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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Descriptor: Infrastructure Perception and Control: Multi-Sensor Object Tracking Dataset (IPC-MSOT)

Traffic intersections are crucial and challenging nodes in transportation networks where multiple lanes of vehicles and pedestrians converge. Traffic accidents often occur at traffic intersections, including a large proportion of traffic fatalities and about one-half of all traffic injuries in the United States. Object detection data were collected in 2024 across three intersections in Colorado Springs, CO, USA, over the course of multiple days and various times to induce a heterogeneous mix of traffic conditions and behaviors. The purpose of the data collection exercises was to learn various attributes about infrastructure sensors and to build a repository of high-resolution, object-level data that can be used for research and development (e.g., to develop multisensor data fusion algorithms). The Infrastructure Perception and Control:Multi-Sensor Object tracking (IPC-MSOT) dataset was collected as part of the U.S. Department of Transportation's Strengthening Mobility and Revolutionizing Transportation (SMART) project, where the city of Colorado Springs, Colorado, and the National Renewable Energy Laboratory collaborated to collect object-level trajectory data from road users using multiple types of infrastructure sensors deployed at different intersections. This dataset allows for testing of late-stage sensor fusion algorithms and their ability to ingest multimodal sensor data, and it can be utilized by traffic engineers to design and evaluate trajectory-based signal control strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1979

Evaluation of use of MPAD trajectory tape and number of orbit points for orbiter mission thermal predictions

The application of using the mission planning and analysis division (MPAD) common format trajectory data tape to predict temperatures for preflight and post flight mission analysis is presented and evaluated. All of the analyses utilized the latest Space Transportation System 1 flight (STS-1) MPAD trajectory tape, and the simplified '136 note' midsection/payload bay thermal math model. For the first 6.7 hours of the STS-1 flight profile, transient temperatures are presented for selected nodal locations with the current standard method, and the trajectory tape method. Whether the differences are considered significant or not depends upon the view point. Other transient temperature predictions are also presented. These results were obtained to investigate an initial concern that perhaps the predicted temperature differences between the two methods would not only be caused by the inaccuracies of the current method's assumed nominal attitude profile but also be affected by a lack of a sufficient number of orbit points in the current method. Comparison between 6, 12, and 24 orbit point parameters showed a surprising insensitivity to the number of orbit points.

Vogt, R. A.↗
NASA NTRS1992

Lunar lander stage requirements based on the Civil Needs Data Base

This paper examines the lunar lander stages that will be necessary for the future exploration and development of the Moon. Lunar lander stage sizing is discussed based on the projected lunar payloads listed in the Civil Needs Data Base. Factors that will influence the lander stage design are identified and discussed. Some of these factors are (1) lunar orbiting and lunar surface lander bases; (2) implications of direct landing trajectories and landing from a parking orbit; (3) implications of landing site and parking orbit; (4) implications of landing site and parking orbit selection; (5) the use of expendable and reusable lander stages; and (6) the descent/ascent trajectories. Data relating the lunar lander stage design requirements to each of the above factors and others are presented in parametric form. These data will provide useful design data that will be applicable to future mission model modifications and design studies.

Mulqueen, John A.↗
NASA NTRS1984

Utilization of spaceborne SAR data for mapping

Recent developments in automated processing of digital SEASAT SAR imagery have made feasible the generation of large-scale high-resolution maps. Standard preprocessing of raw data into digital images results in geometrically distorted imagery. Computer algorithms have been developed for unsupervised pixel location, geometric rectification, and mosaicking of multiple-image frames without ground control points. These algorithms utilize knowledge of the spacecraft trajectory data, the imaging geometry, and the coherent properties of the sensor to generate the required processing parameters. This paper discusses the advantages as well as the inherent limitations of this technique, analyzes the associated errors, and presents results using SEASAT SAR imagery. Also discussed are the results of the recent shuttle imaging radar (SIR-A) experiment as well as a follow-on experiment (SIR-B) planned for 1984.

Curlander, J. C.↗
NASA NTRS2011

Hayabusa Re-Entry: Trajectory Analysis and Observation Mission Design

On June 13th, 2010, the Hayabusa sample return capsule successfully re-entered Earth s atmosphere over the Woomera Prohibited Area in southern Australia in its quest to return fragments from the asteroid 1998 SF36 Itokawa . The sample return capsule entered at a super-orbital velocity of 12.04 km/sec (inertial), making it the second fastest human-made object to traverse the atmosphere. The NASA DC-8 airborne observatory was utilized as an instrument platform to record the luminous portion of the sample return capsule re-entry (~60 sec) with a variety of on-board spectroscopic imaging instruments. The predicted sample return capsule s entry state information at ~200 km altitude was propagated through the atmosphere to generate aerothermodynamic and trajectory data used for initial observation flight path design and planning. The DC- 8 flight path was designed by considering safety, optimal sample return capsule viewing geometry and aircraft capabilities in concert with key aerothermodynamic events along the predicted trajectory. Subsequent entry state vector updates provided by the Deep Space Network team at NASA s Jet Propulsion Laboratory were analyzed after the planned trajectory correction maneuvers to further refine the DC-8 observation flight path. Primary and alternate observation flight paths were generated during the mission planning phase which required coordination with Australian authorities for pre-mission approval. The final observation flight path was chosen based upon trade-offs between optimal viewing requirements, ground based observer locations (to facilitate post-flight trajectory reconstruction), predicted weather in the Woomera Prohibited Area and constraints imposed by flight path filing deadlines. To facilitate sample return capsule tracking by the instrument operators, a series of two racetrack flight path patterns were performed prior to the observation leg so the instruments could be pointed towards the region in the star background where the sample return capsule was expected to become visible. An overview of the design methodologies and trade-offs used in the Hayabusa re-entry observation campaign are presented.

Cassell, Alan M.↗
DOE OSTI2025

An Advanced Microscopic Energy Consumption Model for Automated Vehicle:Development, Calibration, Verification

The automated vehicle (AV) equipped with the Adaptive Cruise Control (ACC) system is expected to reduce the fuel consumption for the intelligent transportation system. This paper presents the Advanced ACC-Micro (AA-Micro) model, a new energy consumption model based on micro trajectory data, calibrated and verified by empirical data. Utilizing a commercial AV equipped with the ACC system as the test platform, experiments were conducted at the Columbus 151 Speedway, capturing data from multiple ACC and Human-Driven (HV) test runs. The calibrated AA-Micro model integrates features from traditional energy consumption models and demonstrates superior goodness of fit, achieving an impressive 90% accuracy in predicting ACC system energy consumption without overfitting. A comprehensive statistical evaluation of the AA-Micro model's applicability and adaptability in predicting energy consumption and vehicle trajectories indicated strong model consistency and reliability for ACC vehicles, evidenced by minimal variance in RMSE values and uniform RSS distributions. Conversely, significant discrepancies were observed when applying the model to HV data, underscoring the necessity for specialized models to accurately predict energy consumption for HV and ACC systems, potentially due to their distinct energy consumption characteristics.

Ma, Ke↗
NASA NTRS2010

Human Spaceflight Recent Conjunctions of Interest

I. During each nine-hour shift (or upon request), the Orbital Safety Analyst (OSA) at JSpOC updates the entire tracked catalog with the latest tracking data from the SSN and screens this catalog against NASA s assets. a) For ISS operations, a 72-hour advance screening is performed. b) For Shuttle orbit operations, a 36-hour advance screening is performed. c) If a vehicle is performing a maneuver during the screening period, OSA uses post-reboost-trajectory data supplied by Mission Control Center-Houston (MCC-H) for screening. II. An automated process is used to detect any conjunctions within 10 x 40 x 40 km box (centered on the vehicle) using Special Perturbation (SP) processing: a) Box dimensions are Radial x Downtrack x Crosstrack. b) Any object found within this box has the tracking tasking level increased to improve (hopefully) its uncertainty in its current and predicted orbital trajectory. c) OSA informs NASA if any object is found inside a 2 x 25 x 25 km box then creates and sends an Orbital Conjunction Message (OCM) to NASA which contains detailed information about the conjunction. d) For Shuttle, the box size used for screening and reporting is altered for special operations (day of rendezvous, launch screening, etc.).

Browns, Ansley C.↗
NASA NTRS1959

Trajectory Control for Vehicles Entering the Earth's Atmosphere at Small Flight Path Angles

Methods of controlling the trajectories of high-drag-low-lift vehicles entering the earth's atmosphere at angles of attack near 90 deg and at initial entry angles up to 3 deg are studied. The trajectories are calculated for vehicles whose angle of attack can be held constant at some specified value or can be perfectly controlled as a function of some measured quantity along the trajectory. The results might be applied in the design of automatic control systems or in the design of instruments which will give the human pilot sufficient information to control his trajectory properly during an atmospheric entry. Trajectory data are compared on the basis of the deceleration, range, angle of attack, and, in some cases, the rate of descent. The aerodynamic heat-transfer rate and skin temperature of a vehicle with a simple heat-sink type of structure are calculated for trajectories made with several types of control functions. For the range of entry angles considered, it is found that the angle of attack can be controlled to restrict the deceleration down to an arbitrarily chosen level of 3g. All the control functions tried are successful in reducing the maximum deceleration to the desired level. However, in order to avoid a tendency for the deceleration to reach an initial peak decrease, and then reach a second peak, some anticipation is required in the control function so that the change in angle of attack will lead the change in deceleration. When the angle of attack is controlled in the aforementioned manner, the maximum rate of aerodynamic heat transfer to the skin is reduced, the maximum skin temperature of the vehicle is virtually unaffected, and the total heat absorbed is slightly increased. The increase in total heat can be minimized, however, by maintaining the maximum desired deceleration for as much of the trajectory as possible. From an initial angle of attack of 90 deg, the angle-of-attack requirements necessary to maintain constant values of deceleration (1g to 4g) and constant values of rate of descent (450 to 1,130 ft/sec) as long as it is aerodynamically practical are calculated and are found to be moderate in both magnitude and rate. Entry trajectories made with these types of control are presented and discussed.

Eggleston, John M.↗
DOE OSTI2026

NREL OpenPATH: An Open-Source, Extensible Platform for Instrumenting Travel Behavior Data

NREL OpenPATH is an open-source, extensible platform that allows communities to instrument their own travel behavior data. The platform consists of a smartphone app, server and analysis pipeline, and enables collection of opt-in, multi-modal, end-to-end travel diaries. It makes the aggregate statistics available via a public dashboard, and allows deployers to download and visualize trip and trajectory data through the admin dashboard. It also allows for customization of the initial demographic survey and the trip-level qualitative information collected. Our goal is to provide an easy-to-use tool that can democratize travel behavior data collection by empower communities of all sizes to recruit participants and obtain a holistic picture of their travel patterns. The platform has been used by close to 40 partners, to collect data from thousands of participants. Upon signing a simple MOU, it is currently available for free to universities, non-profits and public agencies in the United States.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1969

Apollo 14 (mission H-3) Baseline Mission Profile

Preliminary Apollo 14 (Mission H-3) trajectory data for the July 1970 launch window are presented in this document. A general mission profile is presented, and approximate variations of selected mission parameters are indicated. Note that the information in this document is preliminary and will be updated in the Apollo 14 (Mission H-3) operational trajectory documents.

Source record↗
NASA NTRS1982

STS-1 orbital trajectory reconstruction using unmodelled acceleration estimation

A postflight trajectory reconstruction and analysis were conducted in order to determine the effects of unmodeled thrusting on the Space Shuttle Orbiter, to generate representative thrusting uncertainties for input to error analyses, and to evaluate the accuracy of the Ancillary trajectory data product and real-time vectors. The methods of selecting appropriate data arcs, time placement of compensation forces, limitations of the orbit determination process, and methods of evaluating solutions obtained from various force combinations are presented. It is shown that the estimation of forces to compensate for uncoupled attitude maneuvers and attitude hold maintenance thrusting significantly improves the postflight reconstruction of Space Shuttle Orbiter trajectories. A reduction of measurement residual statistics due to dynamic model uncertainties yields improved accuracy for postflight orbit determination.

Lacarna, R. J.↗
NASA NTRS1981

Planetary Flight Handbook: Part 7 - Direct Trajectories to Jupiter, Saturn, Uranus, and Neptune. Supplement A: Tabular Trajectory for Direct Trajectories to Jupiter and Saturn

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 i n Table 4-1 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.

Source record↗
NASA NTRS2015

Internship Abstract - Aerosciences and Flight Mechanics Intern

Mars is a hard place to land on, but my internship with NASA's Aerosciences & Flight Mechanics branch has shown me the ways in which men and women will one day land safely. I work on Mars Aerocapture, an aeroassist maneuver that reduces the fuel necessary to "capture" into Martian orbit before a descent. The spacecraft flies through the Martian atmosphere to lose energy through heating before it exits back into space, this time at a slower velocity and in orbit around Mars. Spacecraft will need to maneuver through the Martian atmosphere to accurately hit their orbit, and they will need to survive the generated heat. Engineering teams need simulation data to continue their designs, and the guidance algorithm that ensures a proper orbit insertion needs to be refined - two jobs that fell to me at the summer's start. Engineers within my branch have developed two concept aerocapture vehicles, and I run simulations on their behavior during the maneuver. I also test and refine the guidance algorithm. I spent the first few weeks familiarizing myself with the simulation software, troubleshooting various guidance bugs and writing code. Everything runs smoothly now, and I recently sent my first set of trajectory data to a Thermal Protection System group so they can incorporate it into their heat-bearing material designs. I hope to generate plenty of data in the next few weeks for various engineering groups before my internship ends mid-August. My major accomplishment so far is improving the guidance algorithm. It is a relatively new algorithm that promises higher accuracy and fuel efficiency, but it hasn't undergone extensive testing yet. I've had the opportunity to work with the principal developer - a professor at Iowa State University - to find and fix several issues. I was also assigned the task of expanding the branch's aerodynamic heating simulation software. I am excited to do this because engineers in the future will use my work to generate meaningful data and make design decisions. My internship has taught me to how to teach myself. There are no tutors, study sessions or professor office hours. When I am given an assignment I am expected to figure out how to accomplish it, and I have grown in my problem solving abilities. My summer experience has reinforced my drive to work at NASA, and I can definitely see myself working full time on the aerocapture project, or something similar.

Rangel, John↗
NASA NTRS1976

A history of the deep space network

The Deep Space Network (DSN) has been managed and operated by the Jet Propulsion Laboratory (JPL) under NASA contract ever since NASA was formed in late 1958. The Tracking and data acquisition tasks of the DSN are markedly different from those of the other NASA network, STDN. STDN, which is an amalgamation of the satellite tracking network (STADAN) and the Manned Space Flight Network (MSFN), is primarily concerned with supporting manned and unmanned earth satellites. In contrast, the DSN deals with spacecraft that are thousands to hundreds of millions of miles away. The radio signals from these distant craft are many orders of magnitude weaker than those from nearby satellites. Distance also makes precise radio location more difficult; and accurate trajectory data are vital to deep space navigation in the vicinities of the other planets of the solar system. In addition to tracking spacecraft and acquiring data from them, the DSN is required to transmit many thousands of commands to control the sophisticated planetary probes and interplanetary monitoring stations. To meet these demanding requirements, the DSN has been compelled to be in the forefront of technology.

Corliss, W. R.↗
NASA NTRS2020

Analysis of Sample Acquisition Dynamics Using Discrete Element Method

The analysis presented in this paper is conducted in the framework of the Ocean Worlds Autonomy Testbed for Exploration Research and Simulation (OceanWATERS) project, currently under development at NASA Ames Research Center. OceanWATERS aims at designing a simulation environment which allows for testing autonomy of scientific lander missions to the icy moons of our solar system. Mainly focused on reproducing the end effector interaction with the inherent terrain, this paper introduces a novel discrete element method (DEM)-based approach to determine forces and torques acting on the lander’s scoop during the sample acquisition process. An accurate force feedback from the terrain on the scoop is required by fault-detection and autonomous decision-making algorithms to identify when the requested torque on the robotic arm’s joints exceeds the maximum available torque. Knowledge of the terrain force feedback significantly helps evaluating the arm’s links structural properties and properly selecting actuators for the joints. Models available in literature constitute a partial representation of the dynamics of the interaction. As an example, Balovnev derived an analytical expression of the vertical and horizontal force acting on a bucket while collecting a sample as a function of its geometry and velocity, soil parameters and reached depth. Although the model represents an adequate approximation of the two force components, it ignores the direction orthogonal to the scoop motion and neglects the torque. This work relies on DEM analysis to compensate for analytical models’ deficiencies and inaccuracies, i. e. provide force and torque 3D vectors, defined in the moving reference (body) frame attached to the scoop, at each instant of the sample collection process. Results from the first presented analysis relate to the specific OceanWATERS sampling strategy, which consists of collecting the sample through five consecutive passes with increasing depth, each pass following the same circularlinear- circular trajectory. Data is collected given a specific scoop design interacting with two types of bulk materials, which may characterize the surface of icy planetary bodies: snow and ice. Although specifically concerned with the OceanWATERS design, this first analysis provides the expected force trends for similar sampling strategies and allows to deduce phenomenological information about the general scooping process. In order to further instruct the community on the use of DEM tools as a solution to the sampling collection problem, two more analyses have been carried out, mainly focused on reducing the DEM computation time, which increases with a decrease in particle size. After running a set of identical simulations, where the only changing parameter is the size of the spherical particle, it is observed that the resulting force trajectories, starting from a given particle size, converge to the true trend. It is deducible that a further decrease in size yields negligible improvements in the accuracy, while it sensibly increases computation time. A final analysis aims at discussing limitations of approximating bulk material particles having a complex shape, e. g. ice fragments, with spheres, by comparing force trends resulting in the two cases for the same simulation scenario.

Catanoso, Damiana↗
NASA NTRS2014

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗
DOE OSTI2025

Nonadiabatic Force Matching for Alchemical Free-Energy Estimation

We propose a method to compute free-energy differences from nonadiabatic alchemical transformations by using flow-based generative models. The method, nonadiabatic force matching, hinges on estimating the dissipation along an alchemical switching process in terms of a nonadiabatic force field that can be learned through stochastic flow matching. The learned field can be used in conjunction with short-time trajectory data to evaluate upper and lower bounds on the alchemical free energy that variationally converge to the exact value if the field is optimal. Applying the method to evaluate the alchemical free energy of atomistic models shows that it can substantially reduce the simulation cost of a free-energy estimate at a negligible loss of accuracy when compared with thermodynamic integration.

Computational chemistry↗
DOE OSTI2024

CORSAIR: A Framework for Human Mobility Prediction through Visit Characterization and Spatial Behavior Modeling

The rapid advancement of location acquisition technologies has led to the daily collection of vast amounts of mobile trajectory data, facilitating in-depth research on human mobility and enabling more accurate mobility prediction models. However, existing methodologies often fall short in capturing the intricate dynamics of human navigation and spatial behavior. This paper addresses this gap by exploring the multifaceted relationships between individuals and their environments, considering the diverse influences of personal preferences and experiences. Some places hold sentimental value and are visited frequently, while others serve as transient points of passage. To model these differences, we introduce CORSAIR, a novel visit characterization framework that leverages visitation patterns and dwell times to delineate an individual’s relationship with specific places. CORSAIR classifies visits into seven distinct types: casual, occasional, routine, special, anchor, important, and resettling. Also, we show that explicitly recognizing these distinct visit types and incorporating nuanced visit intents into mobility prediction models leads to a substantial improvement in prediction accuracy. This distinction allows for more precise modeling of the individual’s transitions, enhancing the personalization and relevance of location-based services. Our findings suggest that a deeper understanding of the complexities of individual-environment interactions is crucial for developing effective predictive tools in mobility research.

Amichi, Licia [ORNL] (ORCID:0000000177631394)↗