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Payan,
Alexia P.
Payan,
Alexia P.
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ItemHelicopter Operations Weather Information Survey Dataset(Georgia Institute of Technology, 2020-11-23) Payan, Alexia P. ; Ramee, Coline ; Speirs, Andrew ; Mavris, Dimitri N. ; Feigh, Karen M.To better understand the kind of weather information used by rotorcraft operators and get their opinion on the weather products that are available to them, the research team created an online survey. The survey consisted of three main sections: 1) Demographics, 2) Flight environment, and 3) Safety Operations. The information collected was used to analyze the number and types of weather information sources used by pilots in different phases of flight, identify differences between industries and study pilots training for adverse weather conditions. The data contained here is an anonymized version of answers to the survey.
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ItemOptimal Siting of Sub-Urban Air Mobility (sUAM) Ground Architectures using Network Flow Formulation(Georgia Institute of Technology, 2020-06) Venkatesh, Nikhil ; Payan, Alexia P. ; Justin, Cedric Y. ; Kee, Ethan C. ; Mavris, Dimitri N.Air Mobility (AM) operating models have steadily made their way into public conscience over the past decade due to increased research activity pioneered by large technology corporations such as Uber and Amazon. Estimates concur that there are around 250 startup businesses with 22 major players working on such technologies with over $25 billion dollars in venture capital funding in 2017[1]. Given the meteoric rise of Air Mobility as one of the leading 21st century disruptive technologies, research effort across the spectrum of functions that can make AM concepts a reality are burgeoning - ranging from vehicle design to operations planning. More specifically, research efforts within the operations planning space deal with service route identification, ground infrastructure (such as charging stations and ports) placement and others. To this effect, the present study seeks to evaluate the feasibility and tractability of a formalized optimization method towards the siting of "vertiports" - ground infrastructure that aids the embarkation and disembarkation of AM commuters - as applied to a Sub-Urban Air Mobility (sUAM) operating model. Mixed-Integer Programming (MIP) formulations offer qualified benefits over other heuristic methods and the authors are confident of their relative performance given the proven track record of such methods in solving generalized facility location problems (GFLP). In this study, two optimization problems were considered: capacitated vertiport siting, where any vertiport considered would need to adhere to capacity constraints; and uncapacitated vertiport siting, where any vertiport considered does not have any capacity limit and can service unlimited demand. Results indicate that a network flow formulation using an MIP methodology is able to adequately place vertiports for sUAM business operations to satisfy demand flows associated with home-work commute.
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ItemKnowledge Discovery Within ADS-B Data from Routine Helicopter Tour Operations(Georgia Institute of Technology, 2020-06) Chin, Hsiang-Jui ; Payan, Alexia P. ; Mavris, Dimitri N. ; Johnson, Charles C.Knowledge discovery or data mining techniques are widely used for anomaly detection in the commercial aviation domain to retrospectively improve operational safety. However, in the general aviation domain, especially for rotorcraft, analyses of flight data records for anomaly detection are not as prevalent. In this study, ADS-B data from a helicopter tour operator will be used to develop a prototype framework for uncovering patterns from routine flights. The ADS-B data contains two types of information: 1) time series of various flight parameters and 2) trajectory parameters. Various knowledge discovery techniques able to handle the aforementioned data types are explored and a few promising methods are applied to the ADS-B data of a helicopter tour operator in Hawaii. From the clustering results, patterns in the flight data records can be observed and can then be used by Subject-Matter Experts (SMEs) to facilitate the detection of anomalies. With this framework in place, rotorcraft operators will be able to analyze their routine flight data to not only monitor the safety of their operations but also to acquire knowledge on their operational patterns.
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ItemPower optimized battery swap and recharge strategies for electric aircraft operations(Georgia Institute of Technology, 2020-06) Justin, Cedric Y. ; Payan, Alexia P. ; Briceno, Simon I. ; German, Brian J. ; Mavris, Dimitri N.Electric propulsion for commuter air transportation is becoming promising because of significant strides in battery specific energy and motor specific power. Energy storage and rapid battery recharge remain nonetheless challenging owing to the significant energy and power requirements of even small aircraft. By modifying algorithms developed in the field of scheduling theory, we propose power optimized and power-investment optimized strategies for electric aircraft battery swaps and recharges. Several aspects are considered: electric energy expenditures, capital expenditures, and flight schedule integrity. The first strategy optimizes the swaps and recharges to minimize the peak-power draw from the grid and to reduce electric energy expenditures. The second strategy optimizes the swaps and recharges to minimize electricity expenditures and capital expenditures associated with battery and charger procurement. In both cases, the optimization is decomposed into two simpler problems. The first is a recharge schedule feasibility analysis given a number of chargers and batteries, which is based on a network flow representation of the battery swap and recharge. The second is a recharge schedule generation given a number of chargers and batteries. Both strategies are applied to the operations of two commuter airlines and are contrasted with a benchmark non-optimized power-as-needed strategy. Promising results are obtained with up to 61% reduction in peak-power draw and up to 25% reduction in electricity costs.
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ItemFramework for Multi-Asset Comparison and Rapid Down-selection for Earth Observation Missions(Georgia Institute of Technology, 2019) Gilleron, Jerome ; Muehlberg, Marc ; Payan, Alexia P. ; Choi, Youngjun ; Briceno, Simon ; Mavris, Dimitri N.Observing the Earth, whether it be from space or from the air, has become easier in recent years with the advent of new space-borne and airborne technologies. First, satellites constantly provide data about almost any point on the globe with varying resolutions and in various spectral bands. Second,Unmanned Aerial Vehicles (UAV) are becoming more readily accessible to the public and may be rapidly deployed to take high resolution images of ground features or areas of interest. Third, manned aircraft may be used to image large areas of land at a higher resolution than satellites and have been used regularly in disaster monitoring and surveillance missions. However, when multiple heterogeneous assets compete to perform a given aerial imaging mission, deciding which asset is better suited and/or less costly to operate in a timely manner is challenging. Every acquisition mode is different, resolution values are computed differently and there currently does not exist a common framework to compare UAV, manned aircraft and satellites. To address this challenge, this paper describes a methodology to rapidly compare various types of aerial assets (such as UAVs and manned aircraft) and space assets (such as satellites) to decide which one would be better able to perform an Earth observation mission depending on a set of requirements. To demonstrate the proposed methodology, this paper executes numerical simulations with three different representative scenarii in California.
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ItemEnabling methods for the design and optimization of detection architectures(Georgia Institute of Technology, 2013-04-08) Payan, Alexia P.The surveillance of geographic borders and critical infrastructures using limited sensor capability has always been a challenging task in many homeland security applications. While geographic borders may be very long and may go through isolated areas, critical assets may be large and numerous and may be located in highly populated areas. As a result, it is virtually impossible to secure each and every mile of border around the country, and each and every critical infrastructure inside the country. Most often, a compromise must be made between the percentage of border or critical asset covered by surveillance systems and the induced cost. Although threats to homeland security can be conceived to take place in many forms, those regarding illegal penetration of the air, land, and maritime domains under the cover of day-to-day activities have been identified to be of particular interest. For instance, the proliferation of drug smuggling, illegal immigration, international organized crime, resource exploitation, and more recently, modern piracy, require the strengthening of land border and maritime awareness and increasingly complex and challenging national security environments. The complexity and challenges associated to the above mission and to the protection of the homeland may explain why a methodology enabling the design and optimization of distributed detection systems architectures, able to provide accurate scanning of the air, land, and maritime domains, in a specific geographic and climatic environment, is a capital concern for the defense and protection community. This thesis proposes a methodology aimed at addressing the aforementioned gaps and challenges. The methodology particularly reformulates the problem in clear terms so as to facilitate the subsequent modeling and simulation of potential operational scenarios. The needs and challenges involved in the proposed study are investigated and a detailed description of a multidisciplinary strategy for the design and optimization of detection architectures in terms of detection performance and cost is provided. This implies the creation of a framework for the modeling and simulation of notional scenarios, as well as the development of improved methods for accurate optimization of detection architectures. More precisely, the present thesis describes a new approach to determining detection architectures able to provide effective coverage of a given geographical environment at a minimum cost, by optimizing the appropriate number, types, and locations of surveillance and detection systems. The objective of the optimization is twofold. First, given the topography of the terrain under study, several promising locations are determined for each sensor system based on the percentage of terrain it is covering. Second, architectures of sensor systems able to effectively cover large percentages of the terrain at minimal costs are determined by optimizing the number, types and locations of each detection system in the architecture. To do so, a modified Genetic Algorithm and a modified Particle Swarm Optimization are investigated and their ability to provide consistent results is compared. Ultimately, the modified Particle Swarm Optimization algorithm is used to obtain a Pareto frontier of detection architectures able to satisfy varying customer preferences on coverage performance and related cost.