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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 55 records · Page 3

Clean Cities Coalitions 2020 Activity Report

The U.S. Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy's Vehicle Technologies Office (VTO) works with local Clean Cities coalitions across the country as part of its Technology Integration Program. These efforts help businesses and consumers make smarter/more-informed transportation energy choices that can save energy, lower costs, reduce reliance on imported oil, and reduce air emissions. This report summarizes the success and impact of coalition activities based on data and information provided in their annual progress reports.

33 ADVANCED PROPULSION SYSTEMS↗

2015-2017 California Vehicle Survey

The 2015-2017 California Vehicle Survey of residential and commercial light-duty vehicle owners in California assessed consumer preferences for vehicles and included a targeted sample of plug-in electric vehicle (PEV) owners. Resource Systems Group conducted the survey on behalf of the California Energy Commission. In addition to economic and demographic data, the survey integrated light-duty vehicle holding and use information with vehicle choice data collected via the stated preferences survey's set of eight vehicle and fuel type choice exercises. The PEV owner survey participants provided additional data on charging behavior, electricity rates, and their main motivations for purchasing PEVs.

1Hz data↗

Advanced satellite servicing facility studies

A NASA-sponsored systems analysis designed to identify and recommend advanced subsystems and technologies specifically for a manned Sun-synchronous platform for satellite management is discussed. An overview of system design, manned and unmanned servicing facilities, and representative mission scenarios are given. Mission areas discussed include facility based satellite assembly, checkout, deployment, refueling, repair, and systems upgrade. The ferrying of materials and consumables to and from manufacturing platforms, deorbit, removal, repositioning, or salvage of satellites and debris, and crew rescue of any other manned vehicles are also examined. Impacted subsytems discussed include guidance navigation and control, propulsion, data management, power, thermal control, structures, life support, and radiation management. In addition, technology issues which would have significant impacts on the system design are discussed.

Qualls, Garry D.↗

Structures IVHM for 3rd Generation RLVs

The primary goal of a Structures Integrated Vehicle Health Management (IVHM) system for 3rd generation Reusable Launch Vehicles (RLV) is to provide near 100% structural sensing coverage and thus eliminate both routine, and especially unplanned, inspections which are costly and time consuming. To meet this goal, significant advances in sensing and measurement system technology, data systems architectures, and structures based analysis methodology will be required to enable the needed large numbers of sensors with little weight penalty. This program will leverage X-33, 2nd Gen RLV, Shuttle, and Aviation Safety SIVHM system development experience to address this goal.

Rogowski, Robert S.↗

China’s Booming Plug-in Electric Vehicle Market—How Will It Continue?

The plug-in electric vehicle (PEV) is recognized as a pivotal component in transportation energy security and technology innovation in China. With government incentives and substantial capital investments, the Chinese PEV market has become one of the largest in the world. However, is this market boom ephemeral or will it be continuing? This chapter provides a general review of the Chinese PEV market mainly from the consumer per-spective, by summarizing the characteristics of the vehicle policies and consumer concerns in the PEV market in recent years. Moreover, it intro-duces detailed examples on how the policies, technologies, and infrastruc-ture shape the market. In addition to employing large amounts of reliable data from the China Automotive Technology and Research Center for the review, the authors also adopted a quantitative analysis tool—the New Energy and Oil Consumption Credits model—to project the potential in-dustrial impacts of the government policies in multiple scenarios. Based on the findings, it seems that the prosperity of the PEV market, incentivized by the government, will continue in the short run. The direct subsidies to PEV from the government will gradually phase out, and the supply-side push policies such as the dual-credit policy and other charging infrastruc-ture incentives will work as the invisible hand to force the automakers to invest more in the PEV segment. However, in the long run, innovation in PEV technology and business models, together with consumer acceptance will be the major factors that can sustain further market growth.

Ou, Shawn↗

Secure and Privacy Aware Data Sharing Approach for Smart Electric Vehicles

The integration of smart electric vehicles (SEVs) into smart cities marks a significant step toward creating efficient, sustainable, and connected urban spaces. However, secure and private data sharing is a major challenge as SEVs connect with smart city systems. The interaction between SEVs and consumer electronic devices (CEDs) raises serious concerns about data security and privacy. Here, to address these challenges, this article presents how blockchain technology and federated learning (FL) can address these issues. The proposed approach provides a secure and privacy-aware framework for data exchange between SEVs and CEDs in smart cities. The experiment results demonstrate the effectiveness of the proposed framework for secure data sharing and maintaining system reliability in smart city environments. It also enables trust and promotes the widespread adoption of interconnected urban technologies.

Das, Debashis [Meharry Medical College, Nashville,↗

HITMAN

HITMAN (Hermite Interpolation of Trajectories and Measurement Synthesis for Analysis of Navigators) interpolates—or estimates the unknown values between known values—flight trajectories and generates synthetic inertial measurement unit (IMU) data using Hermite splines. This Python library provides modeling and simulation capabilities to synthesize inertial measurements from discrete trajectory points, enabling researchers to create exemplar datasets for evaluating navigation algorithms in various applications, including consumer devices like smartphones and vehicles. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Walker II, Michael [Sandia National Lab. (SNL-CA),↗

Habitat Concepts for Deep Space Exploration

Future missions under consideration requiring human habitation beyond the International Space Station (ISS) include deep space habitats in the lunar vicinity to support asteroid retrieval missions, human and robotic lunar missions, satellite servicing, and Mars vehicle servicing missions. Habitat designs are also under consideration for missions beyond the Earth-Moon system, including transfers to near-Earth asteroids and Mars orbital destinations. A variety of habitat layouts have been considered, including those derived from the existing ISS designs and those that could be fabricated from the Space Launch System (SLS) propellant tanks. This paper presents a comparison showing several options for asteroid, lunar, and Mars mission habitats using ISS derived and SLS derived modules and identifies some of the advantages and disadvantages inherent in each. Key findings indicate that the larger SLS diameter modules offer built-in compatibility with the launch vehicle, single launch capability without on-orbit assembly, improved radiation protection, lighter structures per unit volume, and sufficient volume to accommodate consumables for long duration missions without resupply. The information provided with the findings includes mass and volume comparison data that should be helpful to future exploration mission planning efforts.

Smitherman, David↗

Aeroelastic effects on space shuttle dynamics

A simplified analysis procedure for determining static and dynamic aeroelastic loads for the space shuttle and space shuttle configuration is presented. A computer program, originally developed for the Atlas launch vehicle, was modified for simplifying the turbulence response analysis approach. This approach is compared with a more detailed and time consuming approach to space shuttle response to turbulence which was developed earlier. The simplification in going from interference to lumped aerodynamic data was found to give adequate results for preliminary design aeroelastic analysis. Gust loads were found to be approximately 50% of the maximum static aeroelastic loads. The autopilot became unstable when ten elastic modes were included. Elastic effects were found to be less than 3% in the static aeroelastic analysis.

Source record↗

An Approach to Reasoning Service Migration in Data and Reasoning Fabric (DRF) Implementation

In this paper we consider service migration problem for Data and Reasoning Fabric (DRF) enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage and power resources, where cloud/edge servers, and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, estimates of the response time, consumed energy and total cost are defined for each potential compute location. A mixed-integer linear program is then formulated and solved to identify optimal compute locations given QoS constraints, network infrastructure limitations, with worst-case vehicle positioning. The approach is applied to trajectory re-planning use case to avoid a collision with an emergency vehicle in real time.

Air mobility↗

Development of Automated Pipeline for Time-Resolved Link-Wise Vehicular Energy Consumption in the Chattanooga, TN Road Network

The Department of Energy (DOE) has shown strong interest in detecting energy inefficiencies in regional road networks, so as to derive energy consumed at a high spatial temporal resolution. We have developed a workflow to automate the estimation of time-resolved vehicular energy consumption over each link in a road network of interest. The road network used in the current work is centered around the city of Chattanooga, Tennessee and its bordering regions. Utilizing the most mature road network for the Chattanooga, TN region, vehicle speed & count data from TomTom in conjunction with machine learning methods, we have developed an automated pipeline to estimate energy consumption for every link in the network. The first step in the pipeline is ingesting vehicle probe counts and speed estimates from TomTom API. In the next step, the probe counts, speed profiles and other exogenous data (i.e. road types, weather data, ground-truth volume counts and more) were used as input to a supervised learning algorithm to estimate the number of vehicles throughout the entire region for each road segment. These volume estimates were then mapped to a unified road network that contained additional important information such as percentage change in gradient across a link, number of lanes and link lengths that are features in pre-trained single vehicle energy-consumption models available with the RouteE software developed at NREL. The per vehicle energy consumption on each road link predicted using appropriate RouteE vehicular models were multiplied by the volume estimate for the corresponding link over a given time period to predict energy consumed per link for the time interval of interest. Currently, work is underway to improve both the RouteE per vehicle energy estimate and the volume estimates derived from TomTom probe counts. We have also explored the correlation of the link-wise energy estimates with the features of the pre-trained RouteE machine learning model in order to gain insight into what factors contribute most to the link-wise energy consumption.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗

Apollo Soyuz Test Project Weights and Mass Properties Operational Management System

The Apollo Soyuz Test Project (ASTP) Weights and Mass Properties Operational Management System was established to assure a timely and authoritative method of acquiring, controlling, generating, and disseminating an official set of vehicle weights and mass properties data. This paper provides an overview of the system and its interaction with the various aspects of vehicle and component design, mission planning, hardware and software simulations and verification, and real-time mission support activities. The effect of vehicle configuration, design maturity, and consumables updates is discussed in the context of weight control.

Collins, M. A., Jr.↗

The Integrated Medical Model: Statistical Forecasting of Risks to Crew Health and Mission Success

The Integrated Medical Model (IMM) helps capture and use organizational knowledge across the space medicine, training, operations, engineering, and research domains. The IMM uses this domain knowledge in the context of a mission and crew profile to forecast crew health and mission success risks. The IMM is most helpful in comparing the risk of two or more mission profiles, not as a tool for predicting absolute risk. The process of building the IMM adheres to Probability Risk Assessment (PRA) techniques described in NASA Procedural Requirement (NPR) 8705.5, and uses current evidence-based information to establish a defensible position for making decisions that help ensure crew health and mission success. The IMM quantitatively describes the following input parameters: 1) medical conditions and likelihood, 2) mission duration, 3) vehicle environment, 4) crew attributes (e.g. age, sex), 5) crew activities (e.g. EVA's, Lunar excursions), 6) diagnosis and treatment protocols (e.g. medical equipment, consumables pharmaceuticals), and 7) Crew Medical Officer (CMO) training effectiveness. It is worth reiterating that the IMM uses the data sets above as inputs. Many other risk management efforts stop at determining only likelihood. The IMM is unique in that it models not only likelihood, but risk mitigations, as well as subsequent clinical outcomes based on those mitigations. Once the mathematical relationships among the above parameters are established, the IMM uses a Monte Carlo simulation technique (a random sampling of the inputs as described by their statistical distribution) to determine the probable outcomes. Because the IMM is a stochastic model (i.e. the input parameters are represented by various statistical distributions depending on the data type), when the mission is simulated 10-50,000 times with a given set of medical capabilities (risk mitigations), a prediction of the most probable outcomes can be generated. For each mission, the IMM tracks which conditions occurred and decrements the pharmaceuticals and supplies required to diagnose and treat these medical conditions. If supplies are depleted, then the medical condition goes untreated, and crew and mission risk increase. The IMM currently models approximately 30 medical conditions. By the end of FY2008, the IMM will be modeling over 100 medical conditions, approximately 60 of which have been recorded to have occurred during short and long space missions.

Fitts, M. A.↗

Semi-Automatic Geographic Information System Framework for Creating Photo-Realistic Digital Twin Cities to Support Autonomous Driving Research

Digital twin cities are frequently used in vehicle and traffic simulations to render realistic on-road driving scenarios under various traffic and environmental conditions. These digital twins provide a high-fidelity replica of the physical world (e.g., buildings, roads, infrastructures, traffic) to create three-dimensional (3D) virtual-physical environments to support various emerging vehicle and transportation technologies such as connected and automated vehicles. These virtual environments provide a cost-effective digital proving ground to evaluate, validate, and test emerging technologies that include control algorithms, localization, perception, and sensors. Replicating a real-world traffic scenario in a digital twin using a traditional 3D modeling approach is a time-consuming and labor-intensive effort. Here this paper presents a semi-automated spatial framework to construct realistic 3D digital twin cities to support autonomous driving research using readily available geographic information system (GIS) data and 3D prefabricated (prefab) models. We start with a comprehensive review of geospatial data sources of essential digital entities required in a 3D digital twin city and present an integrated GIS-3D modeling pipeline using customized QGIS/GDAL and Blender scripting in Python. The pipeline outputs are realistic 3D digital twin cities compatible with common vehicle simulation software, such as CARLA and IPG CarMaker. The paper closes with a showcase to demonstrate the quality and usability of a digital twin city created to replicate the Shallowford Road corridor in Chattanooga in both Unity and Unreal engine-based virtual environment. The generated digital twin city can be applied to a hardware-in-the-loop simulation environment with an actual testing vehicle to facilitate autonomous driving research.

33 ADVANCED PROPULSION SYSTEMS↗

Current CFD Practices in Launch Vehicle Applications

The quest for sustained space exploration will require the development of advanced launch vehicles, and efficient and reliable operating systems. Development of launch vehicles via test-fail-fix approach is very expensive and time consuming. For decision making, modeling and simulation (M&S) has played increasingly important roles in many aspects of launch vehicle development. It is therefore essential to develop and maintain most advanced M&S capability. More specifically computational fluid dynamics (CFD) has been providing critical data for developing launch vehicles complementing expensive testing. During the past three decades CFD capability has increased remarkably along with advances in computer hardware and computing technology. However, most of the fundamental CFD capability in launch vehicle applications is derived from the past advances. Specific gaps in the solution procedures are being filled primarily through "piggy backed" efforts.on various projects while solving today's problems. Therefore, some of the advanced capabilities are not readily available for various new tasks, and mission-support problems are often analyzed using ad hoc approaches. The current report is intended to present our view on state-of-the-art (SOA) in CFD and its shortcomings in support of space transport vehicle development. Best practices in solving current issues will be discussed using examples from ascending launch vehicles. Some of the pacing will be discussed in conjunction with these examples.

Kwak, Dochan↗

Some aspects of hybrid-zeppelins

To increase an airship's maneuverability and payload capacity as well as to save bouyant gas it is proposed to outfit it with a slender delta-wing, which carries about one half of the total take-off weight of the vehicle. An optimization calculation based on the data of LZ 129 (the last airship, which saw passenger-service) leads to a Hybrid-Zeppelin with a wing of aspect-ratio 1.5 and 105 m span. The vehicle carries a payload of 40% of it's total take-off weight and consumes 0.8 t fuel per ton payload over a distance of 10000 km.

Mackrodt, P. A.↗

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗

Logistics Rates and Assumptions for Future Human Spaceflight Missions Beyond LEO

As NASA prepares for future human spaceflight missions with extended crew duration in destinations beyond low Earth orbit (LEO), the Agency has focused itself on understanding the drivers to sustainably support human life beyond Earth’s atmosphere. Future missions to deep space, the lunar surface, and eventually the Martian surface pose new challenges in ensuring the crew is sufficiently supplied with all necessary materials. To mitigate the risk of not delivering sufficient consumables and logistics for human spaceflight missions, NASA has examined past human space mission data and developed metabolic modeling to determine estimates for the crew consumption rates of fluids, solid consumables, and additional equipment needed. This paper is a compilation of guidelines, rates, and assumptions necessary to evaluate the logistics needs for future human exploration conceptual missions beyond LEO, providing a starting point and resource of information regarding usage rates and overall logistics supply planning for crewed exploration missions. Logistics represent all equipment and supplies not installed as part of the vehicle that are needed to support mission activities. Logistics can be further divided into specific categories, including consumables, maintenance items, spares, utilization, outfitting, as well as any packaging required. This paper will also provide use case examples of logistics needs to support human missions in deep space, including a conceptual lunar surface mission. The paper provides information necessary to calculate the mass and volume of known logistics for conceptual future human exploration missions beyond LEO. The assumptions in the paper are updated versions of previous assumptions made by the Agency and were derived from a number of sources, including International Space Station (ISS) historical usage and resupply rates, the Life Support Baseline Values and Assumptions Document (BVAD) 2022, Human Integration Design Handbook (HIDH) 2014, and data gathered from NASA human spaceflight programs and projects. The primary goal of the paper is to establish a set of consistent reference rates that multiple teams and groups can utilize to conduct logistics analysis and compare cases. This methodology is for initial estimates of conceptual human missions and does not take the place of detailed analysis for programs, nor does it provide requirements for programs.

Human Spaceflight↗