Aircraft Icing Severity Evaluation

Li, Sibo; Paoli, Roberto

doi:10.3390/encyclopedia2010005

Title: Aircraft Icing Severity Evaluation

Journal Article · 05 January 2022 · Encyclopedia

DOI: https://doi.org/10.3390/encyclopedia2010005 · OSTI ID:1838922

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Aircraft icing refers to the ice buildup on the surface of an aircraft flying in icing conditions. The ice accretion on the aircraft alters the original aerodynamic configuration and degrades the aerodynamic performances and may lead to unsafe flight conditions. Evaluating the flow structure, icing mechanism and consequences is of great importance to the development of an anti/deicing technique. Studies have shown computational fluid dynamics (CFD) and machine learning (ML) to be effective in predicting the ice shape and icing severity under different flight conditions. CFD solves a set of partial differential equations to obtain the air flow fields, water droplets trajectories and ice shape. ML is a branch of artificial intelligence and, based on the data, the self-improved computer algorithms can be effective in finding the nonlinear mapping relationship between the input flight conditions and the output aircraft icing severity features.

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Sponsoring Organization:: USDOE

OSTI ID:: 1838922

Journal Information:: Encyclopedia, Journal Name: Encyclopedia Journal Issue: 1 Vol. 2; ISSN 2673-8392

Publisher:: MDPICopyright Statement

Country of Publication:: Country unknown/Code not available

Language:: English

References (36)

On predicting particle-laden turbulent flows Elghobashi, S. Applied Scientific Research, Vol. 52, Issue 4 https://doi.org/10.1007/BF00936835	journal	June 1994
Support-vector networks Cortes, Corinna; Vapnik, Vladimir Machine Learning, Vol. 20, Issue 3 https://doi.org/10.1007/BF00994018	journal	September 1995
A New Version of Detached-eddy Simulation, Resistant to Ambiguous Grid Densities Spalart, P. R.; Deck, S.; Shur, M. L. Theoretical and Computational Fluid Dynamics, Vol. 20, Issue 3 https://doi.org/10.1007/s00162-006-0015-0	journal	May 2006
A framework for sensitivity analysis of decision trees Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław Central European Journal of Operations Research, Vol. 26, Issue 1 https://doi.org/10.1007/s10100-017-0479-6	journal	May 2017
Not So Naive Bayes: Aggregating One-Dependence Estimators Webb, Geoffrey I.; Boughton, Janice R.; Wang, Zhihai Machine Learning, Vol. 58, Issue 1 https://doi.org/10.1007/s10994-005-4258-6	journal	January 2005
PoliMIce: A simulation framework for three-dimensional ice accretion Gori, G.; Zocca, M.; Garabelli, M. Applied Mathematics and Computation, Vol. 267 https://doi.org/10.1016/j.amc.2015.05.081	journal	September 2015
Numerical simulation of ice accretions on an aircraft wing Cao, Yihua; Ma, Chao; Zhang, Qiang Aerospace Science and Technology, Vol. 23, Issue 1 https://doi.org/10.1016/j.ast.2011.08.004	journal	December 2012
Aircraft icing: An ongoing threat to aviation safety Cao, Yihua; Tan, Wenyuan; Wu, Zhenlong Aerospace Science and Technology, Vol. 75 https://doi.org/10.1016/j.ast.2017.12.028	journal	April 2018
NNICE – a neural network aircraft icing algorithm McCann, Donald W. Environmental Modelling & Software, Vol. 20, Issue 10 https://doi.org/10.1016/j.envsoft.2004.09.027	journal	October 2005
Numerical simulation of three-dimensional ice accretion on an aircraft wing Cao, Yihua; Huang, Junsen; Yin, Jun International Journal of Heat and Mass Transfer, Vol. 92 https://doi.org/10.1016/j.ijheatmasstransfer.2015.08.027	journal	January 2016
Heat and mass transfer during ice accretion on aircraft wings with an improved roughness model Fortin, Guy; Laforte, Jean-Louis; Ilinca, Adrian International Journal of Thermal Sciences, Vol. 45, Issue 6 https://doi.org/10.1016/j.ijthermalsci.2005.07.006	journal	June 2006
Machine learning for continuous liquid interface production: Printing speed modelling He, Haiyang; Yang, Yang; Pan, Yayue Journal of Manufacturing Systems, Vol. 50 https://doi.org/10.1016/j.jmsy.2019.01.004	journal	January 2019
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
A tensorial approach to computational continuum mechanics using object-oriented techniques Weller, H. G.; Tabor, G.; Jasak, H. Computers in Physics, Vol. 12, Issue 6 https://doi.org/10.1063/1.168744	journal	January 1998
Exact, infinite energy, blow-up solutions of the three-dimensional Euler equations Gibbon, J. D.; Moore, D. R.; Stuart, J. T. Nonlinearity, Vol. 16, Issue 5 https://doi.org/10.1088/0951-7715/16/5/315	journal	July 2003
Effects of ice geometry on airfoil performance using neural networks prediction Cao, Yihua; Yuan, Kungang; Li, Guozhi Aircraft Engineering and Aerospace Technology, Vol. 83, Issue 5 https://doi.org/10.1108/00022661111159870	journal	September 2011
Aircraft ice accretion prediction using neural network and wavelet packet transform Chang, Shinan; Leng, Mengyao; Wu, Hongwei Aircraft Engineering and Aerospace Technology, Vol. 88, Issue 1 https://doi.org/10.1108/AEAT-05-2014-0057	journal	January 2016
Development of a Fast Fluid Dynamics Model Based on PISO Algorithm for Simulating Indoor Airflow Li, Sibo; Qiao, Hongtao ASME 2021 Heat Transfer Summer Conference collocated with the ASME 2021 15th International Conference on Energy Sustainability https://doi.org/10.1115/ht2021-63909	conference	July 2021
Modeling of Ice Accretion over Aircraft Wings Using a Compressible OpenFOAM Solver Li, Sibo; Paoli, Roberto International Journal of Aerospace Engineering, Vol. 2019 https://doi.org/10.1155/2019/4864927	journal	June 2019
Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization Scheme Li, Sibo; Paoli, Roberto; D’Mello, Michael Scientific Programming, Vol. 2020 https://doi.org/10.1155/2020/9083620	journal	March 2020
Neural networks for topology optimization Sosnovik, Ivan; Oseledets, Ivan Russian Journal of Numerical Analysis and Mathematical Modelling, Vol. 34, Issue 4 https://doi.org/10.1515/rnam-2019-0018	journal	August 2019
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression Altman, N. S. The American Statistician, Vol. 46, Issue 3 https://doi.org/10.2307/2685209	journal	August 1992
Aircraft Ice Accretion Prediction Based on Neural Networks Ogretim, E.; Huebsch, W.; Shinn, A. Journal of Aircraft, Vol. 43, Issue 1 https://doi.org/10.2514/1.16241	journal	January 2006
Comparison of Machine Learning Models for Data-Driven Aircraft Icing Severity Evaluation Li, Sibo; Paoli, Roberto Journal of Aerospace Information Systems, Vol. 18, Issue 12 https://doi.org/10.2514/1.I011047	journal	December 2021
Surface Roughness and Heat Transfer Improved Predictions for Aircraft Ice-Accretion Modeling Han, Yiqiang; Palacios, Jose AIAA Journal, Vol. 55, Issue 4 https://doi.org/10.2514/1.J055217	journal	April 2017
Improved Prediction of Flow Around Airfoil Accreted with Horn or Ridge Ice Xiao, Maochao; Zhang, Yufei AIAA Journal, Vol. 59, Issue 6 https://doi.org/10.2514/1.J059744	journal	June 2021
Data-Driven Machine Learning Model for Aircraft Icing Severity Evaluation Li, Sibo; Qin, Jingkun; Paoli, Roberto Journal of Aerospace Information Systems, Vol. 18, Issue 11 https://doi.org/10.2514/1.i010978	journal	November 2021
Ice Accretion Prediction on Multielement Airfoils Mingione, Giuseppe; Brandi, Vincenzo Journal of Aircraft, Vol. 35, Issue 2 https://doi.org/10.2514/2.2290	journal	March 1998
FENSAP-ICE's Three-Dimensional In-Flight Ice Accretion Module: ICE3D Beaugendre, Héloïse; Morency, François; Habashi, Wagdi G. Journal of Aircraft, Vol. 40, Issue 2 https://doi.org/10.2514/2.3113	journal	March 2003
Icing flight research - Aerodynamic effects of ice and ice shape documentation with stereo photography Mikkelsen, K.; Mcknight, R.; Ranaudo, R. 23rd Aerospace Sciences Meeting https://doi.org/10.2514/6.1985-468	conference	August 1985
Determining the effects of weather in aircraft accident investigations Mclean, Jr., J. 24th Aerospace Sciences Meeting https://doi.org/10.2514/6.1986-323	conference	August 1986
Overview and risk assessment of icing for transport category aircraft and components Vukits, T. 40th AIAA Aerospace Sciences Meeting & Exhibit https://doi.org/10.2514/6.2002-811	conference	August 2002
Aerodynamic Performance of a Swept Wing with Ice Accretions Papadakis, Michael; Yeong, Hsiung Wei; Vargas, Mario 41st Aerospace Sciences Meeting and Exhibit https://doi.org/10.2514/6.2003-731	conference	November 2003
Equilibrium Temperature of an Unheated Icing Surface as a Function of Air Speed Messinger, Bernard L. Journal of the Aeronautical Sciences, Vol. 20, Issue 1 https://doi.org/10.2514/8.2520	journal	January 1953
Fast Evaluation of Aircraft Icing Severity Using Machine Learning Based on XGBoost Li, Sibo; Qin, Jingkun; He, Miao Aerospace, Vol. 7, Issue 4 https://doi.org/10.3390/aerospace7040036	journal	March 2020
A CFD Approach for Predicting 3D Ice Accretion on Aircraft Kinzel, Michael P.; Noack, Ralph W.; Sarofeen, Christian M. SAE 2011 International Conference on Aircraft and Engine Icing and Ground Deicing, SAE Technical Paper Series https://doi.org/10.4271/2011-38-0044	conference	June 2011

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