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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Machine Learning Classification

During and after flooding events, mapping the extent of floodwaters aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. Complimentary optical and SAR images from domestic and foreign entities are brought together through activations of the International Charter: Space and Major Disasters to support response efforts, from true-color, near-infrared, and thermal remote sensing data obtained by NASA, NOAA, and international satellites to the collection of high-resolution true color aerial photography by NOAA and the National Geodetic Survey. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. In this study, true-color NOAA aerial and commercial satellite imagery are used in conjunction with four UAVSAR data swaths centered on the Lumberton and Cape Fear River basins in southeastern North Carolina to develop a Random Forest classification model focused on mapping open water and floodwater otherwise obscured by vegetation or lingering cloud cover. Ancillary building footprint, transportation route, and population data will also be incorporated into the classification scheme to estimate the societal impacts of flooding based on the proximity of features to detected inundation. Preliminary results from the Hurricane Florence case study will be discussed in addition to the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander M Melancon↗
NASA NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Random Forest Classification

Mapping the extent of floodwaters following extreme rainfall aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. This study seeks to determine how accurately maps of inundation can be generated from L-band SAR imagery through Random Forest classification. Once the extent of water and inundated vegetation is classified, cleanup operations are performed using fuzzy logic to reduce false detections. Estimates of water extent are then combined with datasets describing the distribution of population, buildings, and roads throughout the domain to evaluate societal impacts. Results from the Hurricane Florence case study will be discussed along with the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander Melancon↗
NASA NTRSDate not supplied

NASA’s Space Launch System Successfully Launches Artemis I Mission and Hardware Progress Continues for Next Artemis Missions

NASA’s Artemis I mission will be the first flight of the agency’s Space Launch System (SLS) rocket. It will launch an uncrewed Orion spacecraft to the Moon, where it will enter a highly elliptical retrograde orbit. In 2022, final tests, checkouts, and preparations were on schedule to be completed, including the vital wet dress rehearsal (WDR) at Kennedy Space Center’s (KSC) Launch Complex 39B. Following successful completion of WDR, which is expected in summer 2022, the Artemis I launch vehicle will be returned to the Vehicle Assembly Building (VAB) at KSC where it will be prepared for launch. Important data were collected during launch and will be shared as applicable. While the Artemis I hardware and mission took center stage in 2022, significant progress was made on hardware and software for future Artemis missions, including for the first crewed launch and the following SLS Block 1B and Block 2 variants of the rocket.

John Honeycutt↗
DOE OSTI2024

From Sim to Real: A Pipeline for Training and Deploying Traffic Smoothing Cruise Controllers

Designing and validating controllers for connected and automated vehicles to enhance traffic flow presents significant challenges, from the complexity of replicating real-world stop-and-go traffic dynamics in simulation, to the intricacies involved in transitioning from simulation to actual deployment. In this work, we present a full pipeline from data collection to controller deployment. Specifically, we collect 772 km of driving data from the I-24 in Tennessee, and use it to build a one-lane simulator, placing simulated vehicles behind real-world trajectories. Using policy-gradient methods with an asymmetric critic, we improve fuel efficiency by over 10% when simulating congested scenarios. Our comprehensive approach includes reinforcement learning for controller training, software verification, hardware validation and setup, and navigating various sim-to-real challenges. Furthermore, we analyze the controller's behavior and wave-smoothing properties, and deploy it on four Toyota Rav4’s in a real-world validation experiment on the I-24. Lastly, we release the driving dataset, the simulator and the trained controller, to enable future benchmarking and controller design.

42 ENGINEERING↗
DOE OSTI2020

Plug-in electric vehicles in China and the USA: a technology and market comparison

As the top two plug-in electric vehicle (PEV) markets in the world, China and the United States of America (USA) have developed different market structures that are influenced by government policies, test procedures, customer acceptance, and vehicle performance. There are differences in PEV test procedures and vehicle class definitions as well. This paper quantifies such differences and compares PEV characteristics and markets in the two countries using actual data collected over several years. First, although China surpasses the USA in annual PEV production because of generous PEV policies and higher charging infrastructure availability, the USA has a higher PEV adoption rate per capita (2.3 versus 0.81 per 1000 people at the end of 2017). Second, the most popular vehicle classes are A00 battery electric vehicles (BEVs) in China but long-range mid-size BEVs in the USA. Moreover, China's electric and plug-in hybrid sport utility vehicle market is growing quickly. Third, the electricity consumption rated under Chinese test procedures is 20%-40% lower than that rated under US procedures. The sales-weighted electricity consumption of an average BEV in China is 23% lower than that in the USA because of larger proportions of small and micro BEVs in China. Fourth, PEV battery cost in the two countries is currently close to $210-$220 kWh. Finally, China has higher numbers of charging infrastructure, and the ratio of PEVs to public chargers is 9.0 in China versus 17.9 in the USA at the end of 2018.

China EV market↗
NASA NTRS2006

International Space Station Increment-6/8 Microgravity Environment Summary Report November 2002 to April 2004

This summary report presents the analysis results of some of the processed acceleration data measured aboard the International Space Station during the period of November 2002 to April 2004. Two accelerometer systems were used to measure the acceleration levels for the activities that took place during Increment-6/8. However, not all of the activities during that period were analyzed in order to keep the size of the report manageable. The National Aeronautics and Space Administration sponsors the Microgravity Acceleration Measurement System and the Space Acceleration Measurement System to support microgravity science experiments that require microgravity acceleration measurements. On April 19, 2001, both the Microgravity Acceleration Measurement System and the Space Acceleration Measurement System units were launched on STS-100 from the Kennedy Space Center for installation on the International Space Station. The Microgravity Acceleration Measurement System unit was flown to the station in support of science experiments requiring quasi-steady acceleration measurements, while the Space Acceleration Measurement System unit was flown to support experiments requiring vibratory acceleration measurement. Both acceleration systems are also used in support of the vehicle microgravity requirements verification as well as in support of the International Space Station support cadre. The International Space Station Increment-6/8 reduced gravity environment analysis presented in this report uses acceleration data collected by both sets of accelerometer systems: 1. The Microgravity Acceleration Measurement System, which consists of two sensors: the Orbital Acceleration Research Experiment Sensor Subsystem, a low frequency range sensor (up to 1 Hz), is used to characterize the quasi-steady environment for payloads and vehicle, and the High Resolution Accelerometer Package, which is used to characterize the vibratory environment up to 100 Hz. 2. The Space Acceleration Measurement System measures vibratory acceleration data in the range of 0.01 to 400 Hz. This summary report presents analysis of some selected quasi-steady and vibratory activities measured by these accelerometers during Increment-6/8 from November 2002 to April 2004.

Jules, Kenol↗
DOE OSTI2021

In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery

Abstract We autonomously directed a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or “drone” to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, were varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones.

42 ENGINEERING↗
DOE OSTI2022

Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)

This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1977

Comparative vibration environments of transportation vehicles

Measured vibration data are presented for a number of air and surface vehicles. Consideration is given to the importance of direction effects; of vehicle operating modes such as takeoff, cruise, and landing; and of measurement location on the level and frequency of the measurements. Various physical measurement units or descriptors are used to quantify and compare the data. Results suggest the range of vibration associated with a particular mode of transportation and illustrate the comparative levels in terms of each of the descriptors. Collectively, the results form a data base which may be useful in assessing the ride of existing or future systems relative to vehicles in current operation. In addition, subjective response data obtained from vibration simulator studies are presented to illustrate human response characteristics as well as to indicate a laboratory approach for the development of ride-quality criteria.

Stephens, D. G.↗
NASA NTRS1999

The X-33 Flight Test Challenge

Low cost access to space has eluded present launch system technologies. Our objective is to reduce the cost of putting a payload into space from $10,000 per pound to $1000 per pound. In July 1996, a cooperative agreement was initiated between the Lockheed Martin Skunk Works and NASA to help accomplish this goal. The X-33 is the first step in the process to make low cost space access a reality. The X-33 is a suborbital, hypersonic lifting body, proof of concept of a reusable launch vehicle. The X-33 flight test program will validate technologies such as a metallic thermal protection system, Linear Aerospike Engines, use of tanks and struts as fundamental structural elements, as well as quick turnaround time. Flight testing will begin in July 2000, with launches originating from Edwards Air Force Base and initial landings at Michael Army Airfield in Utah. Data collected from these flight tests will aid in the decision to build an economically viable single stage to orbit reusable launch vehicle. This paper will explore the technical challenges facing the X-33 Flight Test Team.

Borden, David↗
NASA NTRS2015

Plume and Shock Interaction Effects on Sonic Boom in the 1-foot by 1-foot Supersonic Wind Tunnel

The desire to reduce or eliminate the operational restrictions of supersonic aircraft over populated areas has led to extensive research at NASA. Restrictions are due to the disturbance of the sonic boom, caused by the coalescence of shock waves formed by the aircraft. A study has been performed focused on reducing the magnitude of the sonic boom N-wave generated by airplane components with a focus on shock waves caused by the exhaust nozzle plume. Testing was completed in the 1-foot by 1-foot supersonic wind tunnel to study the effects of an exhaust nozzle plume and shock wave interaction. The plume and shock interaction study was developed to collect data for computational fluid dynamics (CFD) validation of a nozzle plume passing through the shock generated from the wing or tail of a supersonic vehicle. The wing or tail was simulated with a wedgeshaped shock generator. This test entry was the first of two phases to collect schlieren images and off-body static pressure profiles. Three wedge configurations were tested consisting of strut-mounted wedges of 2.5- degrees and 5-degrees. Three propulsion configurations were tested simulating the propulsion pod and aft deck from a low boom vehicle concept, which also provided a trailing edge shock and plume interaction. Findings include how the interaction of the jet plume caused a thickening of the shock generated by the wedge (or aft deck) and demonstrate how the shock location moved with increasing nozzle pressure ratio.

Shock wave interaction↗
NASA NTRS1975

Review of measured vibration and noise environments experienced by passengers in aircraft and in ground transportation systems

Measured vibration and interior noise data are presented for a number of air and surface vehicles. Consideration is given to the importance of direction effects; of vehicle operations such as take-off, cruise, and landing; and of measurement location on the level and frequency of the measurements. Various physical measurement units or descriptors are used to quantify and compare the data. Results suggest the range of vibration and noise associated with a particular mode of transportation and illustrate the comparative levels in terms of each of the descriptors. Collectively, the results form a data base which may be useful in assessing the ride of existing or future systems relative to vehicles in current operation.

Stephens, D. G.↗
DOE OSTI2022

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. Finally, these results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

42 ENGINEERING↗
NASA NTRS2012

Large Eddy/Reynolds-Averaged Navier-Stokes Simulations of CUBRC Base Heating Experiments

ven with great advances in computational techniques and computing power during recent decades, the modeling of unsteady separated flows, such as those encountered in the wake of a re-entry vehicle, continues to be one of the most challenging problems in CFD. Of most interest to the aerothermodynamics community is accurately predicting transient heating loads on the base of a blunt body, which would result in reduced uncertainties and safety margins when designing a re-entry vehicle. However, the prediction of heat transfer can vary widely depending on the turbulence model employed. Therefore, selecting a turbulence model which realistically captures as much of the flow physics as possible will result in improved results. Reynolds Averaged Navier Stokes (RANS) models have become increasingly popular due to their good performance with attached flows, and the relatively quick turnaround time to obtain results. However, RANS methods cannot accurately simulate unsteady separated wake flows, and running direct numerical simulation (DNS) on such complex flows is currently too computationally expensive. Large Eddy Simulation (LES) techniques allow for the computation of the large eddies, which contain most of the Reynolds stress, while modeling the smaller (subgrid) eddies. This results in models which are more computationally expensive than RANS methods, but not as prohibitive as DNS. By complimenting an LES approach with a RANS model, a hybrid LES/RANS method resolves the larger turbulent scales away from surfaces with LES, and switches to a RANS model inside boundary layers. As pointed out by Bertin et al., this type of hybrid approach has shown a lot of promise for predicting turbulent flows, but work is needed to verify that these models work well in hypersonic flows. The very limited amounts of flight and experimental data available presents an additional challenge for researchers. Recently, a joint study by NASA and CUBRC has focused on collecting heat transfer data on the backshell of a scaled model of the Orion Multi-Purpose Crew Vehicle (MPCV). Heat augmentation effects due to the presence of cavities and RCS jet firings were also investigated. The high quality data produced by this effort presents a new set of data which can be used to assess the performance of CFD methods. In this work, a hybrid LES/RANS model developed at North Carolina State University (NCSU) is used to simulate several runs from these experiments, and evaluate the performance of high fidelity methods as compared to more typical RANS models. .

Salazar, Giovanni↗
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
NASA NTRS1980

A flight investigation of performance and loads for a helicopter with 10-64C main rotor blade sections

A flight investigation produced data on performance and rotor loads for a teetering rotor, AH-1G helicopter flown with a main rotor that had the NLR-1T airfoil as the blade section contour. The test envelope included hover, forward flight speeds from 34 to 83 m/sec (65 to 162 knots), and collective fixed maneuvers at about 0.25 tip speed ratio. The data set for each test point describes vehicle flight state, control positions, rotor loads, power requirements, and blade motions. Rotor loads are reviewed primarily in terms of peak to peak and harmonic content. Lower frequency components predominated for most loads and generally increased with increased airspeed, but not necessarily with increased maneuver load factor. Detailed data for an advanced airfoil on an AH-1G are presented.

Morris, C. E. K.↗
NASA NTRS1986

Review and analysis of high temperature chemical reactions and the effect of non-equilibrium conditions

Chemical reactions at high temperatures have been considered extensively because of their importance to the heating effects on re-entry of space vehicles. Data on these reactions however, are not abundant and even when found there are discrepancies in data collected by various investigators. In particular, data for recombination reactions are calculated from the dissociation reactions or vice versa through the equilibrium constant. This involves the use of the principle of detailed balancing. This principle is discussed in reference to conditions where it is valid as well as to those where it is not valid. Related topics that merit further study or for which applicable information was available are briefly mentioned in an appendix to this report.

Johnson, R. E.↗