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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Interactions Between Climate Policy and Technology-influenced Travel Behavior: Mitigating Induced Demand from CACC

Advances in vehicle technology have influenced the development of automated vehicle systems, where vehicles that do not require human intervention are already deployed in the roadway networks. While these advances are proved to increase roadway safety and highway capacity, more research is needed to understand the long-term and regional-level impacts on mobility, land use, energy consumption, and emissions. This study proposes a multi-model approach to analyze the effect of vehicle automation and deep decarbonization policies over a period from 2020 to 2040 in Austin, Texas. We use the Global Change Analysis Model (GCAM) to develop internally the scenarios that are then passed to the SMART Mobility modeling workflow, a large-scale simulation framework combining the POLARIS activity-based travel demand model and mesoscopic traffic simulator with the Autonomie vehicle energy consumption model and the UrbanSim land use simulator. Results suggest that the introduction of vehicles with advanced automation could increase fuel consumption when no decarbonization policies are implemented. Also, advances in vehicle technology research and development could lead to a decline in energy use in the long-term. Energy pricing and vehicle electrification incentives could help reduce the impact of vehicle automation. Finally, our analysis indicates the relevance of introducing land use processes in longterm vehicle automation studies.

land use↗
NASA NTRSDate not supplied

From the Knowledge-based Digital Platform (KbDP) Concept for Advanced Air Mobility Research to a Preliminary Prototype

Advanced Air Mobility (AAM) encompasses a range of innovative operational and technological changes to aviation (electric aircraft, increasingly automated aircraft, increasingly automated airspace operations, etc.) that are transforming aviation’s role in everyday movement of people and goods. There are multiple associated concepts and use cases for AAM, all interrelated, including small Unmanned Aircraft System (UAS) Traffic Management (UTM), Upper-Class E Traffic Management (ETM), Extensible Traffic Management (xTM), Regional Air Mobility (RAM), and Urban Air Mobility (UAM). These AAM operations must integrate with traditional Air Traffic Management (ATM) operations, as well as non-aviation modes of transportation and logistics. National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from the information database, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Expected benefits of this concept include improved technology transfers from research to production, improved research portfolio investments, and research outcomes that are more integrated with all aspects of the multi-modal transportation problem. The preliminary KbDP prototype has been realized using UAM as a pathfinder use case and developed by a team of system engineer, software developer, data scientist, and interns.

Systems Engineering↗
DOE OSTI2023

Deliver Signal Phase and Timing (SPAT) for Energy Optimization of Vehicle Cohort Via Cloud-Computing and LTE Communications

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques. An emulation-based algorithm is applied to predict the traffic signal state changes to provide adequate prediction horizon (e.g., at minimum 2 minutes) for the cohort energy optimization. In fact, the same connectivity and SPAT delivery methodology has been applied to traffic signalized intersections nationwide in the United States upon public agency approvals for access to their firewalled traffic control network and signal control systems or directly to individual controllers. This methodology proves its effectiveness and potential for rapid growth of such SPAT deliveries at mass production scale without needing infrastructure hardware retrofit or excessive communication means. To support the energy optimization of light and heavy-duty vehicle cohorts of mixed automation and propulsion systems (EV, ICE and hybrid), the connection and SPAT deliveries at two sites were completed, including public roads in Washtenaw County, Michigan and closed track test sites at American Center for Mobility (ACM) in Ypsilanti, Michigan. However, only closed test track results at ACM will be presented in this paper. A neuroevolution based optimizer is developed and implemented to control the speed of a vehicle cohort with different propulsion systems and automation levels. Closed track tests showed significant energy savings of the cohort operation.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRSDate not supplied

Integration of Automated Systems (IAS) Flight Test Overview

The Integration of Automated Systems (IAS) Project is conducting a series of 2023 flight tests supporting NASA's Advanced Air Mobility (AAM) and National Campaign efforts. These flights include crewed, test (i.e., ownship) and traffic (i.e., intruder) aircraft that will fly with unique technologies onboard. The presentation will include overviews of the Hazard Perception and Avoidance (HPA) and Flight Path Management (FPM) technical areas but will focus primarily on HPA. HPA will test the FAA's Airborne Collision Avoidance System X (ACAS X), a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant, ACAS Xr, is designed to accommodate existing helicopter platforms and in-development, vertical takeoff and landing (VTOL) concepts, which are critical to the emerging AAM concept of operations. Two configurations of ACAS Xr will be examined: Collision Avoidance System (CAS, similar to the Traffic Collision Avoidance System [TCAS] II) and Detect and Avoid (DAA, previously developed to provide added situational awareness for uncrewed aircraft). Additionally, this system will be explored during cruise and low-speed flight as well as flights within en-route, structured (i.e., dense/urban), and terminal airspaces. Scripted flight conflicts will be conducted, and these conflicts will be mitigated through maneuvers that are manual (i.e., performed by the pilots) or automated (i.e., achieved by the cooperation of the program middleware and onboard ownship systems). Objective data will be collected involving system and pilot performance as well as pilot decisions; subjective data will include pilot opinions of ACAS Xr's alerting and guidance as well as the automated maneuvers.

detect and avoid↗
NASA NTRS2015

Data-Driven Surface Traversability Analysis for Mars 2020 Landing Site Selection

The objective of this paper is three-fold: 1) to describe the engineering challenges in the surface mobility of the Mars 2020 Rover mission that are considered in the landing site selection processs, 2) to introduce new automated traversability analysis capabilities, and 3) to present the preliminary analysis results for top candidate landing sites. The analysis capabilities presented in this paper include automated terrain classification, automated rock detection, digital elevation model (DEM) generation, and multi-ROI (region of interest) route planning. These analysis capabilities enable to fully utilize the vast volume of high-resolution orbiter imagery, quantitatively evaluate surface mobility requirements for each candidate site, and reject subjectivity in the comparison between sites in terms of engineering considerations. The analysis results supported the discussion in the Second Landing Site Workshop held in August 2015, which resulted in selecting eight candidate sites that will be considered in the third workshop.

Ono, Masahiro↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

- NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future. - (see https://www.nasa.gov/uam-overview/ for more information) - The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). - The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and artificial intelligence capabilities. This presentation gives an overview of the Knowledge Graph and ChatGPT applications within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

systems engineering↗
NASA NTRSDate not supplied

Evaluation of Novel eVTOL Aircraft Automation Concepts

As new electric propulsion technologies have matured, many new electric propulsion Vertical Takeoff and Landing (eVTOL) aircraft concepts have been proposed. These new aircraft concepts enable the creation of new aviation markets including Urban Air Mobility (UAM) but also pose new challenges in safety assurance. One early challenge is the evaluation of the diverse aircraft configurations and accompanying advanced control systems and automation designed to aid controllability. This paper describes a research activity to propose candidate means of evaluation for the aircraft concepts and automation in the context of UAM operations. The research activity proposes the adaptation of an evaluation design standard used by the military for advanced rotorcraft along with proposed descriptions and definitions to support evaluation of diverse automated concepts in the civilian eVTOL community.

AAM↗
DOE OSTI2021

FY2020 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2020 focused on analytical research and large-scale modeling and simulation to understand the impacts that new mobility technologies and services will have at the vehicle-, traveler-, and overall transportation system-level. This research included the development of a multi-fidelity, end-to-end transportation system models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results from projects within each of the EEMS activity areas. The Computational Modeling and Simulation key activity area summarizes work within the sub-areas of (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) Artificial Intelligence, High-Performance Computing, and Data Analytics, and (3) Core Simulation and Evaluation Tools. Additionally, the program’s advanced R&D projects are summarized within (4) the Connectivity and Automation Technology key activity area. Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

FY2019 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2019 focused on analytical research to understand the impacts that new mobility technologies and services will have at the vehicle, traveler, and overall transportation system-level. This research included the development of vehicle and transportation system simulation models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results for projects within four of the five EEMS activity areas: (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) High Performance Computing and Big Data Solutions for Mobility Data, (3) Advanced R&D Projects conducted by industry and academia, and (4) Core Modeling, Simulation, and Evaluation, Similarly, the remaining EEMS activity area – (5) Living Labs (managed under VTO’s Technology Integration Program). Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2024

Integration of Automated Systems Test Campaign NC-IAS

NASA’s 2022 strategic plan included the goal to catalyze economic growth and drive innovation in the aviation industry, and to address challenges in air transportation and airspace management within the National Airspace System (NAS). The Aeronautics Research Mission Directorate (ARMD) specifically was tasked with leading aviation innovation to enable safe and sustainable air transportation through revolutionary vehicle advances and efficient flight operations. As a part of meeting these objectives and advancing air mobility concepts, NASA initiated the National Campaign (NC) in the Advanced Air Mobility (AAM) Project within the Airspace Operations and Safety Program (AOSP). The NC was designed to support operational demonstrations with industry as well as the research and development needed to support NASA-led research flight demonstrations. Within NC, the Integration of Automated Systems (IAS), an NC activity, tested and evaluated flight deck automation and airspace operations management functions needed to enable Urban Air Mobility (UAM) operations. This was accomplished through a partnership with Sikorsky Aircraft (specifically Sikorsky Innovations), a Lockheed Martin company, and DARPA (Defense Advanced Research Projects Agency), by leveraging two automation-enabled helicopters equipped with unique capabilities that enabled NASA to develop and test two-ship conflict encounters to demonstrate flight path management and hazard avoidance technologies. The enabler in this testing was NASA-developed “Middleware” (MW) software (also known as Expandable Variable Autonomy Architecture, or EVAA), which among other things allowed multiple algorithms to be incorporated into one software build that was hosted on the dissimilar-type Sikorsky helicopters. The IAS test campaign period of performance was from March 2022 through October 2023 and was structured as a phased, or spiral, approach that ultimately led to the first-ever demonstration of two-ship UAM/AAM operations designed to safely choreograph specific conflict encounters and mission scenarios to test the research algorithms for strategic and tactical aircraft deconfliction. Lessons learned are included in the body of the report. Data collected will be used to inform FAA and industry standards groups on the increasingly automated systems needed for future AAM operations. The test encounters developed for these flight tests were proven to be highly predictable, repeatable, and safely exercised flight path planning and Detect and Avoid (DAA) algorithms. These same test encounters should be leveraged by future flight test campaigns to verify that operational safety is not compromised as the AAM architecture matures. Next steps include repeating similar encounters using unmanned aircraft carrying DAA sensors in the National Airspace.

AAM↗
DOE OSTI2021

The Highly Integrated Vehicle Ecosystem (HIVE): A Platform for Managing the Operations of On-Demand Vehicle Fleets

This paper introduces the Highly Integrated Vehicle Ecosystem (HIVE), a transportation modeling tool developed by within the Center for Integrated Mobility Sciences (CIMS) group at the National Renewable Energy Laboratory (NREL). HIVE is an agent-based supply/demand model for Mobility on Demand (MoD) which mixes agent-based modeling and centralized dispatch for automated and human-driven fleets and ride hail passengers. Research questions using HIVE span multiple categories, including intelligent fleet planning (assessing fleet, battery, and infrastructure investment decisions), intelligent fleet control (charge management, vehicle dispatching) and strategic business model decision-making (depot-based full-time drivers versus gig-based drivers, human-driven versus automated). The components of the HIVE model are explained and then HIVE is demonstrated in a case study using demand data from the New York City Taxi & Limousine Commission data set.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1988

Systems integration for the Kennedy Space Center (KSC) Robotics Applications Development Laboratory (RADL)

A laboratory for developing robotics technology for hazardous and repetitive Shuttle and payload processing activities is discussed. An overview of the computer hardware and software responsible for integrating the laboratory systems is given. The center's anthropomorphic robot is placed on a track allowing it to be moved to different stations. Various aspects of the laboratory equipment are described, including industrial robot arm control, smart systems integration, the supervisory computer, programmable process controller, real-time tracking controller, image processing hardware, and control display graphics. Topics of research include: automated loading and unloading of hypergolics for space vehicles and payloads; the use of mobile robotics for security, fire fighting, and hazardous spill operations; nondestructive testing for SRB joint and seal verification; Shuttle Orbiter radiator damage inspection; and Orbiter contour measurements. The possibility of expanding the laboratory in the future is examined.

Davis, V. Leon↗
NASA NTRS1994

ASI's space automation and robotics programs: The second step

The strategic decisions taken by ASI in the last few years in building up the overall A&R program, represent the technological drivers for other applications (i.e., internal automation of the Columbus Orbital Facility in the ESA Manned Space program, applications to mobile robots both in space and non-space environments, etc...). In this context, the main area of application now emerging is the scientific missions domain. Due to the broad range of applications of the developed technologies, both in the in-orbit servicing and maintenance of space structures and scientific missions, ASI foresaw the need to have a common technological development path, mainly focusing on: (1) control; (2) manipulation; (3) on-board computing; (4) sensors; and (5) teleoperation. Before entering into new applications in the scientific missions field, a brief overview of the status of the SPIDER related projects is given, underlining also the possible new applications for the LEO/GEO space structures.

Dipippo, Simonetta↗
NASA NTRS2013

Beyond NextGen: AutoMax Overview and Update

Main Message: National and Global Needs - Develop scalable airspace operations management system to accommodate increased mobility needs, emerging airspace uses, mix, future demand. Be affordable and economically viable. Sense of Urgency. Saturation (delays), emerging airspace uses, proactive development. Autonomy is Needed for Airspace Operations to Meet Future Needs. Costs, time critical decisions, mobility, scalability, limits of cognitive workload. AutoMax to Accommodate National and Global Needs. Auto: Automation, autonomy, autonomicity for airspace operations. Max: Maximizing performance of the National Airspace System. Interesting Challenges and Path Forward.

ATM↗
NASA NTRSDate not supplied

Digital Assistance for System Requirement Discovery and Analysis using Machine Learning Natural Language Processing Algorithm

NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future (see https://www.nasa.gov/uam-overview/ for more information). The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and machine learning natural language processing (ML NLP, or just NLP) capabilities. This presentation gives an overview of the NLP application within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

ATM↗