Data-Driven Machine Learning Model for Aircraft Icing Severity Evaluation
Author:
Affiliation:
1. University of Illinois at Chicago, Chicago, Illinois 60607
2. China Literature Limited, 100105 Beijing, People’s Republic of China
Funder
NSF
Argonne National Laboratory
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Aerospace Engineering
Link
https://arc.aiaa.org/doi/pdf/10.2514/1.I010978
Reference16 articles.
1. Determining the effects of weather in aircraft accident investigations
2. Three-Dimensional Eulerian Approach to Droplet Impingement Simulation Using FENSAP-ICE, Part 1: Model, Algorithm, and Validation
3. Aircraft icing: An ongoing threat to aviation safety
4. Fast Evaluation of Aircraft Icing Severity Using Machine Learning Based on XGBoost
5. Aircraft Ice Accretion Prediction Based on Neural Networks
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