﻿ 兼顾水面航态UUV性能和功能综合优化设计
 舰船科学技术  2020, Vol. 42 Issue (12): 36-40    DOI: 10.3404/j.issn.1672-7649.2020.12.007 PDF

Comprehensive optimal design analysis considering the main performance and function of surface UUV
CHENG Zhan-yuan, YANG Song-lin, WANG Bao-jiang, SHI Yan, CAI Cheng-qi
Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003,China
Abstract: Based on an unmanned underwater vehicle, this paper improves its design, make it effectively take into account the surface navigation state, andestablishes the optimization mathematical model of rapidity, maneuverability and functionality, improves the optimization software, uses genetic, chaos and particle swarm optimization algorithms to carry out different algebra optimization calculation, summarizes the calculation principle of the three algorithms, and compares the fitness function of each calculation algebra calculation result Maximum, the best parameters are obtained, and the three algorithms are analyzed to meet the constraints. The results show that the particle swarm optimization algorithm is relatively better and easier to meet the constraints. The calculation results provide a reference for the development and research of the submarine unmanned vehicle.
Key words: unmanned underwater vehicle     considering water surface     optimization algorithm     particle swarm optimization algorithm
0 引　言

1 无人艇优化设计数学模型 1.1 主艇体设计

 图 1 主体和柱体基本型线图 Fig. 1 Basic profile of main body and column

1.2 优化数学模型 1.2.1 设计变量

24个设计变量：首段 ${L_{{h}}}$ ，平行中体段 ${L_{m}}$ ，尾段 ${L_{{a}}}$ ，浮心纵向位置 ${X_{{f}}}$ ，螺旋桨直径 ${D_p}$ ，螺旋桨螺距比 ${P}_{Dp}$ ，艇体中横剖面直径 ${D}_{p}$ ，螺旋桨盘面比 ${A_{{{eo}}}}$ ，上部柱体长度 ${L_{{u}}}$ ，上部柱体宽度 ${B_{{u}}}$ ，上部柱体高度 ${H_{{u}}}$ ，螺旋桨转速 $N$ ，航速 ${V_{{s}}}$ ，水平舵的翼端弦长 ${d_{oh}}$ ，水平舵的根部弦长 ${d_{ih}}$ ，水平舵的展长 ${Z_h}$ ，垂直舵的翼端弦长 ${d_{ov}}$ ，垂直舵的根部弦长 ${d_{iv}}$ ，垂直舵的展长 ${Z_v}$ ，精度 ${X_1}$ 、工作温度 ${X_2}$ 、监测温度 ${X_3}$ 、测温精度 ${X_4}$ 、单个价格 ${X_5}$

1.2.2 目标函数

1）快速性子目标函数

 ${f_1}(X) = {C_{sp}}\left( x \right) = \frac{{{\Delta ^{2/3}} \cdot {V_s}^2}}{{{R_t}/{\eta _0} \cdot {\eta _H} \cdot {\eta _R} \cdot {\eta _S}}}{\text{。}}$ (1)

 ${R_t} = {R_f} + {R_{pv}} + {R_{ap}} = \frac{1}{2}\rho {V_S}^2S\left( {{C_f} + {C_{pv}} + {C_{ap}}} \right){\text{。}}$ (2)

2）操纵性子目标函数

UUV的航行可从水平面和垂直面运动两方面探讨，共选择水平面稳定性横准数、相对回转半径、升速率3个指标构成操纵性的目标函数，即

 ${f_2}\left( X \right) = {C^{'{\beta _1}}} \cdot {\left( {1/{C_{RS}}} \right)^{{\beta _2}}} \cdot V_\chi ^{'{\beta _3}}{\text{，}}$ (3)

 $C = {Y_v}{N_r} - {N_v}({Y_r} - m{u_1}){\text{，}}$ (4)

 $C' = C/\frac{1}{4}{\rho ^2}{L^6}{V^2} = {Y_v}^\prime {N_r}^\prime - {N_v}^\prime ({Y_r}^\prime - m'){\text{，}}$ (5)

 ${C_{RS}} = \frac{{{R_S}}}{L} = \frac{1}{{r_s^{'}}} = \frac{1}{{{K^{'}}\delta }} = \frac{{N_v^{'}\left( {{m^{'}} - Y_r^{'}} \right) + N_r^{'}Y_v^{'}}}{{N_v^{'}Y_\delta ^{'} - N_\delta ^{'}Y_v^{'}}} \cdot \frac{1}{\delta }{\text{，}}$ (6)

 $\frac{{\partial {V_{{x^{'}}}}}}{{\partial {\delta _h}}} = - \frac{{{V^3}}}{{{m^{'}}gh}}\Bigg[\frac{{M_{{\delta _h}}^{'}}}{{Z_{{\delta _h}}^{'}}} - \frac{{M_w^{'}}}{{Z_w^{'}}} - \frac{{M_\theta ^{'}}}{{Z_w^{'}}}\Bigg]Z_{{\delta _h}}^{'}{\text{，}}$ (7)

 $_{}\chi = - \frac{1}{{M_\theta ^{'}}}Z_{{\delta _s}}^{'} \cdot l_{cs}^{'}{\text{。}}$ (8)

3）功能性子目标函数

 $\begin{split} {f_3}(X) =&Y = {\rm{0.266}} + {\rm{0.190}}{{X}_{\rm{1}}} + {\rm{0.237}}{{X}_{\rm{2}}} +\\ &{\rm{0.063}}{{X}_{\rm{3}}} + {\rm{0.317}}{{X}_{\rm{4}}} - {\rm{0.405}}{{X}_{\rm{5}}} + {\rm{0.0293}}{{V}_s}{\text{。}}\end{split}$ (9)

4）总目标函数

 $F(X) = {f_1}{(X)^{{\varepsilon _1}}}*{f_2}{(X)^{{\varepsilon _2}}}*{f_3}{(X)^{{\varepsilon _3}}}{\text{，}}$ (10)

1.2.3 约束条件

1）浮性约束

 $\nabla = {\nabla _1} + {\nabla _2}{\text{。}}$ (11)

2）推力阻力平衡约束

 ${N_p}{K_T}\rho {N^2}D_P^4(1 - t) = {R_t}{\text{。}}$ (12)

3）转矩平衡约束

 $\frac{{{\eta _R}{\eta _s}{P_s}}}{{2\text{π} N}} = {K_Q}\rho {N^2}D_p^5{\text{。}}$ (13)

4）定深直线航行平衡纵倾角约束

5）定深直线航行平衡舵角约束

UUV在水中一定深度直航时，不排除外流场不稳定等因素的干扰，促使必须转尾舵的舵角来恢复平衡，一般这个舵角不超过在±5°。

2 优化算法及综合优化计算分析 2.1 优化算法 2.1.1 遗传算法

2.1.2 混沌算法

2.1.3 粒子群算法

 图 2 PSO算法的基本流程图 Fig. 2 Flow chart of PSO algorithm
2.2 优化计算及分析

2.2.1 遗传算法优化计算

 图 3 适应度函数值随遗传代数的变化曲线 Fig. 3 Change curve of fitness function value with genetic algebra

2.2.2 混沌算法优化计算

 图 4 适应度函数值随计算代数的变化曲线 Fig. 4 Change curve of fitness function value with calculation algebra
2.2.3 粒子群算法优化计算

 图 5 适应度函数值随计算代数的变化曲线 Fig. 5 Variation curve of fitness function value with calculation algebra
2.3 优化结果

3 结　语

1）混沌算法计算结果的适应度函数值相对其他2个算法较低，虽然遗传与粒子群算法计算结果更接近，但整体还是粒子群算法所计算的更优，相对更稳定；

2）随着计算次数的增加，3种计算方法所计算的结果都呈现正向增加的变化趋势，且当计算次数到达5000时，适应度函数一般达到最大值，并满足约束条件；

3）相比其他2种优化方法，使用粒子群算法得出的浮性约束、力的约束和转矩约束的值多在99.9%以上，惩罚值更易达到1，更易满足约束；

4）功能方面的结果提供了一个更优的参考值，可以更好选择一个适合实际需求的功能设备，有效避免成本的过高与资源的浪费。

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