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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Leveraging Real-World Driving Data for Design and Impact Evaluation of Energy Efficient Control Strategies

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies. In this study, two methods are presented of leveraging real-world data in both design optimization of energy efficient control strategies and in evaluating the real-world impact of those control strategies upon large-scale deployment. Through these methodologies, strategies with highest impact on energy savings were selected to be implemented as control algorithms. The developed algorithms were incorporated into a vehicle dynamics and powertrain control architecture implemented on a Cadillac CT6 demonstration vehicle. The control algorithms were then exercised on real-world driving scenarios to determine their impact on collective energy savings. The methodology utilizes the large-scale driving data sets maintained by the National Renewable Energy Laboratory to extract real-world driving scenarios and efficient simulation software tools. The insights obtained through this research help in guiding technology selection for energy efficient driving controls.

ARPA-E↗
DOE OSTI2023

Virtual Community Trains

When access to transit solutions is restricted to parts of a community, a division occurs what leads to disadvantaged socioeconomic conditions that only grows over time. This restriction causes difficulties in accessing needed resources such as medical facilities, employment, healthy food, education, healthy food, and more. To solve this restricted access, Labyrinth Smart Mobility and partners are developing a virtual community train that encourages scalability, energy efficiency, and equitability to all regardless of individual circumstances and limitation. The implementation of virtual community trains allows access to resources previously out of reach. The virtual community train enables one operator to drive a lead vehicle. This lead vehicle would then provide direction to a follow vehicle through vehicle-to-vehicle communication. Labyrinth Smart Mobility in conjunction with partners used various methods and resources to prove market feasibility, technical feasibility, economic need and viability, and more as well as design system requirements and architecture emphasis efficiency and safety. In doing so, Labyrinth Smart Mobility and partners will continue these advancements to further develop the virtual community train.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

NREL Infrastructure Perception and Control Workshop

A lack of highly reliable, full state-space awareness of roadway situations is the current bottleneck for the incremental introduction of smart infrastructure control. NREL's Infrastructure Perception and Control (IPC) lab applies advanced sensing and computation controls to the coordinated movement of vehicles on the road as well as people in large facilities and has produced field test results from a Colorado Springs intersection. In this presentation, NREL discusses the state of smart infrastructure control and opportunities for partnership.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data

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

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2019

Challenges and Opportunities of International Cooperation for Safety & Mission Assurance (SMA) on the European Service Module (ESM) of the Orion Program

ESA is providing the ESM to NASA for the Orion Program in accordance with the Implementing Arrangement (IA) established between the two Agencies in 2012. This IA is a barter agreement, in which ESA provides Service Module vehicles for the first two flights of Orion in exchange for two servicing missions to the ISS. This arrangement has provided opportunities and challenges to both communities. It represents an important opportunity for ESA to build on its experience in the development and operations of ATV and play an active role in the development and flight of a manned space vehicle. This paper will explore some of the unique aspects of this arrangement as it affects Safety & Mission Assurance (SMA).The assessment of ESM compliance with applicable safety requirements is the responsibility of the Joint Safety and Engineering Review Panel (JSERP). The JSERP has two features that are relatively unique amongst NASA safety panels. First, NASA Engineering and SMA organizations co-chair this Panel. Previously, Safety has chaired, with Engineering as a member of the Panel. Inclusion of Engineering as a co-chair has offered greater leverage within the technical community. Second, the JSERP includes a second set of co-chairs from ESA Engineering and ESA Product Assurance and Safety (PA/S) organizations. This recognizes the international arrangement as one of partnership.Another source of both opportunity and challenge is the differing experience base of ESA and NASA. NASA has decades of experience in manned spaceflight, dating back to the Mercury program and following through Gemini, Apollo, Apollo-Soyuz, Skylab, Shuttle and the International Space Station. The risk posture for manned missions is much more rigorous than for unmanned missions. ESA brings a wealth of knowledge as well, with their flights of Ariane and Automated Transfer Vehicle (ATV), development and operation of the Columbus module on ISS, and satellite programs. The result is that both parties have developed paradigms related to risk and failure that contribute to ESM discussions.Orion represents humanity's first venture beyond Low-Earth Orbit (LEO) in over fifty years. Much of that experience is captured in program documents, but first-hand knowledge is limited to a few spaceflight veterans. Missions to LEO have offered the opportunity of direct, near-instantaneous communications and assistance, and the ability to return to Earth within a matter of hours in case of emergency. Outward-bound missions do not have these features and will require a more autonomous spacecraft with attendant safety-related issues.This paper will explore these and other challenges as we approach the flight of the first Orion vehicle.

Ciancone, Michael L.↗
NASA NTRS2012

Prevention of Over-Pressurization During Combustion in a Sealed Chamber

The combustion of flammable material in a sealed chamber invariably leads to an initial pressure rise in the volume. The pressure rise is due to the increase in the total number of gaseous moles (condensed fuel plus chamber oxygen combining to form gaseous carbon dioxide and water vapor) and, most importantly, the temperature rise of the gas in the chamber. Though the rise in temperature and pressure would reduce with time after flame extinguishment due to the absorption of heat by the walls and contents of the sealed spacecraft, the initial pressure rise from a fire, if large enough, could lead to a vehicle over-pressure and the release of gas through the pressure relief valve. This paper presents a simple lumped-parameter model of the pressure rise in a sealed chamber resulting from the heat release during combustion. The transient model considers the increase in gaseous moles due to combustion, and heat transfer to the chamber walls by convection and radiation and to the fuel-sample holder by conduction, as a function of the burning rate of the material. The results of the model are compared to the pressure rise in an experimental chamber during flame spread tests as well as to the pressure falloff after flame extinguishment. The experiments involve flame spread over thin solid fuel samples. Estimates of the heat release rate profiles for input to the model come from the assumed stoichiometric burning of the fuel along with the observed flame spread behavior. The sensitivity of the model to predict maximum chamber pressure is determined with respect to the uncertainties in input parameters. Model predictions are also presented for the pressure profile anticipated in the Fire Safety-1 experiment, a material flammability and fire safety experiment proposed for the European Space Agency (ESA) Automated Transfer Vehicle (ATV). Computations are done for a range of scenarios including various initial pressures and sample sizes. Based on these results, various mitigation approaches are suggested to prevent vehicle over-pressurization and help guide the definition of the space experiment.

Gokoglu, Suleyman A.↗
NASA NTRS2009

The International Space Station and the Space Debris Environment: 10 Years On

For just over a decade the International Space Station (ISS), the most heavily protected vehicle in Earth orbit, has weathered the space debris environment well. Numerous hypervelocity impact features on the surface of ISS caused by small orbital debris and meteoroids have been observed. In addition to typical impacts seen on the large solar arrays, craters have been discovered on windows, hand rails, thermal blankets, radiators, and even a visiting logistics module. None of these impacts have resulted in any degradation of the operation or mission of the ISS. Validating the rate of small particle impacts on the ISS as predicted by space debris environment models is extremely complex. First, the ISS has been an evolving structure, from its original 20 metric tons to nearly 300 metric tons (excluding logistics vehicles) ten years later. Hence, the anticipated space debris impact rate has grown with the increasing size of ISS. Secondly, a comprehensive visual or photographic examination of the complete exterior of ISS has never been accomplished. In fact, most impact features have been discovered serendipitously. Further complications include the estimation of the size of an impacting particle without knowing its mass, velocity, and angle of impact and the effect of shadowing by some ISS components. Inadvertently and deliberately, the ISS has also been the source of space debris. The U.S. Space Surveillance Network officially cataloged 65 debris from ISS from November 1998 to November 2008: from lost cameras, sockets, and tool bags to intentionally discarded equipment and an old space suit. Fortunately, the majority of these objects fall back to Earth quickly with an average orbital lifetime of less than two months and a maximum orbital lifetime of a little more than 15 months. The cumulative total number of debris object-years is almost exactly 10, the equivalent of one piece of debris remaining in orbit for 10 years. An unknown number of debris too small to be tracked and cataloged have also been generated, but normally with even shorter orbital lifetimes. Finally, eight collision avoidance maneuvers have been performed to avoid potential collisions between ISS and large, tracked space debris. The most recent such maneuver was accomplished by ESA's Automated Transfer Vehicle, the Jules Verne, just three months before the 10th anniversary of the launch of ISS's first element.

Johnson, Nicholas↗
DOE OSTI2022

Automated Mobility Platforms: A Framework for Versatile, Energy-Efficient Urban Transportation for the 21st Century: Preprint

This paper presents automated mobility platforms (AMPs) as a framework to deliver high-quality urban mobility. AMPs leverage advances in sensing technology developed from automated roadway vehicles to provide opportunities for efficient movement of people and goods in dense urban environments, campuses, and large facilities like airports. In this paper, we layout the concept of AMPs and how they can serve the mobility needs of the population by providing service at the intersection of micromobility (services such as electric scooters or bikes), personal rapid transit, and moving walkways. Several key attributes and justification for the AMPs concept are described, including improvements to accessibility (inherent in design) covering both socioeconomic and disability equity, safety, energy efficiency, cost-effectiveness, urban land and space management, and scalability. We summarize the results from an ongoing national laboratory-sponsored, faculty-supported, student-led investigation into AMPs that includes stakeholder engagement and technical feasibility assessment.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Real-World Driving Features for Identifying Intelligent Driver Model Parameters

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

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2021

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

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

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2022

HIVE™ [SWR-19-36]

The HIVE™ platform is a mobility services simulation platform developed to provide insight on the energy, infrastructure, service, and economic outcomes of various mobility as a service (MaaS) options. The HIVE platform takes a set of spatiotemporal travel origin-destination pairs and simulates the operation of a predefined mobility service fleet, incorporating request pooling, and various operational and charging behaviors. Hive specializes at modeling fleets of automated electric vehicles (AEVs) and can be used to site and size direct current fast charge (DCFC) stations and measure grid impacts of large-scale AEV fleets serving real-world MaaS trip demand (similar to taxis, Uber, Lyft, etc.). Potential outcomes from a Hive simulation include level of service, total vehicle miles traveled (VMT), deadheading (zero passenger) miles, simultaneous and total energy loads, average occupancy, and more. Hive is developed to generalize to new regions and can be customized to handle many scenarios and operating conditions.

Rames, Clement↗
DOE OSTI2024

Automated Inspection of Criticality Control Overpacks for Surplus Plutonium Disposition: Qualification Update – 25313

In an effort to reduce the amount of nuclear waste in South Carolina, the Department of Energy (DOE) tasked the Savannah River Site (SRS) with diluting and disposing of the amount of plutonium in the state. This process involves the movement and shipment of over 100,000 criticality control overpacks (CCOs) throughout the project lifespan, lending itself to the use of automation to reduce worker radiation exposure and more efficiently utilize human capital. Due to the large scope, this overarching process was broken down into several different “automation projects” to be developed. The first opportunity pursued was the receipt and inspection of empty CCO drums coming into SRS, identified as Automation Project 1 (AP1), and is the focus of this paper. AP1 was developed to unpack incoming CCOs and inspect them for unwanted foreign objects and any damage to the drum or its contents. This process is accomplished by the combination of an automated guided vehicle (AGV) that delivers CCOs to a robotic arm which uses a suite of custom tools to disassemble a CCO, inspect the inside and outside of the CCO and its inner criticality control container (CCC), reassemble the CCC and CCO, and apply a tamper indicating device (TID) to the inspected drum. In past years, the robotic work cell had been developed in a small-scale testing facility for proof-of-concept. This year, major improvements were made to the robotic work cell to perform the process, including integration into the final facility where CCOs will be inspected. Other technical improvements include the implementation of sensor feedback and safety relays into the control system to allow the state of the work cell to be better tracked, and additional development of the TID application process to complete the robotic inspection. Further enhancements were made to the robotic vision processes and robot pathing, as well as development on a computer vision inspection process to detect inspection criteria anomalies in CCOs. In addition to developmental improvements, the work cell underwent a six-month testing period to ensure the project requirements were met. Results of this testing period demonstrate the work cell’s capability to meet project throughput goals at an acceptable level, successfully document the status of each CCO inspected, and reduce the toll on technical operations’ human power by two thirds. At the time of this paper, the work cell is capable of autonomously handling up to eight CCOs with an AGV, delivering CCOs to and from the robot work cell, and having a robotic arm perform a full receipt and inspection procedure on each CCO. Moving forward, repeatability will be improved so that these CCOs can be run back-to-back seamlessly, as well as improving the system to handle more significant edge cases and failure modes.

Spivey, Nicholas↗
DOE OSTI2022

Energy Efficient Mobility Systems 2021 Annual Progress Report

DOE conducts research to understand how the changing mobility landscape will affect transportation energy consumption and identifies opportunities to create more efficient, affordable, reliable, accessible, equitable, and secure transportation options that enhance mobility for individuals and businesses. Within EERE, the EEMS Program is responsible for this research portfolio. This APR describes work that the EEMS Program conducted during FY 2021 in support of the EEMS Program goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Insights Into On-Demand Transit: A Case Study of Houston METRO's curb2curb Service

As modern on-demand transit (ODT) deployments increase, the insights gained from studying real-world systems prove more useful. In this study, a shared ODT system called "METRO curb2curb" in the suburbs of Houston, Texas was studied, with a particular focus on the Missouri City zone, which has the highest ridership out of the four total zones. The Missouri City zone is 18 square miles with an average daily ridership of nearly 400 as of November 2024. In order to gain a passenger perspective, data collection was performed through using the ODT system first-hand, completing 50 rides in March and April of 2023. These rides included a mix of three different types of on-demand travel accommodations - 1.) advance bookings (30 min - 2 hours ahead), 2.) as-soon-as-possible / real-time dispatch bookings, and 3.) no-reservation trips at two designated "anchor points." Wait and travel time were collected for each ride. Anecdotal findings related to ease of locating the driver/vehicle, the process of booking a ride, and popular trip purposes were also documented. This study provides an overview of the METRO curb2curb system, including its history, service provider, service area, ridership, booking options, wait time, and travel time. Intricacies related to different definitions of wait time are included. Finally, relevant considerations for automating the on-demand transit system and discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Variability Analysis of FMVSS-121 Air Brake Systems: 60-mi/hr Service Brake System Performance Data for Truck Tractors

In support of the Federal Motor Carrier Safety Administration’s (FMCSA’s) ongoing interest in connected and automated commercial vehicles, this report summarizes analyses conducted to quantify variability in stopping distance tests conducted on commercial truck tractors. The data used were retrieved from tests performed under the controlled conditions specified for FMVSS-121 air brake system compliance testing. The report explores factors affecting the variability of the service brake stopping distance as defined by 49 CFR 571.121, S5.3.1 Stopping Distance—trucks and buses stopping distance. Variables examined in this analysis include brake type, weight, wheelbase, and tractor antilock braking system (ABS). This analysis uses existing test data collected between 2010 and 2019. Several of the examined parameters affected both tractor stopping distance and stopping distance variability. First, the average stopping distance for disc/disc brakes was shorter than either drum/drum or disc/drum brakes, but brake type did not have a statistically significant effect on stopping distance variability. Second, tractors with a gross vehicle weight rating (GVWR) of 45,000-50,000 lb had shorter stopping distances than any other examined weight category. Weight was found to have a significant effect on stopping distance variability; the 50,000-55,000-lb GVWR range had more variability than both the next lower (45,000-50,000 lb) and next higher (55,000-60,000 lb) range.

Lascurain, Mary Beth↗
NASA NTRS1974

TERSSE: Definition of the Total Earth Resources System for the Shuttle Era. Volume 4: The Role of the Shuttle in the Earth Resources Program

The potential of the space shuttle as a platform for captive earth resources payloads in the sortie mode, and as a launch and services vehicle for automated earth resources spacecraft is examined. The capabilities of the total space transportation system which are pertinent to earth resources sorties and automated spacecraft are included.

Source record↗
NASA NTRS1979

Mining and beneficiation of lunar ores

The beneficiation of lunar plagioclase and ilmenite ores to feedstock grade permits a rapid growth of the space manufacturing economy by maximizing the production rate of metals and oxygen. A beneficiation scheme based on electrostatic and magnetic separation is preferred over conventional schemes, but such a scheme cannot be completely modeled because beneficiation processes are empirical and because some properties of lunar minerals have not been measured. To meet anticipated shipping and processing needs, the peak lunar mining rate will exceed 1000 tons/hr by the fifth year of operation. Such capabilities will be best obtained by automated mining vehicles and conveyor systems rather than trucks. It may be possible to extract about 40 kg of volatiles (60 percent H2O) by thermally processing the less than 20 micron ilmenite concentrate extracted from 130 tons of ilmenite ore. A thermodynamic analysis of an extraction process is presented.

Bunch, T. E.↗