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Characterizing Wake Vortices of Landing Aircraft Using Artificial Neural Networks and LiDAR Measurements

Wartha, Niklas Louis and Stephan, Anton and Holzäpfel, Frank and Rotshteyn, Grigory (2021) Characterizing Wake Vortices of Landing Aircraft Using Artificial Neural Networks and LiDAR Measurements. In: AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021. AIAA AVIATION 2021 FORUM, 2021-08-02 - 2021-08-06, VIRTUAL EVENT. doi: 10.2514/6.2021-2635. ISBN 978-162410610-1.

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Official URL: http://dx.doi.org/10.2514/6.2021-2635


The characterization of wake vortices with Light Detection and Ranging (LiDAR) instruments is commonly facilitated using analytical algorithms such as the Radial Velocities (RV) method. However, these can either not be employed for all LiDAR types, require time-consuming semi-automatic processing, or lack accuracy requirements for fast-time hazard prediction at airports. The approach taken in this paper employs Artificial Neural Networks (ANNs) for the estimation of the location and strength of the primary wake vortices trailing behind landing aircraft, going beyond the qualitative wake vortex identification of previous literature. Custom Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) architectures are generated, and compared to state of the art LiDAR processing algorithms. For this, LiDAR measurements taken at Vienna International Airport that were processed with the RV method are utilized for supervised training of the networks. In addition, feature engineering is performed, allowing to increase the performance of the ANNs by mitigating crosswind effects, optimizing measurement grid positions, and minimizing interfering boundary layer effects. Results indicate the superior performance of the custom CNNs over the custom MLPs in nearly all characterization parameters, while the evaluation speed of a single LiDAR scan turns out to be substantially faster compared to the current state of the art RV method. The custom CNN architecture results in circulation errors as low as 26 m^2/s and localization errors as low as 13 m. A hazard prediction reliability of up to 91% is obtained, given the accuracy of the RV method which constitutes a natural limit of the performance capabilities of ANNs.

Item URL in elib:https://elib.dlr.de/143800/
Document Type:Conference or Workshop Item (Speech)
Title:Characterizing Wake Vortices of Landing Aircraft Using Artificial Neural Networks and LiDAR Measurements
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Wartha, Niklas LouisDLR, IPAhttps://orcid.org/0000-0002-9672-2360UNSPECIFIED
Holzäpfel, FrankDLR, IPAhttps://orcid.org/0000-0003-3182-1832UNSPECIFIED
Date:28 July 2021
Journal or Publication Title:AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:Wake Vortices, Lidar, Artificial Neural Networks
Event Location:VIRTUAL EVENT
Event Type:international Conference
Event Start Date:2 August 2021
Event End Date:6 August 2021
Organizer:American Institute of Aeronautics and Astronautics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Air Transportation and Impact
DLR - Research area:Aeronautics
DLR - Program:L AI - Air Transportation and Impact
DLR - Research theme (Project):L - Climate, Weather and Environment
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Transport Meteorology
Deposited By: Wartha, Niklas Louis
Deposited On:06 Sep 2021 14:44
Last Modified:24 Apr 2024 20:43

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