Organizational Unit:

Aerospace Systems Design Laboratory (ASDL)

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https://hdl.handle.net/1853/70744

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Organizational Unit

Daniel Guggenheim School of Aerospace Engineering

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https://finding-aids.library.gatech.edu/agents/corporate_entities/1135

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Publication Search Results

Now showing 1 - 10 of 17

Evaluating Optimal Paths for Aircraft Subsystem Electrification in Early Design

(Georgia Institute of Technology, 2019-06) Bendarkar, Mayank ; Rajaram, Dushhyanth ; Yu, Cai ; Briceno, Simon ; Mavris, Dimitri N.

The aerospace industry’s push for More-Electric Aircraft (MEA) has motivated numerous studies to quantify and optimize the impact of subsystem electrification in early design phases. Past studies on multi-objective optimization of MEA show a clear benefit over conventional architectures when no constraints are placed on the number of subsystems electrified at once. In reality however, aircraft manufacturers are more likely to progressively electrify subsystems over multiple aircraft generations. While step-by-step electrification may lead to sub-optimal intermittent MEA architectures when compared with scenarios with no such imposition on number of subsystems electrified, little or no literature was found to address the optimal paths towards such electrification changes. The primary aim of this study is the creation of a mathematically defensible methodology that provides decision makers with the ability to analyze several paths for electrification of MEA subsystems while considering Pareto-optimality and other metrics based on objectives of interest in early design. It is hoped that decision makers will be able to understand the performance trade-offs between different electrification paths under different scenarios, constraints, and uncertainties. The resulting methodology is demonstrated on an exercise in the electrification of Small Single Aisle aircraft.
A Model-Based System Engineering Approach to Normal Category Airplane Airworthiness Certification

(Georgia Institute of Technology, 2019-06) Bleu-Laine, Marc-Henri ; Bendarkar, Mayank ; Xie, Jiacheng ; Briceno, Simon ; Mavris, Dimitri N.

Airworthiness certification is to ensure the safety of aircraft. With the surge in novel general aviation aircraft configurations and technologies, the Federal Aviation Administration replaced prescriptive design requirements with performance-based airworthiness standards in Federal Aviation Regulations Part 23 that governs the airworthiness of normal category airplane. The amendment ported over the accepted means of compliance (MoC) from prescriptive advisory circulars to a number of consensus standards from aviation community. Because these MoCs are scattered in multiple documents and cross-reference one another, the certification practice with this new format may be cumbersome and time-consuming.This paper proposes a Model Based System Engineering (MBSE) approach that is envisioned to parametrically transform the document-centric exercise to a model-based process. The approach helps collect the FAR23 regulations and the associated MoC in an integrated system model along with the relevant mappings between them. This allows users to automatically generate a compliance checklist for any specific certification requirement. Other benefits of the MBSE approach include circular referencing check, automatically propagating any future changes to the FARs or MoC standards through the model, and potential incorporation with early aircraft design.
Development of A Certification Module for Early Aircraft Design

(Georgia Institute of Technology, 2019-06) Xie, Jiacheng ; Briceno, Simon ; Mavris, Dimitri N. ; Chakraborty, Imon

The airworthiness certification process of civil transportation aircraft is expensive, timeconsuming, and subject to uncertainty. To reduce the cost and time spent on the certification process, this paper proposes an approach to incorporate certification considerations into early design stages using virtual certification techniques. As a proof of concept, this paper focuses on flight performance certification requirements and developed a certification analysis module for aircraft conceptual and early preliminary design based on FAR-25 Subpart B. The module transforms the regulations from textual documents to quantitative constraint functions and ensures the certification constraint check of the design through physics-based analysis. To validate the module, a Small Single-aisle Aircraft testing model is developed and virtually certified by the module. The certification analysis result of the testing model is benchmarked with public domain data.
A Multi-UAS Trajectory optimization Methodology for Complex Enclosed Environments

(Georgia Institute of Technology, 2019-06) Barlow, Sarah ; Choi, Youngjun ; Briceno, Simon ; Mavris, Dimitri N.

This paper explores a multi-UAV trajectory optimization methodology for confined environments. One potential application of this technology is performing warehouse inventory audits; this application is used to evaluate the methodology's impact on minimizing total mission times. This paper investigates existing algorithms and improves upon them to better address the constraints of warehouse-like environments. An existing inventory scanning algorithm generates sub-optimal, collision free paths for multi-UAV operations, which has two sequential processes: solving a vehicle routing problem, and determining optimal deployment time without any collision. To improve the sub-optimal results, this paper introduces three possible improvements on the multi-UAV inventory tracking scenario. First, a new algorithm logic which seeks to minimize the total mission time once collision avoidance has been ensured rather than having separate processes. Next, an objective function that seeks to minimize the maximum UAV mission time rather than minimizing the total of all UAV mission times. Last, an operational setup consisting of multiple deployment locations instead of only one. These algorithms are evaluated individually and in combination with one another to assess their impact on the overall mission time using a representative inventory environment. The best combination will be further analyzed through a design of experiments by varying several inputs and examining the resulting fleet size, computation time, and overall mission time.
A Bayesian Safety Assessment Methodology for Novel Aircraft Architectures and Technologies using Continuous FHA

(Georgia Institute of Technology, 2019-06) Bendarkar, Mayank ; Behere, Ameya ; Briceno, Simon ; Mavris, Dimitri N.

Novel architectures and technologies carry with them an uncertainty related to their reliability and associated safety risk. Existing safety assessment methods involve determining the severity of discrete functional failure and the corresponding probability. However, with the advent of novel aircraft architectural and operational concepts, traditional methods of establishing severity and probabilities failures are found lacking due to the scarcity of available data. The current work proposes a safety assessment method that uses architecture-specific performance models along with continuous functional hazard assessments to inform hazard severity. The probability of failures is determined using a Bayesian framework that does not falter when data is scarce. Taken together, it is expected that this new proposed methodology will enable a more accurate safety assessment of novel aircraft architectures and technologies. A safety assessment of an electric propulsion system powered by a fuel cell is conducted using the proposed methodology to serve as a proof of concept.
Multi-UAS path-planning for a large-scale disjoint disaster management

(Georgia Institute of Technology, 2019-06) Choi, Younghoon ; Choi, Youngjun ; Briceno, Simon ; Mavris, Dimitri N.

A UAS-based disaster management method has been adopted to monitor the disaster impact and protect human lives since it can be rapidly deployed, execute an aerial imaging mission, and provide a cost-efficient operation. In the case of a wildfire disaster, a disaster management is highly complex because of large-scale wildfires that can occur simultaneously and disjointly in a large area. In order to effectively manage these large-scale wildfires, it requires multiple UAS with multiple ground stations. However, conventional UAS-based management methods relies on a single ground station that can have a limitation to handle the large-scale wildfire problem. This paper presents a new path-planning framework for UAS operations including a fleet of UAVs and multiple ground stations. The framework consists of two parts: creating coverage paths for each wildfire and optimizing routes for each UAV. To test the developed framework, this paper uses representative wildfire scenarios in the State of California.
Rapid and Automated Urban Modeling Techniques for UAS Applications

(Georgia Institute of Technology, 2019-06) Choi, Youngjun ; Pate, David ; Briceno, Simon ; Mavris, Dimitri N.

Urban models for testing UAV path-planning algorithms commonly apply simple representations using cuboid or cylinderical shapes which may not capture the characteristics of a urban environment. To address this limitation of existing urban models, this paper presents two urban modeling techniques for an unmanned aircraft flight simulation in an urban environment. The first proposed urban modeling technique is an airborne LiDAR source-based approach that incorporates machine learning algorithms to identify the number of buildings and characterize them from the LiDAR information. The second proposed urban modeling technique is an artificial urban modeling technique without any airborne LiDAR resources that applies an adaptive spacing method, an iterative algorithm to define an artificial urban environment. Unlike the LiDAR source-based approach that creates an approximated urban model, the adaptive spacing-based urban modeling algorithm generates an artificial urban environment that is visually different from a reference city, but has similar the characteristics to it. To demonstrate the two proposed urban modeling techniques, numerical simulations are conducted using open-source datasets to construct several realistic urban models.
Multi-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.
Energy-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.
Framework 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.