Person:
Mavris,
Dimitri N.
Mavris,
Dimitri N.
Permanent Link
Associated Organization(s)
Organizational Unit
Organizational Unit
ORCID
ArchiveSpace Name Record
Publication Search Results
Now showing
1 - 4 of 4
-
ItemSupervised 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, SimonMany 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.
-
ItemA 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.
-
ItemA 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.
-
ItemAircraft 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%.