UUV optimization design system based on ocean simulator and use method

Abstract

The invention provides a UUV optimization design system based on an ocean simulator and a use method, and belongs to the field of UUV optimization design. The optimization design system comprises an ocean simulator, a digital prototype module and a design workstation. Meanwhile, the invention discloses a using method based on the optimization design system, and by the adoption of the UUV optimization design system based on the ocean simulator and the using method, various working conditions possibly encountered in the future service process of the UUV are fully considered in the early stage of design; multi-round iteration of a virtual world and one-time success of a physical world are realized through a virtual test and an optimization engine, and the research and development efficiency and the design quality are improved.

Description

Technical Field

[0001] The present invention belongs to the field of UUV design, and in particular relates to a UUV optimization design system based on an ocean simulator and a use method thereof. Background Art

[0002] In the optimization design of unmanned underwater vehicles (UUVs), traditional deterministic optimization methods have the following defects: they adopt a static environment assumption, which cannot reflect the dynamic disturbance characteristics of the real ocean environment and makes it difficult to evaluate the performance of the UUV under complex sea conditions. Summary of the Invention

[0003] The purpose of this invention is to provide a UUV optimization design system and method based on an ocean simulator. This system aims to achieve multiple iterations in the virtual world and first-pass success in the physical world through virtual testing and an optimization engine, thereby improving R&D efficiency and design quality. This invention proposes a closed-loop framework consisting of an ocean simulator, a digital prototype module, and a design workstation. The ocean simulator accurately reconstructs the dynamic ocean field, including random velocity perturbations and temperature and salinity noise. This allows the digital prototype to be deeply coupled with the multi-physical domain digital prototype module simulation and optimization engine under uncertain operating conditions, thereby verifying the comprehensive performance of the design solution under simulated operating conditions and achieving optimized design.

[0004] In order to achieve the above technical features, the object of the present invention is achieved as follows: a UUV optimization design system based on an ocean simulator includes a digital prototype module, an ocean simulator and a design workstation;

[0005] The digital prototype module includes a unified digital prototype of multiple physical domains, which is used to build a UUV digital prototype and perform multi-physical domain simulation;

[0006] The ocean simulator includes a virtual ocean field constructed by fusing multi-source data, is connected to the design workstation, is used to generate dynamic ocean environment parameters and spatial configuration data, and provides simulation environment input to the design workstation;

[0007] The design workstation is connected to the digital prototype module and the ocean simulator, and is used to generate a design space according to design requirements, build a response surface model, execute experimental design and optimization decisions, and output optimization results.

[0008] Another aspect of the present invention provides a method for using a UUV optimization design system based on an ocean simulator, comprising:

[0009] Step S1, ocean simulator initialization: load the ocean model of the target sea area, configure the initial flow field parameters and grid division rules, and output the environmental parameters to the design workstation;

[0010] Step S2, digital prototype module simulation: establishing a unified digital prototype in multiple physical domains, receiving the design space and environmental parameters transmitted by the design workstation, and using them to simulate the UUV;

[0011] Step S3, design workstation optimization: Based on the simulation results of the digital prototype, a response surface model is constructed, and the design space of the UUV geometric parameters and the location environment parameters of the UUV are automatically iteratively updated until the convergence conditions are met, and the optimization results are output.

[0012] Preferably, the ocean simulator initialization specifically includes:

[0013] S11. Load the ocean model of the target sea area, configure the initial flow field parameters and grid division rules, and generate a virtual ocean field containing random disturbances, specifically including superimposing a velocity field with random pulsating components on the average velocity, and adding a temperature-salinity field with Gaussian white noise on the temperature-salinity gradient;

[0014] S12. Outputting the environmental parameters of the simulation area to the design workstation, wherein the environmental parameters include latitude and longitude information and flow velocity and temperature-salinity gradient of the corresponding positions.

[0015] Preferably, in S11, the velocity field disturbance is: based on the average velocity given by the initial field, a random pulsating component with a certain intensity and spatial correlation is superimposed to simulate the flow field fluctuations caused by small and medium-scale eddies and tidal mixing in the real ocean;

[0016] The temperature-salinity field disturbance is to introduce Gaussian white noise that obeys normal distribution on the background gradient of temperature and salinity to enhance the spatiotemporal heterogeneity of the model output.

[0017] Preferably, in step S2, the digital prototype module simulation specifically includes:

[0018] S21. Construct the mechanism model of the main subsystems of the UUV to obtain the UUV digital prototype;

[0019] S22, importing the UUV digital prototype into the digital prototype module, receiving the initial design space, initial latitude and longitude positions and corresponding environmental parameters of the design workstation, running the model to obtain and transmit the obtained initial simulation results to the design workstation.

[0020] Preferably, the UUV main subsystems include an overall system, a payload system, a power system and an energy system.

[0021] Preferably, in step S3, the design workstation optimization specifically includes:

[0022] S31, generating the initial design space and initial longitude and latitude position of the UUV and driving the simulation. After completing one simulation step, the new longitude and latitude position is updated according to the movement of the UUV, and an update parameter signal is sent to the ocean simulator. After receiving the signal, the ocean simulator resends the environmental parameters to update the environmental parameters. The above process is repeated until the UUV movement ends;

[0023] S32. Based on the simulation results, an adjustable full factorial experimental design method is used to sample the design variables of the lengths of the first section, middle section, and tail section of the UUV and the diameter of the middle section in the optimization design space to obtain multiple sets of sampling data;

[0024] S33, establishing a response surface model of performance indicators of the UUV including drag, dimensionless position lever arm, stability margin, and turning radius based on the sampling data;

[0025] S34. Optimize the design based on the response surface model of performance indicators.

[0026] Preferably, in step S33, a response surface model of the performance index of the UUV is established based on the sampling data using a Kriging fitting method.

[0027] Preferably, in step S34, the resistance response surface model is optimized by adopting a non-dominated sorting differential evolution algorithm.

[0028] The present invention has the following beneficial effects:

[0029] The present invention transmits high-precision ocean current and temperature-salinity data to the digital prototype module in real time through an ocean simulator, replacing the traditional static environment assumption, thereby reducing the flow field coupling error of the UUV multi-physical domain simulation and improving the simulation credibility; based on the collaborative mechanism of the design workstation and the digital prototype module, the design workstation receives the performance parameters such as navigation resistance and control stability output by the digital prototype, generates new design variables through an adaptive multi-objective optimization algorithm, and feeds them back to the digital prototype module to start iterative simulation, forming a closed-loop link of "simulation-optimization-resimulation", reducing the number of physical prototype tests and shortening the research and development cycle of the entire life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 This is a principle block diagram of a UUV optimization design system based on an ocean simulator according to the present invention.

[0032] Figure 2 This is a timing diagram of a UUV optimization design system based on an ocean simulator of the present invention.

[0033] Figure 3 The present invention is a flow chart of a method for using a UUV optimization design system based on an ocean simulator.

[0034] Figure 4 This is an appearance diagram of the UUV in an embodiment of the present invention.

[0035] Figure 5 Schematic diagram of the distribution of sampling points in the adjustable full factorial design method in an embodiment of the present invention.

[0036] Figure 6 4 is a flowchart of the Kriging interpolation method in an embodiment of the present invention.

[0037] Figure 7 Flowchart of the NSDE optimization algorithm in an embodiment of the present invention.

[0038] Figure 8 The figure is a detailed flow chart of a method for using a UUV optimization design system based on an ocean simulator in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0040] Example 1:

[0041] like Figure 1 As shown in the figure, a UUV optimization design system based on ocean simulator includes ocean simulator, digital prototype module and design workstation. The timing diagram of the interaction between the three is shown in the figure. Figure 2 shown.

[0042] The digital prototype module includes a unified digital prototype of multiple physical domains, which is used to build a UUV digital prototype and perform multi-physical domain simulation.

[0043] The ocean simulator includes a virtual ocean field constructed by fusing multi-source data, which is connected to the design workstation to generate dynamic ocean environment parameters and spatial configuration data, and provide simulation environment input to the design workstation.

[0044] The design workstation is connected to the digital prototype module and the ocean simulator, and is used to generate a design space according to design requirements, build a response surface model, execute experimental design and optimization decisions, and output optimization results.

[0045] Example 2:

[0046] like Figure 3 As shown, a method for using the UUV optimization design system based on the above-mentioned ocean simulator includes the following steps S1-S3:

[0047] S1, ocean simulator initialization, including two sub-steps S11-S12;

[0048] S11. Load the ocean model of the target sea area, configure the initial flow field parameters and grid division rules, and generate a virtual ocean field containing random disturbances, specifically including a velocity field with random pulsating components superimposed on the average velocity, and a temperature-salinity field with Gaussian white noise added to the temperature-salinity gradient.

[0049] The virtual ocean field can be established by using a regional ocean model (ROMS). It should be noted that the present invention does not limit the method of establishing the virtual ocean field.

[0050] The simulated waters in this embodiment have a longitude range of 115°E-135°E and a latitude range of 19N°-42°N, with a resolution of 0.3°; the vertical layer is divided into 15 layers; the terrain uses the GEBCO data (General Bathymetric Chart of the Ocean 2019) with a resolution of 15″; the initial field and boundary temperature and salinity flow data are derived from the monthly average data of the Simple Ocean Data Assimilation (SODA) project, which covers a total of 40 years of climatological monthly data from January 1980 to December 2020, with a spatial resolution of 0.5°×0.5°.

[0051] The simulation period is from January 1980 to December 2020, with an output interval of 1 hour. The model initial conditions use WOA 09 (world ocean atlas) climate data, the boundary conditions use 5-day interval SODA data, and the wind field drive uses 6-hour interval data from ERA5 (Fifth generation ECMWF atmospheric reanalysis). The block formula is used to convert the atmospheric forcing data of ERA5, including 10m wind speed, shortwave and longwave radiation, sea surface air temperature and humidity, precipitation rate, etc., into the parameters required for the model, such as wind stress and heat flux. Flather boundary conditions are used for the four open boundaries, and the KPP scheme (K-pro-file parameterization) is used for vertical mixing.

[0052] During the simulation, a certain degree of random perturbation mechanism was introduced to improve the realism and complexity of the ocean field, including:

[0053] Velocity field perturbation: Based on the average velocity given by the initial field, a random pulsating component with certain intensity and spatial correlation is superimposed to simulate the flow field fluctuations caused by small and medium-scale eddies, tidal mixing, etc. in the real ocean;

[0054] Temperature-salinity field disturbance: Gaussian white noise that obeys the normal distribution is introduced into the background gradient of temperature and salinity to enhance the spatiotemporal heterogeneity of the model output.

[0055] The specific parameters of the random perturbation can be adjusted according to actual research needs to meet the simulation requirements of the diversity of marine environments in different application scenarios. The present invention does not limit the method and type of perturbation setting.

[0056] S12. Outputting the environmental parameters of the simulation area to the design workstation, wherein the environmental parameters include latitude and longitude information and flow velocity and temperature-salinity gradient of the corresponding positions.

[0057] The environmental parameters include but are not limited to latitude and longitude information and its corresponding flow velocity v o , temperature T and salinity S, etc.

[0058] The relationship between the total resistance coefficient and environmental parameters:

[0059] C x =f(Re);

[0060] Re=f(ρ、μ、V a );

[0061] ρ = f(T, S);

[0062] μ=f(T,S);

[0063] v a =|vv o |;

[0064] Where: Re is the Reynolds number, ρ is the density of seawater, μ is the viscosity, v is the UUV navigation speed, v a is the relative speed between UUV and seawater.

[0065] S2. The digital prototype module simulation includes sub-steps S21-S22.

[0066] S21. The digital prototype can be constructed using the multi-domain unified modeling language Modelica. Similarly, the present invention does not limit the method of constructing the UUV digital prototype.

[0067] The mechanism models of the main subsystems of the UUV are constructed to obtain a digital prototype. In this embodiment, the mechanism models of the main subsystems of the UUV are constructed, including the overall system, payload system, power system and energy system.

[0068] The overall shape directly affects the resistance of the UUV in the water. Figure 4 As shown in the figure, it is generally composed of three parts: the head, the parallel midsection, and the tail. The head and tail are formed by rotating the internationally accepted Myring curve. When designing the UUV's appearance, the main focus is on designing the lengths of these three parts.

[0069] The line shape formulas of the head and tail of the UUV are:

[0070]

[0071] Where: d is the diameter of the parallel midsection of the UUV; a is the length of the UUV head; b is the length of the parallel midsection of the UUV; c is the length of the UUV tail, and θ is the tail cone angle.

[0072] Effective volume of payload system payload compartment:

[0073]

[0074] Where: d is the diameter of the parallel middle section of the UUV; b is the length of the parallel middle section of the UUV; V e is the volume of the energy module; V is the volume coefficient.

[0075] In this embodiment, the power system uses a brushless DC motor as the main component of the UUV power system, which can be simplified into motor voltage, torque and motion equations. The mathematical models of each part are as follows.

[0076] Motor voltage equation:

[0077]

[0078] Where: u A 、u A 、u A is the stator three-phase winding voltage; i A 、i B 、i C is the phase current of the three-phase winding; R is the resistance of the three-phase winding; L is the inductance of the three-phase winding; M is the inductance between the two sets of windings; e A 、e B 、e C is the back electromotive force of the three-phase winding; u n is the voltage at the center point of the winding.

[0079] Motor torque equation:

[0080] P e =e A i A +e B i B +eC i C ;

[0081] When the motor is in no-load condition, ignoring the rotor loss, the motor's electromagnetic power is completely converted into rotor kinetic energy:

[0082]

[0083] Where: P e is the electromagnetic power; T e is the electromagnetic torque; ω is the motor mechanical angular velocity; x and y are correction coefficients that can be obtained through actual experimental data to reduce errors.

[0084] Motor motion equation:

[0085]

[0086] Where: T L is the load torque; J is the motor rotor inertia; B V is the viscous friction factor.

[0087] The energy system of UUV is divided into two categories: control power and power power. In this embodiment, a 500AH / 300V lithium-ion battery is selected. The volume energy density of the battery is v = 200Wh / L. The maximum design speed of the UUV is 20kn, and the longest voyage at the maximum speed takes 10 hours.

[0088] Assuming the battery is discharged to 90%, the total energy required by the power supply is:

[0089]

[0090] Based on volume energy density, the volume of the battery V e It can be calculated by the following formula:

[0091]

[0092] S12, constructing a mathematical model of the optimization target based on the digital prototype;

[0093] The formula for calculating the resistance of a UUV when moving below sea level is as follows:

[0094]

[0095] Where: ρ is the seawater density; V is the UUV speed; C x is the total drag coefficient of UUV; S is the headwind area of ​​UUV.

[0096] Dimensionless position arm ( The smaller the value, the more stable the object. When , static stability is achieved):

[0097]

[0098] Where: L α is the position lever arm; L is the total length of the UUV.

[0099] Stability margin ( The larger the value, the more dynamically stable the object is).

[0100]

[0101] Where: is the dimensionless position arm; is the dimensionless rotational lever arm.

[0102] Turning radius (turning radius R t The smaller the object, the more maneuverable it is):

[0103]

[0104] Where: v is the navigation speed of UUV; L is the total length of UUV, is the dimensionless angular velocity of steady rotational motion.

[0105] S3, design workstation optimization, including four sub-steps S31-S34:

[0106] S31, generating the initial design space and initial longitude and latitude position of the UUV and driving the simulation. After completing one simulation step, the new longitude and latitude position is updated according to the movement of the UUV, and an update parameter signal is sent to the ocean simulator. After receiving the signal, the ocean simulator resends the environmental parameters to update the environmental parameters. The above process is repeated until the UUV movement ends;

[0107] The design space of the UUV digital prototype is obtained as follows:

[0108] The optimization design is performed based on each sampling point in the optimization design space of the optimization design variables, wherein the optimization design variables include: head length a, parallel midsection length b, tail length c, and parallel midsection diameter d; however, it is worth noting that in actual application, it is not limited to these types of optimization design variables.

[0109] The design space in this example can be: 180mm≤a≤220mm, 360mm≤b≤440mm, 180mm≤c≤220mm, 45mm≤d≤55mm.

[0110] The optimal design space of the above-mentioned optimal design variables can be obtained through engineering manuals, experience or other methods. The present invention does not limit the method for obtaining the optimal design space of the optimal design variables.

[0111] S32. Based on the simulation results, an adjustable full factorial experimental design method is used to sample the design variables such as the length of the first section, middle section, tail section, and the diameter of the middle section of the UUV in the optimization design space to obtain multiple sets of sampling data:

[0112] One hundred simulations were performed using the design parameters in S31, and the average value was taken as the simulation result. Based on the simulation results, the Adjustable Full Factorial (AFF) method was selected for the experiment. The UUV's head and tail lengths were classified into six levels, the parallel midsection length was classified into three levels, and the parallel midsection diameter was classified into two levels. A total of 6 × 3 × 6 × 2 = 216 experiments were conducted to cover all variable interactions.

[0113] Figure 5 3 is a schematic diagram of the distribution of sampling points in an adjustable full factorial design method according to an exemplary embodiment.

[0114] S33. Based on the sampling data, a response surface model of the UUV's performance indicators such as resistance, dimensionless position arm, stability margin and turning radius is established.

[0115] The flowchart of Kriging interpolation method is as follows Figure 6 shown.

[0116] The Kriging interpolation method has two parameters, θ and P, that describe the spatial correlation between data. In the present invention, the length and diameter of the parallel midsection of the UUV have a significant impact on the UUV drag and dimensionless position lever arm. Therefore, when performing the Kriging interpolation method to fit the response surface, the corresponding P parameter is set to a larger value.

[0117] S34. Optimization design based on response surface model of performance indicators

[0118] In S12, the optimization objectives are resistance R, dimensionless position arm Stability margin and turning radius R t The non-dominated sorting differential evolution algorithm (NSDE) is selected for multi-objective optimization; the NSDE optimization algorithm process is as follows: Figure 7 shown.

[0119] Principle and specific settings of optimization conditions: The optimization variables selected for this optimization problem include the head length a, the parallel middle section length b, the tail length c, and the parallel middle section diameter d; the constraints are:

[0120] a_min≤a≤a_max;

[0121] b_min≤b≤b_max;

[0122] c_min≤c≤c_max;

[0123] d_min≤d≤d_max;

[0124] L min ≤L≤L max ;

[0125] f_min≤f≤f_max;

[0126] v_min≤v;

[0127] s_min≤s;

[0128] V l _min≤V l ;

[0129] Among them, a_min, a_max, b_min, b_max, c_min, c_max, d_min, d_max, f_min, f_max are the ranges of the diameter and slenderness ratio of the UUV head, parallel midsection, tail, and parallel midsection respectively; L max , L min are the minimum and maximum values ​​of the total length of UUV respectively; v_min, s_min, V l _min is the minimum speed, range and payload volume required by the UUV.

[0130] The constraints of the optimization problem are that the total length L is in the range [L min , L max ], the range of slenderness ratio f is [f_min, f_max], the range of speed v is [v_min, +∞], the range of range s is [s_min, +∞], and the load volume V l The range is [V l _min,+∞].

[0131] The UUV overall shape optimization model cannot obtain any set of design parameters that can simultaneously meet the four optimization objectives. Therefore, designers need to make trade-offs based on their own requirements for UUV design and ultimately select a set of appropriate design parameters from the Pareto frontier solution to achieve the best comprehensive motion performance of the UUV.

[0132] Figure 8 is a flow chart of a UUV optimization design method according to an exemplary embodiment. Figure 8As shown, the method includes the following steps:

[0133] Step 1: Load the ocean model of the target sea area, configure the initial flow field parameters and grid division rules, and generate a virtual ocean field containing random disturbances. Specifically, this includes superimposing a velocity field with random pulsating components on the average velocity and adding a temperature-salinity field with Gaussian white noise on the temperature-salinity gradient.

[0134] Step 2: Output the environmental parameters of the simulation area to the design workstation, wherein the environmental parameters include latitude and longitude information and the flow velocity and temperature-salinity gradient of the corresponding position;

[0135] Step 3: Construct the mechanism model of the main subsystems of the UUV to obtain the UUV digital prototype;

[0136] Step 4: Import the UUV digital prototype into the digital prototype module, receive the initial design space, initial latitude and longitude positions and corresponding environmental parameters of the design workstation, run the model to obtain and transmit the obtained initial simulation results to the design workstation;

[0137] Step 5: Generate the initial design space and initial longitude and latitude positions of the UUV and drive the simulation. After completing one simulation, update the new longitude and latitude positions according to the movement of the UUV, and update the environmental parameters according to the environmental parameters of S12. Repeat the above process until the UUV movement ends;

[0138] Step 6: Based on the simulation results, an adjustable full factorial experimental design method is used to sample the design variables such as the length of the first section, middle section, tail section, and the diameter of the middle section of the UUV in the optimization design space to obtain multiple sets of sampling data;

[0139] Step 7: Based on the sampling data, a response surface model of the UUV's performance indicators such as drag, dimensionless position arm, stability margin and turning radius is established;

[0140] Step 8: Optimize the design based on the response surface model of performance indicators.

[0141] The present invention is not limited to the exact construction that has been described above and shown in the drawings, and various modifications and changes may be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. A UUV optimization design system based on an ocean simulator, characterized in that: Includes digital prototype modules, marine simulators, and design workstations; The digital prototype module includes a unified digital prototype of multiple physical domains, which is used to build a UUV digital prototype and perform multi-physical domain simulation; The ocean simulator includes a virtual ocean field constructed by fusing multi-source data, is connected to the design workstation, is used to generate dynamic ocean environment parameters and spatial configuration data, and provides simulation environment input to the design workstation; The design workstation is connected to the digital prototype module and the ocean simulator, and is used to generate a design space according to design requirements, build a response surface model, execute experimental design and optimization decisions, and output optimization results.

2. A method for using the UUV optimization design system based on an ocean simulator according to claim 1, characterized in that: include: Step S1, ocean simulator initialization: load the ocean model of the target sea area, configure the initial flow field parameters and grid division rules, and output the environmental parameters to the design workstation; Step S2, digital prototype module simulation: establishing a unified digital prototype in multiple physical domains, receiving the design space and environmental parameters transmitted by the design workstation, and using them to simulate the UUV; Step S3, design workstation optimization: Based on the simulation results of the digital prototype, a response surface model is constructed, and the design space of the UUV geometric parameters and the location environment parameters of the UUV are automatically iteratively updated until the convergence conditions are met, and the optimization results are output.

3. The method for using the UUV optimization design system based on the ocean simulator according to claim 2, characterized in that: In step S1, the ocean simulator initialization specifically includes: S11. Load the ocean model of the target sea area, configure the initial flow field parameters and grid division rules, and generate a virtual ocean field containing random disturbances, specifically including superimposing a velocity field with random pulsating components on the average velocity, and adding a temperature-salinity field with Gaussian white noise on the temperature-salinity gradient; S12. Outputting the environmental parameters of the simulation area to the design workstation, wherein the environmental parameters include latitude and longitude information and flow velocity and temperature-salinity gradient of corresponding positions.

4. The method for using the UUV optimization design system based on the ocean simulator according to claim 3 is characterized in that: In S11, the velocity field disturbance is: based on the average velocity given by the initial field, a random pulsating component with a certain intensity and spatial correlation is superimposed to simulate the flow field fluctuations caused by small and medium-scale eddies and tidal mixing in the real ocean; The temperature-salinity field disturbance is to introduce Gaussian white noise that obeys normal distribution on the background gradient of temperature and salinity to enhance the spatiotemporal heterogeneity of the model output.

5. The method for using the UUV optimization design system based on the ocean simulator according to claim 2, characterized in that: In step S2, the digital prototype module simulation specifically includes: S21. Construct the mechanism model of the main subsystems of the UUV to obtain the UUV digital prototype; S22, importing the UUV digital prototype into the digital prototype module, receiving the initial design space, initial latitude and longitude positions and corresponding environmental parameters of the design workstation, running the model to obtain and transmit the obtained initial simulation results to the design workstation.

6. The method for using the UUV optimization design system based on the ocean simulator according to claim 5, characterized in that: The main subsystems of the UUV include the overall system, payload system, power system and energy system.

7. The method for using the UUV optimization design system based on the ocean simulator according to claim 2, characterized in that: In step S3, the design workstation optimization specifically includes: S31, generating the initial design space and initial longitude and latitude position of the UUV and driving the simulation. After completing one simulation step, the new longitude and latitude position is updated according to the movement of the UUV, and an update parameter signal is sent to the ocean simulator. After receiving the signal, the ocean simulator resends the environmental parameters to update the environmental parameters. The above process is repeated until the UUV movement ends; S32. Based on the simulation results, an adjustable full factorial experimental design method is used to sample the design variables of the lengths of the first section, middle section, and tail section of the UUV and the diameter of the middle section in the optimization design space to obtain multiple sets of sampling data; S33, establishing a response surface model of performance indicators of the UUV including drag, dimensionless position lever arm, stability margin, and turning radius based on the sampling data; S34. Optimize the design based on the response surface model of performance indicators.

8. The method for using the UUV optimization design system based on an ocean simulator according to claim 5, characterized in that: In step S33, a response surface model of the UUV performance index is established based on the sampling data using a Kriging fitting method.

9. The method for using the UUV optimization design system based on an ocean simulator according to claim 5, characterized in that: In step S34, the resistance response surface model is optimized by adopting a non-dominated sorting differential evolution algorithm.

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