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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1969

Space Flight Handbooks: Volume III. Planetary Flight Handbook: Part 7 - Direct Trajectories to Jupiter, Saturn, Uranus and Neptune. Supplement B. Tabular Trajectory Data for Direct Trajectories to Uranus and Neptune

The trajectory data are presented chronologically and are organized by holding the arrival date constant while varying the Earth departure date in increments of 10 days. Upon completion of the specified range of Earth departure dates, the arrival date is incremented and the range of departure dates is repeated. For long trip times, where the variation of the trajectory parameters is relatively small, the size of the increment of the arrival date is increased. The range of departure and arrival dates and their corresponding increments are given in Table 5-1 for each launch opportunity. The criterion for the selection of these dates is that they encompass the region in which the Earth departure hyperbolic excess speed is less than or equal to 0.65 EMOS. There are two lines of print for each trajectory (departure-date/arrival date pair). In the first line the two left most columns contain the dates of departure and arrival. The next 18 columns of the first line can be divided into three groups: six columns of data related to departure, six columns pertinent to the heliocentric phase of the mission, and six columns related to arrival at the target planet. The second line of print contains, respectively, the Delta V requirements for departure and arrival, the total Delta V requirement, the heliocentric transfer trajectory type, and four parameters defining conditions at arrival. The value computed for the arrival Delta V is for entry into a circular orbit. The radius selected for this orbit, while necessarily somewhat arbitrary, is representative of the broad range of orbit radii which tend to minimize the arrival Delta V for the range of excess speeds between 0.1 and 0.8 EMOS. The value selected for Uranus and Neptune is 3 planet radii. Significant reductions in the computed Delta V can be realized by assuming entry into an elliptical orbit having a periapsis radius equal to the selected circular-orbit radius. The magnitude of the reduction can be determined from Figures 2-6 and 2-8 i n Section 2.

Source record↗
NASA NTRS1971

Space Flight Handbooks, Volume 3 - Planetary Flight Handbook. Part 8 - Jupiter Swingby Missions to Saturn, Uranus, Neptune, and Pluto. Supplement - Tabular Trajectory Data

The trajectory data are presented chronologically and are organized by holding the arrival date constant while varying the Earth departure date in increments of 10 days. Upon completion of the specified range of Earth departure dates, the arrival date is incremented and the range of departure dates is repeated. The range of departure and arrival dates and the corresponding increments are given in Table 5-1 for each launch opportunity. It should be noted that the interval in arrival date is increased in the long flight time region where the variation of the trajectory parameters is relatively small. The criteria used for the selection of these dates are, in general: (i) the minimum Earth departure hyperbolic excess speed (across the Earth departure window) shall not exceed 0.65 EMOS and (2) the periapsis radius at Jupiter shall not be less than 0.95 planet radii.

Source record↗
DOE OSTI2020

Impact of transportation network companies on urban congestion: Evidence from large-scale trajectory data

We collect vehicle trajectory data from major transportation network companies (TNCs) in New York City (NYC) in 2017 and 2019, and we use the trajectory data to understand how the growth of TNCs has impacted traffic congestion and emission in urban areas. By mining the large-scale trajectory data and conduct the case study in NYC, we confirm that the rise of TNC is the major contributing factor that makes urban traffic congestion worse. From 2017 to 2019, the number of for-hire vehicles (FHV) has increased by over 48% and served 90% more daily trips. These resulted in an average citywide speed reduction of 22.5% on weekdays, and the average speed in Manhattan decreased from 11.76 km/h in April 2017 to 9.56 km/h in March 2019. The heavier traffic congestion may have led to 136% more NOx, 152% more CO and 157% more HC emission per kilometer traveled by the FHV sector. Our results show that the traffic condition is consistently worse across different times of the day and at different locations in NYC. And we build the connection between the number of available FHVs and the reduction in travel speed between the two years of data and explain how the rise of TNC may impact traffic congestion in terms of moving speed and congestion time. Our findings provide valuable insights for different stakeholders and decision-makers in framing regulation and operation policies towards more effective and sustainable urban mobility.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

ARM Trajectories Data Set Value-Added Product Report

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s ARM Trajectories Data Set (ARMTRAJ) Value-Added Product (VAP) provides trajectory data sets initialized at ARM deployment coordinates and configured using ARM data sets. The four trajectory data sets support aerosol, cloud, and planetary boundary-layer research. Trajectory calculations use the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation atmospheric reanalysis (ERA5) data set at its highest spatial resolution (~31 km). HYSPLIT also runs at multiple initial starting locations surrounding ARM deployments (in latitude/longitude and/or vertical coordinates), facilitating an ensemble for each sample in the data sets. The ensemble mean and variability reported in ARMTRAJ improve the fidelity and provide uncertainty estimates of trajectory coordinates, thermodynamic properties, and other output fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

ARM Trajectories Data Set Value-Added Product Report

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s ARM Trajectories Data Set (ARMTRAJ) Value-Added Product (VAP) provides trajectory data sets initialized at ARM deployment coordinates and configured using ARM data sets. The six trajectory data sets support aerosol, cloud, planetary boundary layer, and related research (aerosol-cloud interactions, etc.), as well as studies using ARM Aerial Facility (AAF) and tethered balloon system (TBS) measurements. Trajectory calculations use the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation atmospheric reanalysis (ERA5) data set at its highest spatial resolution (~31 km). HYSPLIT also runs at multiple initial starting locations surrounding ARM deployments (in latitude/longitude and/or vertical coordinates), facilitating an ensemble for each sample in the data sets. The ensemble mean and variability reported in ARMTRAJ improve the fidelity and provide uncertainty estimates of trajectory coordinates, thermodynamic properties, and other output fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Scalable and Actionable Performance Measures for Traffic Signal Systems using Probe Vehicle Trajectory Data

Scalable and actionable performance measures for traffic signal systems provide opportunities for practitioners to measure and improve the transportation network. Historically, traffic signal improvements have relied on scheduled signal retiming based on limited data collection, or on the public to call and alert engineers of an issue. This inefficient method of improving signal timing led to the creation of automated traffic signal performance measures (ATSPMs). These metrics rely on expensive infrastructure, including detection and communications, which has produced barriers for numerous agencies to fully adopt. Recently, third-party data providers have begun to release vehicle trajectory data, which allows for enhanced signal metrics with no investment in physical equipment. The purpose of this study is to demonstrate the use of these data and summarize the scalability of the created metrics. This work builds on previous efforts to quantify signal performance on nine intersections in Michigan, U.S. Ten signalized corridors in Columbus, Ohio, were chosen to scale a performance assessment using crowdsourced trajectory data. A total of 136 intersections were assessed in 2-h intervals using data from all weekdays in 2017. High-level corridor summary metrics including average percent of vehicles stopping (18%–32%), average delay (9.4–20.5 s), and level of travel time reliability (1.23–2.73) were calculated for each corridor direction. Intersection-level metrics were also introduced, which can be used by practitioners to identify problems, improve signal timings, and prioritize future infrastructure investments.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data

Autonomous vehicle-based traffic smoothing con- trollers are often not transferred to real-world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline to sidestep such calibration issues by collecting trajectory data and learning controllers directly from trajectory data that are then deployed zero-shot onto the highway. We construct a dataset of 772.3 kilometers of recorded drives on the I-24. We then construct a simple simulator using the recorded drives as the lead vehicle in front of a simulated platoon consisting of one autonomous vehicle and five human followers. Using policy-gradient methods with an asymmetric critic to learn the controller, we show that we are able to improve average MPG by 11% in simulation on congested trajectories. We deploy this controller to a mixed platoon of 4 autonomous Toyota RAV-4’s and 7 human drivers in a validation experiment and demonstrate that the expected time-gap of the controller is maintained in the real world test. Finally, we release the driving dataset [1], the simulator, and the trained controller at https://github.com/nathanlct/trajectory-training-icra.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Hazard-Based Model of Activity Generation Using Vehicle Trajectory Data

Travel behavior modeling suffers from lack of longitudinal data due to the high cost of procurement and difficulty with ensuring data quality. In the recent years ubiquitous data intercepted from social media, cell detail record, GPS traces are increasingly being explored to analyze human mobility. However, these data lack key demographic information which limits their use for developing causal models of traveler behavior. The primary contribution of the paper lies in using vehicle trajectory data from Ann Arbor along with multiple other data sources for developing a hazard-based activity generation model. The different category of data used in the analysis include (i) vehicle trajectory data (VTD), (ii) American Census Survey (ACS), (iii) land use data, (iv) household travel survey (HTS) data, and (v) smart location data from Environmental Protection Agency (EPA). Home and work are identified as the two most frequently visited locations from the VTD and validated against land use categories of the region. Next a random utility-based model is developed using HTS data to identify other activity purposes as a function of the polynomials of distance from home, activity duration and departure time. The developed model is then applied to identify the non-home activity purposes in the VTD. Demographic and accessibility information are appended to the VTD from ACS and smart location databases respectively. The fmal database is then used to develop a hazard-based activity generation model. (C) 2020 The Authors. Published by Elsevier B.V.

activity generation↗
NASA NTRS1963

Space Flight Handbooks. Volume 3- Planetary Flight Handbook: Supplementary Trajectory Data: Venus to Earth and Mars to Earth - Part 2

Parts 2 and 3 present tabulations of trajectory data for scheduling flights to and from Venus and Mars during the period 1960-2000. Part 2 contains information for outbound flights to these planets; Part 3 contains information for trajectories returning from the planets to Earth. Each Part contains data for single-plane transfers, as well as for broken-plane transfers which employ a midcourse plane-change to eliminate the high speed "ridges." The mathematical analyses employed for all calculations are described in Part 1 of this handbook. To facilitate the construction of round-trip trajectories, the date at the target planet is held fixed while the trip duration is varied in 10-day increments from zero days to the length of that planet's synodic period with Earth. Dates of arrival at the target planet are presented in the extreme right-hand column of Part 2, and dates of departure from the target planet are presented in the extreme left-hand column of Part 3. Thus, by holding Part 2 directly to the left of Part 3, the analyst may easily and rapidly scan all trip possibilities which involve any desired stopover time at the target planet. Within approximately 200 days of each conjunction or opposition, data are presented in 10-day increments at the target planet. Only those trips are listed for which the hyperbolic excess speeds at either or both ends of the trajectory do not exceed 0.6 EMOS (Earth Mean Orbital Speed). For the remaining mission regions, the requirements are so smoothly varying that a 50-day interval in dates at the target planet may be employed; the 10-day interval in trip times is, however, preserved here. In these regions, only those trips are listed for which either or both speeds do not exceed 0.3 EMOS.

INTERPLANETARY FLIGHT↗
NASA NTRS1963

Space Flight Handbooks. Volume 3- Planetary Flight Handbook: Supplementary Trajectory Data: Venus to Earth and Mars to Earth - Part 3

Parts 2 and 3 present tabulations of trajectory data for scheduling flights to and from Venus and Mars during the period 1960-2000. Part 2 contains information for outbound flights to these planets; Part 3 contains information for trajectories returning from the planets to Earth. Each Part contains data for single-plane transfers, as well as for broken-plane transfers which employ a midcourse plane-change to eliminate the high speed "ridges." The mathematical analyses employed for all calculations are described in Part 1 of this handbook. To facilitate the construction of round-trip trajectories, the date at the target planet is held fixed while the trip duration is varied in 10-day increments from zero days to the length of that planet's synodic period with Earth. Dates of arrival at the target planet are presented in the extreme right-hand column of Part 2, and dates of departure from the target planet are presented in the extreme left-hand column of Part 3. Thus, by holding Part 2 directly to the left of Part 3, the analyst may easily and rapidly scan all trip possibilities which involve any desired stopover time at the target planet. Within approximately 200 days of each conjunction or opposition, data are presented in 10-day increments at the target planet. Only those trips are listed for which the hyperbolic excess speeds at either or both ends of the trajectory do not exceed 0.6 EMOS (Earth Mean Orbital Speed). For the remaining mission regions, the requirements are so smoothly varying that a 50-day interval in dates at the target planet may be employed; the 10-day interval in trip times is, however, preserved here. In these regions, only those trips are listed for which either or both speeds do not exceed 0.3 EMOS.

Source record↗
DOE OSTI2023

Predicting rare events using neural networks and short-trajectory data

Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic dynamical systems. When the event is rare in comparison with the timescales of simulation and/or measurement needed to resolve the elemental dynamics, accurate prediction from direct observations becomes challenging. In such cases a more effective approach is to cast statistics of interest as solutions to Feynman-Kac equations (partial differential equations). Here, we develop an approach to solve Feynman-Kac equations by training neural networks on short-trajectory data. Our approach is based on a Markov approximation but otherwise avoids assumptions about the underlying model and dynamics. This makes it applicable to treating complex computational models and observational data. Additionally, we illustrate the advantages of our method using a low-dimensional model that facilitates visualization, and this analysis motivates an adaptive sampling strategy that allows on-the-fly identification of and addition of data to regions important for predicting the statistics of interest. Finally, we demonstrate that we can compute accurate statistics for a 75-dimensional model of sudden stratospheric warming. This system provides a stringent test bed for our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Massive Trajectory Data Based on Patterns of Life

Individual human location trajectory and check-in data have been the driving force for human mobility research in recent years. However, existing human mobility datasets are very limited in size and representativeness. For example, one of the largest and most commonly used datasets of individual human location trajectories, GeoLife, captures fewer than two hundred individuals. To help fill this gap, this Data and Resources paper leverages an existing data generator based on fine-grained simulation of individual human patterns of life to produce large-scale trajectory, check-in, and social network data. In this simulation, individual human agents commute between their home and work locations, visit restaurants to eat, and visit recreational sites to meet friends. We provide large datasets of months of simulated trajectories for two example regions in the United States: San Francisco and New Orleans. In addition to making the datasets available, we also provide instructions on how the simulation can be used to re-generate data, thus allowing researchers to generate the data locally without downloading prohibitively large files.

Amiri, Hossein↗