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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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7 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Surface Hold Advisor Using Critical Sections

The Surface Hold Advisor Using Critical Sections is a system and method for providing hold advisories to surface controllers to prevent gridlock and resolve crossing and merging conflicts among vehicles traversing a vertex-edge graph representing a surface traffic network on an airport surface. The Advisor performs pair-wise comparisons of current position and projected path of each vehicle with other surface vehicles to detect conflicts, determine critical sections, and provide hold advisories to traffic controllers recommending vehicles stop at entry points to protected zones around identified critical sections. A critical section defines a segment of the vertex-edge graph where vehicles are in crossing or merging or opposite direction gridlock contention. The Advisor detects critical sections without reference to scheduled, projected or required times along assigned vehicle paths, and generates hold advisories to prevent conflicts without requiring network path direction-of-movement rules and without requiring rerouting, rescheduling or other network optimization solutions.

Law, Caleb Hoi Kei↗
NASA NTRS1990

Coverability graphs for a class of synchronously executed unbounded Petri net

After detailing a variant of the concurrent-execution rule for firing of maximal subsets, in which the simultaneous firing of conflicting transitions is prohibited, an algorithm is constructed for generating the coverability graph of a net executed under this synchronous firing rule. The omega insertion criteria in the algorithm are shown to be valid for any net on which the algorithm terminates. It is accordingly shown that the set of nets on which the algorithm terminates includes the 'conflict-free' class.

Stotts, P. David↗
NASA NTRSDate not supplied

Optimal Communication Topology Determination and Sensor Selection for Independent Airspace Surveillance

The paper presents an approach to sensors selection and network topology determination for independent airspace surveillance with maximum outcome and minimum cost using ground based distributed sensing, computing and communication network infrastructure. The selection criteria includes minimum estimation error, maximum airspace coverage, minimum communication time and power consumption while guaranteeing the system observability and providing in-time quality information to a monitoring observer. The developed algorithm uses multi-objective optimization strategy taking into account trade-offs between conflicting objectives and relaxations for in time implementation. It is implemented utilizing graph theoretic tools. The approach is validated in a desktop simulation environment using synthetic sensors data generated for a simulated multi-vehicle flight scenario in the selected regional airspace.

Distributed sensing↗
NASA NTRS1991

The effect of global visual flow on simulator sickness

Simulator-induced sickness is investigated in experiments performed at the NASA Ames Army Crew Station Research Facility using the fixed-base helmet-mounted-display flight simulator described by Lypaczewski et al. (1986). The focus of the tests was on the possible roles of (1) global visual flow, as defined by Warren et al. (1982), and (2) maneuvering intensity (in the conflict hypothesis of Reason and Brand, 1975). The results, based on subjective evaluations, physiological measurements, and physical tests on 19 Army helicopter pilots performing a 40-min river-valley following task, are presented in extensive tables and graphs and discussed. The data are found to be in agreement with (1) and inconsistent with (2), indicating more sickness at lower altitude instead of with increased maneuvering. Shorter simulator sessions and postponement of low-altitude work until later in the training period are recommended.

Sharkey, Thomas J.↗
NASA NTRSDate not supplied

Aerial Vehicle Routing and Scheduling for UAS Traffic Management: A Monte Carlo Tree Search Approach

Numerous unmanned aircraft systems operating at low altitudes to deliver goods and services may one day become ubiquitous in our cities. In the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) framework, such a concept is envisioned, where aerial vehicles operate beyond visual line of sight (BVLOS) within specifically reserved and time stamped “corridors” in the airspace. For example, these corridors or operational intent volumes can connect an aerial vehicle’s origin site to its destination site for package delivery operations. There may also be more than one corridor available for an aerial vehicle to choose from and often different corridors may intersect with one another. Thus, it is imperative to ensure flight trajectories belonging to different aerial vehicles are not in conflict. Per the UTM CONOPs, we assume that a vehicle almost always stays inside its corridor or operational volume. This work provides a framework for strategic deconfliction of UTM or package delivery drones, where we schedule the departure time of all vehicles subject to various temporal constraints (including the corridor deconfliction at the intersections). We present the “multi-route weighted package delivery problem” which serves as an exemplifying model for strategic deconfliction in UTM. In the multi-route weighted package delivery problem, a graph network is given which consists of a set of depots (source) and drop-off (destination) nodes, with multiple routes (defined as a sequence of waypoints) connecting the depots to drop-off nodes. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is for a known set of aerial vehicles to depart from the depots, choose a route and take off time, while avoiding conflicts with other aerial vehicles, and minimizing both risk and distance traveled. We provide a mixed integer linear programming (MILP) formulation of the problem, as well as a heuristic solution based on Monte Carlo Tree Search (MCTS) – a method used in game theory and artificial intelligence – to overcome limitations inherent to optimal solvers. Computational results show the advantages of using MCTS over the MILP formulation; the former can provide a sub-optimal solution quickly, and may sometimes even reach an optimal solution, whereas the latter may not even produce a solution in reasonable time. Furthermore, results from both the MILP formulation and MCTS methods were validated using a preliminary agent-based simulator implementing the UTM concept of operations. Thus, the MCTS method can be seen as a scalable solution to the complex multi-route weighted package delivery problem and may possibly be extended to similar complex optimization problems.

Kenny Chour↗
NASA NTRS2006

Vulnerabilities, Influences and Interaction Paths: Failure Data for Integrated System Risk Analysis

We describe graph-based analysis methods for identifying and analyzing cross-subsystem interaction risks from subsystem connectivity information. By discovering external and remote influences that would be otherwise unexpected, these methods can support better communication among subsystem designers at points of potential conflict and to support design of more dependable and diagnosable systems. These methods identify hazard causes that can impact vulnerable functions or entities if propagated across interaction paths from the hazard source to the vulnerable target. The analysis can also assess combined impacts of And-Or trees of disabling influences. The analysis can use ratings of hazards and vulnerabilities to calculate cumulative measures of the severity and importance. Identification of cross-subsystem hazard-vulnerability pairs and propagation paths across subsystems will increase coverage of hazard and risk analysis and can indicate risk control and protection strategies.

Malin, Jane T.↗
NASA NTRS2008

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Solakiewiz, Richard↗