An Optimization Method for the Design of an Unmanned Underwater Vehicle under Size and Weight Constraints

Abstract

The present invention proposes an optimization method for the design of an unmanned underwater vehicle (UUV) under size and weight constraints. First, based on the assumptions of the hydrodynamic model, the UUV model is divided into three basic parts, and mathematical models for the water flow resistance, wetted surface area, pressure, and mass of the UUV are established. Taking key dimensions such as length and diameter as variables, through the particle swarm optimization algorithm and the genetic optimization algorithm, the optimal size parameters of the UUV under different optimization objectives are solved and the optimization results of the algorithms are compared. According to the comparison results, the genetic optimization algorithm is selected to perform multidisciplinary joint optimization on the UUV, and three-dimensional modeling and simulation analysis are carried out on the results to verify the effectiveness of the optimization results. The present invention realizes multidisciplinary optimization of the UUV under size and weight constraints, provides a new idea for the structural design of the UUV, solves key problems such as resistance optimization, energy efficiency, and pressure-bearing capacity under size and weight limitations in the design of the UUV, improves the structural design efficiency and performance of the UUV, and reduces the development cost and time.

Description

Technical Field

[0001] The present invention relates to the technical field of performance optimization of unmanned underwater vehicles, and particularly to a design optimization method for unmanned underwater vehicles under size and weight constraints. Background Art

[0002] An unmanned underwater vehicle (UUV) is an important tool for marine resource exploration and development, and is the design result of a comprehensive interdisciplinary project. Its design involves multiple disciplines, such as structural strength, power system, control system, and environmental adaptability. The optimization of UUVs involves multidisciplinary intersections. Through the optimization method of multidisciplinary integration, design challenges can be solved, thereby improving the design level and overall performance of the UUV system.

[0003] Currently, in terms of UUV optimization, the existing problems are as follows: First, due to the complexity of multidisciplinary coupling, the design difficulty increases. Most of the existing technologies are single-objective and single-disciplinary optimizations, resulting in low efficiency in optimization methods and design processes. Usually, it requires a long development time and high development costs. Especially for complex UUV systems, designers need to spend a lot of time on the adjustment and testing of each subsystem, resulting in slow project progress. Second, there are often problems such as the excessive weight affecting the endurance of the power system, or the functional weakening due to size limitations, making it difficult to find the best-performing solution. Third, existing technologies often adopt a single optimization algorithm or means, which usually cannot take into account multiple performance indicators at the same time, such as the compressive capacity, endurance, and movement flexibility of the hull. A single optimization algorithm shows limitations in multi-objective optimization and often only improves the performance in one aspect while ignoring other aspects. Summary of the Invention

[0004] The purpose of the present invention is to at least solve one of the above technical defects.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A design optimization method for an unmanned underwater vehicle under size and weight constraints, comprising:

[0007] Constructing a multidisciplinary mathematical model of the unmanned underwater vehicle, including a water flow resistance model, a wetted surface area model, a pressure model, and a mass model;

[0008] Setting the optimization conditions of the unmanned underwater vehicle, including resistance optimization conditions and mass optimization conditions, and solving the optimal parameters through a particle swarm optimization algorithm and a genetic optimization algorithm;

[0009] Comparing the optimal parameters solved by the particle swarm optimization algorithm and the genetic optimization algorithm, and selecting a better optimization algorithm for multidisciplinary index optimization;

[0010] Visualize and simulate the optimization results for verification;

[0011] The water flow resistance model includes the water flow resistance model of the nose cone, the water flow resistance model of the main body, and the water flow resistance model of the tail cone. Specifically,

[0012] Water flow resistance model of the nose cone:

[0013]

[0014] Among them, represents the pressure resistance of the nose cone, L nose is the length of the nose cone part, p is the fluid pressure, r(z) is the radius of the nose cone shell at the axial position z, and θ(z) is the angle between the streamline at this position and the horizontal plane;

[0015] Water flow resistance model of the main body:

[0016]

[0017] Among them, represents the frictional resistance of the main body, τ w is the wall shear stress, r(x) is the radius of the main body, v is the kinematic viscosity, u is the fluid velocity, and y is the distance perpendicular to the wall;

[0018] Water flow resistance model of the tail cone:

[0019]

[0020] Where: represents the turbulent resistance of the tail cone, τ t is the turbulent shear stress calculated by the turbulence model, L tail is the length of the tail cone, and r(z) is the radius of the tail cone;

[0021] The overall water flow resistance of the underwater vehicle is:

[0022]

[0023] Preferably, the underwater vehicle includes three parts: a nose cone, a main body, and a tail cone.

[0024] Preferably, the method further includes: after visualizing and simulating the optimization results for verification, deploying the optimization results of the underwater vehicle to the actual application scenario according to the verified model.

[0025] Preferably, the wet surface area model includes the wet surface area model of the nose cone, the wet surface area model of the main body, and the wet surface area model of the tail cone. Specifically,

[0026] Wet surface area model of the nose cone:

[0027]

[0028] where r n is the radius function of the vertex z of the nose cone, l n is the length of the nose cone part, h n is the total height of the nose cone;

[0029] Wet surface area model of the main body:

[0030] S body = 2πr b L b

[0031] where r b is the radius of the cylinder, L b is the length of the cylinder;

[0032] Wet surface area model of the tail cone:

[0033]

[0034] where r t is the radius function starting from the vertex z of the tail cone, h t is the total height of the tail cone;

[0035] The wet surface area of the whole unmanned underwater vehicle is:

[0036] S total = S n + S b + S t .

[0037] Preferably, the pressure model includes a nose cone pressure model, a main body pressure model and a tail cone pressure model. Specifically,

[0038] Nose cone pressure model:

[0039]

[0040] where P is the pressure, t is the wall thickness, a is the local curvature radius at the front end, and σ y is the yield strength of the material;

[0041] Main body pressure model:

[0042]

[0043] where P is the pressure, t is the wall thickness, R is the radius of the cylinder, and σ y is the yield strength of the material;

[0044] Tail cone pressure model:

[0045]

[0046] Among them, σ axial is the axial stress, σ hoop is the circumferential stress, P is the pressure, t is the wall thickness, and r is the radius of the bottom of the cone.

[0047] Preferably, the mass model includes:

[0048] Volume of the nose cone:

[0049]

[0050] V hnet = V hext - V hint

[0051] Volume of the main body:

[0052] V c-ext = πt 2 l

[0053]

[0054] V cnet = V c_ext - V c_int

[0055] Volume of the tail cone:

[0056]

[0057] V tnet = V t_ext - V t_int

[0058] Total volume of the unmanned submersible vehicle:

[0059] V totalnet = V hnet + V cnet + V tnet

[0060] Total weight of the unmanned submersible vehicle:

[0061] M = ρ material · V totalnet

[0062] Among them, R1 is the outer radius of the nose cone, r 1i is the inner radius of the nose cone, r is the outer radius of the main body, r i is the inner radius of the main body, l is the length of the main body, h is the height of the tail cone, r 2i is the inner radius of the top of the tail cone.

[0063] Preferably, the drag optimization conditions of the underwater vehicle include the total length, length-diameter ratio, and four variables: nose cone length, body length, tail cone length, and body diameter. The total length L: 4000 mm ≤ L ≤ 9000 mm; the length-diameter ratio s: 5 ≤ s ≤ 12; the nose cone length X1: 500 mm ≤ X1 ≤ 1500 mm; the body length X2: 2500 mm ≤ X2 ≤ 5000 mm; the tail cone length X3: 500 mm ≤ X3 ≤ 1500 mm; the body diameter d: 400 mm ≤ d ≤ 800 mm.

[0064] Preferably, the mass optimization conditions of the underwater vehicle include the total length, stress state, and length-diameter ratio, and four variables: nose cone length, body length, tail cone length, and wall thickness. The total length L: 4000 mm ≤ L ≤ 9000 mm; the length-diameter ratio s: 5 ≤ s ≤ 12; the stress state σ max : σ max ≤ σ y ,σ y is the yield strength of the material; the nose cone length X1: 500 mm ≤ X1 ≤ 1500 mm; the body length X2: 2500 mm ≤ X2 ≤ 5000 mm; the tail cone length X3: 500 mm ≤ X3 ≤ 1500 mm; the wall thickness t: 5 mm ≤ t ≤ 20 mm.

[0065] Preferably, it also includes setting the parameters of the genetic optimization algorithm and the particle swarm optimization algorithm. Specifically:

[0066] The particle swarm optimization algorithm sets the number of particles to 300, the inertia weight to 0.5, and the number of iterations to 3000 times; the genetic algorithm sets the population size to 300, the crossover rate to 0.7, the mutation rate to 0.05, and the number of iterations to 3000 times.

[0067] Preferably, the selection of the better optimization algorithm is specifically: select the optimization algorithm with the smaller optimal parameters of drag and mass for multi-disciplinary index optimization.

[0068] Preferably, the visualization simulation verification of the optimization result is specifically: perform three-dimensional modeling of the underwater vehicle according to the multi-disciplinary index optimization, set the material and material properties of the underwater vehicle, set the simulation environment conditions, and verify the effectiveness of the optimization result according to the visualization finite element simulation result.

[0069] The present invention also provides an underwater vehicle obtained by the underwater vehicle design optimization method under the above-mentioned size and weight constraints

[0070] Therefore, the present invention has the following beneficial effects:

[0071] The method of the present invention establishes a multidisciplinary mathematical model for an unmanned underwater vehicle (UUV), and uses a parallel optimization method to co-optimize each subsystem of the UUV, so as to dynamically adjust parameters to achieve overall multidisciplinary optimization, improve the optimization efficiency of multidisciplinary coupling, improve the actual efficiency and performance of the UUV structure design, reduce the time for designers to debug and adjust subsystems, shorten the development cycle, and thus reduce the development cost and time of the UUV.

[0072] Among them, the present invention constructs a multidisciplinary mathematical model for an unmanned underwater vehicle. Under the constraints of size and weight, through the combination of genetic optimization and particle swarm optimization, the best combination of structural design and power configuration is found, the efficiency of the UUV power system, the hull's compressive capacity and endurance are optimized, and the problem that it is difficult to balance weight and size in the prior art is solved, achieving the optimal balance between size and mass. At the same time, a variety of intelligent optimization algorithms are used. Through the combination of genetic optimization and particle swarm optimization, effective trade-offs can be made between different performance indicators, and the scheme is continuously improved during the design process through genetic algorithms and particle swarm algorithms, so as to obtain the design result with the best comprehensive performance and improve the multi-objective optimization design effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is the overall flowchart of the method of the present invention;

[0074] Figure 2 is the method logic block diagram of an embodiment of the present invention;

[0075] Figure 3 is the framework diagram of the particle swarm optimization algorithm of an embodiment of the present invention;

[0076] Figure 4 is the framework diagram of the genetic optimization algorithm of an embodiment of the present invention;

[0077] Figure 5 is the particle swarm optimization iteration effect (drag) diagram of an embodiment of the present invention;

[0078] Figure 6 is the specific parameter iteration process (drag) diagram of the particle swarm optimization of an embodiment of the present invention;

[0079] Figure 7 is the genetic optimization iteration effect (drag) diagram of an embodiment of the present invention;

[0080] Figure 8 is the specific parameter iteration process (drag) diagram of the genetic optimization of an embodiment of the present invention;

[0081] Figure 9 is the particle swarm optimization iteration effect (mass) diagram of an embodiment of the present invention;

[0082] Figure 10It is the iterative process (quality) diagram of the specific parameters of the particle swarm optimization in the embodiment of the present invention;

[0083] Figure 11 It is the iterative effect (quality) diagram of the genetic optimization in the embodiment of the present invention;

[0084] Figure 12 It is the iterative process (quality) diagram of the specific parameters of the genetic optimization in the embodiment of the present invention;

[0085] Figure 13 It is the iterative process diagram of the specific parameters of the collaborative optimization in the embodiment of the present invention;

[0086] Figure 14 It is the simplified model diagram of the UUV in the embodiment of the present invention;

[0087] Figure 15 It is the mesh division diagram of the UUV model in the embodiment of the present invention;

[0088] Figure 16 It is the overall strain diagram of the UUV model in the embodiment of the present invention;

[0089] Figure 17 It is the safety factor diagram of the UUV model in the embodiment of the present invention;

[0090] Figure 18 It is the stress distribution diagram of the UUV model in the embodiment of the present invention.

[0091] In the figure: 1. Head cone; 2. Main body; 3. Tail cone. Detailed implementation manners

[0092] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, in which the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention, but should not be construed as limiting the present invention.

[0093] As Figure 1 shown, a design optimization method for an unmanned underwater vehicle under size and weight constraints includes:

[0094] Construct a multidisciplinary mathematical model of the unmanned underwater vehicle, including a water flow resistance model, a wetted surface area model, a pressure model, and a mass model;

[0095] Set the optimization conditions of the unmanned underwater vehicle, including resistance optimization conditions and mass optimization conditions, and solve the optimal parameters through the particle swarm optimization algorithm and the genetic optimization algorithm;

[0096] Compare the optimal parameters solved by the particle swarm optimization algorithm and the genetic optimization algorithm, and select a better optimization algorithm for multidisciplinary index optimization;

[0097] Visualize and simulate the optimization results for verification;

[0098] The water flow resistance model includes the water flow resistance model of the head cone, the water flow resistance model of the main body, and the water flow resistance model of the tail cone. Specifically,

[0099] Water flow resistance model of the head cone:

[0100]

[0101] Among them, represents the pressure resistance of the head cone, L nose is the length of the head cone part, p is the fluid pressure, r(z) is the radius of the head cone shell at the axial position z, and θ(z) is the angle between the streamline at this position and the horizontal plane;

[0102] Water flow resistance model of the main body:

[0103]

[0104] Among them, represents the frictional resistance of the main body, τ w is the wall shear stress, r(x) is the radius of the main body, v is the kinematic viscosity, u is the fluid velocity, and y is the distance perpendicular to the wall;

[0105] Water flow resistance model of the tail cone:

[0106]

[0107] Among them: represents the turbulent resistance of the tail cone, τ t is the turbulent shear stress calculated by the turbulence model, L tail is the length of the tail cone, and r(z) is the radius of the tail cone;

[0108] The overall water flow resistance of the underwater vehicle is:

[0109]

[0110] The present invention establishes a multidisciplinary mathematical model for an unmanned underwater vehicle (UUV), and uses a parallel optimization method to collaboratively optimize each subsystem of the UUV, so as to dynamically adjust parameters to achieve overall multidisciplinary optimization, improve the optimization efficiency of multidisciplinary coupling, improve the actual efficiency and performance of the UUV structure design, reduce the time for designers to debug and adjust subsystems, shorten the development cycle, and thus reduce the development cost and time of the UUV. The present invention constructs a multidisciplinary mathematical model for an unmanned underwater vehicle, and under the constraints of size and weight, through the combination of genetic optimization and particle swarm optimization, finds the optimal combination of structure design and power configuration, optimizes the efficiency of the UUV power system, the hull's compressive capacity and endurance, solves the problem in the prior art that it is difficult to balance weight and size, achieves the optimal balance between size and mass, and at the same time uses a variety of intelligent optimization algorithms. Through the combination of genetic optimization and particle swarm optimization, it can effectively balance between different performance indicators, continuously improve the scheme during the design process through genetic algorithms and particle swarm algorithms, so as to obtain the design result with the best comprehensive performance and enhance the multi-objective optimization design effect.

[0111] Preferably, the unmanned underwater vehicle includes a head cone, a main body, and a tail cone.

[0112] Preferably, it further includes: after visually simulating and verifying the optimization results, deploying the optimization results of the unmanned underwater vehicle to the actual application scenario according to the verified model.

[0113] Preferably, the wetted surface area model includes the wetted surface area model of the head cone, the wetted surface area model of the main body, and the wetted surface area model of the tail cone. Specifically,

[0114] Wetted surface area model of the head cone:

[0115]

[0116] where r n is the radius function of the vertex z of the head cone, l n is the length of the head cone part, and h n is the total height of the head cone;

[0117] Wetted surface area model of the main body:

[0118] S body =2πr b L b

[0119] where r b is the radius of the cylinder, and L b is the length of the cylinder;

[0120] Wetted surface area model of the tail cone:

[0121]

[0122] Among them, r t is the radius function starting from the vertex z of the tail cone, and h t is the total height of the tail cone;

[0123] The wetted surface area of the entire unmanned submersible is:

[0124] S total = S n + S b + S t .

[0125] Preferably, the pressure model includes a head cone pressure model, a main body pressure model, and a tail cone pressure model. Specifically,

[0126] Head cone pressure model:

[0127]

[0128]

[0129] Among them, P is the pressure, t is the wall thickness, a is the local curvature radius at the front end, and σ y is the yield strength of the material;

[0130] Main body pressure model:

[0131]

[0132] Among them, P is the pressure, t is the wall thickness, R is the radius of the cylinder, and σ y is the yield strength of the material;

[0133] Tail cone pressure model:

[0134]

[0135] Among them, σ axial is the axial stress, σ hoop is the circumferential stress, P is the pressure, t is the wall thickness, and r is the radius of the bottom of the cone.

[0136] Preferably, the mass model includes:

[0137] Head cone volume:

[0138]

[0139] V hnet = V hext - V hint

[0140] Main body volume:

[0141]

[0142] V cnet = V c_ext - V c_int

[0143] Coccyx volume:

[0144]

[0145] V tnet = V t_ext - V t_int

[0146] Total volume of the unmanned submersible vehicle:

[0147] V totalnet = V hnet + V cnet + V tnet

[0148] Total weight of the unmanned submersible vehicle:

[0149] M = ρ material ·V totalnet

[0150] Wherein, R1 is the outer radius of the head cone, r 1i is the inner radius of the head cone, r is the outer radius of the main body, r i is the inner radius of the main body, l is the length of the main body, h is the height of the coccyx, r 2i is the inner radius at the top of the coccyx.

[0151] Preferably, the optimization conditions of the unmanned submersible vehicle include drag optimization conditions and mass optimization conditions. Specifically,

[0152] The drag optimization conditions include: total length and length-diameter ratio, and four variables: head cone length, body length, coccyx length and body diameter. Total length L: 4000 mm ≤ L ≤ 9000 mm; Length-diameter ratio s: 5 ≤ s ≤ 12; Head cone length X1: 500 mm ≤ X1 ≤ 1500 mm; Body length X2: 2500 mm ≤ X2 ≤ 5000 mm; Coccyx length X3: 500 mm ≤ X3 ≤ 1500 mm; Body diameter d: 400 mm ≤ d ≤ 800 mm;

[0153] The mass optimization conditions include: total length, stress state and length-diameter ratio, and four variables: head cone length, body length, coccyx length and wall thickness. Total length L: 4000 mm ≤ L ≤ 9000 mm; Length-diameter ratio s: 5 ≤ s ≤ 12; Stress state σ max : σ max ≤ σ y σ yis the yield strength of the material; nose cone length X1: 500mm ≤ X1 ≤ 1500mm; body length X2: 2500mm ≤ X2 ≤ 5000mm; tail cone length X3: 500mm ≤ X3 ≤ 1500mm; wall thickness t: 5mm ≤ t ≤ 20mm.

[0154] Preferably, it also includes setting the parameters of the genetic optimization algorithm and the particle swarm optimization algorithm. Specifically,

[0155] The particle swarm optimization algorithm sets the number of particles to 300, the inertia weight to 0.5, and the number of iterations to 3000 times. The genetic algorithm sets the population size to 300, the crossover rate to 0.7, the mutation rate to 0.05, and the number of iterations to 3000 times.

[0156] Preferably, the better optimization algorithm selected is specifically: select the optimization algorithm with the smallest optimal parameter resistance and mass for multi-disciplinary index optimization.

[0157] Preferably, the visualization simulation verification of the optimization result is specifically: perform three-dimensional modeling of the unmanned underwater vehicle according to the multi-disciplinary index optimization, set the material and material properties of the unmanned underwater vehicle, set the simulation environment conditions, and verify the effectiveness of the optimization result according to the visualization finite element simulation result.

[0158] Furthermore, the present invention also provides an unmanned underwater vehicle obtained by the design optimization method of the unmanned underwater vehicle under the above-mentioned size and weight constraints.

[0159] In another embodiment, the design optimization method of an unmanned underwater vehicle under size and weight constraints of the present invention is described in detail with reference to the accompanying drawings.

[0160] The logic diagram of the design optimization method of an unmanned underwater vehicle under size and weight constraints of the present invention is as Figure 2 shown, and specifically includes the following steps:

[0161] S1: Construct a multi-disciplinary mathematical model of the UUV. The established multi-disciplinary mathematical model of the UUV includes a water flow resistance model, a wetted surface area model, a pressure model, and a mass model. The wetted surface area model is the reference basis for frictional resistance. The mathematical model is related to the key dimensions of the basic parts of the UUV, namely the nose cone, the main body, and the tail cone.

[0162] The construction of the water flow resistance model is as follows: The hydrodynamic model of the unmanned submersible can have three basic parts: the nose cone, the main body, and the tail cone, as Figure 14As shown. The head cone is streamlined to achieve minimum air flow separation; the main body is a long cylindrical shape, providing space for internal equipment with relatively low form drag; the tail cone is used to maintain streamline stability and suppress wake effects. In the head and middle sections, that is, the head cone and the main body, the fluid velocity is low, the streamlines are close to the surface, and it is easier to maintain laminar flow. When the fluid passes through the middle section of the UUV to reach the tail, that is, the tail cone, the streamlined structure of the tail will cause fluid separation and form turbulence.

[0163] The water flow resistance model of the head cone includes the contribution of the pressure distribution along the surface of the head cone and its variation with the axial position:

[0164]

[0165] Where: represents the pressure drag of the head cone, L nose is the length of the head cone part, p is the fluid pressure obtained through the Bernoulli equation, r(z) is the radius of the head cone shell at the axial position z, and θ(z) is the angle between the streamline and the horizontal plane at this position.

[0166] The frictional drag of the main body is calculated from the shear force generated by the fluid flowing over the surface of the UUV:

[0167]

[0168] Where: represents the frictional drag of the main body, L body is the length of the main body part, τ w is the wall shear stress, r(x) is the radius of the main body, v is the kinematic viscosity, u is the fluid velocity, and y is the distance perpendicular to the wall.

[0169] The turbulent drag is estimated through the turbulent shear stress model:

[0170]

[0171] Where: represents the turbulent drag of the tail cone, L tail is the length of the tail cone part, τ t is the turbulent shear stress calculated by the turbulent model, r(z) is the radius of the tail cone.

[0172] The overall water flow resistance R of the UUV total :

[0173]

[0174] The construction of the wetted surface area model is as follows: In addition to the influence of water flow resistance, frictional drag is also a major factor affecting the performance of the UUV. Calculating the wetted surface area of the UUV is a key step in evaluating the drag characteristics. The wetted surface area is the sum of the surface areas of the UUV in contact with water.

[0175] Wet surface area model of the nose cone:

[0176] The wet surface area of the conical nose cone is calculated by the following formula:

[0177]

[0178] Where: r n is the radius function of the nose cone vertex z, l n is the length of the nose cone part, h n is the total height of the nose cone.

[0179] The main body is a cylinder, and the wet surface area is the area of the cylindrical surface:

[0180] S body = 2πr b L b

[0181] Where: r b is the radius of the cylinder, L b is the length of the cylinder.

[0182] The calculation of the wet surface area of the tail cone is the same as that of the nose cone. It is calculated by the following formula:

[0183]

[0184] Where: r t is the radius function starting from the tail cone vertex z, h t is the total height of the tail cone.

[0185] The wet surface area S of the UUV total :

[0186] S total = S n + S b + S t

[0187] The wet surface area S of the UUV is obtained total , and the frictional resistance of the entire UUV is further estimated. Through resistance modeling, the resistance distribution of the UUV during underwater navigation can be analyzed in detail, and its hull design can be optimized through simulation. By changing the flow line, that is, the shape of the hull, the resistance caused by the water flow can be reduced to achieve better performance.

[0188] The construction of the pressure model is as follows: When constructing the model of the UUV structure, it is considered that the structure is mainly affected by the external water pressure. The UUV consists of three parts: the front, middle, and rear, namely the nose cone, the main body, and the tail cone, and each part uses a unified wall thickness. In this paper, the stealth depth is set to 100m, and titanium alloy is selected as the construction material. Under the considered working environment, the performance of titanium alloy is set to remain unchanged.

[0189] The streamlined front part can usually be approximated as a hemispherical shell, which bears uniform external pressure. The shell mainly bears the uniformly distributed external water pressure, and its stress can be estimated by the following formula:

[0190]

[0191] Pressure condition:

[0192]

[0193] Where: P is the pressure, t is the wall thickness, a is the local curvature radius at the front end, and σ y is the yield strength of the material.

[0194] The middle cylinder needs to consider circumferential and radial stresses:

[0195] Circumferential stress σ θ :

[0196]

[0197] Where: P is the circumferential pressure, R is the radius of the cylinder, and t is the wall thickness.

[0198] Axial stress σ z :

[0199]

[0200] Where: P is the axial pressure, R is the radius of the cylinder, and t is the wall thickness.

[0201] Pressure condition:

[0202]

[0203] Where: P is the pressure, R is the radius of the cylinder, and σ y is the yield strength of the material.

[0204] For a conical shell, the axial pressure and circumferential pressure are different, and the following formula is used for estimation:

[0205]

[0206] Where: σ axial is the axial stress, σ hoop is the circumferential stress, P is the pressure, t is the wall thickness, and r is the radius of the bottom of the cone.

[0207] Considering that the UUV may be affected by external irregular loads, its structural stability is particularly important. Especially for long cylindrical structures, buckling stability analysis needs to be carried out, and the Euler buckling formula is used for calculation:

[0208]

[0209] Among them: P cr is the critical force, E is the elastic modulus of the titanium alloy, I is the moment of inertia of the cross-section of the cylindrical part, R is the outer diameter of the cylinder, t is the wall thickness of the cylinder, K is the length coefficient, usually 1, and L is the effective length of the cylindrical part.

[0210] The construction of the mass model is as follows: The UUV is hollow, and the volume of the internal cavity needs to be considered when calculating the volume to accurately estimate the mass.

[0211] Streamlined front end, i.e., the volume of the hollow hemisphere:

[0212]

[0213] V hnet =V hext -V hint

[0214] Cylindrical middle part, i.e., the volume of the hollow cylinder:

[0215] V c-ext =πr 2 l

[0216]

[0217] V cnet =V c_ext -V c_int

[0218] Conical tail, i.e., the volume of the hollow cone:

[0219]

[0220] V tnet =V t_ext -V t_int

[0221] The total volume is the sum of the volumes of all parts:

[0222] V totalnet =V hnet +V cnet +V tnet

[0223] Total weight:

[0224] M = ρ material ·V totalnet

[0225] Among them: R1 is the outer radius of the streamlined front end, r 1iis the inner radius of the streamlined front end, r is the outer radius of the cylindrical middle part, r i is the inner radius of the cylindrical middle part, l is the length of the cylindrical middle part, h is the height of the conical tail, r 2i is the inner radius at the top of the conical tail, usually 0, assumed to be a tip.

[0226] S2: Set the optimization conditions for the unmanned underwater vehicle. Specifically, set the optimization indicators, parameters to be optimized, initial values of the parameters, and theoretical boundary values of the parameters. The optimization indicators are resistance and mass. The parameters to be optimized include the lengths and diameters of different parts of the UUV. At the same time, the total length and length-diameter ratio limit of the UUV also need to be considered. The initial values of the parameters are selected according to the initial values of the UUV model design, and the theoretical boundary values need to be set according to the design range.

[0227] Principle and specific setting of the resistance optimization conditions: In order to make the UUV shape more in line with the dynamic model, two constraint conditions are set: the total length L and the length-diameter ratio s, and four variables: the head cone length, body length, tail cone length, and body diameter. Total length L: 4000mm ≤ L ≤ 9000mm; length-diameter ratio s: 5 ≤ s ≤ 12; head cone length X1: 500mm ≤ X1 ≤ 1500mm; body length X2: 2500mm ≤ X2 ≤ 5000mm; tail cone length X3: 500mm ≤ X3 ≤ 1500mm; body diameter d: 400mm ≤ d ≤ 800mm. Optimization goal: When restricting the overall size of the UUV, the total resistance R (N) of the UUV is minimized. The particle swarm optimization algorithm sets the number of particles to 300, the inertia weight to 0.5, and the number of iterations to 3000 times. The genetic algorithm sets the population size to 300, the crossover rate to 0.7, the mutation rate to 0.05, and the number of iterations to 3000 times. The particle swarm optimization algorithm and the genetic algorithm belong to existing mature conventional algorithms, and their flowcharts are as Figure 3 、 Figure 4 shown, and the present invention will not elaborate here.

[0228] Principle and specific setting of the mass optimization conditions: Since the types and magnitudes of the pressures on different shapes are different, the UUV is divided into three parts: the front streamlined part, the middle cylindrical part, and the reduced tail part, that is, the head cone, the main body, and the tail cone of the unmanned underwater vehicle. Here, three constraints are set: the total length, the stress state, and the length-diameter ratio, and four variables: the lengths of the front, middle, and tail, and the wall thickness. Total length L: 4000mm ≤ L ≤ 9000mm; length-diameter ratio s: 5 ≤ s ≤ 12; stress state σ max : σ max ≤ σ y ,σ yis the yield strength of the material; nose cone length X1: 500mm ≤ X1 ≤ 1500mm; body length X2: 2500mm ≤ X2 ≤ 5000mm; tail cone length X3: 500mm ≤ X3 ≤ 1500mm; wall thickness t: 5mm ≤ t ≤ 20mm. The optimization goal is to optimize the pressure effect at the minimum weight so that the UUV can operate in water at a certain depth. The particle swarm optimization algorithm specifies that the number of particles is 300, the inertia weight is 0.5, and the number of iterations is 3000 times. The genetic algorithm sets the population size to 300, the crossover rate to 0.7, the mutation rate to 0.05, and the number of iterations to 3000 times.

[0229] S3: Solve for the optimal parameters through the particle swarm optimization algorithm and the genetic optimization algorithm.

[0230] The particle swarm optimization algorithm needs to set the number of particles, the inertia weight, and the number of iterations, and then perform iterative optimization; the genetic optimization algorithm needs to set the population size, the crossover rate, the mutation rate, and the number of iterations, and then perform iterative optimization. According to the conditions set in S2 and the relevant parameters of the optimization algorithm set in S3, the particle swarm optimization algorithm and the genetic optimization algorithm are used to solve respectively. The optimization process is as Figures 5 - 12 . Figure 5 , Figure 7 is the process of the particle swarm algorithm and the genetic algorithm iteratively seeking the optimal solution with the resistance as the optimization goal; Figure 6 , Figure 8 is the solution process of the nose cone length (shown as the red curve in the figure), the body length (shown as the orange curve in the figure), the tail cone length (shown as the blue curve in the figure), and the body diameter (shown as the green curve in the figure) during the iterative process. It can be seen that during the optimization process of the two algorithms, the sizes of the four design variables are continuously adjusted and iterated within the range to seek the optimal value, while the resistance is continuously decreasing and finally oscillating around a fixed value. Figure 9 , Figure 11 is the process of the particle swarm algorithm and the genetic algorithm iteratively seeking the optimal solution with the mass as the optimization goal; Figure 8 , Figure 12 is the solution process of the nose cone length (shown as the red curve in the figure), the body length (shown as the orange curve in the figure), the tail cone length (shown as the blue curve in the figure), and the wall thickness (shown as the green curve in the figure) during the iterative process. During the optimization process of the two algorithms, the sizes of the four design variables are also continuously adjusted and iterated within the range to seek the optimal value, while the mass is also continuously decreasing and finally oscillating around a fixed value. The solution results of the resistance optimization are shown in Table 1; the solution results of the mass optimization are shown in Table 2.

[0231] Table 1: Solution Results of Resistance Optimization

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[0233] Table 2: Solution Results of Mass Optimization

[0234]

[0235]

[0236] S4: Use the final result of the optimization objective as the judgment basis, compare the optimization results of the optimization algorithms, and select a better optimization algorithm for further multidisciplinary (index) optimization or multidisciplinary index optimization.

[0237] Specifically, selecting a better optimization algorithm specifically means selecting an optimization algorithm with the smallest optimal parameter resistance and mass obtained from the solution.

[0238] Under the condition that the resistance is the optimization index, the genetic algorithm has a better optimization effect on the scale of the UUV than the particle swarm optimization algorithm, and the scale of the UUV is also smaller. In terms of the structural optimization under the weight constraint, the optimization result of the genetic algorithm has better performance, which can make the UUV smaller in volume, while maintaining better compression performance and smaller weight. Through comparison, the genetic algorithm has a better optimization effect. Therefore, the genetic algorithm is selected for subsequent optimization, and the parameter selection is the same as before. According to the above single-system optimization results, the collaborative optimization method is selected to conduct multidisciplinary optimization design research on the UUV. The optimization objective is to minimize the total resistance and total mass of the unmanned underwater vehicle. The optimization process is as Figure 13 shown. Figure 13 For the optimization objectives of resistance and mass, the iterative process of the genetic algorithm to seek the optimal solution for the head cone length (shown as the red curve in the figure), body length (shown as the orange curve in the figure), tail cone length (shown as the dark blue curve in the figure), and wall thickness (shown as the light blue curve in the figure). The optimization results are shown in Table 3.

[0239] Table 3: Multidisciplinary Optimization Results

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[0241] S5: Visual simulation verification of the optimization results. First, perform 3D modeling of the UUV based on the 3D drawing software for the size optimization results obtained in S4, and then set the simulation environment conditions to visualize the finite element simulation results to verify the effectiveness of the size optimization results.

[0242] Perform 3D modeling on the optimization results obtained in S4, as Figure 14 , set the material and material properties of the UUV: yield strength σ y = 887 MPa, tensile strength σ u = 970 MPa, elastic modulus E = 110 GPA, shear modulus G = 48 GPA, density ρ = 4.48 g / cm 3 .

[0243] According to the planned diving depth of 100 meters, analyze the mechanical strength and stability of the entire hull and its internal structure under static loads. For the finite element analysis, the UUV model needs to be meshed first for subsequent simulation and solution. The meshing uses standard triangular meshes with a size ranging from 10 mm to 20 mm, and the meshing results are as shown in Figure 15 . Set the external loads according to the direction and magnitude of the underwater pressure, and simulate the 100-meter underwater environment as a whole. The overall strain results of the UUV are as shown in Figure 16 , the safety factor is as shown in Figure 17 , and the stress distribution is as shown in Figure 18 . It can be seen from Figure 16 that the maximum displacement of the UUV hull is only 0.06 mm, meeting the safety standard requirements; it can be seen from Figure 17 that the minimum safety factor of the UUV hull is 13.7, meeting the international safety standards; it can be seen from Figure 18 that the stress is mainly distributed in the main part of the UUV, and it is also less than the yield strength σ y = 887 MPa. Therefore, the method of collaborative optimization is feasible, and it can effectively reduce the mass of the UUV and the resistance of the UUV while meeting the design requirements. The results of the visual simulation verification show that the optimized results of the UUV structure size meet the requirements of the planned environment, further proving that the method of collaborative optimization is feasible. The optimization strategy of the present invention can effectively reduce the mass of the UUV, reduce the resistance, and improve the energy efficiency ratio while meeting the design requirements.

[0244] S6: Deploy the design optimization results of the unmanned underwater vehicle to the actual application scenario according to the verified model.

[0245] The present invention provides a design optimization method for an unmanned underwater vehicle under size and weight constraints. First, based on the assumptions of the hydrodynamic model, the UUV model is divided into three basic parts: the head cone, the main body, and the tail cone, and the mathematical models of the water flow resistance, wetted surface area, pressure, and mass of the UUV are established. Taking the key dimensions such as length and diameter as variables, through the particle swarm optimization algorithm and the genetic optimization algorithm, the optimal size parameters of the UUV under different optimization objectives are solved and the algorithm optimization results are compared. According to the comparison results, the genetic optimization algorithm is selected to perform multidisciplinary joint optimization on the UUV, and the results are subjected to three-dimensional modeling and simulation analysis to verify the effectiveness of the optimization scheme and the optimization results. The design optimization results of the unmanned underwater vehicle are deployed to the actual application scenario according to the verified model. The design optimization method for the UUV under size and weight constraints provided by the present invention improves the design efficiency and performance of the UUV structure, and reduces the development cost and time.

[0246] It should be noted that although the above embodiments describe the various steps in a specific order, those skilled in the art can understand that in order to achieve the effects of the present disclosure, it is not necessary for different steps to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the protection scope of the present disclosure.

[0247] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0248] To solve the above technical problems, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of an optimization method for the design of an underwater vehicle under size and weight constraints as described above.

[0249] An embodiment of the present invention further provides a computer device, including a memory, a processor, and a network interface that are communicatively connected to each other through a system bus. It should be noted that this embodiment only shows a computer device having components such as a memory, a processor, a network interface, and an operating system. However, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Among them, those skilled in the art of the present technology can understand that a computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0250] The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touchpad or a voice control device and other means.

[0251] There can be one or more memories, and at least one type of readable storage medium is included. The readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory can be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the memory can also be an external storage device of the computer device, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, FlashCard, etc. equipped on the computer device. Of course, the memory can also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the memory is generally used to store the operating system and various application software installed on the computer device, such as the program code of a method for optimizing the design of an underwater vehicle under size and weight constraints. In addition, the memory can also be used to temporarily store various data that have been output or will be output.

[0252] In some embodiments, the processor can be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chips. The processor is generally used to control the overall operation of the computer device. In this embodiment, the processor is used to run the program code stored in the memory or process data, such as the program code of a method for optimizing the design of an underwater vehicle under size and weight constraints.

[0253] The network interface can include a wireless network interface and / or a wired network interface, and this network interface is generally used to establish a communication connection between the computer device and other electronic devices.

[0254] The present invention also provides another implementation manner, that is, to provide a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for optimizing the design of an underwater vehicle under size and weight constraints as described above are implemented.

[0255] It can be seen that, first of all, the design method of the present invention improves the optimization efficiency of multi-disciplinary coupling. This design adopts a parallel optimization method to synergistically optimize each subsystem of the UUV, sets independent design variables and objective functions, so as to dynamically adjust parameters to achieve the overall multi-disciplinary optimization. Through this method, on the premise of meeting the performance indicators of each subsystem, the best performance of the UUV system is achieved. Secondly, this design realizes the optimal balance between size and mass. Under the constraints of size and weight, through the combination of genetic optimization and particle swarm optimization, the best combination of structural design and power configuration is found, and the efficiency of the power system, the hull's compressive resistance and endurance are optimized, solving the problem that it is difficult to balance weight and size in the prior art. Furthermore, this design improves the multi-objective optimization effect. The present invention adopts the combination of multiple intelligent optimization algorithms, which can effectively balance between different performance indicators, such as compressive resistance and endurance. The genetic algorithm and particle swarm algorithm are used to continuously improve the scheme during the design process, so as to obtain the design result with the best comprehensive performance. Finally, this design reduces the development cost and time of the UUV. The multi-disciplinary optimization method of the present invention can reduce the time for designers to debug and adjust the subsystems, shorten the development cycle, and reduce the development cost.

[0256] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0257] It is not difficult for those skilled in the art to understand that the present invention includes any combination of the above-mentioned invention content and specific implementation part of the specification and each part shown in the drawings. Due to space limitations and to make the specification concise, the various schemes formed by these combinations are not described one by one. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0258] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present invention without departing from the principle and purpose of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An optimization method for the design of an underwater unmanned vehicle under size and weight constraints, characterized in that Including: Construct a multidisciplinary mathematical model of the unmanned underwater vehicle, including a water flow resistance model, a wetted surface area model, a pressure model, and a mass model; Set the optimization conditions of the unmanned underwater vehicle, including resistance optimization conditions and mass optimization conditions, and solve the optimal parameters through the particle swarm optimization algorithm and the genetic optimization algorithm; Compare the optimal parameters solved by the particle swarm optimization algorithm and the genetic optimization algorithm, and select a better optimization algorithm for multidisciplinary index optimization; Conduct visual simulation verification on the optimization results; The water flow resistance model includes the water flow resistance model of the head cone, the water flow resistance model of the main body, and the water flow resistance model of the tail cone. Specifically, Water flow resistance model of the head cone: Among them, represents the pressure drag of the nose cone, is the length of the nose cone part, is the fluid pressure, is the radius of the nose cone shell at the axial position r, is the angle between the streamline at this position and the horizontal plane; Water flow resistance model of the main body: Among them, represents the frictional resistance of the main body, is the wall shear stress, is the radius of the main body, is the kinematic viscosity, is the fluid velocity, is the distance perpendicular to the wall surface; Water flow resistance model of the tail cone: Wherein: represents the turbulent drag of the tail cone, is the turbulent shear stress calculated by the turbulence model, is the length of the tail cone, is the radius of the tail cone; The overall water flow resistance of the unmanned underwater vehicle is: 。 2. The design optimization method of an underwater vehicle under size and weight constraints according to claim 1, characterized in that It also includes: After conducting visual simulation verification on the optimization results, deploy the optimization results of the unmanned underwater vehicle to the actual application scenario according to the verified model.

3. The design optimization method of an underwater vehicle under size and weight constraints according to claim 1, characterized in that The wetted surface area model includes the wetted surface area model of the head cone, the wetted surface area model of the main body, and the wetted surface area model of the tail cone. Specifically, Wetted surface area model of the head cone: Among them, is the curve radius function of the nose cone part, is the length of the nose cone part, is the total height of the nose cone; Wetted surface area model of the main body: Among them, is the radius of the cylinder, is the length of the cylinder; Wetted surface area model of the tail cone: Among them, is the curve radius function of the tail cone part, is the total height of the tail cone; The overall wetted surface area of the unmanned underwater vehicle is: 。 4. The design optimization method of an underwater vehicle under size and weight constraints according to claim 1, characterized in that The pressure model includes the head cone pressure model, the main body pressure model, and the tail cone pressure model. Specifically, Head cone pressure model: Among them, is the pressure, is the wall thickness, is the local curvature radius at the front end, is the yield strength of the material; Main body pressure model: Among them, is the pressure, is the wall thickness, is the radius of the cylinder, is the yield strength of the material; Tail cone pressure model: Among them, is the axial stress, is the circumferential stress, is the pressure, is the wall thickness, is the radius at the bottom of the cone.

5. A method for optimizing the design of an unmanned underwater vehicle under size and weight constraints according to claim 1, characterized in that The mass model includes: Volume of the head cone: Volume of the main body: Volume of the tail vertebra: Total volume of the unmanned underwater vehicle: Total weight of the unmanned underwater vehicle: Among them, is the outer radius of the nose cone, is the inner radius of the nose cone, is the outer radius of the main body, is the inner radius of the main body, is the length of the main body, is the height of the tail cone, is the inner radius at the top of the tail cone.

6. The design optimization method of an underwater vehicle under size and weight constraints according to claim 1, characterized in that The drag optimization conditions of the unmanned submersible include: total length and length-diameter ratio, as well as four variables: nose cone length, body length, tail cone length, and body diameter, total length : ; length-diameter ratio : ; nose cone length : ; body length : ; tail cone length : ; body diameter : ; The mass optimization conditions of the unmanned submersible include: total length, stress state, and length-diameter ratio, as well as four variables: nose cone length, body length, tail cone length, and wall thickness, total length : ; length-diameter ratio : ; stress state : , , where : ; body length : ; tail cone length : ; wall thickness : .

7. A method for optimizing the design of an unmanned underwater vehicle under size and weight constraints according to claim 1, characterized in that, It also includes setting the parameters of the genetic optimization algorithm and the particle swarm optimization algorithm. Specifically, For the particle swarm optimization algorithm, set the number of particles to 300, the inertia weight to 0.5, and the number of iterations to 3000 times; for the genetic algorithm, set the population size to 300, the crossover rate to 0.7, the mutation rate to 0.05, and the number of iterations to 3000 times.

8. A method for optimizing the design of an underwater vehicle under size and weight constraints according to claim 1, characterized in that, The selection of the better optimization algorithm specifically means: select the optimization algorithm with smaller resistance and mass of the solved optimal parameters for multidisciplinary index optimization.

9. A method for optimizing the design of an unmanned underwater vehicle under size and weight constraints according to any one of claims 1-8, characterized in that, The visual simulation verification of the optimization results specifically means: conduct 3D modeling of the unmanned underwater vehicle according to the multidisciplinary index optimization, set the materials and material properties of the unmanned underwater vehicle, set the simulation environment conditions, and verify the effectiveness of the optimization results according to the visual finite element simulation results.

10. An unmanned underwater vehicle obtained by the unmanned underwater vehicle design optimization method under the size and weight constraints described in any one of claims 1 - 9.

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