Mavris, Dimitri N.

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Now showing 1 - 10 of 21
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    Supervised Machine Learning-based Wind Prediction to Enable Real-Time Flight Path Planning
    (Georgia Institute of Technology, 2021-01) Kim, Junghyun ; Zhang, Chao ; Mavris, Dimitri N. ; Briceno, Simon
    Many research groups have been committed to developing numerical models for weather forecasts. The models are currently used to predict weather patterns and trends in the aviation industry. In particular, pilots receive wind information predicted by the models and use the forecast to not only calculate how much fuel is needed for a flight but also optimize flight routes by seeking favorable winds. One potential issue is that the models provide relatively coarse wind information in both space and time, which potentially leads to inaccurate calculation of fuel consumption. This research aims to yield a continuous wind prediction model by combining a supervised learning algorithm with the Inverse Distance Weighting technique. Specifically, this research compares three different supervised learning algorithms that include Gaussian Process, Multi-Layer Perceptron, and Support Vector Machine to identify the most appropriate algorithm. The selected algorithm is then compared to a linear interpolation method that is widely used in current flight planning systems for obtaining continuous wind information. A case study is performed with the real Delta Airlines flight 1944 to evaluate the proposed methodology. The results show that 1) the Support Vector Machine provides a better wind prediction compared to the other models, 2) the supervised learning-based regression method performs better than the linear interpolation method in wind predictions, and 3) there are 16 seconds of difference between the real flight (12,117 seconds) and the simulated flight (12,101 seconds) for the cruise portion, indicating that the proposed methodology generates valid results as long as input wind data is provided accurately.
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    A Data-Driven Approach using Machine Learning to Enable Real-Time Flight Path Planning
    (Georgia Institute of Technology, 2020-06) Kim, Junghyun ; Briceno, Simon ; Justin, Cedric Y. ; Mavris, Dimitri N.
    As aviation traffic continues to grow, most airlines are concerned about flight delays, which increase operating costs for the airlines. Since most delays are caused by weather, pilots and flight dispatchers typically gather all available weather information prior to departure to create an efficient and safe flight plan. However, they may have to perform in-flight re-planning because weather information can significantly change after the original flight plan is created. One potential issue is that weather forecasts being currently used in the aviation industry may provide relatively unreliable information and are not accessible fast enough so that it challenges pilots to perform in-flight re-planning more accurately and frequently. In this paper, we propose a data-driven approach that uses an unsupervised machine learning technique to provide a more reliable and up-to-date area of convective weather. To evaluate the proposed methodology, we collect the American Airlines flight (AA1300) information and actual weather-related data on October 6th, 2019. Preliminary results show that the proposed methodology provides a better picture of the nearby convective weather activity compared to the most well-known convective weather product.
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    A Model-Based Aircraft Certification Framework for Normal Category Airplanes
    (Georgia Institute of Technology, 2020-06) Bendarkar, Mayank ; Xie, Jiacheng ; Briceno, Simon ; Harrison, Evan D. ; Mavris, Dimitri N.
    A typical aircraft certification process consists of obtaining a type, production, airworthiness, and continued airworthiness certificate. During this process, a type certification plan is created that includes the intended regulatory operating environment, the proposed certification basis, means of compliance, and a list of documentation to show compliance. This paper extends previous work to demonstrate a model-based framework for the management of these certification artifacts for normal category airplanes. The developed framework integrates the regulatory rules and approved means of compliance in a single model while using best-practices found in Model-Based Systems Engineering (MBSE) literature. This framework, developed using SysML in MagicDraw captures not just the textual requirements and verification artifacts, but also their relationships and any inherent meta-data properties via custom defined stereotype profiles. Additionally, a simulation capability that automates the extraction and export of the applicable rules (certification basis) and corresponding means of compliance for any aircraft under consideration at the click of a button has been developed. The framework also provides numerous additional benefits to different stakeholders that have been described in detail with examples where necessary.
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    Aircraft Flight Plan Optimization with Dynamic Weather and Airspace Constraints
    (Georgia Institute of Technology, 2020) Ramee, Coline ; Junghyun, Kim ; Deguignet, Marie ; Justin, Cedric ; Briceno, Simon ; Mavris, Dimitri N.
    Flight planning is the process of producing a flight plan which describes a proposed aircraft trajectory. This task is typically performed ahead of departure with the intent of minimizing operating costs, while accounting for weather, airspace, traffic, and comfort considerations. Recent improvements in cockpit connectivity present new opportunities for flight crews to continuously re-assess the trajectories once in the air using the latest information sets (weather observations and forecasts, traffic). In turn, this enables flight crews to proactively respond to the uncertain evolution of the weather by steering the aircraft along optimal trajectories. This also brings new challenges as flight crews are ill-equipped to continuously process vast amount of information to perform the trajectory optimization. A framework is therefore proposed to automate the fusion of various sources of information (severe weather, winds aloft, restricted airspace) to feed a trajectory optimizer that continuously updates the aircraft trajectory. This relies on the implementation of the A* algorithm with the objective to minimize cruise fuel burn and emissions. Use-cases are investigated by comparing continuously updated trajectories with actual flight trajectories retrieved from the FAA Traffic Flow Management Systems through consumer-oriented websites. Promising results are observed with fuel burn savings reaching 8%.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.