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Results for “mobility challenges”

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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

High Performance Torso Cooling Garment

The concept proposed in this paper is to improve thermal efficiencies of the liquid cooling and ventilation garment (LCVG) in the torso area, which could facilitate removal of LCVG tubing from the arms and legs, thereby increasing suited crew member mobility. EVA space suit mobility in micro-gravity is challenging, and it becomes even more challenging in the gravity of Mars. By using shaped water tubes that greatly increase the contact area with the skin in the torso region of the body, the heat transfer efficiency can be increased. This increase in efficiency could provide the required liquid cooling via torso tubing only; no arm or leg LCVG tubing would be required. Benefits of this approach include increased crewmember mobility, enhanced evaporation cooling, increased comfort during Mars EVA tasks, and easing of the overly dry condition in the helmet associated with the Advanced Extravehicular Mobility Unit (EMU) ventilation loop currently under development. This report describes analysis and test activities performed to evaluate the potential improvements to the thermal performance of the LCVG. Analyses evaluated potential tube shapes for improving the thermal performance of the LCVG. The analysis results fed into the selection of flat flow strips to improve thermal contact with the skin of the suited test subject. Testing of small segments was performed to compare thermal performance of the tubing approach of the current LCVG to the flat flow strips proposed as the new concept. Results of the testing is presented along with recommendations for future development of this new concept.

Conger, Bruce↗
DOE OSTI2022

Transforming Energy Through Sustainable Mobility: Expanding Low-Carbon Transportation R&D Solutions

As the nation's premier laboratory for cutting-edge transportation decarbonization research and development solutions, the National Renewable Energy Laboratory (NREL) pioneers the creation and deployment of sustainable mobility technologies and strategies, with a focus on slashing transportation sector greenhouse gas emissions and combatting climate change. Trucks, planes, cargo ships, and other difficult-to-decarbonize vehicles are part of this essential transition for the transportation sector, which is currently the nation's largest source of the greenhouse gas emissions. NREL provides the scientific building blocks needed to spur innovation through multifaceted analysis, research, and engineering. This work acts as a catalyst to help industry bring affordable, high-performance, energy-efficient, and low-emission modes of transport and related infrastructure to market sooner. Our researchers collaborate closely with academic, government, and industry partners to design better batteries, drivetrains, and engines. They develop technologies for high-power charging, thermal management, energy storage, and power electronics. They are also reimagining fuels and combustion while creating sustainable lightweight materials. Unbiased expert guidance - backed by credible data and analysis, tools, and scientific rigor - empowers partners to make informed decisions about sustainable transportation. NREL recognizes that communities with limited mobility options face reduced access to employment opportunities, health care, and education, lowering overall quality of life. Alongside partners, NREL experts are creating transportation solutions that meet community-identified needs and increase mobility equity in historically underserved and overburdened communities. Rather than providing a one-size-fits-all solution, we take an interdisciplinary approach to mobility equity that considers the needs and challenges of diverse groups, maximizing benefits at the individual, community, and societal levels.

ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

Mobile Lidar as A Tool for Terrestrial and Planetary Cave Exploration and Mapping

The Kinematic Navigation and Cartography Knapsack (KNaCK) team at NASA’s Marshall Space Flight Center in Huntsville, Alabama is developing tools that enable ultra-high resolution terrain mapping and navigation using mobile LiDAR (Light Detection and Ranging) and SLAM (Simultaneous Localization and Mapping) algorithms in fully GPS-denied and unilluminated environments [1,2]. The backpack mounted LiDAR instrument under development by our team demonstrates the potential of mobile SLAM LiDAR for use in challenging lunar and planetary surface environments as well as for lunar, planetary, and terrestrial cave exploration, study, and utilization.

Lunar Skylight↗
DOE OSTI2019

LLOCUS: Learning-based Localization Using crowdSourcing

We propose a novel learning-based system, LLOCUS, enabled by crowdsourcing on mobile devices, for localizing mobile transmitters with unknown transmit power. Our approach carefully tackles several challenges in learning and localizing using a crowdsourced system with both ends’ mobility, the ‘ends’ being the transmitters and the receivers. We decouple the problem of localizing a transmitter with unknown transmit power into two problems, 1) predicting the transmit power of an unknown transmitter, and 2) localizing a transmitter with known transmit power. LLOCUS first estimates the transmit power of the unknown transmitter and then scales the reported RSS values such that the unknown transmit power problem is transparent to the method of localization. We evaluate LLOCUS using four experiments, in different indoor and outdoor environments, with the size of the areas ranging from 225 to 5500 square meters. We find that LLOCUS reduces the localization error by 17-47%, over a number of non-learning methods. LLOCUS also estimates the transmit power of an unknown transmitter with average error in the range of 3-5 dB, across our experiments.

42 ENGINEERING↗
DOE OSTI2019

Joint Optimization Scheme for the Planning and Operations of Shared Autonomous Electric Vehicle Fleets Serving Mobility on Demand

As the transportation sector undergoes three major transformations—electrification, shared/on-demand mobility, and automation—there are new challenges to analyzing the impacts of these trends on both the transportation system and the power sector. Most models that analyze the requirements of fleets of shared autonomous electric vehicles (SAEVs) operate at the scale of an urban region, or smaller. A quadratically constrained, quadratic programming problem is formulated, designed to model the requirements of SAEVs at a national scale. The size of the SAEV fleet, the necessary charging infrastructure, the fleet charging schedule, and the dispatch required to serve demand for trips in a region are treated as decision variables. By minimizing both the amortized cost of the fleet and chargers as well as the operational costs of charging, it is possible to explore the coupled interactions between system design and operation. To apply the model at a national scale, key complications about fleet operations are simplified; but a detailed agent-based regional simulation model to parameterize those simplifications is leveraged. Preliminary results are presented, finding that all mobility in the United States (U.S.) currently served by 276 million personally owned vehicles could be served by 12.5 million SAEVs at a cost of $\$ 0.27$/vehicle-mile or $ 0.18/passenger-mile. In conclusion, the energy requirements for this fleet would be 1142 GWh/day (8.5% of 2017 U.S. electricity demand) and the peak charging load 76.7 GW (11% of U.S. power peak). Several model sensitivities are explored, and it is found that sharing is a key factor in the analysis.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

SU-F-T-80: A Mobile Application for Intra-Operative Electron Radiotherapy Treatment Planning

Purpose: Intraoperative electron radiotherapy (IORT) poses a unique set of challenges for treatment planning. Planning must be performed in a busy operating room environment over a short timeframe often with little advance knowledge of the treatment depth or applicator size. Furthermore, IORT accelerators can have a large number of possible applicators, requiring extensive databooks that must be searched for the appropriate dosimetric parameters. The goal of this work is to develop a software tool to assist in the planning process that is suited to the challenges faced in the IORT environment. Methods: We developed a mobile application using HTML5 and Javascript that can be deployed to tablet devices suitable for use in the operating room. The user selects the desired treatment parameters cone diameter, bevel angle, and energy (a total of 141 datasets) and desired bolus. The application generates an interactive display that allows the user to dynamically select points on the depth-dose curve and to visualize the shape of the corresponding isodose contours. The user can indicate a prescription isodose line or depth. The software performs a monitor unit calculation and generates a PDF report. Results: We present our application, which is now used routinely in our IORT practice. It has been employed successfully in over 23 cases. The interactivity of the isodose distributions was found to be of particular use to physicians who are less-frequent IORT users, as well as for the education of residents and trainees. Conclusion: This software has served as a useful tool in IORT planning, and demonstrates the need for treatment planning tools that are designed for the specialized challenges encountered in IORT. This software is the subject of a license agreement with the IntraOp Medical Corporation. This software is the subject of a license agreement between Massachusetts General Hospital / Partners Healthcare and the IntraOp Medical Corporation. CLW is consulting on software development with the IntraOp Medical Corporation.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Caves as Terrestrial Analogs for Mapping and Exploration in Planetary Surface and Subsurface Environments.

The Kinematic Navigation and Cartography Knapsack (KNaCK) team is developing tools that will enable ultra-high resolution terrain mapping and navigation at the lunar surface. KNaCK uses mobile light detection and ranging (LiDAR) and simultaneous localization and mapping (SLAM) algorithms to operate in fully GPS-denied and unilluminated environments. Testing in relevant analogs is key to perfecting this technology for planetary applications. After utilizing desert and volcanic environments for multiple tests, the team is now using caves as an additional proving ground. Tests in terrestrial caves have demonstrated the potential of mobile SLAM LiDAR for use in challenging surface environments such as steep walled craters and permanently shadowed regions as well as for lunar, planetary, and terrestrial cave exploration, study, and utilization.

LiDAR↗
DOE OSTI2015

A self-consistent first-principle based approach to model carrier mobility in organic materials

Transport through thin organic amorphous films, utilized in OLEDs and OPVs, has been a challenge to model by using ab-initio methods. Charge carrier mobility depends strongly on the disorder strength and reorganization energy, both of which are significantly affected by the details in environment of each molecule. Here we present a multi-scale approach to describe carrier mobility in which the materials morphology is generated using DEPOSIT, a Monte Carlo based atomistic simulation approach, or, alternatively by molecular dynamics calculations performed with GROMACS. From this morphology we extract the material specific hopping rates, as well as the on-site energies using a fully self-consistent embedding approach to compute the electronic structure parameters, which are then used in an analytic expression for the carrier mobility. We apply this strategy to compute the carrier mobility for a set of widely studied molecules and obtain good agreement between experiment and theory varying over several orders of magnitude in the mobility without any freely adjustable parameters. The work focuses on the quantum mechanical step of the multi-scale workflow, explains the concept along with the recently published workflow optimization, which combines density functional with semi-empirical tight binding approaches. This is followed by discussion on the analytic formula and its agreement with established percolation fits as well as kinetic Monte Carlo numerical approaches. Finally, we skatch an unified multi-disciplinary approach that integrates materials science simulation and high performance computing, developed within EU project MMM@HPC.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

Analysis of the Electrical Grid for UAM

A wide range of challenges must be overcome to conduct Urban Air Mobility (UAM) operations at scale with electric vertical takeoff and landing aircraft. This presentation is on an initial analysis of one potential infrastructure challenge for UAM: available electrical grid capacity. The success of UAM is dependent upon the availability of electricity, which will be increasingly impacted by the proliferation of ground electric vehicles over time. Under the best-case assumption that electricity can be transmitted and distributed as needed within each major interconnection (i.e., electrical grid), it is estimated that the maximum number of UAM aircraft that can charge simultaneously in the continental United States will decrease by 20% from 594,878 today to 475,177 in the year 2050.

electrical grid↗
NASA NTRSDate not supplied

A Simulation Architecture for Air Traffic Over Urban Environments Supporting Autonomy Research in Advanced Air Mobility

As part of its research, NASA investigates concepts, aircraft, and operations related to Advanced Air Mobility (AAM). One of the most challenging scenarios for AAM will be enabling safe routine access near densely populated urban centers. AAM flight operations over a regional area require a moderately high-fidelity simulation capability to develop and evaluate autonomy technologies in the urban environment. This paper aims to describe a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (tens to hundreds of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to simulate all flight phases accurately. The simulation incorporates a detailed simulated urban environment and includes glass cockpit displays to monitor aircraft operations. Simulation models integrate to simulate air and ground-based sensors, such as Radar and LiDAR. As a commercially available rendering engine, X-Plane 11 is used as the renderer to simulate vision-based sensors (such as onboard and ground-based cameras) with a detailed graphical model of a city at different times of the day and weather conditions. This paper presents the simulation and software architecture used for simulating AAM traffic over this urban region. This system enables the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward flight test evaluation.

distributed sensing↗
NASA NTRSDate not supplied

A Simulation Architecture for Air Traffic Over Urban Environments Supporting Autonomy Research in Advanced Air Mobility

As part of its research, NASA investigates concepts, aircraft, and operations related to Advanced Air Mobility (AAM). One of the most challenging scenarios for AAM will be enabling safe routine access near densely populated urban centers. AAM flight operations over a regional area require a moderately high-fidelity simulation capability to develop and evaluate autonomy technologies in the urban environment. This paper aims to describe a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (tens to hundreds of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to simulate all flight phases accurately. The simulation incorporates a detailed simulated urban environment and includes glass cockpit displays to monitor aircraft operations. Simulation models integrate to simulate air and ground-based sensors, such as Radar and LiDAR. As a commercially available rendering engine, X-Plane 11 is used as the renderer to simulate vision-based sensors (such as onboard and ground-based cameras) with a detailed graphical model of a city at different times of the day and weather conditions. This paper presents the simulation and software architecture used for simulating AAM traffic over this urban region. This system enables the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward flight test evaluation.

Distributed sensing↗
NASA NTRS2005

Location Management in a Transport Layer Mobility Architecture

Mobility architectures that place complexity in end nodes rather than in the network interior have many advantageous properties and are becoming popular research topics. Such architectures typically push mobility support into higher layers of the protocol stack than network layer approaches like Mobile IP. The literature is ripe with proposals to provide mobility services in the transport, session, and application layers. In this paper, we focus on a mobility architecture that makes the most significant changes to the transport layer. A common problem amongst all mobility protocols at various layers is location management, which entails translating some form of static identifier into a mobile node's dynamic location. Location management is required for mobile nodes to be able to provide globally-reachable services on-demand to other hosts. In this paper, we describe the challenges of location management in a transport layer mobility architecture, and discuss the advantages and disadvantages of various solutions proposed in the literature. Our conclusion is that, in principle, secure dynamic DNS is most desirable, although it may have current operational limitations. We note that this topic has room for further exploration, and we present this paper largely as a starting point for comparing possible solutions.

Eddy, Wesley M.↗
NASA NTRSDate not supplied

Analysis of the Electrical Grid for UAM

A wide range of challenges must be overcome to conduct Urban Air Mobility (UAM) operations at scale with electric vertical takeoff and landing aircraft as envisioned. This presentation is on an analysis of one potential infrastructure challenge for UAM: available electrical grid power capacity. The success of UAM depends on the availability of electricity, which will be increasingly impacted by the proliferation of ground electric vehicles over time. Under the best-case assumption that electricity can be transmitted and distributed as needed within each interconnection (i.e., electrical grid), it is estimated that the maximum number of UAM aircraft that can charge simultaneously in the continental United States will decrease by 20% from about 595 thousand in the year 2021 to about 475 thousand in the year 2050 due to the projected increase in ground electric vehicles.

electrical grid; urban air mobility; electric vehi↗
DOE OSTI2026

Cyclodextrin-Derived Porous Liquids Enabled by In Situ Solvation Shell Formation

Porous liquids (PLs) represent a unique platform for molecular separations by combining permanent porosity with liquid-phase mobility. However, it remains a formidable challenge to construct and stabilize PLs with sub-5 Å pores using readily available porous host and liquid media. Here, we report the construction of cyclodextrin (CD)-derived PLs enabled by in situ solvation shell formation. The acid–base neutralization reaction between CD and an organic base was leveraged to generate a thin ionic solvation shell around the CD host, effectively liquefying CD and preventing its segregation in the liquid base medium while preserving accessible molecular-scale cavities. Spectroscopic analysis, neutron scattering, density functional theory calculations, and molecular dynamics simulations collectively confirm the structural evolution and existence of abundant internal porosity in PLs. The unique architectures of CD-derived PLs enable highly selective encapsulation of fluorinated alkanes and significantly enhanced uptake of inert gases. This facile and generalizable strategy enables construction of high-quality PLs with engineered ultramicroporosity to facilitate molecular separations.

Cavities↗
DOE OSTI2022

Enhancement of 2D topological semimetal transport properties by current annealing

Observation of intrinsic quantum transport properties of two-dimensional (2D) topological semimetals can be challenging due to suppression of high mobility caused by extrinsic factors introduced during fabrication. We demonstrate current annealing as a method to substantially improve electronic transport properties of 2D topological semimetal flakes. Contact resistance and resistivity were improved by factors up to 2 × 10 6 and 2 × 10 4 , respectively, in devices based on exfoliated flakes of two topological semimetals, ZrSiSe and BaMnSb 2 . Using this method, carrier mobility in ZrSiSe was improved by a factor of 3800, resulting in observation of record-high mobility for exfoliated ZrSiSe. Quantum oscillations in annealed ZrSiSe appeared at magnetic fields as low as 5 T, and magnetoresistance increased by a factor of 10 4 . We argue that a thermal process underlies this improvement. Finally, Raman spectroscopy and analysis of quantum oscillations in ZrSiSe indicate that the phonon modes and Fermi surface area are unchanged by current annealing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2022

Resolving Lipid Structures for Threat-Agnostic Signatures

Development of threat-agnostic biosignatures is critical for the detection of and response to biological threats that go beyond the historical list-based approach. Lipids are a class of structurally diverse biomolecules that hold great promise as relevant threat-agnostic biosignatures, yet the fundamental challenge of resolving and decoding structural differences among lipids remains an obstacle in the pursuit of signature development. Structural analysis of complex lipids remains stymied due to spectral complexity and the inability to resolve low abundance lipids. To address these challenges, we integrated ozonolysis with ion mobility mass spectrometry (IMS-MS) analysis to determine the structural information for complex lipid species and (2) reduced lipid sample complexity prior to introduction into the IMS-MS using normal phase high pressure liquid chromatography (HPLC) to enable the ozonolysis of low abundance lipids. We applied these new capabilities to define the lipid structures in antimicrobial resistant (AMR) and antimicrobial susceptible (AMS) biothreat organism Yersinia pestis and cells infected with Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) variants.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2013

Real-Time Optimization for use in a Control Allocation System to Recover from Pilot Induced Oscillations

Integration of the Control Allocation technique to recover from Pilot Induced Oscillations (CAPIO) System into the control system of a Short Takeoff and Landing Mobility Concept Vehicle simulation presents a challenge because the CAPIO formulation requires that constrained optimization problems be solved at the controller operating frequency. We present a solution that utilizes a modified version of the well-known L-BFGS-B solver. Despite the iterative nature of the solver, the method is seen to converge in real time with sufficient reliability to support three weeks of piloted runs at the NASA Ames Vertical Motion Simulator (VMS) facility. The results of the optimization are seen to be excellent in the vast majority of real-time frames. Deficiencies in the quality of the results in some frames are shown to be improvable with simple termination criteria adjustments, though more real-time optimization iterations would be required.

optimal control↗