Design of Blended-wing-body Underwater Gliders Based on Dual-layer Multi-objective Optimization

doi:10.12382/bgxb.2025.0012

Abstract

Abstract:

To enhance the performance and optimization efficiency of blended-wing-body underwater glider (BWBUG),this study proposes a dual-layer multi-objective optimization method for shape design of BWBUGs.A fully parameterized model of the shape of BWBUG is established,an analysis platform for shape performance is built,and a performance database and high-precision neural network mapping models are established.These mapping models are used to study the correlations among design parameters and performance metrics.Furthermore,a dual-layer constrained multi-objective optimization algorithm is constructed to optimize the BWBUG shape by taking the lift-to-drag ratio and internal capacity as objectives.The optimization process is conducted using the developed mapping models.The results demonstrate the lift-to-drag ratio and internal capacity of the optimized BWBUG are improved by 31.49% and 11.33%,respectively.Additionally,the weighted score analyses under varying weight scenarios further validate the effectiveness and robustness of the proposed optimization method.

Key words: underwater glider, blended-wing-body, neural network, dual-layer constrained multi-objective optimization, correlation analysis

CLC Number:

TJ67
U662.9

WANG Wenxin, WANG Peng, LI Jinglu, JIANG Yichen, DONG Huachao. Design of Blended-wing-body Underwater Gliders Based on Dual-layer Multi-objective Optimization[J]. Acta Armamentarii, 2025, 46(11): 250012-.

Figures/Tables 17

Fig.1 The concept diagram of BWBUG

Fig.2 Plane geometric shape of BWBUG

Table 1 The range of design parameters

参数	范围	参数	范围	参数	范围
s₁	[-0.0573,0.0573]	x₁	[0.05,0.20]	x₈	[0.30,0.64]
s₂	[-0.0494,0.0494]	x₂	[-0.20,-0.07]	x₉	[-0.20,-0.04]
s₃	[-0.0557,0.0557]	x₃	[0.06,0.68]	x₁₀	[0.04,0.19]
s₄	[-0.0383,0.0383]	x₄	[0.2,0.6]	x₁₁	[-0.14,0]
s₅	[-0.0574,0.0574]	x₅	[0.16,0.65]	x₁₂	[0.00,1.00]
		x₆	[0.10,0.28]	x_L/mm	[2000,5500]
		x₇	[0.050,0.198]	x_D/mm	[1000,4000]

Fig.3 CFD calculation of watershed and boundary layer mesh

Fig.4 The model of BWBUG

Fig.5 Hydrodynamic coefficients calculated with different mesh sizes

Fig.6 Calibration of numerical simulation method

Fig.7 Multi-software automated numerical simulation platform

Fig.8 Performance parameters:training accuracy of neural network

Fig.9 Thermal diagram of the correlation among design parameters and performance parameters

Fig.10 Double-layer optimization method based on NSGA III

Fig.11 Parameter optimization transfer diagram

Fig.12 Pareto front plot of optimized results at different stages

Table 2 Performance comparison at various stages

参数	第一阶段	第二阶段	提升/%
最大升阻比	10.83	14.24	31.49
最大内部容量	7.24	8.06	11.33
加权(0.2,0.8)	6.73	7.38	9.72
加权(0.4,0.6)	6.30	6.69	6.19
加权(0.5,0.5)	6.07	7.19	18.45
加权(0.6,0.4)	6.74	8.6	27.60
加权(0.8,0.2)	8.79	11.42	29.92

Fig.13 Curve graph of internal capacity,L/D,and related parameters

Fig.14 Cloud map of pressure on the lower surface of BWBUGs

Fig.15 Cloud map of pressure on the upper surface of BWBUGs

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