A runway icing prediction method based on physics-informed neural network

Abstract

Abstract:

Runway icing poses a threat to the safety of aircraft ground operation, accurate prediction of pavement temperature

and icing state is essential. To overcome the limitations of traditional runway icing predication methods in handling

complex scenarios, limited data, and physical inconsistency, this study proposes a runway icing prediction method

based on physics-informed neural network (PINN). The model embeds multilayer structure heat conduction and

water-ice phase change mechanisms into a deep neural network, enabling precise prediction of temperature fields

and water-ice state under limited data. Experimental data from a self-designed icing simulation are used to compare the solution results of PINN with the finite difference method (FDM). Results show that the jointly driven data physics PINN reduces average prediction error of temperature by about 90% compared to FDM, with just 0.21 ℃,

which is able to reconstruct full-field temperature field from limited data. Furthermore, the study analyzes the

mechanisms of salinity lowers the freezing point, delays icing, and suppresses ice growth. These findings can provide a new technological path for runway icing prediction.

Key words:

physics-informed neural network (PINN), runway icing, heat conduction, phase change, finite difference method

(FDM)

CLC Number:

V351.11

XING Xinyuana, b , LIU Shifua, b , LING Jianminga, b , TAO Zefenga, b, .

A runway icing prediction method based on physics-informed neural network

[J]. Journal of Civil Aviation University of China, 2025, 43(3): 38-44.

References 25

	[1]
	ICAO. Annual report-2018 air transport statistics[R]. Montreal: ICAO,
	2018: 1-12.
[2]	王立文, 郭贤, 陈斌, 等．多传感器跑道积冰预警无线数据采集
	系统设计(英文)[J]．机床与液压, 2019, 47(6): 38-45．
[3]	陈斌, 高大枘, 汤凯峰, 等．基于 LADRC 的跑道积冰主动探测装
	置温度精准控制[J]．控制理论与应用, 2024, 41(9): 1610-1618．
	[4]
	DAN H C, TAN J W, DU Y F, et al． Simulation and optimization of road
	deicing salt usage based on water-ice-salt model[J]． Cold Regions Science and Technology, 2020, 169: 102917．
	[5]
	NUIJTEN A D W． Runway temperature prediction, a case study for Oslo
	Airport, Norway[J]． Cold Regions Science and Technology, 2016, 125:
	72-84．
	[6]
	NUIJTEN A D W, H覫YLAND K V． Modelling the thermal conductivity
	of a melting snow layer on a heated pavement[J]． Cold Regions Science
	and Technology, 2017, 140: 20-29．
	[7]
	FUJIMOTO A, SAIDA A, FUKUHARA T． A new approach to modeling
	vehicle-induced heat and its thermal effects on road surface tempera－
	ture[J]． Journal of Applied Meteorology and Climatology, 2012, 51(11):
	1980-1993．
	[8]
	FUJIMOTO A, TOKUNAGA R A, KIRIISHI M, et al． A road surface
	freezing model using heat, water and salt balance and its validation by
	field experiments[J]． Cold Regions Science and Technology, 2014, 106/
	107: 1-10．
	[9]
	CHEN J Q, SUN C Q, SUN X, et al． Finite difference model for predict－
	ing road surface ice formation based on heat transfer and phase transi－
	tion theory[J]． Cold Regions Science and Technology, 2023, 207: 103772．
[10]	CHEN J Q, WANG H, XIE P Y． Pavement temperature prediction: the－
	oretical models and critical affecting factors[J]． Applied Thermal Engi－
	neering, 2019, 158: 113755．
[11]	LIU W, RAO Z. Pavement icing forecasting based on long short-term
	memory network[C]//2019 5th International Conference on Environmental Science and Material Application, Xi′an, China, 2020: 052070.
[12]	WANG S H, WANG T L, PEI X, et al． Highway icing time prediction
	with deep learning approaches based on data from road sensors[J]． Sci－
	ence China Technological Sciences, 2023, 66(7): 1987-1999．
[13]	DILORENZO T, HABIBZADEH-BIGDARVISH O, LI D F, et al． Rigid
	pavement icing: misting tests on a model pavement column under simu－
	lated cold fronts inside a freezer[J]． International Journal of Pavement
	Engineering, 2023, 24(2): 2044036．
[14]	陈斌, 刘悦, 尹开浪, 等. 冰雪天气下基于 LSTM 的跑道温度数
	据-机理联合预测[J]. 北京航空航天大学学报, 2024, 50(7): 2184-
	2194.
[15]	陈斌, 刘悦, 李庆真, 等．冰雪天气下基于 MFOA-KELM 残差修
	正的跑道温度混合预测[J]．北京航空航天大学学报, 2022, 48(11):
	2153-2164．
[16]	余波, 甘子玉, 张森林, 等. 基于物理信息神经网络预测 2D/3D 非
	稳态温度场及热源[J/OL]. 工程力学, 113, [2025-05-21].http://kns.
	cnki.net/kcms/detail/11.2595.O3.20231013.1641.006.html.
[17]	赵暾, 周宇, 程艳青, 等．基于内嵌物理机理神经网络的热传导方
	程的正问题及逆问题求解[J]．空气动力学学报, 2021, 39(5): 19-26．
[18]	MADIR B E, LUDDENS F, LOTHOD譩 C, et al． Physics informed neural
	Networks for heat conduction with phase change[EB/OL]． (2024-10-18)
	[2025-05-20]. https://arxiv．org/abs/2410.14216v1．
[19]	CAI S Z, WANG Z C, WANG S F, et al． Physics-informed neural net－
	works for heat transfer problems[J]． Journal of Heat Transfer, 2021, 143
(6)	: 060801．
[20]	XING Z B, CHENG H, CHENG J． Deep learning method based on physics-informed neural network for 3D anisotropic steady-state heat con－
	duction problems[J]． Mathematics, 2023, 11(19): 4049．
[21]	CHU F Q, LI S X, ZHAO C J, et al． Interfacial ice sprouting during salty
	water droplet freezing[J]． Nature Communications, 2024, 15(1): 2249．
[22]	王海瑞, 王兴鹏, 李朝阳, 等．南疆咸水结冰与融化过程中盐分及离
	子的变化[J]．环境化学, 2022, 41(4): 1392-1400．
[23]	吴赣昌. 层状路面体系温度场分析[J]. 中国公路学报, 1992(4): 17-25.
[24]	RAISSI M, PERDIKARIS P, KARNIADAKIS G E． Physics-informed
	neural networks: a deep learning framework for solving forward and in－
	verse problems involving nonlinear partial differential equations[J]． Journal of Computational Physics, 2019, 378: 686-707．
[25]	LING J M, XING X Y, ZHANG J, et al． An experimental validation based study of airport pavement icing mechanisms in saline environ－
	ments and the development of a simplified prediction model[J]． Applied
	Sciences, 2024, 14(19): 8867．