基于POD和代理模型的热气防冰性能预测方法

doi:10.7527/S1000-6893.2022.26992

Abstract

Abstract:

Most large civil aircraft use hot air anti-icing systems as anti-icing strategies for airfoils and nacelles. A novel estimation method for hot air anti-icing system performance based on Proper Orthogonal Decomposition （POD） and surrogate models is proposed to shorten the design cycle of hot air anti-icing system. POD is adopted for data compression and characteristics extraction for the anti-icing performance snapshot matrix obtained by numerical calculation， and a lower-dimensional approximation for the snapshot matrix is derived from the projection subspace consisting of a set of basis modes. Support vector regression method is used to construct the surrogate models between the fitting coefficients of basis modes and the piccolo tube geometric parameters. The validation of the estimation method on a three-dimensional slat piccolo tube hot air anti-icing system shows that this method has a good surface temperature prediction and can accurately predict the runback water distribution within the droplet impingement area. The time consumption of the established estimation method reduces significantly compared to the numerical simulation method， which is of great significance for the hot air anti-icing system optimal design.

Key words: hot air anti-icing, performance estimation, proper orthogonal decomposition, surrogate model, support vector regression

CLC Number:

V221⁺.92

Qian YANG, Xiaofeng GUO, Qin LI, Wei DONG. Hot air anti-icing performance estimation method based on POD and surrogate model[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(1): 626992-626992.

Figures/Tables 18

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 1

Range of hot air anti-icing geometric parameters

Parameter	Range
L/mm	［20， 40］
$x p i c$ /mm	［5， 20］
$y p i c$ /mm	［-10， 10］
$θ 0$ /（°）	［-20， 20］
$θ 1$ /（°）	［0， 45］

Table 1

Table 2

Training samples

No.	L/mm	$x p i c$ /mm	$y p i c$ /mm	$θ 0$ /（°）	$θ 1$ /（°）
1	31.49	17.99	-5.23	2.78	13.52
2	37.95	6.21	5.95	-1.02	32.56
3	23.21	15.41	5.01	0.10	20.05
4	25.39	6.11	0.33	14.34	7.76
$⋮$	$⋮$	$⋮$	$⋮$	$⋮$	$⋮$
1 000	20.55	17.56	0.01	15.50	2.50

Table 2

Table 3

Testing samples

No.	L/mm	$x p i c$ /mm	$y p i c$ /mm	$θ 0$ /（°）	$θ 1$ /（°）
1	39.75	15.19	1.15	8.10	40.84
2	30.45	5.19	-2.35	-8.50	11.36
3	38.45	18.19	3.95	-9.90	12.49
4	21.45	-2.06	0.75	-8.90	26.44
$⋮$	$⋮$	$⋮$	$⋮$	$⋮$	$⋮$
200	26.05	14.44	3.05	-2.70	23.96

Table 3

Fig. 6

Fig. 7

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Hot air anti-icing performance estimation method based on POD and surrogate model

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