基于神经网络的S弯喷管-涡扇发动机多维度仿真

doi:10.7527/S1000-6893.2024.30791

Abstract

Abstract:

The internal flow of the serpentine nozzle is complex， with numerous parameters affecting its aerodynamic performance. Traditional component-based zero-dimensional engine models cannot accurately assess the aerodynamic performance impact of the serpentine nozzle on the overall engine. In this paper， a high-fidelity performance prediction model for serpentine nozzles is established using the back propagation （BP） neural network， and is coupled with a zero-dimensional turbofan engine model. This integrated approach is employed to investigate the influence of the baseline serpentine nozzle on engine speed， altitude characteristics， and component behaviors， as well as the differences in nozzle and engine performance with various geometric parameters. The results indicate that compared to axisymmetric nozzles， the engine equipped with the serpentine nozzle experiences a decline in performance. Specifically， at sea-level static conditions， the engine’s thrust decreases by 4.50%， while the fuel consumption increases by 4.75%； the fan bypass ratio decreases by a maximum of 0.33% at sea level， accompanied by a reduction in surge margin. Conversely， at an altitude of 12 km， the fan bypass ratio increases by a maximum of 0.28%， and the surge margin increases. These variation trends in fan operating characteristics can be attributed to the differences in throttling effects of the serpentine nozzle on the core and bypass of mixing chamber at different altitudes. Additionally， increasing the length-to-diameter ratio of the serpentine nozzle from 2.2 to 3.0 enhances its performance， resulting in an 8.0% increase in thrust coefficient and a 4.8% increase in discharge coefficient. The multi-dimensional coupling model between serpentine nozzle and engine established in this study can effectively evaluate the changes in engine performance and component characteristics following the installation of serpentine nozzles of varying geometric parameters.

Key words: numerical zooming, serpentine nozzle, turbofan engine, multi-dimensional coupling, neural network, geometric parameter

CLC Number:

V231.1

Chengxi WANG, Li ZHOU, Xiaolin SUN, Xiaobo ZHANG, Zhanxue WANG. Multi-dimensional simulation between serpentine nozzle and turbofan based on neural network[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(15): 130791.

Figures/Tables 26

Fig.1

Fig.2

Table 1

Table 2

Parameters range of serpentine nozzle

参数	下限	上限
L/D	2.2	3
W/H_n	2	11
A_e/A_i	0.5	0.9
P $c $ /P $b $	0.9	1.1
NPR_c	1.5	4.5
T $c *$ /K	600	1 000
T $b *$ /K	250	500

Table 2

Fig.3

Table 3

Fig.4

Fig.5

Table 4

Fig.6

Fig.7

Table 5

Table 6

Fig.8

Fig.9

Table 7

Fig.10

Fig.11

Fig.12

Fig.13

Fig.14

Fig.15

Fig.16

Fig.17

Fig.18

Fig.19

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参数所属部件	参数名称	参数符号
混合室	长度	L_M
	外涵直径	D_b
	内涵直径	D_c
喷管	进口直径	D
	总长度	L
	出口宽度	W
	出口高度	H_n
	进口面积	A_i
	出口面积	A_e
	长径比	L/D
	出口宽高比	W/H_n
	出进口面积比	A_e/A_i

边界名称	边界类型
混合室外涵进口	压力进口
混合室内涵进口	压力进口
混合室-喷管交界面	interface
混合室壁面	绝热无滑移壁面
喷管壁面	绝热无滑移壁面
喷管-远场交界面	interface
远场进口	压力进口
远场边界	开放边界（Opening）
远场出口	压力出口

网格数量/10⁴	推力系数	流量系数
相对误差/%	0.06	0.05
630	0.977 213	0.983 522
870	0.977 776	0.983 996

设计参数	数值
飞行高度/km	0
飞行马赫数	0
风扇压比	1.81
涵道比	2.01
压气机压比	4.36
涡轮前总温/K	1 255.00

部件	迭代变量	残差变量
风扇	涵道比、β	质量流量
高压压气机	β	质量流量
燃烧室	出口总温T_t4	质量流量
高压涡轮	β	质量流量
低压涡轮	β	质量流量
混合室		静压
喷管		质量流量
低压轴	转速	功率
高压轴	转速	功率

Multi-dimensional simulation between serpentine nozzle and turbofan based on neural network

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Figures/Tables 26

References 28

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结果	F/F_0，e	G_sfc/G_sfc0，e	P_t3/P_t3，0，e
相对误差/%	0.357 3	0.348 7	0.508 6
实验数据	0.951 62	1.043 83	0.994 97
模型计算	0.955 02	1.047 47	1.000 03

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