基于自适应采样的高超声速飞行器气动热全局快速预示

doi:10.7527/S1000-6893.2022.27391

Abstract

Abstract:

High-fidelity aerothermodynamics analysis models in the thermal protection system of the hypersonic vehicles significantly increase the computational budget of engineering design， drawing extensive attention on data-driven based rapid prediction methods. This paper proposes a batch adaptive sampling method based on fuzzy clustering to improve the global prediction accuracy with the limited computational budget of high fidelity models. The sampling influence domain is constructed by clustering and hypersphere segmentation with the distribution characteristics of the prediction error， considering both the key sampling domain with larger errors and global exploration. The sampling refused domain is developed by the local error scoring coefficient weighted to reduce the redundancy of newly added samples. The method adds new samples in the comprehensively determined key sampling space to improve the sampling quality based on the maxmin criterion， thereby rapidly improving the global accuracy of the prediction models. The comparison results show that the proposed method outperforms One-Shot， APSFC and CV-Voronoi in terms of reducing the sampling scale required and accelerating prediction accuracy improvement. The rapid prediction results of the HTV-2 typed vehicle aerothermodynamics demonstrate the practicality and effectiveness of the proposed method in engineering practices.

Key words: adaptive sampling, fuzzy clustering, rapid prediction, hypersonic, aerothermodynamics

CLC Number:

V221

Guotao YANG, Zhenjiang YUE, Li LIU. Rapid prediction of global hypersonic vehicle aerothermodynamics based on adaptive sampling[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(6): 127391.

Figures/Tables 30

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Results comparison of One-Shot， CV-Voronoi， APSFC and FCHSW

测试函数	One-Shot	CV-Voronoi	APSFC	FCHSW
TG	27.7 $±$ 1.5	27.3 $±$ 2.1	30.5 $±$ 2.4	27.2 $±$ 2.1
ES	41.2 $±$ 7.1	26.8 $±$ 2.3	50.0 $±$ 11.6	26.5 $±$ 2.3
PK	54.3 $±$ 4.8	43.9 $±$ 6.2	57.9 $±$ 8.8	41.8 $±$ 4.3
AK	163.2 $±$ 8.3	165.8 $±$ 12.2	226.4 $±$ 45.1	132.4 $±$ 9.5
GP	36.4 $±$ 2.5	40.4 $±$ 4.1	40.4 $±$ 5.8	36.3 $±$ 3.3
MZ	303.3 $±$ 13.9	289.9 $±$ 27.1	417.6 $±$ 93.5	227.6 $±$ 15.7
HT	157.9 $±$ 9.9	132.6 $±$ 13.7	193.0 $±$ 49.7	132.1 $±$ 10.3
CH	132.9 $±$ 11.9	126.9 $±$ 9.8	150.6 $±$ 14.7	125.0 $±$ 10.3
DP	280.8 $±$ 31.8	283.2 $±$ 20.2	288.6 $±$ 33.1	279.0 $±$ 19.8

Table 1

Fig. 5

Fig. 6

Fig. 7

Table 2

Fig. 8

Table 3

Results comparison of influential domain

测试函数	超立方体影响域	超球体影响域
TG	27.9 $±$ 2.2	27.2 $±$ 2.1
ES	29.5 $±$ 5.9	26.5 $±$ 2.3
PK	48.4 $±$ 7.4	41.8 $±$ 4.3
AK	150.9 $±$ 17.1	132.4 $±$ 9.5
GP	40.4 $±$ 6.2	36.3 $±$ 3.3
MZ	230.7 $±$ 16.7	227.6 $±$ 15.7
HT	132.4 $±$ 8.7	132.1 $±$ 10.3
CH	129.8 $±$ 15.4	125.0 $±$ 10.3
DP	296.6 $±$ 22.0	279.0 $±$ 19.8

Table 3

Fig. 9

Table 4

Summary of test results about number of new samples per round

测试函数	$n a d d = n v$	$n a d d = 2 n v$	$n a d d = 3 n v$	$n a d d = 4 n v$	$n a d d = 5 n v$
TG	27.2 $±$ 2.1	29.8 $±$ 2.8	30.8 $±$ 2.9	31.6 $±$ 3.7	32.2 $±$ 4.2
ES	26.5 $±$ 2.3	27.9 $±$ 3.0	30.9 $±$ 4.6	33.0 $±$ 4.5	36.6 $±$ 5.2
PK	41.8 $±$ 4.3	44.0 $±$ 5.1	43.7 $±$ 5.9	45.8 $±$ 5.2	48.3 $±$ 4.8
AK	132.4 $±$ 9.5	133.9 $±$ 7.0	134.5 $±$ 9.0	134.3 $±$ 7.0	137.9 $±$ 8.7
GP	36.3 $±$ 3.3	39.0 $±$ 4.1	40.5 $±$ 5.5	41.8 $±$ 4.6	43.9 $±$ 5.3
MZ	227.6 $±$ 15.7	229.4 $±$ 15.0	229.2 $±$ 14.3	231.7 $±$ 12.9	235.6 $±$ 17.6
HT	132.1 $±$ 10.3	140.7 $±$ 7.7	138.1 $±$ 9.5	139.4 $±$ 8.3	141.4 $±$ 8.7
CH	125.0 $±$ 10.3	132.8 $±$ 13.8	128.7 $±$ 13.6	135.6 $±$ 14.8	134.8 $±$ 14.7
DP	279.0 $±$ 19.8	287.9 $±$ 22.1	297.9 $±$ 23.7	301.9 $±$ 22.5	297.6 $±$ 23.3

Table 4

Table 5

Summary of test results about fluctuation coefficient

测试函数	$μ = 1$	$μ = 1.2$	$μ = 1.5$	$μ = 2$	$μ = 2.5$	$μ = 3$	$μ = 5$
TG	26.8 $±$ 1.8	26.5 $±$ 1.8	27.0 $±$ 2.1	27.2 $±$ 2.1	28.3 $±$ 2.3	28.6 $±$ 2.3	29.1 $±$ 2.0
ES	38.8 $±$ 7.6	34.4 $±$ 7.3	30.5 $±$ 5.9	26.5 $±$ 2.3	26.8 $±$ 2.5	28.4 $±$ 4.8	30.6 $±$ 5.7
PK	44.8 $±$ 5.7	43.5 $±$ 5.0	42.3 $±$ 4.8	41.8 $±$ 4.3	41.8 $±$ 4.6	42.7 $±$ 4.6	46.8 $±$ 5.7
AK	130.4 $±$ 7.8	129.6 $±$ 7.6	131.3 $±$ 6.3	132.4 $±$ 9.5	141.2 $±$ 10.2	143.5 $±$ 9.7	156.7 $±$ 11.8
GP	35.8 $±$ 2.8	35.3 $±$ 2.3	36.2 $±$ 3.6	36.3 $±$ 3.3	37.2 $±$ 3.7	37.1 $±$ 3.2	40.6 $±$ 3.9
MZ	232.7 $±$ 14.7	233.4 $±$ 11.5	229.4 $±$ 14.2	227.6 $±$ 15.7	232.2 $±$ 18.0	233.7 $±$ 17.9	244.7 $±$ 17.3
HT	147.1 $±$ 10.2	142.3 $±$ 8.7	137.7 $±$ 9.4	132.1 $±$ 10.3	133.0 $±$ 9.4	136.1 $±$ 10.0	136.6 $±$ 11.3
CH	134.6 $±$ 17.1	129.7 $±$ 15.6	131.0 $±$ 14.6	125.0 $±$ 10.3	123.6 $±$ 15.0	127.9 $±$ 14.4	126.7 $±$ 15.5
DP	287.1 $±$ 25.2	293.9 $±$ 32.2	292.4 $±$ 25.4	279.0 $±$ 19.8	283.9 $±$ 23.1	288.8 $±$ 24.2	288.5 $±$ 23.5

Table 5

Fig. 10

Fig. 11

Table 6

Design space of flight parameters

参数	参数空间
马赫数Ma	$5.0 ~ 20.0$
飞行攻角α/（°）	$0 ~ 20$
飞行高度H/km	$30 ~ 60$

Table 6

Fig. 12

Fig. 13

Fig. 14

Table 7

Results comparison of 10 test cases using RPM

测试工况	Ma	α/（°）	H/km	驻点热流/（ $k W · m - 2$ ）		驻点误差/%	整体误差/%
测试工况	Ma	α/（°）	H/km	RPM	CFD	驻点误差/%	整体误差/%
Test1	5.0	8.9	43.3	207.8	215.3	3.6	6.9
Test2	6.7	17.8	53.3	291.1	299.6	2.9	5.0
Test3	8.3	2.2	56.7	475.5	455.5	4.2	7.1
Test4	10.0	15.6	33.3	3 661.9	3 698.7	1.0	5.0
Test5	11.7	0.0	36.7	4 703.2	4 679.3	0.5	4.8
Test6	13.3	11.1	60.0	1 525.8	1 519.6	0.4	2.6
Test7	15.0	20.0	46.7	5 312.3	5 287.0	0.5	4.9
Test8	16.7	6.7	30.0	22 447.9	22 863.0	1.9	5.1
Test9	18.3	4.4	50.0	8 062.7	8 054.3	0.1	3.3
Test10	20.0	13.3	40.0	18 983.5	19 233.9	1.3	3.4

Table 7

Fig. 15

Fig. 16

Fig. A1

Fig. A2

Fig. A3

Fig. A4

Fig. A5

Fig. A6

Fig. A7

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测试函数	CV-Voronoi		APSFC		FCHSW
测试函数	建模次数	训练时间/s	建模次数	训练时间/s	建模次数	训练时间/s
TG	335.0±59.7	1.4±0.2	209.7±37.1	0.5±0.2	153.3±31.1	0.8±0.2
ES	320.0±62.8	1.3±0.2	633.9±314.4	1.3±0.8	147.8±33.4	0.6±0.2
PK	952.0±297.7	3.1±0.9	833.8±267.1	1.7±0.6	399.5±83.7	1.5±0.3
AK	13 850.1±2 054.2	50.3±12.6	13 284.7±5 809.0	77.1±82.5	4 880.1±1 099.4	18.3±9.3
GP	792.2±173.2	2.7±0.6	393.0±120.9	0.8±0.2	313.7±65.5	1.3±0.3
MZ	42 466.6±7 781.3	539.8±20.7	45 708.1±23 123.0	1 346.5±113.4	14 624.6±2 448.4	114.8±23.0
HT	8 831.3±1 885.8	52.2±15.9	6 542.3±3 395.1	67.9±67.9	2 862.4±436.4	19.8±4.2
CH	7 953.6±1 255.7	66.7±13.5	2 778.5±546.8	18.1±6.4	1 683.7±272.5	22.7±14.7
DP	39 985.3±5 723.6	1 201.9±360.3	7 030.8±1 527.0	127.8±54.8	6 870.2±956.5	353.4±73.8

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Rapid prediction of global hypersonic vehicle aerothermodynamics based on adaptive sampling

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