CN113761645A - Method, device and equipment for designing underwater vehicle shell - Google Patents
Method, device and equipment for designing underwater vehicle shell Download PDFInfo
- Publication number
- CN113761645A CN113761645A CN202110579100.4A CN202110579100A CN113761645A CN 113761645 A CN113761645 A CN 113761645A CN 202110579100 A CN202110579100 A CN 202110579100A CN 113761645 A CN113761645 A CN 113761645A
- Authority
- CN
- China
- Prior art keywords
- shell
- design
- target
- optimization
- underwater vehicle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Computer Hardware Design (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Molecular Biology (AREA)
- Genetics & Genomics (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Automation & Control Theory (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computing Systems (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Physiology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Feedback Control In General (AREA)
Abstract
The application relates to the technical field of underwater vehicles, and discloses a method, a device and equipment for designing an underwater vehicle shell. The method comprises the following steps: carrying out test design according to appearance parameter design variables of the underwater vehicle shell and corresponding value ranges to obtain a plurality of groups of parameter test variable samples, and obtaining an optimization target sample corresponding to each group of parameter test variable samples according to the determined property of an optimization target; establishing a target approximate model of each optimized target relative to the shell appearance parameter design variable according to the parameter test variable sample and the corresponding optimized target sample; and performing multi-objective optimization on the underwater vehicle shell based on a genetic algorithm and a target approximate model to obtain an optimized shell shape parameter value and an optimized target value, and designing the underwater vehicle shell. Therefore, the design optimization period of the shell can be shortened through multi-objective optimization, the design optimization efficiency is improved, and resources are saved.
Description
Technical Field
The present application relates to the field of underwater vehicle technology, and for example, to methods, apparatus and devices for underwater vehicle hull design.
Background
The ocean contains extremely rich energy and mineral resources. An underwater vehicle is one of important means for exploring the ocean, and observation and operation can be carried out on the deep sea by means of the underwater vehicle. The underwater vehicle is a recyclable small-sized underwater self-navigation carrier and has autonomous planning, autonomous decision making and autonomous navigation capabilities; and the ship does not need to be supported by a special mother ship, has the advantages of strong flexibility, good concealment and the like, and plays an important role in the aspects of military reconnaissance, marine scientific investigation and the like.
Currently, underwater vehicles are moving towards great depths, long range and light weight. The hull of the underwater vehicle is mainly used for bearing loads, and is used for supporting and containing internal equipment, and the hull has pressure resistance. Thus, the design parameters of the hull are directly related to the system performance of the aircraft, such as hydrodynamic performance, endurance, and carrying capacity. Therefore, the design and optimization of the pressure hull is of great importance in the overall aircraft system design.
The design optimization method of the pressure-resistant shell of the related underwater vehicle mostly depends on the experience of designers, designs a preliminary scheme on the basis of an empirical formula, and then continuously modifies parameters through a plurality of model experiments until the design index requirements are met, so that the design period of the method is long, the cost is high, and the optimal performance is difficult to achieve; or, a large amount of simulation analysis is carried out through model tests and computational fluid dynamics software, and the process has poor target performance, much time consumption and high cost. Therefore, the design process of the underwater vehicle shell is more complex and consumes more resources.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.
The embodiment of the disclosure provides a method, a device and equipment for designing an underwater vehicle shell, which aim to solve the technical problem of complex design of the underwater vehicle shell.
In some embodiments, the method comprises:
carrying out test design according to appearance parameter design variables of the underwater vehicle shell and corresponding value ranges to obtain a plurality of groups of parameter test variable samples, and obtaining an optimization target sample corresponding to each group of parameter test variable samples according to the determined property of an optimization target;
establishing a target approximate model of each optimized target relative to the shell appearance parameter design variable according to the parameter test variable sample and the corresponding optimized target sample;
and performing multi-objective optimization on the underwater vehicle shell based on a genetic algorithm and a target approximate model to obtain an optimized shell shape parameter value and an optimized target value, and designing the underwater vehicle shell.
In some embodiments, the apparatus comprises:
the test module is configured to carry out test design according to appearance parameter design variables of the underwater vehicle shell and corresponding value ranges to obtain a plurality of groups of parameter test variable samples, and obtain optimization target samples corresponding to each group of parameter test variable samples according to the determined property of an optimization target;
an approximation module configured to establish a target approximation model of each of the optimization targets with respect to the shell shape parameter design variables based on the parameter test variable samples and the corresponding optimization target samples;
and the optimization design module is configured to perform multi-objective optimization on the underwater vehicle shell based on a genetic algorithm and a target approximation model to obtain an optimized shell shape parameter value and an optimized target value, and perform design on the underwater vehicle shell.
In some embodiments, the apparatus for underwater vehicle hull design includes a processor and a memory storing program instructions, the processor being configured to, upon execution of the program instructions, perform the method for underwater vehicle hull design described above.
In some embodiments, the apparatus comprises the above apparatus for underwater vehicle hull design.
The method, the device and the equipment for designing the underwater vehicle shell provided by the embodiment of the disclosure can realize the following technical effects:
based on experimental design, an approximate model technology and a genetic algorithm, multi-objective optimization design is carried out on the pressure-resistant shell of the underwater vehicle, so that the shell design optimization period can be shortened through multi-objective optimization, the design optimization efficiency is improved, and resources are saved.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are shown as like elements and not in limitation thereof, and wherein:
FIG. 1 is a schematic flow diagram of a method for designing an underwater vehicle hull provided by an embodiment of the present disclosure;
FIG. 2 is a schematic structural view of an underwater vehicle hull provided by an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart diagram of a method for designing an underwater vehicle hull provided by an embodiment of the present disclosure;
FIG. 4 is a Pareto optimal solution set provided by an embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating experimental results of a withstand voltage test provided by an embodiment of the present disclosure;
FIG. 6 is a schematic structural diagram of an apparatus for designing an underwater vehicle hull provided by an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a design device for an underwater vehicle hull provided by an embodiment of the disclosure.
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
The terms "first," "second," and the like in the description and in the claims, and the above-described drawings of embodiments of the present disclosure, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the present disclosure described herein may be made. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions.
The term "plurality" means two or more unless otherwise specified.
In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
In the embodiment of the disclosure, the multi-objective optimization design is performed on the pressure-resistant shell of the underwater vehicle based on experimental design, an approximate model technology and a genetic algorithm, so that the optimization period of the shell design can be shortened through the multi-objective optimization, the design optimization efficiency is improved, and resources are saved. And sensitivity analysis can be carried out based on the established target approximate model, the influence rule and the influence degree of each shell shape parameter design variable on the optimization target are obtained, the optimization efficiency is further improved, the design optimization is more targeted, and guidance can be provided for the subsequent design optimization. In addition, feasibility verification can be carried out on the designed shell, and feasibility and reliability of shell design are further guaranteed.
Fig. 1 is a schematic flow chart diagram of a design method for an underwater vehicle hull according to an embodiment of the present disclosure. As shown in fig. 1, a process for design of an underwater vehicle hull comprises:
step 101: carrying out test design according to the shape parameter design variables of the underwater vehicle shell and the corresponding value ranges to obtain a plurality of groups of parameter test variable samples, and obtaining an optimization target sample corresponding to each group of parameter test variable samples according to the determined property of the optimization target.
In the embodiment of the disclosure, the shape parameter of the underwater vehicle shell can be used as a design variable, that is, the shape parameter design variable can be determined. The shape of the shell of the underwater vehicle is mostly a revolving body structure, wherein shape parameter design variables comprise: and determining the constraint range of the shape parameter design variables according to the practical situation and design experience of the underwater vehicle application.
The shell of the underwater vehicle is mainly used for bearing load, and is used as a support and a container of internal equipment, and has pressure resistance. Thus, the performance indexes for measuring the pressure shell of the autonomous underwater vehicle comprise: shell mass, envelope volume, shell drag, shell strength and stability, ride-on capability, etc., whereas in embodiments of the present disclosure, optimization goals may include: each optimization target is corresponding to a constraint condition to limit the range of the optimization target, and the optimization targets may contradict each other in the optimization process, so that the optimization problem is a multi-target optimization problem.
After the design variables and the optimization targets of the appearance parameters of the underwater vehicle shell are determined, and the corresponding value ranges and the corresponding constraint conditions are determined, firstly, the design variables and the corresponding value ranges of the appearance parameters of the underwater vehicle shell are subjected to test design to obtain a plurality of groups of parameter test variable samples, and then, the optimization target samples corresponding to each group of parameter test variable samples are obtained according to the properties of the determined optimization targets.
Carrying out experimental design according to the determined appearance parameter design variables and the corresponding value ranges, wherein the experimental design comprises the following steps: parametric tests, full factor design, orthogonal arrays, latin hypercube design, or, optimal latin hypercube, etc., where experimental design can help determine reasonable design points. After the test design is completed, parametric test variable samples can be obtained.
The properties of each optimization target are different, and the solving modes of obtaining corresponding optimization target samples are also different according to the parameter test variable samples. In some embodiments, if the optimization objective comprises: the shell mass samples, the envelope volume samples and the shell resistance can be obtained by establishing a buoy shell model through three-dimensional drawing to obtain the shell mass samples and the envelope volume samples corresponding to each group of parameter test variable samples; and obtaining a shell resistance sample corresponding to each group of parameter test variable samples under the condition of setting the flow rate through fluid mechanics simulation.
Of course, if the optimization objectives include: and (3) obtaining a shell strength and stability sample corresponding to each group of parameter test variable samples through finite element simulation. The properties of different optimization targets and different solving modes are not specifically listed.
Step 102: and establishing a target approximate model of each optimization target about the design variable of the shell appearance parameter according to the parameter test variable sample and the corresponding optimization target sample.
The approximate model technique includes: a polynomial response surface approximation model, a radial basis function neural network model, or a Kriging model, etc., and the approximation model technique can fit a smooth and simple approximation function suitable for finding a global optimal solution in the design space, and thus, in some embodiments, an approximation model can be determined, including: the polynomial response surface approximate model, the radial basis function neural network model or the Kriging model can be selected according to design requirements. Then, based on the approximate model, fitting analysis is carried out on the parameter test variable samples and the corresponding optimization target samples to obtain an approximate function of each optimization target on the shell appearance parameter design variable, and therefore the target approximate model is obtained.
Step 103: and performing multi-objective optimization on the underwater vehicle shell based on a genetic algorithm and a target approximate model to obtain an optimized shell shape parameter value and an optimized target value, and designing the underwater vehicle shell.
The mathematical model of the multi-objective optimization problem can be described as:
min f(x)＝[f1(x),f2(x),…fn(x)]T
s.t.x∈X
gi(x)≥0,i＝1,2,…p
hj(x)＝0,(j＝1,2,…q).
wherein f isn(x) Is an objective function, gi(x)，hj(x) Is a constraint function; x ═ X1,x2,…xn)TRepresenting an n-dimensional design variable.
In multi-objective optimization, optimization objectives are mutually contradictory and restricted, and if a certain objective is improved, another objective is inevitably lost, that is, all objectives cannot be improved at the same time, so that the solution of the multi-objective optimization problem is a non-inferior solution, also called Pareto optimal solution, and a set formed by Pareto solutions is called a Pareto optimal solution set. The Pareto solution set is firstly obtained by the Pareto solution set-based multi-objective optimization method, and then a satisfactory optimal solution is selected from the solution set according to design requirements or actual experience, so that the essence of a multi-objective optimization problem can be objectively reflected.
In the multi-objective optimization solution, a genetic algorithm is adopted for solving, a non-dominated solution sorting genetic algorithm (NSGA) is a multi-objective optimization method for effectively solving a Pareto solution set, and a second-generation non-dominated sorting genetic algorithm NSGA-II improved on the basis can quickly sort non-dominated solutions, can control the quantity of elite, keeps population diversity, can better converge to the optimal Pareto frontier, and is considered to be one of the most effective multi-objective optimization methods at present. Therefore, in some embodiments, the target approximation model is subjected to multi-objective optimization based on a non-dominated sorting genetic algorithm to obtain a Pareto optimal solution set; then, from the Pareto optimal solution set, a set of solutions is determined as the optimized values of the housing profile parameters and the optimized target values.
And determining the optimized shape parameter value and the optimized target value of the shell to obtain a value corresponding to the design parameter, namely designing the underwater vehicle shell to obtain the designed shell.
Therefore, in the embodiment, the multi-objective optimization design is performed on the pressure-resistant shell of the underwater vehicle based on the experimental design, the approximate model technology and the genetic algorithm, so that the shell design optimization period can be shortened through the multi-objective optimization, the design optimization efficiency is improved, and the resources are saved.
In some embodiments, after the target approximation model of each optimization target with respect to the design variables of the housing shape parameters is established in step 102, the reasonability and accuracy of the target approximation model can be analyzed, so as to further improve the reasonability and reliability of the housing design. Performing an analysis of the plausibility and accuracy of the target approximation model may include: determining a complex correlation coefficient corresponding to each target approximate model; and determining the feasibility of the corresponding target approximation model according to the complex correlation coefficient value.
For example: the feasibility analysis is carried out by calculating the complex correlation coefficient, the degree of the response surface conforming to the given data can be reflected, wherein the complex correlation coefficient value is between [0,1], and the closer to 1, the better the fitting effect is, generally speaking, if the complex correlation coefficient value is more than 0.9, the feasibility of the corresponding target approximate model can be determined.
In some embodiments, after the optimized values of the casing shape parameters and the optimized target values are obtained, sensitivity analysis can be further performed when the underwater vehicle casing is designed. The sensitivity analysis refers to qualitatively or quantitatively researching the influence of the uncertainty of the model input on the uncertainty of the model output, and can be used for determining the influence of each parameter of the model on the output result, and the commonly used sensitivity analysis comprises the following steps: the method comprises a Fourier amplitude sensitivity inspection method, a Morris method, a Sobol' method or a Fourier amplitude sensitivity inspection extension method, so that the sensitivity analysis is carried out on a target approximate model, and the influence rule and the influence degree of each shell shape parameter design variable on an optimization target can be obtained. Therefore, the optimization efficiency can be improved, the design optimization is more targeted, and guidance can be provided for the design optimization of the secondary underwater vehicle.
In some embodiments, after obtaining the optimized target value and the hull shape parameter value, when designing the hull of the underwater vehicle, feasibility verification may further be performed, which may include: analyzing the hydrodynamic performance of the designed shell obtained according to the optimized target value and the shell appearance parameter value through hydrodynamic simulation; and/or performing integral linear buckling analysis on the designed shell through finite element Analysis (ANSYS); and/or carrying out a pressure resistance test on the designed shell. In this way, the feasibility and reliability of the housing design are further ensured.
The following operational flow is integrated into a specific embodiment to illustrate the design process for an underwater vehicle hull provided by an embodiment of the present invention.
In this embodiment, the experimental design uses the optimal latin hypercube, the approximation model technique uses a second-order nonlinear response surface model, and the genetic algorithm uses a second-generation non-dominated sorting genetic algorithm (NSGA-II).
Fig. 2 is a schematic structural diagram of an underwater vehicle hull provided by an embodiment of the present disclosure. As shown in figure 2, the pressure-resistant chamber has an outer diameter D of 298mm and a wall thickness tcThe head and the tail of the 22mm carbon fiber composite material cylinder are all provided with a minimum wall thickness tf＝ta10mm titanium alloy semi-ellipsoidThe bow and stern parts are connected with the carbon fiber cylinder body through a titanium alloy end ring, and the total length L of the buoy is 1160 mm. Therefore, in this embodiment, the design variables of the form factor related to the design of the housing structure include: bow form factor x1Stern shape factor x2Ratio of bow length to total length of buoy x3Ratio of stern length to total length of buoy x4Referring to the design experience of the same type of shell, the value ranges of the 4 design variables are as follows:
1.2≤x1≤2.8，1.2≤x2≤2.8，0.1≤x3≤0.2，0.12≤x4≤0.24。
in addition, according to the application requirements of the underwater vehicle, the shell resistance F, the shell mass m and the envelope volume V are determined as 3 optimization targets, and the shell resistance and the mass are reduced as much as possible in consideration that the autonomous underwater vehicle can only carry limited energy; to increase the net buoyancy of the autonomous underwater vehicle and reduce energy consumption, the envelope volume of the underwater vehicle should be increased as much as possible. The hydrodynamic performance of the autonomous underwater vehicle is affected by the resistance and the envelope volume, so that the maximum envelope volume and the minimum resistance are contradictory. Similarly, the effective carrying capacity of an autonomous underwater vehicle is affected by the envelope volume and hull mass, while the maximum envelope volume and minimum hull mass are contradictory. Therefore, the optimization of the resistance, the mass and the envelope volume of the pressure shell of the autonomous underwater vehicle is a multi-objective optimization problem. In addition, the maximum working water depth of the autonomous underwater vehicle is 4000m, the safety coefficient is set to be 1.2, and therefore the constraint condition is taken as the minimum critical buckling pressure Pcr≥48MPa。
And determining the shape parameter design variables of the underwater vehicle shell, the corresponding value range, the optimization target and the constraint condition, and then designing the underwater vehicle shell.
Fig. 3 is a schematic flow chart diagram of a design method for an underwater vehicle hull according to an embodiment of the present disclosure. The process for design of an underwater vehicle hull in conjunction with fig. 3 includes:
step 301: according to the appearance parameter design variables of the underwater vehicle shell and the corresponding value range, test design is carried out through an optimal Latin hypercube method, and a plurality of groups of parameter test variable samples are obtained.
In the embodiment, the calculation efficiency of fitting and the feasibility limit of engineering are comprehensively considered, each factor takes 24 levels, the 24 levels of the factors are randomly combined, and each level is only adopted once, so that 24 groups of parameter test variable samples are obtained.
Step 302: and obtaining an optimization target sample corresponding to each group of parameter test variable samples according to the determined property of the optimization target.
The three-dimensional drawing can adopt solid works, a model of the buoy shell can be established in the solid works through parametric modeling, after materials of all parts of the buoy are defined, the envelope volume and the shell quality of the shell can be read directly according to a plurality of input groups of parameter test variable samples, and the shell quality samples and the envelope volume samples corresponding to each group of parameter test variable samples are obtained.
And (3) performing fluid mechanics simulation in fluid mechanics simulation software FLUENT, setting the incoming flow speed to be 0.5m/s, adopting a second-order windward format discrete convection term and an RNG k-epsilon model, inputting a plurality of groups of parameter test variable samples, and solving shell resistance samples.
This results in 24 sets of parametric test variable samples and corresponding optimization target samples, as shown in table 1.
TABLE 1
Step 303: and establishing a response surface approximation model of each optimization target relative to the shell shape parameter design variable through a second-order nonlinear response surface model according to the parameter test variable sample and the corresponding optimization target sample.
In this embodiment, the mathematical model of the second-order nonlinear response surface model is:
wherein y represents an approximation, N is the number of design variables, and xiTo design variables, beta0,βi,βii,βijConstant term, primary term, secondary term and cross term. Then, performing regression analysis on the test data in the table 1 by using a least square method to obtain a response surface approximation model of the resistance, the mass and the envelope volume of the buoy shell with respect to the design variable:
step 304: and (4) calculating the complex correlation coefficients of the three response surface approximation models to obtain the complex correlation coefficient of F, the complex correlation coefficient of m and the complex correlation coefficient of V.
Step 305: determine whether each complex correlation coefficient is greater than 0.9? If yes, go to step 306, otherwise, the design process ends.
In this example, if the complex correlation coefficient of F in formula (1) is 0.9335, the complex correlation coefficient of m in formula (2) is 0.9999, and the complex correlation coefficient of V in formula (3) is 0.9995, the complex correlation coefficients are all within the set range [0,1], and are all greater than the set threshold value 0.9, which indicates that the accuracy of the obtained response surface model completely meets the requirement, step 306 may be executed.
Step 306: and (3) performing multi-objective optimization on each response surface approximation model by adopting a second-generation non-dominated sorting genetic algorithm NSGA-II to obtain a Pareto optimal solution set.
Setting the initial population number to be 150, the evolution algebra to be 200, the cross probability to be 0.9, the cross distribution index to be 20, the variation probability to be 0.25 and the variation distribution index to be 20, solving the three response surface approximation models, and obtaining a Pareto optimal solution set as shown in fig. 4.
Step 307: according to the application requirement of the underwater vehicle, a group of solutions can be determined from the Pareto optimal solution set as the optimized shape parameter value and the optimized target value of the shell, and the design of the underwater vehicle shell is carried out.
Fig. 4 is a Pareto optimal solution set provided by the embodiment of the present disclosure, so that a set of solutions can be determined as the optimized values of the housing profile parameters and the optimized target values from the Pareto optimal solution set shown in fig. 4. The optimization target value can be taken as an example, and table 2 is a comparison relationship between the optimization result of the embodiment and the optimization result of the empirical formula provided by the embodiment of the disclosure.
TABLE 2
Of course, the value of the casing outer shape parameter corresponding to the optimized target value may be obtained, which is not specifically exemplified. Therefore, the design of the underwater vehicle shell can be carried out according to the optimized target value and the shell shape parameter value, and the designed shell is obtained.
Step 308: and based on a global sensitivity analysis method, carrying out sensitivity analysis on the target approximate model, and acquiring the influence rule and the influence degree of each shell shape parameter design variable on the optimization target.
In the embodiment, an analysis result shows that in a design variable constraint range, the resistance of the pressure-resistant shell of the autonomous underwater vehicle is approximately linearly increased along with the increase of the shape coefficients of the bow part and the stern part, is reduced and then increased along with the increase of the length of the bow part, and is increased and then reduced along with the increase of the length of the stern part; the mass of the pressure-resistant shell of the autonomous underwater vehicle is increased along with the shape coefficients of the fore and aft parts and is approximately in a linear increasing trend, and the mass of the pressure-resistant shell of the autonomous underwater vehicle is increased along with the length of the fore and aft parts and is in a linear decreasing trend; the envelope volume of the pressure shell of the autonomous underwater vehicle is approximately in a trend of linear increasing along with the increase of the shape coefficients of the fore and the aft, and is in a trend of linear decreasing along with the increase of the length of the fore and the aft.
Therefore, the value ranges of all design variables can be determined according to the requirements of design indexes in the design process of the pressure shell of the underwater vehicle, and the method has very important reference significance for reducing the resistance and the mass of the pressure shell and increasing the envelope volume.
Step 309: and analyzing the hydrodynamic performance of the designed shell obtained according to the optimized target value and the shell appearance parameter value based on FLUENT hydrodynamic simulation.
Step 310: and carrying out integral linear buckling analysis on the designed shell obtained according to the optimized target value and the shell appearance parameter value by adopting finite element analysis ANSYS.
In this embodiment, an overall displacement cloud chart and a first-order buckling chart can be obtained under the external pressure of 48MPa, and an analysis result shows that the characteristic value of the first-order buckling result of the pressure-resistant shell under the external pressure of 48MPa is 2.0, the minimum critical buckling pressure of the shell is 96MPa, and the use requirement of the water depth stability of 4000 meters is completely met.
Step 311: and carrying out a pressure resistance test on the designed shell.
In this embodiment, a 48MPa pressure resistance test is performed by a high pressure test apparatus, a pressure resistance cabin is pressurized by a hydraulic station, five target pressures of 10MPa, 20MPa, 30MPa, 40MPa and 48MPa are set in the pressurizing process, and the pressure is maintained for a period of time when the target pressures are reached, so as to obtain an experimental result as shown in fig. 5. After the withstand voltage test, the design shell has no obvious deformation and no water seepage phenomenon inside.
Therefore, in the embodiment, the multi-objective optimization design is performed on the pressure-resistant shell of the underwater vehicle based on the experimental design, the approximate model technology and the genetic algorithm, so that the feasibility and the reliability are high, the optimization period of the shell design can be shortened through the multi-objective optimization, the design optimization efficiency is improved, and the resources are saved. In addition, the purpose of design optimization can be improved through sensitivity analysis, and guidance is provided for design optimization of the secondary underwater vehicle.
According to the above process for design of an underwater vehicle hull, an apparatus for design of an underwater vehicle hull can be constructed.
Fig. 6 is a schematic structural diagram of a design device for an underwater vehicle hull provided by an embodiment of the disclosure. As shown in fig. 6, the design device for the underwater vehicle shell comprises: a testing module 610, an approximation module 620, and an optimization design module 630.
The test module 610 is configured to perform test design according to the shape parameter design variables of the underwater vehicle shell and the corresponding value ranges to obtain multiple sets of parameter test variable samples, and obtain the optimization target samples corresponding to each set of parameter test variable samples according to the determined property of the optimization target.
And an approximation module 620 configured to establish a target approximation model of each optimization target with respect to the housing form parameter design variables based on the parameter test variable samples and the corresponding optimization target samples.
And the optimization design module 630 is configured to perform multi-objective optimization on the underwater vehicle hull based on the genetic algorithm and the target approximation model, obtain the optimized hull shape parameter value and the optimized target value, and perform design on the underwater vehicle hull.
In some embodiments, the testing module 610 is specifically configured to establish a buoy shell model by three-dimensional mapping, obtain shell mass samples corresponding to each set of parametric test variable samples, and envelope volume samples; and obtaining a shell resistance sample corresponding to each group of parameter test variable samples under the condition of setting the flow rate through fluid mechanics simulation.
In some embodiments, the approximation module 620 is specifically configured to determine an approximation model, the approximation model comprising: a polynomial response surface approximation model, a radial basis function neural network model, or a Kriging model; and fitting and analyzing the parameter test variable samples and the corresponding optimization target samples based on the approximation model to obtain an approximation function of each optimization target on the design variable of the shell appearance parameter.
In some embodiments, further comprising: a rational analysis module configured to determine a complex correlation coefficient corresponding to each target approximation model; and determining the feasibility of the corresponding target approximate model according to each complex correlation coefficient value.
In some embodiments, the optimization design module 630 is specifically configured to perform multi-objective optimization on the objective approximation model based on a non-dominated ranking genetic algorithm to obtain a Pareto optimal solution set; and determining a group of solutions from the Pareto optimal solution set as an optimized shell shape parameter value and an optimized target value.
In some embodiments, further comprising: and the sensitivity analysis module is configured to carry out sensitivity analysis on the target approximate model and obtain the influence rule and the influence degree of each shell appearance parameter design variable on the optimization target.
In some embodiments, further comprising: the feasibility analysis module is configured to analyze hydrodynamic performance of the designed shell obtained according to the optimized target value and the shell appearance parameter value through hydrodynamic simulation; and/or performing integral linear buckling analysis on the designed shell through finite element Analysis (ANSYS); and/or carrying out a pressure resistance test on the designed shell.
Therefore, in the embodiment, the device for designing the underwater vehicle shell can perform multi-objective optimization design on the underwater vehicle pressure-resistant shell based on experimental design, approximate model technology and genetic algorithm, so that the shell design optimization period can be shortened through multi-objective optimization, the design optimization efficiency is improved, and resources are saved. And sensitivity analysis can be carried out based on the established target approximate model, the influence rule and the influence degree of each shell shape parameter design variable on the optimization target are obtained, the optimization efficiency is further improved, the design optimization is more targeted, and guidance can be provided for the subsequent design optimization. In addition, feasibility verification can be carried out on the designed shell, and feasibility and reliability of shell design are further guaranteed.
The disclosed embodiment provides a device for designing a shell of an underwater vehicle, the structure of which is shown in fig. 7, and the device comprises:
a processor (processor)1000 and a memory (memory)1001, and may further include a Communication Interface (Communication Interface)1002 and a bus 1003. The processor 1000, the communication interface 1002, and the memory 1001 may communicate with each other through the bus 1003. Communication interface 1002 may be used for the transfer of information. The processor 1000 may invoke logic instructions in the memory 1001 to perform the method for underwater vehicle hull design of the above-described embodiments.
In addition, the logic instructions in the memory 1001 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products.
The memory 1001 is a computer readable storage medium and can be used for storing software programs, computer executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 1000 implements the method for underwater vehicle hull design in the above described method embodiments by executing program instructions/modules stored in the memory 1001 to perform functional applications and data processing.
The memory 1001 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal incubator, and the like. Further, the memory 1001 may include a high-speed random access memory and may also include a nonvolatile memory.
The embodiment of the present disclosure provides a design device for an underwater vehicle hull, including: a processor and a memory storing program instructions, the processor configured to, when executing the program instructions, perform a design method for an underwater vehicle hull.
The disclosed embodiment provides equipment comprising the shell design device for the underwater vehicle.
Embodiments of the present disclosure provide a computer-readable storage medium having stored thereon computer-executable instructions configured to perform the above-described method for design of an underwater vehicle hull.
The disclosed embodiments provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-described method for design of an underwater vehicle hull.
The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.
The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer incubator (which may be a personal computer, a server, or a network incubator, etc.) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes, and may also be a transient storage medium.
The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the disclosed embodiments includes the full ambit of the claims, as well as all available equivalents of the claims. As used in this application, although the terms "first," "second," etc. may be used in this application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, unless the meaning of the description changes, so long as all occurrences of the "first element" are renamed consistently and all occurrences of the "second element" are renamed consistently. The first and second elements are both elements, but may not be the same element. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other like elements in a process, method, or incubator comprising the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments. It can be clearly understood by the skilled person that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, incubators, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (10)
1. A method for design of an underwater vehicle hull, comprising:
carrying out test design according to appearance parameter design variables of the underwater vehicle shell and corresponding value ranges to obtain a plurality of groups of parameter test variable samples, and obtaining an optimization target sample corresponding to each group of parameter test variable samples according to the determined property of an optimization target;
establishing a target approximate model of each optimized target relative to the shell appearance parameter design variable according to the parameter test variable sample and the corresponding optimized target sample;
and performing multi-objective optimization on the underwater vehicle shell based on a genetic algorithm and a target approximate model to obtain an optimized shell shape parameter value and an optimized target value, and designing the underwater vehicle shell.
2. The method of claim 1, wherein obtaining an optimization objective sample corresponding to each set of the parametric test variable samples comprises:
establishing a buoy shell model through three-dimensional drawing to obtain shell mass samples corresponding to each group of parameter test variable samples and envelope volume samples;
and obtaining a shell resistance sample corresponding to each group of the parameter test variable samples under the condition of setting the flow rate through fluid mechanics simulation.
3. The method of claim 1, wherein said establishing a target approximation model of each of said optimization targets with respect to said housing form parameter design variables comprises:
determining an approximation model, the approximation model comprising: a polynomial response surface approximation model, a radial basis function neural network model, or a Kriging model;
and fitting and analyzing the parameter test variable samples and the corresponding optimization target samples based on the approximate model to obtain an approximate function of each optimization target on the design variable of the shell appearance parameter.
4. The method of claim 1, wherein after establishing the target approximation model for each of the optimization targets with respect to the shell form factor design variables, further comprising:
determining a complex correlation coefficient corresponding to each target approximation model;
and determining the feasibility of the corresponding target approximate model according to each complex correlation coefficient value.
5. The method of claim 1, wherein the obtaining the optimized values of the housing profile parameter and the optimized target values comprises:
performing multi-objective optimization on the target approximation model based on a non-dominated sorting genetic algorithm to obtain a Pareto optimal solution set;
and determining a group of solutions from the Pareto optimal solution set as the optimized shell shape parameter value and the optimized target value.
6. The method of any one of claims 1-5, wherein the designing the underwater vehicle hull comprises:
and carrying out sensitivity analysis on the target approximate model, and acquiring the influence rule and the influence degree of each shell shape parameter design variable on the optimization target.
7. The method of any one of claims 1-5, wherein the designing the underwater vehicle hull comprises:
analyzing the hydrodynamic performance of the designed shell obtained according to the optimized target value and the shell appearance parameter value through hydrodynamic simulation; and/or the presence of a gas in the gas,
performing integral linear buckling analysis on the design shell through finite element Analysis (ANSYS); and/or the presence of a gas in the gas,
and carrying out a pressure resistance test on the designed shell.
8. An apparatus for design of an underwater vehicle hull, comprising:
the test module is configured to carry out test design according to appearance parameter design variables of the underwater vehicle shell and corresponding value ranges to obtain a plurality of groups of parameter test variable samples, and obtain optimization target samples corresponding to each group of parameter test variable samples according to the determined property of an optimization target;
an approximation module configured to establish a target approximation model of each of the optimization targets with respect to the shell shape parameter design variables based on the parameter test variable samples and the corresponding optimization target samples;
and the optimization design module is configured to perform multi-objective optimization on the underwater vehicle shell based on a genetic algorithm and a target approximation model to obtain an optimized shell shape parameter value and an optimized target value, and perform design on the underwater vehicle shell.
9. An apparatus for underwater vehicle hull design, the apparatus comprising a processor and a memory storing program instructions, wherein the processor is configured to perform the method for underwater vehicle hull design of any of claims 1 to 7 when executing the program instructions.
10. An apparatus, comprising: an apparatus for design of an underwater vehicle hull according to claim 8 or 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110579100.4A CN113761645A (en) | 2021-05-26 | 2021-05-26 | Method, device and equipment for designing underwater vehicle shell |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110579100.4A CN113761645A (en) | 2021-05-26 | 2021-05-26 | Method, device and equipment for designing underwater vehicle shell |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113761645A true CN113761645A (en) | 2021-12-07 |
Family
ID=78787235
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110579100.4A Pending CN113761645A (en) | 2021-05-26 | 2021-05-26 | Method, device and equipment for designing underwater vehicle shell |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113761645A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114398719A (en) * | 2021-12-24 | 2022-04-26 | 华中科技大学 | Construction method and application of model in dynamic safety field of underwater vehicle |
| CN117556551A (en) * | 2024-01-11 | 2024-02-13 | 中国人民解放军国防科技大学 | Lightweight design method, device, equipment and medium for engine combustion chamber shell |
| CN117993124A (en) * | 2024-03-14 | 2024-05-07 | 季华实验室 | Shell design value determining method, device, equipment and medium |
| CN114398719B (en) * | 2021-12-24 | 2024-07-26 | 华中科技大学 | Construction method and application of dynamic safety field model of underwater vehicle |
-
2021
- 2021-05-26 CN CN202110579100.4A patent/CN113761645A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114398719A (en) * | 2021-12-24 | 2022-04-26 | 华中科技大学 | Construction method and application of model in dynamic safety field of underwater vehicle |
| CN114398719B (en) * | 2021-12-24 | 2024-07-26 | 华中科技大学 | Construction method and application of dynamic safety field model of underwater vehicle |
| CN117556551A (en) * | 2024-01-11 | 2024-02-13 | 中国人民解放军国防科技大学 | Lightweight design method, device, equipment and medium for engine combustion chamber shell |
| CN117556551B (en) * | 2024-01-11 | 2024-04-09 | 中国人民解放军国防科技大学 | Lightweight design method, device, equipment and medium for engine combustion chamber shell |
| CN117993124A (en) * | 2024-03-14 | 2024-05-07 | 季华实验室 | Shell design value determining method, device, equipment and medium |
| CN117993124B (en) * | 2024-03-14 | 2024-06-25 | 季华实验室 | Shell design value determining method, device, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Alam et al. | Design and construction of an autonomous underwater vehicle | |
| Alvarez et al. | Hull hydrodynamic optimization of autonomous underwater vehicles operating at snorkeling depth | |
| Zhang et al. | Using CFD software to calculate hydrodynamic coefficients | |
| CN113761645A (en) | Method, device and equipment for designing underwater vehicle shell | |
| Liu et al. | Multiple objective multidisciplinary design optimization of heavier-than-water underwater vehicle using CFD and approximation model | |
| Danışman | Reduction of demi-hull wave interference resistance in fast displacement catamarans utilizing an optimized centerbulb concept | |
| Senjanović et al. | Some aspects of structural modelling and restoring stiffness in hydroelastic analysis of large container ships | |
| Liu et al. | Study on carbon fiber composite hull for AUV based on response surface model and experiments | |
| Wang et al. | Investigation and optimization of appendage influence on the hydrodynamic performance of AUVs | |
| hang Hou et al. | AUV hull lines optimization with uncertainty parameters based on six sigma reliability design | |
| Vesting | Marine propeller optimisation-strategy and algorithm development | |
| Hou et al. | Hull shape optimization of small underwater vehicle based on Kriging-based response surface method and multi-objective optimization algorithm | |
| Choi et al. | Composite propeller design optimization for cavitation minimization using deep learning-based objective parameter prediction model | |
| Haase et al. | Numerical analysis of a planing boat in head waves using a 2D+ T method | |
| Marie et al. | Sail-assisted motor vessels weather routing using a fuzzy logic model | |
| Bonfiglio et al. | Multi-fidelity optimization of high speed SWATHs | |
| Sung et al. | Preliminary hull-form design for a semi-submersible vessel using a physics-based digital model | |
| CN108711203B (en) | Damaged hull wave load rapid forecasting method based on proxy model | |
| Sánchez-Caja et al. | A coupled potential–viscous flow approach for the prediction of propeller effective wakes in oblique flow | |
| Karim et al. | Numerical Study on Flow Around Modern Ship Hulls with Rudder-Propeller Interactions | |
| Matulja et al. | Optimization of the ship hull hydrodynamic characteristics in calm water | |
| Peric et al. | Trends in industry applications of computational fluid dynamics for maritime flows | |
| Liu et al. | Rapid data-driven individualised shape design of AUVs based on CFD and machine learning | |
| Perumpulissery Chandran et al. | Effect of slenderness ratio and aft fins on the hydrodynamic forces for an underwater body in oblique flows | |
| Salas et al. | Experimental and CFD resistance calculation of a small fast catamaran |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |