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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Predicting the Operational Acceptability of Route Advisories

NASA envisions a future Air Traffic Management system that allows safe, efficient growth in global operations, enabled by increasing levels of automation and autonomy. In a safety-critical system, the introduction of increasing automation and autonomy has to be done in stages, making human-system integrated concepts critical in the foreseeable future. One example where this is relevant is for tools that generate more efficient flight routings or reroute advisories. If these routes are not operationally acceptable, they will be rejected by human operators, and the associated benefits will not be realized. Operational acceptance is therefore required to enable the increased efficiency and reduced workload benefits associated with these tools. In this paper, the authors develop a predictor of operational acceptability for reroute advisories. Such a capability has applications in tools that identify more efficient routings around weather and congestion and that better meet airline preferences. The capability is based on applying data mining techniques to flight plan amendment data reported by the Federal Aviation Administration and data on requested reroutes collected from a field trial of the NASA developed Dynamic Weather Routes tool, which advised efficient route changes to American Airlines dispatchers in 2014. 10-Fold cross validation was used for feature, model and parameter selection, while nested cross validation was used to validate the model. The model performed well in predicting controller acceptance or rejection of a route change as indicated by chosen performance metrics. Features identified as relevant to controller acceptance included the historical usage of the advised route, the location of the maneuver start point relative to the boundaries of the airspace sector containing the maneuver start (the maneuver start sector), the reroute deviation from the original flight plan, and the demand level in the maneuver start sector. A random forest with forty trees was the best performing of the five models evaluated in this paper.

route advisories↗
NASA NTRS2017

Predicting the Operational Acceptability of Route Advisories

NASA envisions a future Air Traffic Management system that allows safe, efficient growth in global operations, enabled by increasing levels of automation and autonomy. In a safety-critical system, the introduction of increasing automation and autonomy has to be done in stages, making human-system integrated concepts critical in the foreseeable future. One example where this is relevant is for tools that generate more efficient flight routings or reroute advisories. If these routes are not operationally acceptable, they will be rejected by human operators, and the associated benefits will not be realized. Operational acceptance is therefore required to enable the increased efficiency and reduced workload benefits associated with these tools. In this paper, the authors develop a predictor of operational acceptability for reroute advisories. Such a capability has applications in tools that identify more efficient routings around weather and congestion and that better meet airline preferences. The capability is based on applying data mining techniques to flight plan amendment data reported by the Federal Aviation Administration and data on requested reroutes collected from a field trial of the NASA developed Dynamic Weather Routes tool, which advised efficient route changes to American Airlines dispatchers in 2014. 10-Fold cross validation was used for feature, model and parameter selection, while nested cross validation was used to validate the model. The model performed well in predicting controller acceptance or rejection of a route change as indicated by chosen performance metrics. Features identified as relevant to controller acceptance included the historical usage of the advised route, the location of the maneuver start point relative to the boundaries of the airspace sector containing the maneuver start (the maneuver start sector), the reroute deviation from the original flight plan, and the demand level in the maneuver start sector. A random forest with forty trees was the best performing of the five models evaluated in this paper.

operational acceptability↗
NASA NTRS2016

Analysis of Multi-Flight Common Routes for Traffic Flow Management

When severe convective weather requires rerouting aircraft, FAA traffic managers employ severe weather avoidance plans (e.g., Playbook routes, Coded Departure Routes, etc.) These routes provide pilots with safe paths around weather-affected regions, and provide controllers with predictable, and often well-established flight plans. However, they often introduce large deviations to the nominal flight plans, which may not be necessary as weather conditions change. If and when the imposed traffic management initiatives (TMIs) become stale, updated shorter path flight trajectories may be found en route, providing significant time-savings to the affected flights. Multiple Flight Common Routes (MFCR) is a concept that allows multiple flights that are within a specified proximity or region, to receive updated shorter flight plans in an operationally efficient manner. MFCR is believed to provide benefits to the National Airspace System (NAS) by allowing traffic managers to update several flight plans of en route aircraft simultaneously, reducing operational workload within the TMUs of all affected ARTCCs. This paper will explore some aspects of the MFCR concept by analyzing multiple flights that have been selected for rerouting by the NAS Constraint Evaluation and Notification Tool (NASCENT). Various methods of grouping aircraft with common or similar routes will be presented, along with a comparison of the efficacy of these methods.

dynamic weather routes↗
DOE OSTI2020

Use of Multi-Attribute Utility Analyses to Assess Transportation Routes from ISFSIs - 20535

With two consolidated interim storage facilities for used nuclear fuel (UNF) having applied for storage licenses, there is the potential need to transport UNF in the U.S. from shutdown reactor sites to complete the site cleanup and from operating reactor sites who may be running out of storage options (e.g., spent fuel pools are nearly full and dry storage facilities are reaching licensed capacity). At some of the shutdown reactor sites, only the independent spent fuel storage installation (ISFSI) exists and often there remains a degraded transportation infrastructure for performing shipments from these sites. At other sites (e.g., operating reactor plants), the existing transportation infrastructure on the site is unchanged and sufficient for moving UNF, but the environment around the route may have changed (e.g., what was once a rural setting has transformed into a more urban setting) and/or local stakeholders want their input taken into consideration in UNF transportation activities. So, although preliminary evaluations have been performed to identify viable modes of transport from these sites using rail, barge, and/or heavy haul truck, if multiple options exist for moving the UNF from the site (e.g., multiple truck routes or a truck and a barge route both exist) then an evaluation that takes input from multiple stakeholders should be considered to identified the preference (e.g., ranking) of routes from a site. Orano has supported the development of several studies examining the viable routes for removing UNF from some of these sites and has utilized a multi-attribute utility analysis (MUA) to establish a ranking of routes and associated modes to move the UNF from the ISFSIs to a Class I rail carrier. The ultimate result from the MUA is a list(s) of the most to the least favored/preferred routes from the site. This paper provides an overview of the MUA methodology and provides an example of its application to a site with a shutdown reactor. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Passage Route and Upstream Migration Success: A Case Study of Snake River Salmonids Ascending Lower Granite Dam

Abstract Lower Granite Dam is the last dam that federally protected Snake River salmonids Oncorhynchus spp. must ascend during their spawning migration. The dam has an adult fish ladder equipped with a trapping system to facilitate fisheries research and hatchery broodstock collection. There are three possible passage routes through the adult ladder: trapped, shunted, and free passage. During the adult trapping season, all fish must swim through 0.305-m shunt pipes outfitted with PIT tag arrays that allow the selection of fish for trapping. Selected fish use the “trapped” route and are kept in a holding area for up to 20 h before being sampled and returned to the ladder. Unselected fish use the “shunted” route and immediately resume upstream migration after swimming through the pipes. When the trap is not in operation, the shunted route is inaccessible, and all fish use the “free passage” route to ascend the ladder without additional impediment. In 2016, a temporary change in ladder operations permitted free passage for a portion of the trapping season. Our study used this rare opportunity to evaluate how different passage routes affect in-ladder transit time and upstream homing success for five salmonid stocks: Sockeye Salmon Oncorhynchus nerka; steelhead O. mykiss; and spring-, summer-, and fall-run Chinook Salmon O. tshawytscha. In 2016, only Sockeye Salmon and spring- and summer-run Chinook Salmon were given access to free passage, and we found evidence that free passage increased subsequent detection at natal sites upstream. An expanded analysis of shunted versus trapped fish during the years 2012–2016 found no difference in rates of detection to home tributaries by route of passage for any of the five fish stocks examined.

Morrisett, Christina N.↗
DOE OSTI2018

Part 1: Tracing Russian VX to its synthetic routes by multivariate statistics of chemical attribution signatures

Chemical attribution signatures (CAS) associated with different synthetic routes used for the production of Russian VX (VR) were identified here. The goal of the study was to retrospectively determine the production method employed for an unknown VR sample. Six different production methods were evaluated, carefully chosen to include established synthetic routes used in the past for large scale production of the agent, routes involving general phosphorus-sulfur chemistry pathways leading to the agent, and routes whose main characteristic is their innate simplicity in execution. Two laboratories worked in parallel and synthesized a total of 37 batches of VR via the six synthetic routes following predefined synthesis protocols. The chemical composition of impurities and byproducts in each route was analyzed by GC/MS-EI and 49 potential CAS were recognized as important markers in distinguishing these routes using Principal Component Analysis (PCA). The 49 potential CAS included expected species based on knowledge of reaction conditions and pathways but also several novel compounds that were fully identified and characterized by a combined analysis that included MS-CI, MS-EI and HR-MS. The CAS profiles of the calibration set were then analyzed using partial least squares discriminant analysis (PLS-DA) and a cross validated model was constructed. The model allowed the correct classification of an external test set without any misclassifications, demonstrating the utility of this methodology for attributing VR samples to a particular production method. This work is part one of a three-part series in this Forensic VSI issue of a Sweden-United States collaborative effort towards the understanding of the CAS of VR in diverse batches and matrices. This part focuses on the CAS in synthesized batches of crude VR and in the following two parts of the series the influence of food matrices on the CAS profiles are investigated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Tuning Li 2 O 2 Formation Routes by Facet Engineering of MnO 2 Cathode Catalysts

In lithium-oxygen batteries, the solubility of LiO 2 intermediates in the electrolyte regulates the formation routes of the Li 2 O 2 discharge product. High-donor-number electrolytes with a high solubility of LiO 2 tend to promote the formation of Li 2 O 2 large particles following the solution route, which eventually benefits the cell capacity and cycle life. Here, we propose that facet engineering of cathode catalysts could be another direction in tuning the formation routes of Li 2 O 2 . In this work, fi-MnO 2 crystals with high occupancies of {111} or {100} facets were adopted as cathode catalysts in Li-O 2 batteries with a tetra(ethylene)glycol dimethyl ether electrolyte. The {111}-dominated beta-MnO 2 catalyzed the formation of the Li 2 O 2 discharge product into large toroids following the solution routes, while {100}-dominated beta-MnO 2 facilitated the formation of Li 2 O 2 thin films through the surface routes. Further computational studies indicate that the different formation routes of Li 2 O 2 could be related to different adsorption energies of LiO 2 on the two facets of beta-MnO 2 . Our results demonstrate that facet engineering of cathode catalysts could be a new way to tune the formation route of Li 2 O 2 in a low-donor-number electrolyte. Here, we anticipate that this new finding would offer more choices for the design of lithium-oxygen batteries with high capacities and ultimately a long cycle life.

25 ENERGY STORAGE↗
DOE OSTI2021

Performance Evaluation of Adaptive Routing on Dragonfly-based Production Systems

Performance of applications in production environments can he sensitive to network congestion. Cray Aries supports adaptively routing each network packet independently based on the load or congestion encountered as a packet traverses the network. Software can dictate different routing policies, adjusting between minimal and non-minimal bias, for each posted message. We have extensively evaluated the sensitivity of the routing bias selection on application performance as well as whole system performance in both production and controlled conditions. We show that the default routing bias used in Aries-based systems is often sub-optimal and that using a higher bias towards minimal routes will not only reduce the congestion effects on the application but also will decrease the overall congestion on the network. This routing scheme results in not only improved mean performance (by up to 12%) of most production applications hut also reduced run-to-run variability. Our study prompted the two supercomputing facilities (ALCF and NERSC) to change the default routing mode on their Aries-based systems. We present the substantial improvement measured in the overall congestion management and interconnect performance in production after making this change.

Chunduri, Sudheer↗
DOE OSTI2022

Neural Architecture and Feature Search for Predicting the Ridership of Public Transportation Routes

Accurately predicting the ridership of public-transit routes provides substantial benefits to both transit agencies, who can dispatch additional vehicles proactively before the vehicles that serve a route become crowded, and to passengers, who can avoid crowded vehicles based on publicly available predictions. The spread of the coronavirus disease has further elevated the importance of ridership prediction as crowded vehicles now present not only an inconvenience but also a public-health risk. At the same time, accurately predicting ridership has become more challenging due to evolving ridership patterns, which may make all data except for the most recent records stale. One promising approach for improving prediction accuracy is to fine-tune the hyper-parameters of machine-learning models for each transit route based on the characteristics of the particular route, such as the number of records. However, manually designing a machine-learning model for each route is a labor-intensive process, which may require experts to spend a significant amount of their valuable time. To help experts with designing machine-learning models, we propose a neural-architecture and feature search approach, which optimizes the architecture and features of a deep neural network for predicting the ridership of a public-transit route. Our approach is based on a randomized local hyper-parameter search, which minimizes both prediction error as well as the complexity of the model. We evaluate our approach on real-world ridership data provided by the public transit agency of Chattanooga, TN, and we demonstrate that training neural networks whose architectures and features are optimized for each route provides significantly better performance than training neural networks whose architectures and features are generic.

Ayman, Afiya↗
DOE OSTI2018

Navigation Application Programming Interface Route Fuel Saving Opportunity Assessment on Large-Scale Real-World Travel Data for Conventional Vehicles and Hybrid Electric Vehicles

The green-routing strategy instructing a vehicle to select a fuel-efficient route benefits the current transportation system with fuel-saving opportunities. This paper introduces a navigation application programming interface (API), route fuel-saving evaluation framework for estimating fuel advantages of alternative API routes based on large-scale, real-world travel data for conventional vehicles (CVs) and hybrid electric vehicles (HEVs). Navigation APIs, such as Google Directions API, integrate traffic conditions and provide feasible alternative routes for origin-destination pairs. This paper develops two link-based fuel-consumption models stratified by link-level speed, road grade, and functional class (local/non-local), one for CVs and the other for HEVs. The link-based fuel-consumption models are built by assigning travel from many global positioning system driving traces to the links in TomTom MultiNet and road grade data from the U.S. Geological Survey elevation data set. Fuel consumption on a link is computed by the proposed model. This paper envisions two kinds of applications: (1) identifying alternate routes that save fuel, and (2) quantifying the potential fuel savings for large amounts of travel. An experiment based on a large-scale California Household Travel Survey global positioning system trajectory data set is conducted. The fuel consumption and savings of CVs and HEVs are investigated. At the same time, the trade-off between fuel saving and travel time due to choosing different routes is also examined for both powertrains.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1991

Parallel algorithms for placement and routing in VLSI design

The computational requirements for high quality synthesis, analysis, and verification of very large scale integration (VLSI) designs have rapidly increased with the fast growing complexity of these designs. Research in the past has focused on the development of heuristic algorithms, special purpose hardware accelerators, or parallel algorithms for the numerous design tasks to decrease the time required for solution. Two new parallel algorithms are proposed for two VLSI synthesis tasks, standard cell placement and global routing. The first algorithm, a parallel algorithm for global routing, uses hierarchical techniques to decompose the routing problem into independent routing subproblems that are solved in parallel. Results are then presented which compare the routing quality to the results of other published global routers and which evaluate the speedups attained. The second algorithm, a parallel algorithm for cell placement and global routing, hierarchically integrates a quadrisection placement algorithm, a bisection placement algorithm, and the previous global routing algorithm. Unique partitioning techniques are used to decompose the various stages of the algorithm into independent tasks which can be evaluated in parallel. Finally, results are presented which evaluate the various algorithm alternatives and compare the algorithm performance to other placement programs. Measurements are presented on the parallel speedups available.

Brouwer, Randall Jay↗
NASA NTRS1991

A workstation-based evaluation of a far-field route planner for helicopters

Helicopter flight missions at very low, nap of the Earth, altitudes place a heavy workload on the pilot. To aid in reducing this workload, Ames Research Center has been investigating various types of automated route planners. As part of an automated preflight mission planner, a route planner algorithm aids in selecting the overall (far-field) route to be flown. During the mission, the route planner can be used to replan a new route in case of unexpected threats or change in mission requirements. An evaluation of a candidate route planning algorithm, based on dynamic programming techniques is described. This algorithm meets most of the requirements for route planning, both preflight and during the mission. In general, the requirements are to minimize the distance and/or fuel and the deviation from a flight time schedule, and must be flyable within the constraints of available fuel and time.

Warner, David N., Jr.↗
NASA NTRS1998

Weather Avoidance Using Route Optimization as a Decision Aid: An AWIN Topical Study

The aviation community is faced with reducing the fatal aircraft accident rate by 80 percent within 10 years. This must be achieved even with ever increasing, traffic and a changing National Airspace System. This is not just an altruistic goal, but a real necessity, if our growing level of commerce is to continue. Honeywell Technology Center's topical study, "Weather Avoidance Using Route Optimization as a Decision Aid", addresses these pressing needs. The goal of this program is to use route optimization and user interface technologies to develop a prototype decision aid for dispatchers and pilots. This decision aid will suggest possible diversions through single or multiple weather hazards and present weather information with a human-centered design. At the conclusion of the program, we will have a laptop prototype decision aid that will be used to demonstrate concepts to industry for integration into commercialized products for dispatchers and/or pilots. With weather a factor in 30% of aircraft accidents, our program will prevent accidents by strategically avoiding weather hazards in flight. By supplying more relevant weather information in a human-centered format along with the tools to generate flight plans around weather, aircraft exposure to weather hazards can be reduced. Our program directly addresses the NASA's five year investment areas of Strategic Weather Information and Weather Operations (simulation/hazard characterization and crew/dispatch/ATChazard monitoring, display, and decision support) (NASA Aeronautics Safety Investment Strategy: Weather Investment Recommendations, April 15, 1997). This program is comprised of two phases, Phase I concluded December 31, 1998. This first phase defined weather data requirements, lateral routing algorithms, an conceptual displays for a user-centered design. Phase II runs from January 1999 through September 1999. The second phase integrates vertical routing into the lateral optimizer and combines the user interface into a prototype software testbed. Phase II concludes with a dispatcher and pilot evaluation of the route optimizer decision aid. This document describes work completed in Phase I in contract with NASA Langley August 1998 - December 1998. This report includes: (1) Discuss how weather hazards were identified in partnership with experts, and how weather hazards were prioritized; (2) Static representations of display layouts for integrated planning function (3) Cost function for the 2D route optimizer; (4) Discussion of the method for obtaining, access to raw data of, and the results of the flight deck user information requirements definition; (5) Itemized display format requirements identified for representing weather hazards in a route planning aid.

Source record↗
NASA NTRS2011

Defining Dynamic Route Structure

This poster describes a method for defining route structure from flight tracks. Dynamically generated route structures could be useful in guiding dynamic airspace configuration and helping controllers retain situational awareness under dynamically changing traffic conditions. Individual merge and diverge intersections between pairs of flights are identified, clustered, and grouped into nodes of a route structure network. Links are placed between nodes to represent major traffic flows. A parametric analysis determined the algorithm input parameters producing route structures of current day flight plans that are closest to todays airway structure. These parameters are then used to define and analyze the dynamic route structure over the course of a day for current day flight paths. Route structures are also compared between current day flight paths and more user preferred paths such as great circle and weather avoidance routing.

Zelinski, Shannon↗
NASA NTRS2017

Airspace Technology Demonstration 3 (ATD-3): Multi-Flight Common Route (MFCR) Technology Transfer Document Summary Version 1.0

This summary document and accompanying technology artifacts satisfy the second of three Research Transition Products (RTPs) defined in the ATD-3 Applied Traffic Flow Management (ATFM) Research Transition Team (RTT) Plan. This transfer consists of NASA's Multi-Flight Common Route (MFCR) research for efficient route corrections for en-route weather avoidance. The MFCR concept builds on the experience of the legacy Dynamic Weather Routes (DWR) and focuses on a better balance of potential savings with ATC acceptability, common route corrections options for multiple flights on similar routings, and better use of existing and/or modern automation for communication and coordination of route change options. All of these capabilities are expected to improve system performance significantly in terms of actual delay-reducing clearances issued to flights compared to that of the DWR tool and operating concept.

tech transfer↗
NASA NTRS2016

AIRNOISE: A Tool for Preliminary Noise-Abatement Terminal Approach Route Design

Noise from aircraft in the airport vicinity is one of the leading aviation-induced environmental issues. The FAA developed the Integrated Noise Model (INM) and its replacement Aviation Environmental Design Tool (AEDT) software to assess noise impact resulting from all aviation activities. However, a software tool is needed that is simple to use for terminal route modification, quick and reasonably accurate for preliminary noise impact evaluation and flexible to be used for iterative design of optimal noise-abatement terminal routes. In this paper, we extend our previous work on developing a noise-abatement terminal approach route design tool, named AIRNOISE, to satisfy this criterion. First, software efficiency has been significantly increased by over tenfold using the C programming language instead of MATLAB. Moreover, a state-of-the-art high performance GPU-accelerated computing module is implemented that was tested to be hundreds time faster than the C implementation. Secondly, a Graphical User Interface (GUI) was developed allowing users to import current terminal approach routes and modify the routes interactively to design new terminal approach routes. The corresponding noise impacts are then calculated and displayed in the GUI in seconds. Finally, AIRNOISE was applied to Baltimore-Washington International Airport terminal approach route to demonstrate its usage.

aircraft noise↗
NASA NTRS2018

Airspace Technology Demonstration 3 (ATD-3): Dynamic Weather Routes (DWR) Technology Transfer Document Summary Version 2.0

This summary document and accompanying technology artifacts satisfy the first of three Research Transition Products (RTPs) defined in the Applied Traffic Flow Management (ATFM) Research Transition Team (RTT) Plan. The original transfer, completed in September 2016, consisted of NASA's legacy Dynamic Weather Routes (DWR) work for efficient routing for en-route weather avoidance. This transfer updates the Concept of Operations document to a publicly-available NASA Technical Memorandum. Dynamic Weather Routes (DWR) is a ground-based trajectory automation system that continuously and automatically analyzes active in-flight aircraft in en route airspace to identify opportunities for simple corrections to flight plan routes that can save significant flying time, at least five minutes wind-corrected, while avoiding weather and considering traffic conflicts, airspace sector congestion, special use airspace, and FAA routing restrictions.

DWR↗
NASA NTRSDate not supplied

Sheaf Theoretic Models for Routing in Delay Tolerant Networks

One key to communications scalability is routing; as such the goal of this paper is to build upon successful efforts towards general routing for space-based networks. With the ever-increasing accessibility of space, the number of assets is increasing, which becomes a critical communications burden in terms of scheduling, spectrum allocation, and resource allocation. In order to mitigate these concerns, a true networking approach is necessary; a standard approach for space systems is Delay Tolerant Networking (DTN). For DTN to be a meaningful answer to the Solar System Internet (SSI) question, DTN must offer meaningful routing solutions that span the heterogeneous collection of links and nodes. This, in turn, depends on the general structure of these disconnected networks -- a structure that remains largely unknown. In ground communications networks, routing decisions are made based on several pathfinding algorithms working in tandem. In previous work, we modeled Dijkstra's pathfinding algorithm using sheaves and provided a more general framework for determining paths using sheaves over graphs. Continuing our sheaf-theoretic approach, we introduce here an expansion of our pathfinding sheaf to handle more general information, and we expand on additional pathfinding algorithms that can be represented using sheaves. Moreover, we demonstrate means of combining multiple algorithms into a single sheaf structure so that changes of scale can be presented in the language of sheaves. In addition, space communications networks rely upon radio transmitter antennas which can establish broadcast and multicast communications options, rather than the primarily unicast options available to wired networks. Last year, we also introduced a multicast routing sheaf for presenting broadcast, unicast, and multicast communications over a graph. Extending that work, we also introduce queuing sheaves so that we can blend these communications options together to simulate a variety of routing options across space networks. In addition, we include examples to illustrate the applicability of this abstract theory to routing in disconnected networks.

Robert Short↗