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At least 235 records · Page 13

Opinions from Users Across the Lifespan about Fully Autonomous and Rideshare Vehicles with Associated Features

Fully autonomous vehicles have the potential to fundamentally transform the future transportation system. While previous research has examined individuals’ perceptions towards fully autonomous vehicles, a complete understanding of attitudes and opinions across the lifespan is unknown. Therefore, individuals’ awareness, acceptance, and preferences towards autonomous vehicles were obtained from 75 participants through interviews with three diverse groups of participants: 20 automotive engineering graduate students who were building an autonomous concept vehicle, 21 non-technical adults, and 34 senior citizens. The results showed that regardless of age, an individual’s readiness to ride in a fully autonomous vehicle and the vehicle’s requirements were influenced by the users’ understanding of autonomous vehicles. All of the engineering students understand what a fully autonomous vehicle is and this group was the most willing to ride especially compared to the seniors, where only half of the seniors knew what a fully autonomous vehicle is and 58.8% were not at all ready to ride one. The desire to have a manual control option or the ability to override the vehicle was common (90% of the engineering students, 95.2% of the adults, and 82.4% of the seniors), especially for individuals who reported not being ready to ride in a fully autonomous vehicle. The majority of all three groups of participants (85% of the engineering students, 81% of the adults, and 52.9% of the seniors) considered it essential that the vehicle should convey information about the vehicle’s status and intended behavior. Diagnostic information about the vehicle was desired by the engineering students (71.4%), who had a technical understanding of autonomous vehicles and current automotive related technologies. When autonomous vehicles are available, most participants anticipate preferring to use them as a rideshare service model (75% of the engineering students, 38% of the adults, and 27% of the seniors) rather than owning (5% of the engineering students, 19% of the adults, and 21% of the seniors) the autonomous vehicle themselves. Regarding the topic of sharing rides with strangers, both the automotive engineering students (90%) and the adults (52.6%) were comfortable with the idea of pooled rideshare in comparison to the seniors (29.4%). In future efforts, it will be important to include potential autonomous vehicle users of a wide age range as well as physical, cognitive, and visual abilities.

Gangadharaiah, Rakesh↗

Quantifying System Level Impact of Connected and Automated Vehicles in an Urban Corridor

Numerous studies have demonstrated significant energy reduction for an ego vehicle by up to 20% leveraging Vehicle-to-Everything (V2X) technologies [1-4]. Some studies have also analyzed the impact of such vehicles on the energy consumption of other vehicles in a suburban or a highway corridor [5, 6], but the impact in an urban setting has not been studied yet. Southwest Research Institute (SwRI), in collaboration with Continental and Hyundai, is currently working on a Department of Energy funded project that is focused on quantifying the impact of multiple ego vehicles (smart vehicles) on the total energy consumption of the corridor under various traffic conditions, vehicle electrification level, vehicle-to-vehicle (V2V) technology penetration, and the number of smart (ego) vehicles in an urban setting. A six-kilometer-long urban corridor from Columbus, Ohio was modeled and calibrated with real-world data in PTV Vissim traffic microsimulation software. Five forward-looking powertrain models, consisting of two battery electric vehicles (BEVs), a hybrid electric vehicle (HEV), and two internal combustion engine (ICE) powered vehicles, were developed to estimate the energy consumption of vehicles on the corridor. A comprehensive full factorial simulation study was performed. The simulation results indicate that for a traffic mix based on projected new vehicles sales in 2025, a 15% corridor-level energy consumption reduction can be achieved. The paper details the development and validation of the simulation framework, design of experiments conducted, a discussion of challenges faced, and results under various test conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

High-Fidelity Heavy-Duty Vehicle Modeling Using Sparse Telematics Data

Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate. Captured in the dataset was geospatial information, time series measurements, and vehicle-specific metadata from a subset of 96 vehicles from varied geographic regions across North America. A series of custom algorithms was developed to process vehicle data and derive both vehicle model input parameters and representative drive cycles. Derived models provide a basis on which to simulate real-world vehicles and iterate on vehicle aerodynamics, auxiliary power loads, transmission shift schedules, and other parameters to achieve reduced fuel consumption and increase energy efficiency. Notably, these models were developed without the use of expensive field data collection, using only data collected through fleet telematics. Processed representative drive cycles are used to validate the fuel economy of derived models. The models developed through this research allow for more representative vehicle simulations with increased flexibility regarding vehicle-to-vehicle variations.

ADVANCED PROPULSION SYSTEMS↗

Los Angeles Air Force Base Vehicle-to-Grid Demonstration (Final Project Report)

Electrification of non-tactical vehicle fleets represents a key efficiency and energy security objective for the United States Department of Defense. To achieve electrification, the department targeted vehicle-to-grid services as a way to decrease the overall cost of operating the vehicle fleet and achieve rough parity with traditional internal combustion engine vehicle fleets. This report describes efforts to aggregate a fleet of bi-directional electric vehicles and charging stations to provide regulation up and regulation down in the California Independent System Operator ancillary services market. A 29-vehicle electric vehicle demonstration fleet, consisting of mixed purpose and duty vehicles such as sedans, pickups, vans, and medium-duty trucks, was deployed at the Los Angeles Air Force base. The fleet provided frequency regulation to the California Independent System Operator’s wholesale electricity market to determine the capability of recouping some of the additional costs of procuring electric vehicles and their supporting infrastructure. Lawrence Berkeley National Laboratory, with its partner Kisensum, LLC, developed the fleet scheduling, optimization, and control software to allow the vehicle fleet at the air force base to participate in the ancillary services markets. This report focuses on the control software and market interactions, the significant challenges faced and solutions devised to address them, and examines the potential of using the electric vehicle fleet as an energy storage resource for the base buildings, an application known as vehicle-tobuilding, in providing demand response and emergency backup power. The report discusses key findings related to providing frequency regulation to the California Independent System Operator market, electric vehicle fleet performance, compatibility of varying resource parameters of vehicle fleet aggregation, the need for automated methods for communicating hour-ahead energy bidding, challenges related to battery capacity and charge/discharge rates, and monthly settlement revenue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Cradle-to-Grave Lifecycle Analysis of Greenhouse Gas Emissions of Light-Duty Passenger Vehicles in China: Towards a Carbon-Neutral Future

Vehicle electrification is considered a pathway for on-road transportation decarbonization in China. Different from the conventional gasoline vehicles whose emissions are mainly released from vehicle tailpipes, emissions of battery electric vehicles (BEVs) are from the upstream processes of electricity generation and vehicle manufacturing, thus a comprehensive lifecycle analysis and comparison of BEVs with gasoline vehicles is required to quantify the emission mitigation benefit of vehicle electrification and determine the path to a carbon-neutral future. In the study, we compare the cradle-to-grave (C2G) lifecycle greenhouse gas emissions of gasoline and electric vehicles in China and analyze the greenhouse gas emission reduction of vehicle electrification in different provinces. Results show that under the current technologies, the national average C2G GHG emissions for battery electric vehicles (BEVs) of 100 miles (i.e., 160 km) and 300 miles (i.e., 480 km) all-electric range (AER) are 231 and 279 g CO2eq/km, respectively, 22% and 5% lower than those for gasoline internal combustion engine vehicles (ICEVs). Improving vehicle fuel efficiency by hybridizing gasoline ICEVs can effectively reduce C2G emissions to 212 g CO2eq/km. At the provincial level, C2G GHG emissions of BEVs vary according to the provincial electricity mix. In eight provinces, C2G GHG emissions of BEVs with 300 miles AER (BEV300s) are higher than those of gasoline ICEVs due to the GHG-intensive coal-based electricity mix. In the future scenario, with low carbon fuels (such as high-level bioethanol blending gasoline) and electricity decarbonization, the national average C2G emissions of hybrid electric vehicles (HEVs) and BEV300s can be reduced to 55 and 73 g CO2eq/km, respectively. Further decrease of C2G GHG emissions relies on reducing vehicle-cycle emissions from material processing and vehicle component manufacturing.

33 ADVANCED PROPULSION SYSTEMS↗

Autonomie Simulation Datasets in Support of U.S. DOT-NHTSA Advanced Vehicle Technology Research

Understanding how new vehicle technologies affect fuel economy and energy use is critical to the regulatory work performed by the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA), which sets Corporate Average Fuel Economy (CAFE) standards under the Energy Policy and Conservation Act of 1975. In order to support this work, Argonne National Laboratory uses Autonomie, a full-vehicle simulation tool, to evaluate advanced powertrain architectures and their effects on vehicle energy consumption and performance. A wide range of vehicle classes has been assessed (i.e., internal combustion engine vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, battery-electric vehicles, and fuel cell electric vehicles), as well as the effects of various technology improvements such as lightweighting, aerodynamic refinements, and low-rolling-resistance tires. Simulations have been run across multiple drive cycles to capture fuel and electricity use under realistic operating conditions. The resulting datasets include detailed vehicle-level results, model assumptions, and validation reports, all of which have been made publicly available through NHTSA in support of the 2023 notice of proposed rulemaking covering light-duty vehicles for model years 2027 to 2035. These data are critical to stakeholders working in fuel economy regulation, vehicle technology assessment, and energy policy analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Downloadable Dynamometer Database (D3): Public Test Data on Advanced-Technology Vehicles

Access to high-quality, independent vehicle test data is critical to advancing energy-efficient transportation research. The Downloadable Dynamometer Database (D3) is a public repository of dynamometer test data on advanced-technology vehicles, generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory and hosted by the Transportation and Power Systems Division. The database has been made available to support researchers, students, and professionals engaged in energy-efficient vehicle research, development, and education. A wide range of vehicle categories has been tested (i.e., alternative fuel vehicles, conventional gasoline and diesel vehicles, all-electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles), as well as various drive cycles and test conditions documented in the accompanying D3 user presentation. Stakeholders can select a vehicle type, identify a vehicle of interest, and download the associated test data for use in their own analyses. Data downloaded from D3 must be accompanied by the required attribution: "This data is from the Downloadable Dynamometer Database and was generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory." These data are critical to vehicle modeling, validation, technology assessment, and educational use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A Framework for Integration of IVHM Technologies for Intelligent Integration for Vehicle Management

As a part of the overall goal of developing Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, the NASA Faculty Fellowship Program (NFFP) at Marshall Space Flight Center has performed a pilot study on IVHM principals which integrates researched IVHM technologies in support of Integrated Intelligent Vehicle Management (IIVM). IVHM is the process of assessing, preserving, and restoring system functionality across flight and ground systems (NASA NGLT 2004). The framework presented in this paper integrates advanced computational techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of IIVM. These real-time responses allow the IIVM to modify the effected vehicle subsystem(s) prior to a catastrophic event. Furthermore, the objective of this pilot program is to develop and integrate technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear the IIVM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition, to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle mission Planning; Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations. The representative IVHM technologies for IIVH includes: 1) robust controllers for use in re-usable launch vehicles, 2) scaleable/flexible computer platform using heterogeneous communication, 3) coupled electromagnetic oscillators for enhanced communications, 4) Linux-based real-time systems, 5) genetic algorithms, 6) Bayesian Networks, 7) evolutionary algorithms, 8) dynamic systems control modeling, and 9) advanced sensing capabilities. This paper presents IVHM technologies developed under NASA's NFFP pilot project. The integration of these IVHM technologies forms the framework for IIVM.

Paris, Deidre E.↗

Power Systems Evaluated for Solar Electric Propulsion Vehicles

Solar electric propulsion (SEP) mission architectures are applicable to a wide range NASA missions including the robotic exploration of the outer planets in the next decade and the human exploration of Mars within the next 2 decades. SEP enables architectures that are very mass efficient with reasonable power levels (1-MW class) aerobrake and cryogenic upper-stage transportation technologies are utilized. In this architecture, the efficient SEP stage transfers the payload from low Earth orbit (LEO) High Energy Elliptical Parking Orbit (HEEPO) within a period of 6 to 12 months. highthrust, cryogenic upper stage and payload then separate from the SEP vehicle for injection to the planetary target, allowing for fast heliocentric trip times. This mission architecture offers a potential reduction in mass to LEO in comparison to alternative all-chemical nuclear propulsion schemes. Mass reductions may allow launch vehicle downsizing enable missions that would have been grounded because of cost constraints. The preceding figure illustrates a conceptual SEP stage design for a human Mars mission. Researchers at the NASA Glenn Research Center at Lewis Field designed conceptual SEP vehicle, conceived the mission architecture to use this vehicle, and analyzed the vehicle s performance. This SEP stage has a dry mass of 35 metric tons (MT), 40 MT of xenon propellant, and a photovoltaic array that spans 110 m, providing power to a cluster of eight 100-kW Hall thrusters. The stage can transfer an 80-MT payload and upper stage to the desired HEEPO. Preliminary packaging studies show this space-station-class SEP vehicle meets the proposed "Magnum" launch vehicle and volume requirements with considerable margin. An SEP vehicle for outer planetary missions, such as the Europa Mapper Mission, would be dramatically smaller than human Mars mission SEP stage. In this mission architecture, the SEP power system with the payload to provide spacecraft power throughout the mission. Several photovoltaic array design concepts were considered for the SEP vehicle power system for the human mission to Mars. These include a space station derivative, a SCARLET (Solar Concentrator Arrays with Refractive Linear Element Technology) derivative, and a hybrid inflatable-deployable thin polymer membrane array with thin-film solar cells (as shown in the concept illustration). This concept is based on a design developed for the Next Generation Space Telescope Sun shield. The array is divided into 16 independent electrical sections with 500-V, negative-grounded solar cell strings. The power system employs a channelized, 500-Vdc power management and distribution (PMAD) architecture with lithium ion batteries for energy storage for vehicle and payload secondary loads (the high-power Hall thrusters do not operate in eclipse periods). The 500-V PMAD voltage permits "direct-drive" thruster operation, greatly reducing the power processing unit size, complexity, and power loss. Similar power system architecture, designs, and technology are assumed for the Europa Mapper Mission SEP vehicle. The primary exceptions are that the photovoltaic array is assumed to consist of two rectangular wings and that the power system rating is 15 kW in Earth orbit and 200 W at Europa. To size the SEP vehicle power system, a dedicated Fortran code was developed to predict detailed power system performance, mass, and thermal control requirements. This code also modeled all the relevant Earth orbit environments; that is, the particulate radiation, plasma, meteoroids and debris, ultraviolet radiation, contamination, and thermal conditions. Analysis results for the Human Mars Mission SEP vehicle show a power system mass of 9-MT and photovoltaic array area of 5800-square meters for the thin-membrane design concept with CuInS2 thin-film cells. Power processing unit input power for a thin-membrane array design with three-junction, amorphous SiGe solar cells is shown in the graph. Power falls off rapidly inhe first weeks of the mission because of light-induced (Staebler-Wronksi) solar cell losses. During the next 200 days, power decreases steadily as the SEP stage spirals through the proton belts and sustains the bulk of the mission radiation damage. Once the vehicle apogee is above approximately four Earth radii, little additional degradation is incurred. From 400 to 800 days, a 1100-km "parking" orbit is maintained to await the next payload transfer opportunity. This orbit is below the main proton belt, and thus, little radiation dose is accumulated during this time period. During the second LEO-to-HEEPO transfer, power degrades somewhat further, but power requirements are still met. In comparison, the Europa Mapper SEP vehicle power system had a mass of 150 kg and a thin membrane array area of 100 square meters.

Kerslake, Thomas W.↗

High-Mileage Courier Fleet Vehicle Laboratory Battery Pack Testing

For one of each of the AVTA's plug-in hybrid electric vehicles, battery electric vehicles, and some hybrid electric vehicles tested in high-mileage courier fleets, the high-voltage traction battery packs were removed from the vehicle and tested at the beginning and end of fleet testing. For some vehicles, batteries also were tested at periodic intervals during fleet testing. Standard reference performance tests were conducted to characterize battery degradation over time. This dataset contains results from two or more rounds of battery tests for 21 distinct year/make/model vehicles (see reference ["INL Advanced Vehicle Testing Activity: On-road Logger and Laboratory Battery Pack Testing Vehicle List"](https://avt.inl.gov/sites/default/files/pdf/reports/DatasetVehicleList.pdf) for full list of vehicles). Each round of battery testing included the "Static Capacity Test" and the "Hybrid Pulse Power Characterization (HPPC) Test", conducted according to test procedures published in the United States Advanced Battery Consortium ["Battery Test Manual For Power-Assist Hybrid Electric Vehicles"](https://www.uscar.org/commands/files_download.php?files_id=57), ["Battery Test Manual For Plug-In Hybrid Electric Vehicles"](https://www.uscar.org/commands/files_download.php?files_id=168), and ["Electric Vehicle Battery Test Procedures Manual"](https://www.uscar.org/commands/files_download.php?files_id=5) prior to the time of testing. These tests were performed by Intertek Testing Services, North America. (This dataset will be shared by API; a small sample of the vehicle battery and test data has been extracted and is available for download while the API is being developed.)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

High-Mileage Courier Fleet Vehicle Laboratory Battery Pack Testing

For one of each of the AVTA's plug-in hybrid electric vehicles, battery electric vehicles, and some hybrid electric vehicles tested in high-mileage courier fleets, the high-voltage traction battery packs were removed from the vehicle and tested at the beginning and end of fleet testing. For some vehicles, batteries also were tested at periodic intervals during fleet testing. Standard reference performance tests were conducted to characterize battery degradation over time. This dataset contains results from two or more rounds of battery tests for 21 distinct year/make/model vehicles (see reference ["INL Advanced Vehicle Testing Activity: On-road Logger and Laboratory Battery Pack Testing Vehicle List"](https://avt.inl.gov/sites/default/files/pdf/reports/DatasetVehicleList.pdf) for full list of vehicles). Each round of battery testing included the "Static Capacity Test" and the "Hybrid Pulse Power Characterization (HPPC) Test", conducted according to test procedures published in the United States Advanced Battery Consortium ["Battery Test Manual For Power-Assist Hybrid Electric Vehicles"](https://www.uscar.org/commands/files_download.php?files_id=57), ["Battery Test Manual For Plug-In Hybrid Electric Vehicles"](https://www.uscar.org/commands/files_download.php?files_id=168), and ["Electric Vehicle Battery Test Procedures Manual"](https://www.uscar.org/commands/files_download.php?files_id=5) prior to the time of testing. These tests were performed by Intertek Testing Services, North America. This dataset is shared by API; a small sample of the vehicle battery and test data has been extracted and is also available for download.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

What Makes You Hold on to That Old Car? Joint Insights From Machine Learning and Multinomial Logit on Vehicle-Level Transaction Decisions

What makes you hold on to that old car? While the vast majority of household vehicles are still powered by conventional internal combustion engines, the progress of adopting emerging vehicle technologies will critically depend on how soon the existing vehicles are transacted out of the household fleet. Leveraging a nationally representative longitudinal data set, the Panel Study of Income Dynamics, this study examines how household decisions to dispose of or replace a given vehicle are: 1) influenced by the vehicle’s attributes, 2) mediated by households’ concurrent socio-demographic and economic attributes, and 3) triggered by key life cycle events. Coupled with a newly developed machine learning interpretation tool, TreeExplainer, we demonstrate an innovative use of machine learning models to augment traditional logit modeling to both generate behavioral insights and improve model performance. We find the two gradient-boosting-based methods, CatBoost and LightGBM, are the best performing machine learning models for this problem. The multinomial logistic model can achieve similar performance levels after its model specification is informed by TreeExplainer. Both machine learning and multinomial logit models suggest that while older vehicles are more likely to be disposed of or replaced than newer ones, such probability decreases as the vehicles serve the family longer. Pickup trucks and sport utility vehicles are less likely to be disposed of or replaced than cars, and leased vehicles are more likely to be transacted than owned vehicles. We find that married families, families with higher education levels, homeowners, and older families tend to keep their vehicles longer. Life events such as childbirth, residential relocation, and change of household composition and income are found to increase vehicle disposal and/or replacement. We provide additional insights on the timing of vehicle replacement or disposal, in particular, the presence of children and childbirth events are more strongly associated with vehicle replacement among younger parents.

33 ADVANCED PROPULSION SYSTEMS↗

ASDFL: An adaptive super‐pixel discriminative feature‐selective learning for vehicle matching

Abstract There are a large number of cameras in modern transportation system that capture numerous vehicle images continuously. Therefore, automatic analysis of these vehicle images is helpful for traffic flow management, criminal investigations and vehicle inspections. Vehicle matching, which aims to determine whether two input images depict an identical vehicle, is one of the core tasks in vehicle analysis. Recent relevant studies have focused on local feature extraction instead of global extraction, since local details can provide crucial cues to distinguish between cars. However, these methods do not select local features; that is, they do not assign weights to local features. Therefore, in this research, we systematically study the vehicle matching task, and present a novel annotation‐free local‐based deep learning method called Adaptive super‐pixel discriminative feature‐selective learning (ASDFL) to address this issue. In ASDFL, vehicle images are segmented into clusters of super‐pixels of similar size by considering the location and colour similarities of pixels without using any component‐level annotation. These super‐pixels are deemed to be the virtual components of vehicles. Moreover, a convolutional neural network is used to extract the deep features of these virtual components. Thereafter, an instance‐specific mask generation module driven by the extracted global features is enhanced to produce a mask to select the most distinctive virtual components of each vehicle image pair in the feature space. Finally, the vehicle matching task is accomplished by classifying the selected virtual component features of each imaged vehicle pair. Extensive experiments on two popular vehicle identification benchmarks demonstrate that our method is 1.57% and 0.8% more accurate than the previous baselines in a vehicle matching task on the VeRi and VehicleID datasets, respectively, which demonstrates the effectiveness of our method.

Qin, Rong↗

The next generation of reentry vehicles: NASA's view

The term reentry vehicle is used in the broad sense. The vehicles addressed include not only those that return from orbit to the Earth's surface, but also aeroassisted orbit transfer vehicles that use aerodynamic forces generated during atmospheric passes to achieve orbital changes with a smaller expenditure of energy than is required for an all-propulsion vehicle. Advanced reusable launch vehicles with special emphasis on system concepts and the influence of advanced technology on entry vehicle configuration are considered. Three categories of orbital transfer vehicles discussed: synergetic plane-change vehicles, planetary aerocapture vehicles, and LEO to GEO orbital transfer vehicles. While the orbital transfer and planetary vehicles are quite different from conventional winged Earth entry vehicles, synergetic plane change and high cross-range Earth entry vehicles have many similarities. Finally, a possible scenario for the development of the next generation of reentry vehicles is presented.

Walberg, G. D.↗

From Antarctica to space: Use of telepresence and virtual reality in control of remote vehicles

In the Fall of 1993, NASA Ames deployed a modified Phantom S2 Remotely-Operated underwater Vehicle (ROV) into an ice-covered sea environment near McMurdo Science Station, Antarctica. This deployment was part of the antarctic Space Analog Program, a joint program between NASA and the National Science Foundation to demonstrate technologies relevant for space exploration in realistic field setting in the Antarctic. The goal of the mission was to operationally test the use of telepresence and virtual reality technology in the operator interface to a remote vehicle, while performing a benthic ecology study. The vehicle was operated both locally, from above a dive hole in the ice through which it was launched, and remotely over a satellite communications link from a control room at NASA's Ames Research Center. Local control of the vehicle was accomplished using the standard Phantom control box containing joysticks and switches, with the operator viewing stereo video camera images on a stereo display monitor. Remote control of the vehicle over the satellite link was accomplished using the Virtual Environment Vehicle Interface (VEVI) control software developed at NASA Ames. The remote operator interface included either a stereo display monitor similar to that used locally or a stereo head-mounted head-tracked display. The compressed video signal from the vehicle was transmitted to NASA Ames over a 768 Kbps satellite channel. Another channel was used to provide a bi-directional Internet link to the vehicle control computer through which the command and telemetry signals traveled, along with a bi-directional telephone service. In addition to the live stereo video from the satellite link, the operator could view a computer-generated graphic representation of the underwater terrain, modeled from the vehicle's sensors. The virtual environment contained an animate graphic model of the vehicle which reflected the state of the actual vehicle, along with ancillary information such as the vehicle track, science markers, and locations of video snapshots. The actual vehicle was driven either from within the virtual environment or through a telepresence interface. All vehicle functions could be controlled remotely over the satellite link.

Stoker, Carol↗

IVHM Framework for Intelligent Integration for Vehicle Health Management

Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, is the process of assessing, preserving, and restoring system functionality across flight and techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of Integrated Intelligent Vehicle Management (IIVM). These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, this framework integrates technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear that IIVM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives. These systems include the following: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle Mission Planning, Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations.

Paris, Deidre↗

Intelligent Vehicle Health Management

As a part of the overall goal of developing Integrated Vehicle Health Management systems for aerospace vehicles, the NASA Faculty Fellowship Program (NFFP) at Marshall Space Flight Center has performed a pilot study on IVHM principals which integrates researched IVHM technologies in support of Integrated Intelligent Vehicle Management (IIVM). IVHM is the process of assessing, preserving, and restoring system functionality across flight and ground systems (NASA NGLT 2004). The framework presented in this paper integrates advanced computational techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of INM. These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, the objective of this pilot program is to develop and integrate technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear the INM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition, to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle mission Planning; Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations. The representative IVHM technologies for computer platform using heterogeneous communication, 3) coupled electromagnetic oscillators for enhanced communications, 4) Linux-based real-time systems, 5) genetic algorithms, 6) Bayesian Networks, 7) evolutionary algorithms, 8) dynamic systems control modeling, and 9) advanced sensing capabilities. This paper presents IVHM technologies developed under NASA's NFFP pilot project and the integration of these technologies forms the framework for IIVM.

Paris, Deidre E.↗

Analysis of Launch Vehicle Liftoff Debris: Historical Perspective from Space Shuttle and Application to Artemis I

Human exploration-class launch vehicles are inherently prone to debris due to the extreme environments generated during pre-launch operations, liftoff, and flight. The use of cryogenic propellants often requires thermal protection system (TPS) coatings, typically foam, to maintain the propellant conditions in the tank and prevent an accumulation ice on the external surface of the vehicle. Some ice growth is to be expected at umbilical interfaces, vents, flanges, or brackets where it is difficult to apply TPS. This ice may come loose at any time due to wind on the launch pad, structural vibration and acoustics after rocket ignition, or aerodynamic forces during flight. This phenomena is especially apparent on vehicles with no TPS, such as the Saturn V rockets used in the Apollo Program, see Figure 1. During propellant tanking, the thermal contraction of the underlying substrate may generate cracks in the TPS (Figure 1). Chunks of TPS can release due to the expansion of ingested gas from cryopumping or from aerodynamic forces if the crack creates an offset surface. Most foams will also have a certain amount of “popcorning” where small pieces of foam will pop off during flight because of the differential between the static surface pressure and the pressure of the gas trapped in the foam cell structure. There are a number of other coating or closeout materials that may be shed from the vehicle and become debris. During pre-launch operations and liftoff, the vehicle may also be exposed to debris originating from the launch pad or ground support equipment. This debris is separate from foreign object debris, or FOD, which is not intended to be present and is strictly controlled through operations and maintenance procedures. In this case, debris is generated from hardware and materials that are necessary for launch and are subject to the intense vibration, acoustics, and direct plume impingement of the launch environment. Examples include ice from umbilicals, tape and tie wraps that protect cables, and rust or corrosion from the launch platform. While NASA has historically been aware of debris as a potential issue that could cause a failure resulting in loss of mission, loss of vehicle, or loss of crew, the likelihood and severity of that risk was not always well understood or given sufficient weight in program and flight decisions. After the Space Shuttle Columbia accident (STS-107), the investigation found that foam TPS debris shed from the external tank was the proximate cause of the damage to the orbiter wing. Six previous observations of debris released from the foam ramp that covered the bipod connecting the forward end of the orbiter to the external tank resulted in minor changes or were determined to be accepted flight risks. Two occurrences of bipod ramp foam loss were not identified until the STS-107 investigation. Despite the damage inflicted by these debris strikes, the Shuttle Program Requirements Control Board deemed the vehicle safe to fly. During the Return to Flight effort following the Columbia disaster, NASA Engineering developed a process for the assessment of debris transport, impact, and damage tolerance to support independent assessments of risk by NASA Safety and Mission Assurance (S&MA). Under this system, each element (vehicle or ground system) defines a catalog of all expected debris based on launch history, component testing, or analysis. Debris transport analysis (DTA) is conducted using the debris catalog characteristics and potential flow transport mechanisms (e.g., vehicle aerodynamics, gravity, wind, plume-driven). The predicted debris impact locations and velocities are provided to the hardware owners, who use available test data and analysis to determine whether each component can withstand the impacts. In cases where the element hardware may be severely damaged or fail, the options are to mitigate the debris source through some change in design or operation, or to work with S&MA to try to characterize the probability of the impact and damage for program risk acceptance. Because of the differences in debris characteristics and transport, the DTA has been divided between the Liftoff and Ascent regimes. The development and application of Liftoff DTA methodology from the Shuttle Program to the current Artemis Program is the subject of this paper. Liftoff DTA covers the time from the start of pre-launch operations at the launch pad, up until the vehicle clears the launch tower and there is no longer any interaction with ground systems. Debris transport during this period is broadly classified as either gravity, wind, and plume-entrained (GWPE) or plume driven (PD). GWPE debris is generally lower speed, travelling in a forward-to-aft direction. PD transport includes flow features from the rocket ignition transient, as well as plume impingement and recirculation that occur as the vehicle lifts off the launch platform. In these cases, the debris typically moves in an aft-to-forward direction at higher speeds. The applicable transport mechanisms must be considered for each piece of debris depending on the material, and release location and time. For example, rust or metallic debris from the tower could fall (GWPE) and impact the vehicle before landing on the launch platform deck where it could be also be transported by plume impingement (PD). However, falling ice (GWPE) from an umbilical is unlikely to survive impact with the vehicle or launch platform and be available for PD transport. Modeling of debris transport is accomplished using a set of DTA tools which simulate debris trajectories subject to a reference frame acceleration (i.e., gravity) and aerodynamic drag. Where the trajectory encounters a solid surface, the debris is allowed to rebound with a specified coefficient of restitution. The drag is calculated by interpolating the fluid state at each point in the debris trajectory from high-fidelity computational fluid dynamics (CFD) simulations of the launch vehicle and pad. The CFD data may either be static (steady state or time averaged), typically for GWPE transport, or dynamic (time-accurate) for PD flow features like the ignition transient. Examples of the CFD flow field solutions for the Space Launch System (SLS) rocket and launch pad are shown in Figure 2. Typical SLS debris trajectory predictions from DTA are illustrated in Figure 3. The final version of this paper will include a more detailed examination of the Liftoff DTA process developed during the Shuttle Program, and how it has been augmented and applied to the SLS rocket under the Artemis Program. Comparisons with debris observations from the Artemis I launch will demonstrate validation of the tools and methodology.

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