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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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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Community-Informed Urban Flood Modeling for Impact Mitigation

The intensification of the hydrologic cycle due to climate change poses a threat to aging and under-designed water infrastructure systems which cannot adequately manage intense storm events. Developing a comprehensive plan for managing rain-driven flooding events is challenging due to uncertainties in the magnitude and frequency of future storm events and conflicting stakeholder objectives. In the City of Baltimore, Maryland, stormwater infrastructure is struggling to keep up with rainfall-driven (pluvial) flooding events, which regularly damage housing and disrupt transportation for residents. In this study, a hybrid of community engagement, numerical modeling, and artificial intelligence techniques are employed to explore prospective urban flooding adaptations. Community engagement drives the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed. The model integrates complex surface and subsurface stormwater infrastructure data from the City, high-resolution spatial data, insights from local public works experts, and the lived experiences of City residents. This co-developed model simulates adaptations of interest to stakeholders in the city, including green and grey infrastructure and operational management strategies. Stormwater management scenarios focused on inlet cleaning and spatially concentrated green infrastructure are found to be the most effective in reducing flood depths in community priority locations. Together, these adaptations can reduce the duration of intersection inundation by more than twenty minutes, allowing for quicker emergency response and restoration of typical transportation systems. Future work will combine this community engaged flooding model with the Deep Uncertainties Pathways framework to explore tradeoffs between adaptations and develop dynamic adaptations which align with community objectives, enhance climate resilience in Baltimore, and can be adjusted in response to changing future conditions.

Ava, Spangler [Pennsylvania State University]↗
NASA NTRS1988

Space shuttle phase B wind tunnel model and test information. Volume 2: Orbiter configuration

Archived wind tunnel test data are available for flyback booster or other alternative recoverable configurations as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle. Considerable wind tunnel data was acquired by the competing contractors and the NASA centers for an extensive variety of configurations with an array of wing and body planforms. All contractor and NASA wind tunnel test data acquired in the Phase B development have been compiled into a data base and are available for applying to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Data Base is structured by vehicle component and configuration type. Basic components include the booster, the orbiter, and the launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retro-glide and twin body. Orbiter configuration types include straight and delta wings, lifting body, drop tanks, and double delta wings. Launch configuration types include booster and orbiter components in various stacked and tandem combinations.

Glynn, J. L.↗
NASA NTRS1988

Space shuttle phase B wind tunnel model and test information. Volume 2: Orbiter configuration

Archived wind tunnel test data are available for flyback booster or other alternate recoverable configurations as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle. Considerable wind tunnel data was acquired by the competing contractors and the NASA centers for an extensive variety of configurations with an array of wing and body planforms. All contractor and NASA wind tunnel test data acquiredin the Phase B development have been compiled into a database and are available for applying to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Database is structured by vehicle component and configuration type. Basic components include the booster, the orbiter, and the launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retroglide, and twin body. Orbiter configuration types include straight and delta wings, lifting body, drop tanks, and double delta wings. Launch configration types include booster and orbiter components in various stacked and tandom combinations. The digital database consists of 220 files of data containing basic tunnel recorded data.

Glynn, J. L.↗
NASA NTRS1988

Space shuttle phase B wind tunnel model and test information. Volume 3: Launch configuration

Archived wind tunnel data are available for flyback booster or other alternative recoverable configurations as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle. Considerable wind tunnel data was acquired by the competing contractors and the NASA Centers for an extensive variety of configurations with an array of wing and body planforms. All contractor and NASA wind tunnel data acquired in the Phase B development have been compiled into a data base and are available for application to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Database is structured by vehicle component and configuration type. Basic components include booster, orbiter and launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retroglide and twin body. Orbital configuration types include straight and delta wings, lifting body, drop tanks and double delta wings. This is Volume 3 (Part 2) of the report -- Launch Configuration -- which includes booster and orbiter components in various stacked and tandem combinations.

Glynn, J. L.↗
NASA NTRS1988

Space shuttle phase B wind tunnel model and test information. Volume 1: Booster configuration

Archived wind tunnel test data are available for flyback booster or other alternative recoverable configurations as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle. Considerable wind tunnel data was acquired by the competing contractors and the NASA Centers for an extensive variety of configurations with an array of wing and body planforms. All contractor and NASA wind tunnel test data acquired in the Phase B development have been compiled into a database and are available for application to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Database is structured by vehicle component and configuration type. Basic components include the booster, the orbiter and the launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retroglide and twin body. Orbiter configuration types include straight and delta wings, lifting body, drop tanks, and double delta wings. Launch configurations include booster and orbiter components in various stacked and tandem combinations. This is Volume 1 (Part 1) of the report -- Booster Configuration.

Glynn, J. L.↗
NASA NTRS1988

Space shuttle phase B wind tunnel model and test information. Volume 1: Booster configuration

Archived wind tunnel test data are available for flyback booster or other alternative recoverable configurations as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle. Considerable wind tunnel data was acquired by the competing contractors and the NASA Centers for an extensive variety of configurations with an array of wing and body planforms. All contractor and NASA wind tunnel test data acquired in the Phase B development have been compiled into a database and are available for application to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Database is structured by vehicle component and configuration type. Basic components include the booster, the orbiter, and the launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retroglide and twin body. Orbiter configuration types include straight and delta wings, lifting body, drop tanks and double delta wings. Launch configurations include booster and orbiter components in various stacked and tandem combinations. This is Volume 1 (Part 2) of the report -- Booster Configuration.

Glynn, J. L.↗
NASA NTRS1988

Space shuttle phase B wind tunnel model and test information. Volume 3: Launch configuration

Archived wind tunnel test data are available for flyback booster or other alternate recoverable configuration as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle, including contractor data for an extensive variety of configurations with an array of wing and body planforms. The test data have been compiled into a database and are available for application to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Database is structured by vehicle component and configuration. Basic components include booster, orbiter, and launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retroglide and twin body. Orbiter configurations include straight and delta wings, lifting body, drop tanks and double delta wings. Launch configurations include booster and orbiter components in various stacked and tandem combinations. The digital database consists of 220 files containing basic tunnel data. Database structure is documented in a series of reports which include configuration sketches for the various planforms tested. This is Volume 3 -- launch configurations.

Glynn, J. L.↗
NASA NTRS2003

Mission interactive scenarion studio for autonomous spacecraft

We propose building a Studio enabling the use of diverse existing mission activity and scenario patterns, the creation of new ones, and the modeling of their effects using existing modeling tools. The core of the Studio is a component-based Type Library, which captures years of mission adaptation patterns in various forms. The Studio works as a content server to capture the developed adaptation knowledge for reuse and provides bridging into different mission uplink implementations, including the Mission Data System [l] (MDS) state/goal machinery. Various activities can be coordinated, controlled, and reused through the Studio's component interface to establish and model a mission scenario. A special component Factory mechanism will be in place to facilitate the adaptation of projects into the Studio. The architecture of the Studio reflects and enforces a division of knowledge and actions into three parts: Model, Controller, Viewer. The Model contains information about (a proposed version of) the spacecraft and mission. The Controller contains logic for constructing scenarios of mission activities. The information in the Model and Controller is principally in the form of reusable patterns. A Viewer can be a simple or complex software system. For example, Apgen [2,3] is one possible viewer, MDS is another. A 3-tiered infrastructure is used for the Studio, reflecting the Model, Controller, Viewer architecture [4,5]. The Studio is useful in pre-phase A of a project by enabling spacecraft design options to be played against desired mission scenarios. In later design phases of a project, the construction and modeling of more detailed scenarios is supported by the Studio.

autonomous spacecraft↗
NASA NTRS2008

Using an Informative Missing Data Model to Predict the Ability to Assess Recovery of Balance Control after Spaceflight

Astronauts show degraded balance control immediately after spaceflight. To assess this change, astronauts' ability to maintain a fixed stance under several challenging stimuli on a movable platform is quantified by "equilibrium" scores (EQs) on a scale of 0 to 100, where 100 represents perfect control (sway angle of 0) and 0 represents data loss where no sway angle is observed because the subject has to be restrained from falling. By comparing post- to pre-flight EQs for actual astronauts vs. controls, we built a classifier for deciding when an astronaut has recovered. Future diagnostic performance depends both on the sampling distribution of the classifier as well as the distribution of its input data. Taking this into consideration, we constructed a predictive ROC by simulation after modeling P(EQ = 0) in terms of a latent EQ-like beta-distributed random variable with random effects.

Feiveson, Alan H.↗
NASA NTRS2010

Extracting Space Weather Information from Research Models: Opportunities and Challenges

In addition to supporting space research in the international community, the Community Coordinated Modeling Center (CCMC) has as its second objective to apply the power of modern research models toward space weather specification and forecasting. Motivated by the objectives to test models and to ease the transition of research models to space weather forecasting organizations, the CCMC has developed a number of real-time modeling systems, as well as a large number of modeling and data products for space weather forecasting support. Over time, these activities have produced tailored products for partners, as well as tools, which address the space weather needs of NASA's robotic mission community. All tools are accessible via a configurable, flexible interface. During this process, CCMC has accumulated substantial experience in understanding model performance, as well as in the design and execution of realtime systems. This presentation will focus on lessons learned and it will suggest low hanging fruit for transition to operations at partner agencies.

Hesse, Michael↗
NASA NTRS1994

Representing spatial information in a computational model for network management

While currently available relational database management systems (RDBMS) allow inclusion of spatial information in a data model, they lack tools for presenting this information in an easily comprehensible form. Computer-aided design (CAD) software packages provide adequate functions to produce drawings, but still require manual placement of symbols and features. This project has demonstrated a bridge between the data model of an RDBMS and the graphic display of a CAD system. It is shown that the CAD system can be used to control the selection of data with spatial components from the database and then quickly plot that data on a map display. It is shown that the CAD system can be used to extract data from a drawing and then control the insertion of that data into the database. These demonstrations were successful in a test environment that incorporated many features of known working environments, suggesting that the techniques developed could be adapted for practical use.

Blaisdell, James H.↗
NASA NTRS2014

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Error Covariance↗
NASA NTRS2014

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Data Assimilation↗