Performance Prediction and Optimization of Ramjet for Projectiles Using Support Vector Regression Model

doi:10.12382/bgxb.2022.0493

Abstract

Abstract:

To improve the working performance of solid fuel ramjet for projectiles and shorten the optimization period, the transitionshear stress transfer and vortex concept dissipation equations are used to establish an internal ballistic calculation model, and the flow field and performance parameters are obtained. Then, based on the support vector regression method,a prediction model of the performance parameters is built, and the ramjet structure is optimized using the NSGA-Ⅱ algorithm. The results show that the internal ballistic calculation model can simulate the combustion and flow process in the ramjet well. At the same time, the constructed prediction model has high reliability, and the maximum relative error is less than 3% compared with that of the high-confidence model. After the ramjet is optimized, the combustion chamber is shortened by 13.88%, the aft mixing chamber is increased by 13.50%, and the combustion efficiency, thrust and specific impulse are increased by 12.02%,24.22% and 20.28%, respectively.

Key words: solid fuel ramjet, surrogate model, multi-objective optimization model, performance prediction

CLC Number:

ZHANG Ning, SHI Jinguang, WANG Zhongyuan, ZHAO Xinxin. Performance Prediction and Optimization of Ramjet for Projectiles Using Support Vector Regression Model[J]. Acta Armamentarii, 2023, 44(10): 2944-2953.

Figures/Tables 20

Fig.1 Structure of SFRJ for projectile

Fig.2 Meshing of simulation cases

Table 1 Main parameters of HTPB[20]

参数	数值
ρ/(kg·m^-3)	930
A/(m·s^-1)	3.964 (T<722K) 0.011(T>722K)
E_a/(J·mol^-1)	55803(T<722K) 20523(T>722K)

Table 2 Main parameters of C4H6

参数	数值
密度/(kg·m^-3)	612
标准生成焓/(J·mol^-1)	110000
标准熵/(J·mol^-1·K^-1)	278.78
标准热容/(J·mol^-1·K^-1)	79.88
燃烧热/(J·mol^-1)	-2541490

Table 3 Grid convergence analysis

网格数/10⁴	F/N	η/%	$r ·$ 平均值/(mm·s^-1)
14	253.27	79.17	0.6664
21	328.45	80.77	0.6847
32	383.06	84.26	0.7484
48	402.51	85.15	0.7598
72	412.83	86.31	0.7740
108	414.00	87.15	0.7795

Table 3 Grid convergence analysis

网格数/10⁴	F/N	η/%	$r ·$ 平均值/(mm·s^-1)
14	253.27	79.17	0.6664
21	328.45	80.77	0.6847
32	383.06	84.26	0.7484
48	402.51	85.15	0.7598
72	412.83	86.31	0.7740
108	414.00	87.15	0.7795

Fig.3 Optimization workflow of theramjet

Fig.4 Geometric model of the ramjet

Fig.5 NSGA-Ⅱ flow witha penalty function

Fig.6 Mean axial velocity alongthe combustor centerline

Table 4 Comparison between the calculated results and experimental results

算例	条件		结果
算例	流量/ (kg·m^-2·s^-1)	温度/ K	$r · ¯ r e f$ / (mm·s^-1)	$r · ¯ c a l$ / (mm·s^-1)
1	0.293	624	0.576	0.583
2	0.308	615	0.578	0.589

Table 4 Comparison between the calculated results and experimental results

算例	条件		结果
算例	流量/ (kg·m^-2·s^-1)	温度/ K	$r · ¯ r e f$ / (mm·s^-1)	$r · ¯ c a l$ / (mm·s^-1)
1	0.293	624	0.576	0.583
2	0.308	615	0.578	0.589

Fig.7 Comparison between predicted and calculated values

Fig.8 Pareto solution set for multi-objective optimization design

Fig.9 Optimizedramjet shape when λ=1

Table 5 Structural parameters of the optimized ramjet

结构	参数值/mm	变化量/%
l_c	212.72	-13.88
h_c	24.00	20.00
d_m	41.67	-16.67
l_m	306.45	13.50
h_p	8.40	-30.00
d_r	15.18	-15.67
l_s	34.68	-7.96
d_out	23.55	13.49
l_e	19.98	-5.33

Fig.10 C4H6 mass fraction of the ramjet

Fig.11 O2 mass fraction of the ramjet

Fig.12 Temperaturecontour and streamline of the initial ramjet

Fig.13 Temperature contour and streamline of the optimized ramjet

Fig.14 Regression rate along grain surface

Table 6 Performance of the ramjet

性能指标	优化前	优化后
$r · ¯$ /(mm·s^-1)	0.774	0.822
$m · b$ /(kg·s^-1)	0.041	0.043
F/N	412.83	512.82
I/(N·s·kg^-1)	999.68	1107.81
η/%	86.31	98.33

Table 6 Performance of the ramjet

性能指标	优化前	优化后
$r · ¯$ /(mm·s^-1)	0.774	0.822
$m · b$ /(kg·s^-1)	0.041	0.043
F/N	412.83	512.82
I/(N·s·kg^-1)	999.68	1107.81
η/%	86.31	98.33

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