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Daniel Guggenheim School of Aerospace Engineering
Daniel Guggenheim School of Aerospace Engineering
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ItemIdentification of Instantaneous Anomalies in General Aviation Operations using Energy Metrics(Georgia Institute of Technology, 2019-12) Puranik, Tejas G. ; Mavris, Dimitri N.Quantification and improvement of safety is one of the most important objectives among the General Aviation community. In recent years, machine learning techniques have emerged as an important enabler in the data-driven safety enhancement of aviation operations with a number of techniques being applied to flight data to identify and isolate anomalous (and potentially unsafe) operations. Energy-based metrics provide measurable indications of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions across a heterogeneous fleet of aircraft and operations. In this paper, a novel method of identifying instantaneous anomalies for retrospective safety analysis in General Aviation using energy-based metrics is proposed. Each flight data record is processed by a sliding window across the multi-variate time series of evaluated metrics. A Gaussian Mixture Model using energy metrics and their variability within each window is fit in order to predict the probability of any instant during the flight being nominal. Instances during flights that deviate from the nominal are isolated to identify potential increased levels of risk. The identified anomalies are compared with traditional methods of safety assessment such as exceedance detection to highlight the benefits of the developed method. The methodology is demonstrated using flight data records from two representative aircraft for critical phases of flight.
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ItemA Framework for Electrified Propulsion Architecture and Operation Analysis(Georgia Institute of Technology, 2019-07) Cinar, Gokcin ; Garcia, Elena ; Mavris, Dimitri N.Purpose – The purpose of this paper was to create a generic and flexible framework for the exploration, evaluation and side-by-side comparison of novel propulsion architectures. The intent for these evaluations was to account for varying operation strategies and to support architectural design space decisions, at the conceptual design stages, rather than single-point design solutions. Design/methodology/approach – To this end, main propulsion subsystems were categorized into energy, power and thrust sources. Two types of matrices, namely, the property and interdependency matrices, were created to describe the relationships and power flows among these sources. These matrices were used to define various electrified propulsion architectures, including, but not limited to, turboelectric, series-parallel and distributed electric propulsion configurations. Findings – As a case study, the matrices were used to generate and operate the distributed electric propulsion architecture of NASA’s X-57 Mod IV aircraft concept. The mission performance results were acceptably close to the data obtained from the literature. Finally, the matrices were used to simulate the changes in the operation strategy under two motor failure scenarios to demonstrate the ease of use, rapidness and automation. Originality/value – It was seen that this new framework enables rapid and analysis-based comparisons among unconventional propulsion architectures where solutions are driven by requirements.
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ItemMulti-UAV Trajectory Optimization Utilizing a NURBS-Based Terrain Model for an Aerial Imaging Mission(Georgia Institute of Technology, 2019-05) Choi, Youngjun ; Chen, Mengzhen ; Choi, Younghoon ; Briceno, Simon ; Mavris, Dimitri N.Trajectory optimization precisely scanning an irregular terrain is a challenging problem since the trajectory optimizer needs to handle complex geometry topology, vehicle performance, and a sensor specification. To address these problems, this paper introduces a novel framework of a multi-UAV trajectory optimization method for an aerial imaging mission in an irregular terrain environment. The proposed framework consists of terrain modeling and multi-UAV trajectory optimization. The terrain modeling process employs a Non-Uniform Rational B-Spline (NURBS) surface fitting method based on point cloud information resulting from an airborne LiDAR sensor or other sensor systems. The NURBS-based surface model represents a computationally efficient terrain topology. In the trajectory optimization method, the framework introduces a multi-UAV vehicle routing problem enabling UAV to scan an entire area of interest, and obtains feasible trajectories based on given vehicle performance characteristics, and sensor specifications, and the approximated terrain model. The proposed multi-UAV trajectory optimization algorithm is tested by representative numerical simulations in a realistic aerial imaging environment, namely, San Diego and Death Valley, California.
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ItemEnergy-Constrained Multi-UAV Coverage Path Planning for an Aerial Imagery Mission Using Column Generation(Georgia Institute of Technology, 2019-03) Choi, Younghoon ; Choi, Youngjun ; Briceno, Simon ; Mavris, Dimitri N.This paper presents a new Coverage Path Planning (CPP) method for an aerial imaging mission with multiple Unmanned Aerial Vehicles (UAVs). In order to solve a CPP problem with multicopters, a typical mission profile can be defined with five mission segments: takeoff, cruise, hovering, turning, and landing. The traditional arc-based optimization approaches for the CPP problem cannot accurately estimate actual energy consumption to complete a given mission because they cannot account for turning phases in their model, which may cause non-feasible routes. To solve the limitation of the traditional approaches, this paper introduces a new route-based optimization model with column generation that can trace the amount of energy required for all different mission phases. This paper executes numerical simulations to demonstrate the effectiveness of the proposed method for both a single UAV and multiple UAV scenarios for CPP problems.
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ItemIntegrated Sizing and Optimization of Aircraft and Subsystem Architectures in Early Design(Georgia Institute of Technology, 2018-06) Rajaram, Dushhyanth ; Yu, Cai ; Chakraborty, Imon ; Mavris, Dimitri N.The aerospace industry’s current trend towards novel or More Electric architectures results in some unique challenges for designers due to both the scarcity or absence of historical data and a potentially large combinatorial space of possible architectures. These add to the already existing challenges of attempting to optimize an aircraft design in the presence of multiple possible objective functions while avoiding an overly compartmentalized approach. This paper uses the Integrated Subsystem Sizing and Architecture Assessment Capability to pursue a multi-objective optimization for a large twin-aisle aircraft and a small single-aisle aircraft using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) algorithm with parallel function evaluations. One novelty of the optimization setup is that it explicitly considers the impacts of subsystem architectures in addition to those of traditional aircraft-level design variables. The optimization yields generations of nondominated designs in which substantially electrified subsystem architectures are found to predominate. As a first assessment of the impact of epistemic uncertainty on the results obtained, the optimization is rerun with altered sensitivities for the thrust-specific fuel consumption penalties due to shaft-power and bleed air extraction. This analysis demonstrated that the composition of architectures on the Pareto frontier is sensitive to the secondary power extraction penalties, but more so for the small single-aisle aircraft than the large twin-aisle aircraft.
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ItemMulti-objective Optimization of Departure Procedures at Gimpo International Airport(Georgia Institute of Technology, 2018) Kim, Junghyun ; Lim, Dongwook ; Monteiro, Dylan Jonathan ; Kirby, Michelle ; Mavris, Dimitri N.Most aviation communities have increasing concerns about the environmental impacts, which are directly linked to health issues for local residents near the airport. In this study, the environmental impact of different departure procedures using the Aviation Environmental Design Tool (AEDT) was analyzed. First, actual operational data were compiled at Gimpo International Airport (March 20, 2017) from an open source. Two modifications were made in the AEDT to model the operational circumstances better and the preliminary AEDT simulations were performed according to the acquired operational procedures. Simulated noise results showed good agreements with noise measurement data at specific locations. Second, a multi-objective optimization of departure procedures was performed for the Boeing 737-800. Four design variables were selected and AEDT was linked to a variety of advanced design methods. The results showed that takeoff thrust had the greatest influence and it was found that fuel burn and noise had an inverse relationship. Two points representing each fuel burn and noise optimum on the Pareto front were parsed and run in AEDT to compare with the baseline. The results showed that the noise optimum case reduced Sound Exposure Level (SEL) 80-dB noise exposure area by approximately 5% while the fuel burn optimum case reduced total fuel burn by 1% relative to the baseline for aircraft-level analysis.
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ItemA Categorical Model for Airport Capacity Estimation Using Hierarchical Clustering(Georgia Institute of Technology, 2017-12) Cinar, Gokcin ; Jimenez, Hernando ; Mavris, Dimitri N.Motivated by the need for very inexpensive, easily updated, first-order-accurate estimates of airport capacity required in system-wide analyses, we propose a novel approach to generate a predictive categorical model. The underlying hypothesis tested in this work is that for the same weather conditions airports with a similar runway configuration and fleet mix will have similar capacities. Accordingly, if airport categories with known capacity are defined a-priori on the basis of similarity in fleet mix and runway configuration, then a membership function to the set of categories essentially constitutes a predictive model. We test this hypothesis by formulating and implementing such a model in order to examine its feasibility and discuss key practical considerations. Verification demonstrates model fit error within 4% with a categorical training set of 35 major United States airports. Validation against European airports for model representation error is limited by data availability but shown to be in the order of 7-10%. Results suggest that elemental runway configurations are the primary driver for categorical definition, and variations within each category can be associated to fleet mix variations. The implementation of the proposed method to generate other such models with different data sets is encouraged.
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ItemRequirements Analysis for Design Optimization of Aerobatic Aircraft(Georgia Institute of Technology, 2017-01) Sarojini, Darshan ; Collins, Kyle ; Mavris, Dimitri N.Aerobatic aircraft design and simulation is challenging as these aircraft need to fly at any angle of attack and sideslip angle (full-envelope aerodynamics). They fly at velocities close to stall speed, all the way up to the never exceed velocity. These aircraft are also routinely stressed to 6-12 g's in both upright and inverted flight. Presently, most aerobatic aircraft are designed using heuristic knowledge. There is a need for a systematic approach to design aerobatic aircraft in a multi-disciplinary design framework. Towards this goal, this paper presents an extensive study of requirements, metrics and design variables to define a good aerobatic aircraft. First a historical perspective is given to know the current state-of-the-art. Information obtained from regulations, aircraft performance, subject matter experts and analysis of existing aircraft is used to obtain metrics to evaluate. The possible design configurations is given as a morphological matrix. Finally, possible analysis approaches to evaluate the metrics are discussed.
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ItemA Comparison of Automatic Nap-of-the-Earth Guidance Strategies for Helicopters(Georgia Institute of Technology, 2014-05) Johnson, Eric N. ; Mooney, John G.This paper describes updated results from a partnership between the Sikorsky Aircraft Corporation and the Georgia Institute of Technology to develop, improve, and flight test a sensor, guidance, navigation, control, and real-time flight path optimization system to support high performance Nap-of-the-Earth (NOE) helicopter flight.
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ItemTerrain Height Evidence Sharing for Collaborative Autonomous Rotorcraft Operation(Georgia Institute of Technology, 2013-01) Johnson, Eric N. ; Mooney, John G. ; White, Matthew ; Hartman, Jonathan ; Sahasrabudhe, VineetThis paper describes recent results from a partnership between the Sikorsky Aircraft Corporation and the Georgia Institute of Technology to develop, improve, and flight test a sensor, guidance, navigation, control, and real-time information sharing system to support collaborative autonomy and high performance nap-of-the-Earth helicopter flight. The emphasis here is on smart and selective sharing of terrain data which (1) minimizes the bandwidth consumed by obstacle/terrain-information-sharing between aircraft, (2) assigns an appropriate level of confidence to the data received from other heterogeneous aircraft, (3) is robust to sensor error and failures, and (4) is robust to entry and exit of vehicles from the network. Results from simulation and flight testing are provided.