Design optimization method for unmanned underwater vehicle under size and weight constraints

Abstract

The invention provides an unmanned underwater vehicle design optimization method under size and weight constraints, which comprises the following steps of: firstly, based on a fluid dynamic model hypothesis, dividing a UUV (Unmanned Underwater Vehicle) model into three basic parts, and establishing mathematical models of water flow resistance, wet surface area, pressure and mass of the UUV; and solving optimal size parameters of the UUV under different optimization targets by taking key sizes such as length and diameter as variables through a particle swarm optimization algorithm and a genetic optimization algorithm, and comparing algorithm optimization results. And according to a comparison result, selecting a genetic optimization algorithm to carry out multidisciplinary joint optimization on the UUV, carrying out three-dimensional modeling and simulation analysis on the result, and verifying the effectiveness of the optimization result. According to the method, multidisciplinary optimization under the UUV size and weight constraint is realized, a new thought of UUV structure design is provided, the key problems of resistance optimization, energy efficiency, loading capacity and the like under the size and weight constraint in the UUV design are solved, the UUV structure design efficiency and performance are improved, and the development cost and time are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned underwater vehicle performance optimization, and in particular to an unmanned underwater vehicle design optimization method under size and weight constraints. Background Art

[0002] Unmanned Underwater Vehicle (UUV) is an important tool for marine resource exploration and development. It is a comprehensive interdisciplinary design project, and its design involves multiple disciplines, such as structural strength, power system, control system and environmental adaptability. UUV optimization involves multidisciplinary intersection. Through multidisciplinary integration optimization, design challenges can be solved, thereby improving the design level and overall performance of UUV systems.

[0003] At present, there are the following problems in UUV optimization: First, due to the complexity of multidisciplinary coupling, which increases the difficulty of design, the existing technologies are mostly single-objective single-discipline optimization, resulting in low efficiency in optimization methods and design processes, and usually requiring 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. Secondly, there are usually problems such as excessive weight affecting the endurance of the power system, or size limitations leading to weakened functionality, making it difficult to find the best performance solution. Furthermore, existing technologies often use a single optimization algorithm or method, which usually cannot take into account multiple performance indicators at the same time, such as the hull's pressure resistance, endurance, and movement flexibility. A single optimization algorithm shows limitations in multi-objective optimization, and can often only improve performance in one aspect while ignoring other aspects. Summary of the invention

[0004] The object of the present invention is to solve at least one of the technical drawbacks.

[0005] In order to achieve the above object, 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] Construct a multidisciplinary mathematical model of the UUV, including water flow resistance model, wetted surface area model, pressure model and mass model;

[0008] Set the optimization conditions of the unmanned underwater vehicle, including resistance optimization conditions and quality optimization conditions, and solve the optimal parameters through particle swarm optimization algorithm and genetic optimization algorithm;

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

[0010] Conduct visual simulation verification of optimization results;

[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] The water flow resistance model of the nose cone:

[0013]

[0014] in, Represents the pressure resistance of the nose cone, L nose is the length of the nose cone, 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] The main body of the water flow resistance model:

[0016]

[0017] in, represents the friction resistance of the body, τ w is the wall shear stress, r(x) is the radius of the 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 tail cone:

[0019]

[0020] in: represents the turbulent drag 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, r(z) is the radius of the tail cone;

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

[0022]

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

[0024] Preferably, the method further comprises: after visually verifying the optimization results, deploying the optimization results of the unmanned underwater vehicle to an actual application scenario according to the verified model.

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

[0026] Wetted surface area model of nose cone:

[0027]

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

[0029] Wetted surface area model of the subject:

[0030] S body =2πr b L b

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

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

[0033]

[0034] Among them, 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 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 of the front end, σ y is the yield strength of the material;

[0041] Subject pressure model:

[0042]

[0043] Where P is the pressure, t is the wall thickness, R is the radius of the cylinder, 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 cone base.

[0047] Preferably, the quality model comprises:

[0048] Nose cone volume:

[0049]

[0050] V hnet =V hext -V hint

[0051] Body volume:

[0052] V c-ext =πt 2 l

[0053]

[0054] V cnet =V c_ext -V c_int

[0055] Coccygeal volume:

[0056]

[0057] V tnet =V t_ext -V t_int

[0058] Total volume of unmanned underwater vehicle:

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

[0060] Total weight of unmanned underwater vehicle:

[0061] M=ρ material ·V totalnet

[0062] Where 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 body, and r i is the inner radius of the body, l is the length of the body, h is the height of the tail vertebra, r 2i is the inner radius of the top of the coccyx.

[0063] Preferably, the resistance optimization conditions of the unmanned underwater vehicle include: total length and aspect ratio, and four variables: nose cone length, body length, tail cone length and body diameter, total length L: 4000mm≤L≤9000mm; aspect ratio s: 5≤s≤12; nose 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.

[0064] Preferably, the quality optimization conditions of the unmanned underwater vehicle include: total length, stress state and aspect ratio, and four variables: nose cone length, body length, tail cone length and wall thickness, total length L: 4000mm≤L≤9000mm; aspect ratio s: 5≤s≤12; stress state σ max :σ max ≤σ y , σ y is the yield strength of the material; head 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.

[0065] Preferably, the method further includes setting 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; 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.

[0067] Preferably, the selection of a better optimization algorithm is specifically: selecting an optimization algorithm with smaller optimal parameter resistance and mass for multidisciplinary index optimization.

[0068] Preferably, the visual simulation verification of the optimization results is specifically as follows: three-dimensional modeling of the unmanned underwater vehicle is performed according to multidisciplinary indicator optimization, the materials and material properties of the unmanned underwater vehicle are set, the simulation environment conditions are set, and the validity of the optimization results is verified according to the visual finite element simulation results.

[0069] The present invention also provides an unmanned underwater vehicle, which is obtained by the unmanned underwater vehicle design optimization method under the 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 of an unmanned underwater vehicle, and adopts a parallel optimization method to collaboratively optimize various subsystems of the UUV, so that parameters can be dynamically adjusted to achieve multidisciplinary overall optimization, thereby improving the optimization efficiency of multidisciplinary coupling, improving the actual efficiency and performance of UUV structural design, reducing the time for designers to debug and adjust subsystems, shortening the development cycle, and thus reducing the cost and time of UUV development.

[0072] Among them, the present invention constructs a multidisciplinary mathematical model of an unmanned underwater vehicle, and under the constraints of size and weight, through the combination of genetic optimization and particle swarm optimization, finds the best combination of structural design and power configuration, optimizes the efficiency of the UUV power system, the hull's pressure resistance and endurance, solves the problem of weight and size being difficult to balance in the prior art, and achieves the optimal balance between size and quality. At the same time, a variety of intelligent optimization algorithms are used, and through the combination of genetic optimization and particle swarm optimization, effective trade-offs can be made between different performance indicators. Through genetic algorithms and particle swarm algorithms, the scheme is continuously improved during the design process, thereby obtaining the design result with the best comprehensive performance and improving the multi-objective optimization design effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is the overall flow chart of the method of the present invention;

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

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

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

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

[0078] Figure 6 It is a specific parameter iteration process (resistance) diagram of particle swarm optimization according to an embodiment of the present invention;

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

[0080] Figure 8 It is a diagram of the iterative process (resistance) of specific parameters of genetic optimization according to an embodiment of the present invention;

[0081] Fig. 9 It is a particle swarm optimization iterative effect (quality) diagram of an embodiment of the present invention;

[0082] Fig.10It is a specific parameter iteration process (quality) diagram of the particle swarm optimization of an embodiment of the present invention;

[0083] Fig.11 It is a graph of the effect (quality) of genetic optimization iteration according to an embodiment of the present invention;

[0084] Fig.12 It is a diagram of the iterative process (quality) of specific parameters of genetic optimization according to an embodiment of the present invention;

[0085] Fig.13 is a diagram of a specific parameter iteration process of collaborative optimization according to an embodiment of the present invention;

[0086] Fig.14 This is a simplified model diagram of a UUV according to an embodiment of the present invention;

[0087] Fig.15 It is a grid division diagram of a UUV model according to an embodiment of the present invention;

[0088] Fig.16 is the overall strain diagram of the UUV model according to an embodiment of the present invention;

[0089] Fig.17 is a safety factor diagram of a UUV model according to an embodiment of the present invention;

[0090] Fig.18 3 is a stress distribution diagram of the UUV model according to an embodiment of the present invention.

[0091] In the figure: 1. nose cone; 2. main body; 3. tail cone. DETAILED DESCRIPTION

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

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

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

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

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

[0097] Conduct visual simulation verification of optimization results;

[0098] 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,

[0099] The water flow resistance model of the nose cone:

[0100]

[0101] in, Represents the pressure resistance of the nose cone, L nose is the length of the nose cone, 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;

[0102] The main body of the water flow resistance model:

[0103]

[0104] in, represents the friction resistance of the body, τ w is the wall shear stress, r(x) is the radius of the 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 tail cone:

[0106]

[0107] in: represents the turbulent drag 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, r(z) is the radius of the tail cone;

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

[0109]

[0110] The present invention establishes a multidisciplinary mathematical model of an unmanned underwater vehicle, and adopts a parallel optimization method to collaboratively optimize various subsystems of the UUV, so that parameters can be dynamically adjusted to achieve multidisciplinary overall optimization, thereby improving the optimization efficiency of multidisciplinary coupling, improving the actual efficiency and performance of the UUV structural design, reducing the time for designers to debug and adjust the subsystems, shortening the development cycle, and thus reducing the UUV development cost and time. The present invention constructs a multidisciplinary mathematical model of an unmanned underwater vehicle, and under the constraints of size and weight, finds the best combination of structural design and power configuration through the combination of genetic optimization and particle swarm optimization, optimizes the efficiency of the UUV power system, the hull pressure resistance and the endurance, solves the problem of the difficulty in balancing weight and size in the prior art, and achieves the optimal balance between size and quality. At the same time, a variety of intelligent optimization algorithms are adopted, and through the combination of genetic optimization and particle swarm optimization, effective trade-offs can be made between different performance indicators. The genetic algorithm and the particle swarm algorithm are used to continuously improve the scheme in the design process, thereby obtaining a design result with the best comprehensive performance, and improving the multi-objective optimization design effect.

[0111] Preferably, the unmanned underwater vehicle includes three parts: a nose cone, a main body and a tail cone.

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

[0113] Preferably, the wet surface area model includes a wet surface area model of a nose cone, a wet surface area model of a main body, and a wet surface area model of a tail cone. Specifically,

[0114] Wetted surface area model of nose cone:

[0115]

[0116] Among them, r n is the radius function of the vertex z of the head cone, l n is the length of the nose cone, h n is the total height of the nose cone;

[0117] Wetted surface area model of the body:

[0118] S body =2πr b L b

[0119] Among them, r b is the radius of the cylinder, 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, h t is the total height of the tail cone;

[0123] The wet surface area of ​​the unmanned underwater vehicle is:

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

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

[0126] Nose cone pressure model:

[0127]

[0128]

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

[0130] Subject pressure model:

[0131]

[0132] Where P is the pressure, t is the wall thickness, R is the radius of the cylinder, 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 cone base.

[0136] Preferably, the quality model includes:

[0137] Nose cone volume:

[0138]

[0139] V hnet =V hext -V hint

[0140] Body volume:

[0141]

[0142] V cnet =V c_ext -V c_int

[0143] Coccygeal volume:

[0144]

[0145] V tnet =V t_ext -V t_int

[0146] Total volume of unmanned underwater vehicle:

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

[0148] Total weight of unmanned underwater vehicle:

[0149] M=ρ material ·V totalnet

[0150] Where 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 body, and r i is the inner radius of the body, l is the length of the body, h is the height of the tail vertebra, r 2i is the inner radius of the top of the coccyx.

[0151] Preferably, the optimization conditions of the unmanned underwater vehicle include resistance optimization conditions and quality optimization conditions. Specifically,

[0152] The resistance optimization conditions include: total length and aspect ratio, and four variables: nose cone length, body length, tail cone length and body diameter. The total length L: 4000mm≤L≤9000mm; aspect ratio s: 5≤s≤12; nose 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;

[0153] The quality optimization conditions include: total length, stress state and aspect ratio, as well as four variables: nose cone length, body length, tail cone length and wall thickness. The total length L: 4000mm≤L≤9000mm; aspect ratio s: 5≤s≤12; stress state σ max :σ max ≤σ y , σ yis the yield strength of the material; head 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] As a preference, it also includes setting 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. 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.

[0156] As a preference, the better optimization algorithm is selected as follows: an optimization algorithm with smaller optimal parameter resistance and mass is selected to perform multidisciplinary index optimization.

[0157] Preferably, the optimization results are verified by visual simulation, specifically: three-dimensional modeling of the unmanned underwater vehicle is performed according to multidisciplinary indicator optimization, the materials and material properties of the unmanned underwater vehicle are set, the simulation environment conditions are set, and the validity of the optimization results is verified based on the visualized finite element simulation results.

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

[0159] In another embodiment, a method for optimizing the design of an unmanned underwater vehicle under size and weight constraints of the present invention is described in detail in conjunction with 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 follows: Figure 2 As shown, the specific steps include:

[0161] S1: Construct a multidisciplinary mathematical model of UUV. The established multidisciplinary mathematical model of UUV includes water flow resistance model, wet surface area model, pressure model and mass model. The wet surface area model is a reference for friction resistance. The mathematical model is related to the key dimensions of the basic parts of UUV, namely the nose cone, body and tail cone.

[0162] The water flow resistance model is constructed as follows: The fluid dynamics model of an unmanned submersible can have three basic parts: nose cone, body and tail cone, such as Fig.14As shown. The nose cone is streamlined to achieve minimal airflow separation; the main body is a long cylindrical shape to provide space for internal equipment and has relatively small shape resistance; the tail cone is used to maintain streamline stability and suppress wake effects. In the head and middle sections, namely the nose cone and the main body, the fluid velocity is low, the streamline is close to the surface, and it is easier to maintain laminar flow. When the fluid passes through the middle section of the UUV to the tail, namely the tail cone, the streamlined structure of the tail will cause fluid separation and form turbulence.

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

[0164]

[0165] in: Represents the pressure resistance of the nose cone, L nose is the length of the nose cone, p is the fluid pressure, obtained by Bernoulli's equation, r(z) is the radius of the nose cone shell at axial position z, and θ(z) is the angle between the streamline at that position and the horizontal plane.

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

[0167]

[0168] in: Represents the friction resistance of the body, L body is the length of the main body, τ w is the wall shear stress, r(x) is the radius of the 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 using the turbulent shear stress model:

[0170]

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

[0172] UUV overall water flow resistance R total :

[0173]

[0174] The wet surface area model is constructed as follows: In addition to the influence of water flow resistance, friction resistance is also a major factor affecting the performance of UUVs. Calculating the wet surface area of ​​UUVs is a key step in evaluating resistance characteristics. The wet surface area is the sum of the surface areas of UUVs in contact with water.

[0175] Wetted surface area model of nose cone:

[0176] The wetted surface area of ​​a conical nose cone is calculated using the following formula:

[0177]

[0178] Where: r n is the radius function of the vertex z of the head cone, l n is the length of the nose cone, 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 cylinder:

[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 wetted 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 vertex z of the tail cone, h t is the total height of the tail cone.

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

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

[0187] Get the UUV wet surface area S total , and further estimate the friction resistance of the entire UUV. Through resistance modeling, the resistance distribution of the UUV when it is sailing underwater can be analyzed in detail, and its shell design can be optimized through simulation, changing the streamline, that is, the shape of the shell, to reduce the resistance caused by the water flow and achieve better performance.

[0188] The pressure model is constructed 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: front, middle and rear, namely the nose cone, the main body and the tail cone, and each part uses a uniform 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 the titanium alloy is set to remain unchanged.

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

[0190]

[0191] Pressure conditions:

[0192]

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

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

[0195] Circumferential stressσ 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 conditions:

[0202]

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

[0204] For a tapered housing, the axial pressure is different from the circumferential pressure and can be estimated using the following formula:

[0205]

[0206] Where: 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 cone base.

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

[0208]

[0209] Where: P cr is the critical force, E is the elastic modulus of titanium alloy, I is the moment of inertia of the cylindrical section, 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 cylinder part.

[0210] The mass model is constructed as follows: 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. hollow hemispherical volume:

[0212]

[0213] V hnet =V hext -V hint

[0214] The volume of the middle part of the cylinder, that is, the hollow cylinder:

[0215] V c-ext =πr 2 l

[0216]

[0217] V cnet =V c_ext -V c_int

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

[0219]

[0220] V tnet =V t_ext -V t_int

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

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

[0223] gross weight:

[0224] M=ρ material ·V totalnet

[0225] Where: 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, and r i is the inner radius of the middle of the cylinder, l is the length of the middle of the cylinder, h is the height of the conical tail, r 2i is the inner radius of the top of the tapered tail, usually 0, assuming it is a tip.

[0226] S2: Set the optimization conditions of the unmanned underwater vehicle. Specifically, set the optimization index, the parameters to be optimized, the initial values ​​of the parameters, and the theoretical boundary values ​​of the parameters. The optimization index is resistance and mass. The optimization parameters include the length and diameter of different parts of the UUV. At the same time, the total length and aspect ratio of the UUV must 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] Setting principle and specific setting of resistance optimization conditions: In order to make the UUV shape more consistent with the dynamic model, two constraints are set: total length L and aspect ratio s and four variables head cone length, body length, tail cone length and body diameter. Total length L: 4000mm≤L≤9000mm; aspect 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 the overall size of the UUV is limited, 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. 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. Particle swarm optimization algorithm and genetic algorithm are existing mature conventional algorithms. Their flow chart is as follows: Figure 3 , Figure 4 As shown, the present invention will not be described in detail here.

[0228] The setting principle and specific setting of quality optimization conditions: Due to the different types and sizes of pressure on different shapes, the UUV is divided into three parts: a streamlined front section, a cylindrical middle section and a reduced tail section, namely the nose cone, main body and tail cone of the unmanned underwater vehicle. Three constraints are set here: total length, stress state and aspect ratio, and four variables: front, middle and tail lengths and wall thickness. Total length L: 4000mm≤L≤9000mm; aspect 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 stipulates that the number of particles is 300, the inertia weight is 0.5, and the number of iterations is 3000. 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.

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

[0230] The particle swarm optimization algorithm needs to set the number of particles, inertia weight, and number of iterations, and then perform iterative optimization; the genetic optimization algorithm needs to set the population size, crossover rate, mutation rate, and number of iterations, and then perform iterative optimization. According to the conditions set in S2 and the optimization algorithm related parameters set in S3, the particle swarm optimization algorithm and the genetic optimization algorithm are used to solve the problem respectively. The optimization process is as follows Figure 5-Figure 12 . Figure 5 , Figure 7 In order to optimize the resistance, the particle swarm algorithm and genetic algorithm are iterated to find the optimal solution process; Figure 6 , Figure 8 In the iteration process, the nose cone length is shown by the red curve in the figure, the body length is shown by the orange curve in the figure, the tail cone length is shown by the blue curve in the figure, and the body diameter is shown by the green curve in the figure. It can be seen that in 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 constantly decreasing and finally oscillates around the fixed value. Fig. 9 , Fig.11 To optimize quality, the particle swarm algorithm and genetic algorithm are iteratively used to seek the optimal solution process; Figure 8 , Fig.12 In the iterative process, the nose cone length is shown in the red curve, the body length is shown in the orange curve, the tail cone length is shown in the blue curve, and the wall thickness is shown in the green curve. During the optimization process of the two algorithms, the sizes of the four design variables are constantly adjusted and iterated within the range to seek the optimal value, while the mass is constantly decreasing and finally oscillates around the fixed value. The solution results of resistance optimization are shown in Table 1; the solution results of mass optimization are shown in Table 2.

[0231] Table 1: Solution results of resistance optimization

[0232]

[0233] Table 2: Solution results of quality optimization

[0234]

[0235]

[0236] S4: Take the final result of the optimization target as the basis for judgment, compare the optimization results of the optimization algorithm, and select the better optimization algorithm to further perform multidisciplinary (indicator) optimization or multidisciplinary indicator optimization.

[0237] Specifically, selecting a better optimization algorithm specifically means selecting an optimization algorithm with smaller optimal parameter resistance and mass.

[0238] When resistance is the optimization indicator, the optimization effect of the genetic algorithm on the scale of UUV is better than that of the particle swarm optimization algorithm, and the scale of UUV is also smaller. In terms of structural optimization under weight constraints, the optimization results of the genetic algorithm have better performance, which can make the UUV smaller while maintaining better compression performance and smaller weight. By comparison, the optimization effect of the genetic algorithm is better, so 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 a multidisciplinary optimization design study on UUV. The optimization goal is to minimize the total resistance and total mass of the unmanned underwater vehicle. The optimization process is as follows Fig.13 shown. Fig.13 In order to optimize the resistance and mass, the nose cone length is shown by the red curve in the figure, the body length is shown by the orange curve in the figure, the tail cone length is shown by the dark blue curve in the figure, and the wall thickness is shown by the light blue curve in the figure. The optimization results are shown in Table 3.

[0239] Table 3: Multidisciplinary optimization results

[0240]

[0241] S5: Visualization and simulation verification of optimization results. First, the size optimization results obtained in S4 are used to perform three-dimensional modeling of the UUV based on three-dimensional drawing software, and then the simulation environment conditions are set to visualize the finite element simulation results to verify the effectiveness of the size optimization results.

[0242] The optimization results obtained by S4 are modeled in three dimensions, such as Fig.14 , set the material and material properties of UUV: yield strength σ y =887MPa, tensile strengthσ u =970MPa, elastic modulus E = 110GPA, shear modulus G = 48GPA, density ρ = 4.48g / cm 3 .

[0243] According to the proposed diving depth of 100 meters, the mechanical strength and stability of the entire hull and its internal structure under static load are analyzed. Finite element analysis requires meshing of the UUV model for subsequent simulation and solution. The meshing uses a standard triangle mesh of 10 mm to 20 mm. The meshing results are as follows: Fig.15 As shown. The external load is set according to the direction and magnitude of the underwater pressure, and the overall underwater environment of 100 meters is simulated. The overall strain results of the UUV are shown in Fig.16 As shown, the safety factor is Fig.17 As shown, the stress distribution is Fig.18 As shown. Fig.16 It can be seen that the maximum displacement of the UUV shell is only 0.06mm, which meets the safety standard requirements; Fig.17 It can be seen that the minimum safety factor of the UUV shell is 13.7, which meets the international safety standards; Fig.18 It can be seen that the stress is mainly distributed in the main part of the UUV and is also less than the yield strength σ y =887MPa. Therefore, the collaborative optimization method is feasible and can effectively reduce the mass and resistance of UUV while meeting the design requirements. The visual simulation verification results show that the optimization results of UUV structure size meet the proposed environmental requirements, which further proves that the collaborative optimization method is feasible. The optimization strategy of the present invention can effectively reduce the mass of UUV, reduce resistance and improve energy efficiency while meeting the design requirements.

[0244] S6: Deploy the UUV design optimization results to actual application scenarios based on 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 assumption of a fluid dynamics model, the UUV model is divided into three basic parts: a nose cone, a main body, and a tail cone, and a mathematical model of the water flow resistance, wet surface area, pressure, and mass of the UUV is established. With key dimensions such as length and diameter as variables, the particle swarm optimization algorithm and the genetic optimization algorithm are used to solve the optimal size parameters of the UUV under different optimization objectives and compare the algorithm optimization results. Based on the comparison results, a 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 unmanned underwater vehicle design optimization results are deployed to actual application scenarios according to the verified model. The present invention provides a UUV design optimization method under size and weight constraints, which improves the efficiency and performance of UUV structural design and reduces development costs and time.

[0246] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present disclosure, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present disclosure.

[0247] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0248] In order to solve the above technical problems, an embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the unmanned underwater vehicle design optimization method under size and weight constraints as described above are implemented.

[0249] The embodiment of the present invention also provides a computer device, including a memory, a processor, and a network interface that are interconnected and communicated through a system bus. It should be noted that this embodiment only shows a computer device with component memory, a processor, a network interface, and an operating system, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the 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 microprocessors, application-specific integrated circuits (ASIC), programmable gate arrays (FPGA), digital processors (DSP), embedded devices, etc.

[0250] Computer devices can be computing devices such as desktop computers, notebooks, PDAs, and cloud servers. Computer devices can interact with users through keyboards, mice, remote controls, touch pads, or voice control devices.

[0251] The memory may be one or more, and may include at least one type of readable storage medium, the readable storage medium including flash memory, hard disk, multimedia card, card-type memory (e.g., 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, disk, optical disk, etc. In some embodiments, the memory may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device. Of course, the memory may also include both the internal storage unit of the computer device and its external storage 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 unmanned underwater vehicle under size and weight constraints, etc. In addition, the memory may also be used to temporarily store various types of data that have been output or are to be output.

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

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

[0254] The present invention also provides another embodiment, namely, providing a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the unmanned underwater vehicle design optimization method under size and weight constraints as described above.

[0255] It can be seen that, first of all, the design method of the present invention improves the optimization efficiency of multidisciplinary coupling. The design adopts a parallel optimization method to coordinately optimize the various subsystems of the UUV, set independent design variables and objective functions, so that the parameters can be dynamically adjusted to achieve multidisciplinary overall optimization. Through this method, the optimal performance of the UUV system is achieved under the premise of meeting the performance indicators of each subsystem. Secondly, the design achieves the optimal balance between size and mass. Under the constraints of size and weight, the combination of genetic optimization and particle swarm optimization is used to find the best combination of structural design and power configuration, optimize the efficiency of the power system, the pressure resistance of the hull and the endurance, and solve the problem that weight and size are difficult to take into account in the prior art. Furthermore, the design improves the multi-objective optimization effect. The present invention adopts a combination of multiple intelligent optimization algorithms, which can effectively balance different performance indicators, such as pressure resistance and endurance, and continuously improve the scheme in the design process through genetic algorithms and particle swarm algorithms, so as to obtain the design result with the best comprehensive performance. Finally, the design reduces the cost and time of UUV development. The multidisciplinary optimization method of the present invention can reduce the time for designers to debug and adjust subsystems, shorten the development cycle, and reduce development costs.

[0256] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may 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 invention content and specific implementation methods of the above specification and the various parts shown in the drawings. Due to the limited space and to make the specification concise, the various schemes composed of 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 in the protection scope of the present invention.

[0258] Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. Those skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and purpose of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A design optimization method for an unmanned underwater vehicle under size and weight constraints, characterized in that: include: Construct a multidisciplinary mathematical model of the UUV, including water flow resistance model, wetted surface area model, pressure model and mass model; Set the optimization conditions of the unmanned underwater vehicle, including resistance optimization conditions and quality optimization conditions, and solve the optimal parameters through particle swarm optimization algorithm and genetic optimization algorithm; Compare the optimal parameters solved by particle swarm optimization algorithm and genetic optimization algorithm, and select the better optimization algorithm for multidisciplinary index optimization; Conduct visual simulation verification of optimization results; 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, The water flow resistance model of the nose cone: in, Represents the pressure resistance of the nose cone, L nose is the length of the nose cone, 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; The main body of the water flow resistance model: in, represents the friction resistance of the body, τ w is the wall shear stress, r(x) is the radius of the body, ν is the kinematic viscosity, u is the fluid velocity, and y is the distance perpendicular to the wall; Water flow resistance model of tail cone: in: represents the turbulent drag 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, r(z) is the radius of the tail cone; The overall water flow resistance of the unmanned underwater vehicle is:

2. The method for optimizing the design of an unmanned underwater vehicle under size and weight constraints according to claim 1, characterized in that: Also includes: After visual simulation verification of the optimization results, the unmanned underwater vehicle optimization results are deployed to actual application scenarios based on the verified model.

3. The design optimization method of an unmanned underwater vehicle under size and weight constraints according to claim 1, characterized in that: The wet surface area model includes a wet surface area model of a nose cone, a wet surface area model of a main body, and a wet surface area model of a tail cone. Specifically, Wetted surface area model of nose cone: Among them, r n is the radius function of the vertex z of the head cone, l n is the length of the nose cone, h n is the total height of the nose cone; Wetted surface area model of the subject: S body =2πr b L b Among them, r b is the radius of the cylinder, L b is the length of the cylinder; Wetted surface area model of the tail cone: Among them, 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; The wet surface area of ​​the unmanned underwater vehicle is: S total =S n +S b +S t 。 4. The design optimization method of an unmanned underwater vehicle under size and weight constraints according to claim 1, characterized in that: The pressure model includes a nose cone pressure model, a main body pressure model and a tail cone pressure model. Specifically, Nose cone pressure model: Where P is the pressure, t is the wall thickness, a is the local curvature radius of the front end, σ y is the yield strength of the material; Subject pressure model: Where P is the pressure, t is the wall thickness, R is the radius of the cylinder, σ y is the yield strength of the material; Tail cone pressure model: 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 cone base.

5. The method for optimizing the design of an unmanned underwater vehicle under size and weight constraints according to claim 1, characterized in that: The quality model includes: Nose cone volume: V hnet =V hext -V hint Main body volume: V c-ext ＝πr 2 l V cnet =V c_ext -V c_int Coccygeal volume: V tnet =V t_ext -V t_int Total volume of unmanned underwater vehicle: V totalnet =V hnet +V cnet +V tnet Total weight of unmanned underwater vehicle: M=ρ material ·V totalnet Where 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 body, and r i is the inner radius of the body, l is the length of the body, h is the height of the tail vertebra, r 2i is the inner radius of the top of the coccyx.

6. The method for optimizing the design of an unmanned underwater vehicle under size and weight constraints according to claim 1, characterized in that: The resistance optimization conditions of the unmanned underwater vehicle include: total length and aspect ratio, and four variables: nose cone length, body length, tail cone length and body diameter, total length L: 4000mm≤L≤9000mm; aspect ratio s: 5≤s≤12; nose 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; the quality optimization conditions of the unmanned underwater vehicle include: total length, stress state and aspect ratio, and four variables: nose cone length, body length, tail cone length and wall thickness, total length L: 4000mm≤L≤9000mm; aspect ratio s: 5≤s≤12; stress state σ max :σ max ≤σ y , σ y is the yield strength of the material; head 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.

7. The 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 parameters of the genetic optimization algorithm and the particle swarm optimization algorithm. Specifically, 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; 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.

8. The method for optimizing the design of an unmanned underwater vehicle under size and weight constraints according to claim 1, characterized in that: The method of selecting a better optimization algorithm specifically includes: selecting an optimization algorithm with smaller optimal parameter resistance and mass 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 to 9, characterized in that: The visual simulation verification of the optimization results is specifically as follows: three-dimensional modeling of the unmanned underwater vehicle is performed according to multidisciplinary indicator optimization, the materials and material properties of the unmanned underwater vehicle are set, the simulation environment conditions are set, and the validity of the optimization results is verified according to the visual finite element simulation results.

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

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