A Message Passing Strategy for Decentralized Connectivity Maintenance in Agent Removal

In a multi-agent system, agents coordinate to achieve global tasks through local communications. Coordination usually requires sufficient information flow, which is usually depicted by the connectivity of the communication network. In a networked system, removal of some agents may cause a disconnection. In order to maintain connectivity in agent removal, one can design a robust network topology that tolerates a finite number of agent losses, and/or develop a control strategy that recovers connectivity. This paper proposes a decentralized control scheme based on a sequence of replacements, each of which occurs between an agent and one of its immediate neighbors. The replacements always end with an agent, whose relocation does not cause a disconnection. We show that such an agent can be reached by a local rule utilizing only some local information available in agents' immediate neighborhoods. As such, the proposed message passing strategy guarantees the connectivity maintenance in arbitrary agent removal. Furthermore, we significantly improve the optimality of the proposed scheme by incorporating $\delta$-criticality (i.e. the criticality of an agent in its $\delta$-neighborhood).Comment: 9 pages, 9 figure

Enhanced emission prediction modeling and analysis for conceptual design

Issued as final reportUnited States. National Aeronautics and Space Administratio

Dual-Mission Large Aircraft Feasibility Study and Aerodynamic Investigation

A Dual-Mission Large Aircraft, or DMLA, represents the possibility of a single aircraft capable of fulfilling both a Global Reach Aircraft (GRA) and Very Large Transport (VLT) roles. The DMLA, by combining the GRA and VLT into a single new aircraft, could possibly lower the aircraft manufacturer's production costs through the resulting increase in production quantity. This translates into lower aircraft acquisition costs, a primary concern for both the Air Force and commercial airlines. This report outlines the first steps taken in this study, namely the assessment of technical and economic feasibility of the DMLA concept. In the course of this project, specialized GRA and VLT aircraft were sized for their respective missions, using baseline conventional (i.e., lacking advanced enabling technologies) aircraft models from previous work for the Air Force's Wright Laboratory and NASA-Langley. DMLA baseline aircraft were then also developed, by first sizing the aircraft for the more critical of the two missions and then analyzing the aircraft's performance over the other mission. The resulting aircraft performance values were then compared to assess technical feasibility. Finally, the life-cycle costs of each aircraft (GRA, VLT, and DMLA) were analyzed to quantify economic feasibility. These steps were applied to both a two-engine aircraft set, and a four-engine aircraft set

Advanced Design Methodology for Robust Aircraft Sizing and Synthesis

Contract efforts are focused on refining the Robust Design Methodology for Conceptual Aircraft Design. Robust Design Simulation (RDS) was developed earlier as a potential solution to the need to do rapid trade-offs while accounting for risk, conflict, and uncertainty. The core of the simulation revolved around Response Surface Equations as approximations of bounded design spaces. An ongoing investigation is concerned with the advantages of using Neural Networks in conceptual design. Thought was also given to the development of systematic way to choose or create a baseline configuration based on specific mission requirements. Expert system was developed, which selects aerodynamics, performance and weights model from several configurations based on the user's mission requirements for subsonic civil transport. The research has also resulted in a step-by-step illustration on how to use the AMV method for distribution generation and the search for robust design solutions to multivariate constrained problems

Improved Aircraft Environmental Impact Segmentation via Metric Learning

Accurate modeling of aircraft environmental impact is pivotal to the design of operational procedures and policies to mitigate negative aviation environmental impact. Aircraft environmental impact segmentation is a process which clusters aircraft types that have similar environmental impact characteristics based on a set of aircraft features. This practice helps model a large population of aircraft types with insufficient aircraft noise and performance models and contributes to better understanding of aviation environmental impact. Through measuring the similarity between aircraft types, distance metric is the kernel of aircraft segmentation. Traditional ways of aircraft segmentation use plain distance metrics and assign equal weight to all features in an unsupervised clustering process. In this work, we utilize weakly-supervised metric learning and partial information on aircraft fuel burn, emissions, and noise to learn weighted distance metrics for aircraft environmental impact segmentation. We show in a comprehensive case study that the tailored distance metrics can indeed make aircraft segmentation better reflect the actual environmental impact of aircraft. The metric learning approach can help refine a number of similar data-driven analytical studies in aviation.Comment: 32 pages, 11 figure