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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

From Simple Labels to Time-Use Integrations: Supporting the Spectrum of Qualitative Travel Behavior Data

In transportation research, applications of travel behavior data collection are context-specific and require different types of qualitative inputs. These inputs can be viewed as spanning a spectrum of user burden and data quality, from simple trip labels to complex time-use surveys. However, each currently active smartphone-based travel diary platform appears to only support one type of qualitative input, and the effort required for customization is unclear. In this paper, we characterize the spectrum by defining four canonical use cases: (i) trip labels, (ii) trip questionnaire, (iii) counterfactual trips, and (iv) time-use surveys. We then outline a mechanism for supporting configurable user inputs on the same underlying smartphone-based sensing mechanism and demonstrate that it can support all the use cases without any code changes. We further demonstrate that the flexible data model that underpins this mechanism can enable real-time monitoring and analysis. Finally, we evaluate per-user data collection and engagement metrics for large-scale deployments of three canonical use cases, spanning 10 programs, 435 users, and 251,041 trips, and a maximum duration of 800 days. Future efforts may support additional use cases through an expanded configuration and provide greater insight into user engagement. We hope that these insights enable the research community to look at qualitative inputs through a new lens and experiment with novel use cases to fill in the spectrum.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
DOE OSTI2018

End-System Aware Peak Link Utilization Transport for Big Data Transfer over Dedicated Connections

High-performance networks provisioning 10/40-Gbps dedicated channels have been rapidly developed andincreasingly deployed to support big data transfers on the order of terabytes or petabytes in large-scale network-intensiveapplications. However, end users have not seen corresponding increase in application throughput mainly because traditional end-to-end transport methods are not optimized for such connections. New congestion or flow control mechanisms are desirable to meet the challenges brought to transport protocol design by dedicated connections. The advent and proliferation of multi-core processors make it now possible to improve application throughput by providing multiple processing and network resources to a single data transfer. Based on the existing PLUT method, we proposed a new dynamic buffer management scheme and an automatic parallelism tuning mechanism to achieve the maximal bottleneck rate. The performance superiority of PLUT is justified by the experimental results collected over a local 10Gbps dedicated connection.

Rao, Nageswara↗
NASA NTRS1994

Lagrangian Transport Calculations Using UARS Data. Part I: Passive Tracers

The transport of passive tracers observed by UARS has been simulated using computed trajectories of thousands of air parcels initialized on a three-dimensional stratospheric grid. These trajectories are calculated in isentropic coordinates using horizontal winds provided by the United Kingdom Meteorological Office data assimilation system and vertical (cross-isentropic) velocities computed using a fast radiation code.

UARS computed trajectories↗
NASA NTRS2017

Prediction and Warning of Transported Turbulence in Long-Haul Aircraft Operations

An aviation flight planning system is used for predicting and warning for intersection of flight paths with transported meteorological disturbances, such as transported turbulence and related phenomena. Sensed data and transmitted data provide real time and forecast data related to meteorological conditions. Data modelling transported meteorological disturbances are applied to the received transmitted data and the sensed data to use the data modelling transported meteorological disturbances to correlate the sensed data and received transmitted data. The correlation is used to identify transported meteorological disturbances source characteristics, and identify predicted transported meteorological disturbances trajectories from source to intersection with flight path in space and time. The correlated data are provided to a visualization system that projects coordinates of a point of interest (POI) in a selected point of view (POV) to displays the flight track and the predicted transported meteorological disturbances warnings for the flight crew.

Shipley, Scott T.↗
NASA NTRS1961

Airspeed Operating Practices of Turbine-Powered Commercial Transport Airplanes

Data obtained with NASA VGH and V-G recorders installed on three types of turboprop and one type of turbojet commercial transport air- plane have been analyzed to determine the relation of the maximum operational speeds to the placard normal-operating and never-exceed speeds. The frequency of exceeding the placard speeds is compared with corresponding results for past operations with piston-powered transports. In addition, data pertaining to the operational altitudes and the average airspeeds in rough and smooth air for the turbine-powered transports are presented.

Coleman, Thomas L.↗
DOE OSTI2016

INFERRING THE MAGNETOHYDRODYNAMIC STRUCTURE OF SOLAR FLARE SUPRA-ARCADE PLASMAS FROM A DATA-ASSIMILATED FIELD TRANSPORT MODEL

Supra-arcade fans are highly dynamic structures that form in the region above post-reconnection flare arcades. In these features the plasma density and temperature evolve on the scale of a few seconds, despite the much slower dynamics of the underlying arcade. Further, the motion of supra-arcade plasma plumes appears to be inconsistent with the low-beta conditions that are often assumed to exist in the solar corona. In order to understand the nature of these highly debated structures, it is, therefore, important to investigate the interplay of the magnetic field with the plasma. Here we present a technique for inferring the underlying magnetohydrodynamic processes that might lead to the types of motions seen in supra-arcade structures. Taking as a case study the 2011 October 22 event, we begin with extreme-ultraviolet observations and develop a time-dependent velocity field that is consistent with both continuity and local correlation tracking. We then assimilate this velocity field into a simplified magnetohydrodynamic simulation, which deals simultaneously with regions of high and low signal-to-noise ratio, thereby allowing the magnetic field to evolve self-consistently with the fluid. Ultimately, we extract the missing contributions from the momentum equation in order to estimate the relative strength of the various forcing terms. In this way we are able to make estimates of the plasma beta, as well as predict the spectral character and total power of Alfvén waves radiated from the supra-arcade region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Real-World Driving Features for Identifying Intelligent Driver Model Parameters

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. Although the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “intelligent driver model” (IDM) as our driver behavior model and use real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations, and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.

27 ARPA - Advanced Research Projects Agency-Energy↗
NASA NTRS1986

Impact data from a transport aircraft during a controlled impact demonstration

On December 1, 1984, the FAA and NASA conducted a remotely piloted air-to-ground crash test of a Boeing 720 transport aircraft instrumented to measure crash loads of the structure and the anthropomorphic dummy passengers. Over 330 time histories of accelerations and loads collected during the Full-Scale Transport Controlled Impact Demonstration (CID) for the 1-sec period after initial impact are presented. Although a symmetric 1 deg. nose-up attitude with a 17 ft/sec sink rate was planned, the plane was yawed and rolled 13 deg. at initial (left-wing) impact. The first fuselage impact occurred near the nose wheel well with the nose pitched down 2.5 deg. Peak normal (vertical) floor accelerations were highest in the cockpit and forward cabin near the nose wheel well and were approximately 14G. The remaining cabin floor received normal acceleration peaks of 7G or less. The peak longitudinal floor accelerations showed a similar distribution, with the highest (7G) in the cockpit and forward cabin, decreasing to 4G or less toward the rear. Peak transverse floor accelerations ranged from about 5G in the cockpit to 1G in the aft fuselage.

Fasanella, E. L.↗
NASA NTRS2003

Investigation of Tropical Dynamics and Transport with UARS Data

Our research focused on Kelvin waves in the tropical lower stratosphere, and resulted in three papers published or submitted to the Journal of Geophysical Research. The first of these, published in 2002, used temperature data from the Microwave Limb Sounder to examine the amplitude, frequency, phase, and spatial structure of leading modes of Kelvin waves. The second and third, submitted late in 2002 and currently in revision, described the response to Kelvin waves of various trace constituents measured by MLS and CLAES (methane, nitrous oxide, CFC-12, and ozone in the second paper; water vapor in the third paper). Water vapor is a special case because the vertical structure induced by Kelvin waves is convolved with water vapor's seasonally varying vertical profile induced by seasonal variations in temperature at the tropical tropopause. Forward modeling indicated that the vertical resolution of MLS was indeed adequate to capture this complicated structure, yet it was not visible in the MLS data, though the Kelvin wave signature was clear on certain UARS levels. The effects of Kelvin waves on the tropical tropopause and on stratosphere- troposphere exchange cannot be quantified from UARS data because of poor vertical resolution and sensitivity in that region. It is recommended that this analysis be repeated using data from the new MLS and HIRDLS instruments aboard Aura, and that priority be given to fine-scale retrievals of temperature, water vapor, and ozone in the tropical tropopause region.

Jackman, Charles↗
DOE OSTI2016

Friendly Extensible Transfer Tool Beta Version

Often data transfer software is designed to meet specific requirements or apply to specific environments. Frequently, this requires source code integration for added functionality. An extensible data transfer framework is needed to more easily incorporate new capabilities, in modular fashion. Using FrETT framework, functionality may be incorporated (in many cases without need of source code) to handle new platform capabilities: I/O methods (e.g., platform specific data access), network transport methods, data processing (e.g., data compression.).

Collins, William P.↗
DOE OSTI2021

Real-World Driving Features for Identifying Intelligent Driver Model Parameters: Preprint

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. While the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “Intelligent Driver Model” (IDM) as the driver behavior model to explore and real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.

27 ARPA - Advanced Research Projects Agency-Energy↗