and policy communities because of the need to ensure food security for a growing population. A special challenge is posed by the changes in the frequency and intensity of heat-waves, droughts, and episodic rainstorms already underway in many parts of the world. Changes in production are directly linked to such variations in temperature and precipitation during the growing season, and often to offseason changes in weather affecting soil-water storage and availability to crops. This is not an isolated problem but one of both global and regional importance, because of impacts on the livelihoods of smallholder farmers as well as consequences for the world food trade system. This two-part set the Agricultural Model Intercomparison and Improvement Project (AgMIP): Integrated Crop and Economic Assessments is the first to be entirely devoted to AgMIP (www.agmip.org). AgMIP is a major international research program focused on climate change and agriculture. The goal of the two parts is to advance the field by providing detailed information on new simulation techniques and assessments being conducted by this program. It presents information about new methods of global and regional integrated assessment, results from agricultural regions, and adaptation strategies for maintaining food security under changing climate conditions.

security↗
NASA NTRS2018

The SMAP Level-4 ECO Project: Improving Terrestrial Flux Estimates Through Coupled Hydrology-Vegetation Data Assimilation

Simulations of hydrologic and vegetation states as well as water, energy and carbon fluxes from the land surface to the atmosphere are crucial for a wide range of applications, including agricultural advisories, forecasts of (short-term) atmospheric behavior and seasonal weather predictions including forecasts of extreme events, such as heatwaves or droughts. The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Eco-Hydrology (L4-ECO) project aims to improve modeled estimates of the terrestrial water, energy and carbon fluxes and states by developing a fully-coupled hydrology-vegetation data assimilation system. This system is developed around the NASA Goddard Earth Observing System (GEOS) Catchment-CN land surface model, which combines land hydrology and energy balance components of the GEOS Catchment model with dynamic vegetation components of the Community Land Model version 4. Catchment-CN fully couples the terrestrial water, energy and carbon cycles, allowing feedbacks from the land hydrology to the biosphere and vice versa.Here, we implement a calibration of the Catchment-CN vegetation parameterization against observations of the fraction of absorbed photosynthetically active radiation (FPAR) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve the model's standalone skill. Later, the DA algorithm used to produce the SMAP L4 soil moisture product will be adapted to Catchment-CN to assimilate SMAP brightness temperatures and inform the model's land hydrology component. Finally, the DA system will be further extended to assimilate MODIS FPAR observations in order to constrain the model's dynamic vegetation component.In this presentation, we demonstrate that the Catchment-CN parameter calibration leads to more realistic vegetation simulations and reduces the root mean squared error between modeled and observed vegetation states across the model's various plant functional types. We also show that the assimilation of SMAP observations is able to improve the average correlation, bias and unbiased RMSE between the modeled surface and root zone soil moisture estimates, and ground observations from the SMAP core validation sites.

Kolassa, Jana↗
NASA NTRS2011

Improving Project Management Using Formal Models and Architectures

This talk discusses the advantages formal modeling and architecture brings to project management. These emerging technologies have both great potential and challenges for improving information available for decision-making. The presentation covers standards, tools and cultural issues needing consideration, and includes lessons learned from projects the presenters have worked on.

Kahn, Theodore↗
NASA NTRS2015

The Agricultural Model Intercomparison and Improvement Project (AgMIP) Town Hall

AgMIP (www.agmip.org) is an international community of climate, crop, livestock, economics, and IT experts working to further the development and application of multi-model, multi-scale, multi-disciplinary agricultural models that can inform policy and decision makers around the world. This meeting will engage the AGU community by providing a brief overview of AgMIP, in particular its new plans for a Coordinated Global and Regional Assessment of climate change impacts on agriculture and food security for AR6. This Town Hall will help identify opportunities for participants to become involved in AgMIP and its 30+ activities.

farm crops↗
NASA NTRS1984

Advanced solar electric propulsion (ASEP) for planetary missions

This paper defines an advanced SEP concept based upon demonstrated ion thruster technology improvements and projected improvements in solar array technology. The ASEP concept utilizes an 'integrated' rather than a 'stage' approach and assumes 30 cm Hg ion thrusters capable of operation near 7 kWe each. The mission performance using the ASEP system is presented for three missions: (1) Saturn orbiter, (2) comet rendezvous and (3) comet sample return. The ASEP system provides significant mission performance improvements. The simpler, fewer thruster ASEP system design may provide cost as well as performance benefits for the missions examined when compared to ballistic or SEP stage mission options.

Jones, R. M.↗
NASA NTRS2015

Improving Climate Projections Using "Intelligent" Ensembles

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Baker, Noel C.↗
NASA NTRS2015

Improving Climate Projections Using "Intelligent" Ensembles

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Baker, Noel C.↗
NASA NTRS2022

9- by 15-Foot Low-Speed Wind Tunnel Acoustic Upgrade Part 2: Improvement Comparison

This report describes an acoustic improvement project carried out in the NASA Glenn Research Center (GRC) 9- by 15-Foot Low Speed Wind Tunnel (9x15 LSWT) and the effect on the acoustic measurement capabilities of the facility. The improvement project began in June 2017 and ended in March 2019, with some work stoppages in between. The main purpose of the project was to reduce the background noise of the facility to enable better signal-to-noise ratio measurements of quiet aircraft propulsion concepts. The term background noise refers to the noise measured inside the facility without a model installed. This report discusses the details of the project, the motivation for the upgrade and the history of the facility background noise levels that motivated the upgrade. This report is one of a two-volume set documenting the 9x15 LSWT acoustic improvement. The companion report[1] documents the “supporting studies” that were conducted as part of the improvement project, but do not relate explicitly to the 9x15 LSWT. The present report is focused specifically on studies conducted by NASA regarding the 9x15 LSWT and thus may be less broadly applicable to other facilities or projects.

Wind Tunnel↗