CFD-GA-based ship drag reduction optimization design method and system

Abstract

The invention discloses a ship resistance reduction optimization design method and system based on CFD-GA, and the method comprises the steps: carrying out the CFD numerical simulation of a ship without a fairing, and calculating a ship resistance coefficient; initial design parameters of the fairing are determined, parametric modeling of the fairing is carried out, and a fairing model is generated; cFD numerical simulation is carried out on the multiple sets of ships with the fairings, and the resistance coefficient and the drag reduction rate are calculated; an RBF-GA ship resistance reduction optimization model is constructed, the fairing initial design parameters and the CFD calculation resistance coefficient are input into the RBF-GA ship resistance reduction optimization model, and optimized fairing design parameters are obtained; according to the optimized fairing design parameters, generating a new fairing model, and performing CFD numerical simulation to calculate a ship resistance coefficient; and the steps are repeated until the drag reduction target is achieved, and the optimized design ship fairing appearance is obtained. According to the method, the shape optimization design of the ship fairing can be efficiently carried out, wind resistance is effectively reduced, ship energy consumption is reduced, and the requirements of ship transportation for energy conservation and emission reduction are met.

Description

Technical Field

[0001] The present invention relates to the field of ship and ocean engineering and fluid mechanics, and in particular to a ship drag reduction optimization design method and system based on computational fluid dynamics (CFD) combined with radial basis function (RBF)-genetic algorithm (GA). Background Art

[0002] With the introduction of new international and domestic regulations to further reduce carbon emissions from shipping and the promotion of indexes such as EEDI, the shipping industry has increasingly higher requirements for the energy consumption of container ships. The wind resistance of the superstructure of container ships has a significant impact on its energy consumption. As the scale of container ships continues to expand, the proportion of superstructure wind resistance increases, which can account for 10% to 20% of the total resistance. Adding fairings can effectively reduce the wind resistance of container ships. However, the traditional drag reduction design method based only on CFD requires a lot of computing resources and a long design cycle, and it is challenging to efficiently design the shape of the fairing with a high drag reduction rate.

[0003] Shipping is an important part of international trade, and more than 80% of international trade goods in the world are carried out by sea. Therefore, shipping has also become an important source of greenhouse gas emissions, and carbon emissions in the shipping industry have attracted much attention. On the other hand, international shipping emission reduction rules are becoming increasingly stringent, and the new green shipping regulations are a new opportunity for shipping development. There is a huge market demand for green ship technology. Adding a high drag reduction rate container ship fairing can reduce ship resistance and reduce ship energy consumption. It is an effective and easy-to-promote green ship technology. The CFD-GA ship drag reduction optimization design method proposed in the present invention can efficiently carry out the optimization design of the ship fairing shape, shorten the design cycle of the ship drag reduction solution, and effectively meet the huge market demand for the green transformation of the shipping industry.

[0004] The fairing has a simple structure and flexible installation position. It has significant advantages in drag reduction, energy saving and emission reduction, and plays an important role in various means of transportation. By optimizing the shape design, the streamline shape of the fairing can improve the flow field distribution around the vehicle, reduce turbulence and eddy current phenomena, and significantly reduce air or water flow resistance, thereby improving fuel economy and reducing operating costs. These characteristics have made fairings widely used in the fields of automobiles, aviation, aerospace, ships, etc., making important contributions to improving performance and reducing operating costs. Common fairing design methods include model tests and CFD-based numerical analysis methods. Among them, CFD methods have been widely used due to their advantages such as fine results and flexible research parameter range. However, to meet the design requirements of high efficiency and high drag reduction, it is necessary to establish a large number of geometric models and perform CFD calculations and analysis one by one to determine the optimal geometric shape of the fairing. This process requires a lot of time and computing resources, and it is impossible to complete the design of high-performance ship fairings in a short time. In view of this problem, there is an urgent need for a drag reduction design method that combines optimization algorithms and numerical simulation technology to achieve efficient drag reduction design of fairing shapes.

[0005] In engineering applications, common shape optimization techniques include particle swarm optimization (PSO), ant colony algorithm and genetic algorithm (GA). Among them, the GA method can quickly find the best in a complex design space and is suitable for dealing with optimization problems with multiple peaks and complex constraints. In the optimization of the fairing shape, the relationship between the fairing shape parameters and the drag coefficient is often established by numerical analysis methods. Commonly used numerical analysis methods include multivariate linear fitting, B-spline interpolation and RBF interpolation. RBF interpolation provides high-precision global interpolation and is suitable for various complex optimization problems. Therefore, the present invention will construct a GA ship drag reduction optimization design method based on RBF interpolation.

[0006] In summary, the present invention proposes a ship drag reduction optimization design method based on CFD-GA for ship drag reduction optimization design, which can efficiently carry out the optimization design of the ship fairing shape, effectively reduce wind resistance, reduce ship energy consumption, shorten the optimization design cycle, and meet the needs of energy conservation and emission reduction in ship transportation. Summary of the invention

[0007] Aiming at the problems of large computational resource requirements and long design cycle in the existing ship wind resistance optimization design, the present invention proposes an efficient ship drag reduction optimization design method and system based on CFD-GA.

[0008] The technical solution adopted by the present invention to solve the technical problem is:

[0009] The present invention provides a ship drag reduction optimization design method based on CFD-GA, the method comprising the following steps:

[0010] Step 1: Perform CFD numerical simulation on a ship without a fairing to calculate the ship's drag coefficient; determine the initial design parameters of the fairing, perform parametric modeling of the fairing, and generate a fairing model;

[0011] Step 2, perform CFD numerical simulation on multiple groups of ships with fairings to calculate the drag coefficient and drag reduction rate;

[0012] Step 3: Combining RBF interpolation and GA method, constructing RBF-GA ship drag reduction optimization model, inputting the initial design parameters of fairing and CFD calculated drag coefficient into RBF-GA ship drag reduction optimization model, and obtaining the optimized fairing design parameters;

[0013] Step 4: Generate a new fairing model based on the optimized fairing design parameters, and perform CFD numerical simulation to calculate the ship resistance coefficient;

[0014] Step 5: Determine whether the optimized ship resistance coefficient meets the target drag reduction requirement; if not, repeat steps 1 to 4 until the target drag reduction requirement is met, output the fairing design parameters, drag coefficient and drag reduction rate, and obtain the optimized design of the ship fairing shape.

[0015] Furthermore, the method for performing parametric modeling of the fairing in step 1 of the present invention includes:

[0016] The fairing is set on the bow bulwark of the ship, and the front end vertex of the bulwark is used as the front end vertex of the fairing. The fairing includes a curved surface composed of the following lines: a bulwark top curve, a front end curve, two symmetrical terminal curves, and an endpoint connection spline generated by the front end curve and the vertices of the two terminal curves; the fairing is adjusted by the following two variable parameters: the horizontal distance between the start and end points of the front end curve of the fairing and the angle between the terminal straight line and the horizontal line.

[0017] Furthermore, in step 1 of the present invention, the front end curve is set as a cubic curve, the front end vertex of the front end curve is fixed as the starting endpoint, a vertical line is drawn through the starting endpoint and is tangent to the front end curve, and the tangent point is the starting endpoint; the height of the end point of the front end curve is fixed and can move horizontally to control the horizontal distance from the starting endpoint; at the same time, the tangent line extending backward through the end point of the front end curve is used as the end straight line, and the angle between the end straight line and the horizontal line can be adjusted.

[0018] Furthermore, the method for performing CFD numerical simulation in step 2 of the present invention includes:

[0019] Import the fairing parameterized model of the ship established in step 1, and establish a calculation domain with a length, width, and height of 7L, 1.5L, and 1.5L, respectively, where L is the length of the ship, the velocity inlet is located at 1.5L from the bow, and the pressure outlet is 4.5L from the stern; considering the symmetry of the ship used in the calculation, the half-ship flow field is adopted, the middle section and the width direction boundary 1.5L away from the middle section are set as symmetric boundary conditions, the bottom and the top 1.5L away from the waterline of the ship are set as sliding walls, and the hull part is set as a non-slip wall;

[0020] The wind resistance problem of container ships involves the turbulent flow of viscous incompressible fluids. The k-ε turbulence model is used to simulate the problem of wind field around ships, calculate the resistance coefficient of container ships, and compare and analyze the flow field changes and drag reduction rate of fairing container ships; the drag coefficient and drag reduction rate are as follows:

[0021]

[0022] Fairing drag reduction rate:

[0023]

[0024] Among them, C d0 represents the drag coefficient of the ship without fairing, C di represents the drag coefficient of the i-th fairing.

[0025] Furthermore, the RBF interpolation method in step 3 of the present invention for constructing the RBF-GA ship drag reduction optimization model includes:

[0026] The functional relationship between the fairing design parameters and the drag coefficient is established through RBF interpolation. The RBF calculation formula is as follows:

[0027]

[0028] Among them, x i is the fairing design parameter, y i Output drag coefficient for CFD calculation, λ i is the weight coefficient, and φ(r) is the basis function. After obtaining the functional relationship between the fairing design parameters and the drag coefficient through RBF interpolation, any number of fairing design parameter-drag coefficient data pairs can be obtained as the initial data for subsequent GA optimization.

[0029] Furthermore, the GA method in step 3 of the present invention for constructing the RBF-GA ship drag reduction optimization model includes:

[0030] When using the GA method for optimization, n individuals are randomly selected, such as n = 200, and one individual is a set of fairing design parameters. Each sample is represented by a binary array (composed of multiple columns, each column value is 0 or 1). The binary array is called the gene of the individual. The gene can be converted into the fairing design parameter x through the following formula. This process is called decoding. Assume that the gene of an individual in the sample is b = [b0, b1, ..., b n-1 ],but:

[0031]

[0032] Where x is the fairing design parameter, x max ,x min are the maximum and minimum values ​​of the fairing design parameters;

[0033] Based on RBF interpolation, the functional relationship between x (fairing design parameters) and y (drag coefficient) is obtained, and n pairs of data points (x i ,y i ), x i is the design parameter of the ith fairing, y i To correspond to the drag coefficient and to carry out the optimization design, firstly, n pairs of data points are screened by individual retention probability P:

[0034]

[0035] Among them, pop i =y max -y i is the fitness, y max is the maximum value of the resistance coefficient among n pairs of data points. max is the maximum fitness, which is the difference between the calculated maximum drag coefficient and the minimum drag coefficient (pop max =y max -y min ). i The smaller the fitness, the pop i The larger the retention probability p i The larger the value, the easier it is to retain the data.

[0036] The screening result is called the parent generation (i.e. the screened fairing parameter-drag coefficient data pair, the binary code corresponding to the fairing parameter is the parent gene), and a child gene is generated by combining two parent genes. This process is called gene inheritance. During the gene inheritance process, part of the binary code will mutate randomly (also called gene mutation), that is, the binary code changes from 0 to 1 or from 1 to 0. A new generation of genes (i.e. the binary code corresponding to the fairing design parameters) is generated by combining gene inheritance and gene mutation. The drag coefficient corresponding to the new generation of genes is smaller, which is an iteration. After multiple iterations, the fairing design parameters corresponding to the minimum drag coefficient corresponding to this optimization are found.

[0037] The present invention provides a ship drag reduction optimization design system based on CFD-GA, comprising:

[0038] a memory for storing executable computer programs;

[0039] The processor is used to implement a ship drag reduction optimization design method based on CFD-GA when executing an executable computer program stored in the memory.

[0040] The present invention provides a computer-readable storage medium storing a computer program for implementing the above-mentioned ship drag reduction optimization design method based on CFD-GA when executed by a processor.

[0041] The beneficial effects produced by the present invention are:

[0042] The present invention proposes a ship drag reduction optimization design method combining genetic algorithm (GA) and CFD, which can efficiently carry out the optimization design of ship fairing shape, effectively reduce wind resistance, reduce energy consumption of container ships, improve navigation efficiency, and meet the new green shipping regulations. The present invention will effectively serve the energy conservation and emission reduction needs of ship transportation and has broad market prospects. The method can efficiently obtain excellent drag reduction effects, and the results have been verified to be accurate and effective.

[0043] The present invention innovatively proposes a fairing optimization design method, which can effectively meet the fairing modeling requirements and facilitate the fairing shape adjustment and visualization. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0045] Figure 1 is a method flow chart of an embodiment of the present invention;

[0046] Figure 2a 1 is a structural diagram of a fairing according to an embodiment of the present invention;

[0047] Figure 2bis a structural diagram b of a fairing according to an embodiment of the present invention;

[0048] Figure 3 is a cross-sectional view of a fairing according to an embodiment of the present invention;

[0049] Figure 4a Figure a is a flow field simulation diagram of an embodiment of the present invention without a fairing

[0050] Figure 4b FIG. b is a flow field simulation diagram of an embodiment of the present invention without a fairing;

[0051] Figure 5a FIG. 1 is a flow field simulation diagram of an embodiment of the present invention with a fairing

[0052] Figure 5b FIG. b is a flow field simulation diagram of an embodiment of the present invention with a fairing;

[0053] Figure 6 is an optimized surface according to an embodiment of the present invention;

[0054] Figure 7a FIG. a is a flow field simulation diagram of an optimal fairing optimized by an embodiment of the present invention;

[0055] Figure 7b FIG. b is a flow field simulation diagram of the optimal fairing optimized according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 used to limit the present invention.

[0057] like Figure 1 As shown, the present invention provides a ship drag reduction optimization design method based on CFD-GA, the method comprising the following steps:

[0058] Step 1: Perform CFD numerical simulation on a ship without a fairing to calculate the ship's drag coefficient; determine the initial design parameters of the fairing, perform parametric modeling of the fairing, and generate a fairing model;

[0059] Step 2, perform CFD numerical simulation on multiple groups of ships with fairings to calculate the drag coefficient and drag reduction rate;

[0060] Step 3: Combining RBF interpolation and GA method, constructing RBF-GA ship drag reduction optimization model, inputting the initial design parameters of fairing and CFD calculated drag coefficient into RBF-GA ship drag reduction optimization model, and obtaining the optimized fairing design parameters;

[0061] Step 4: Generate a new fairing model based on the optimized fairing design parameters, and perform CFD numerical simulation to calculate the ship resistance coefficient;

[0062] Step 5: Determine whether the optimized ship resistance coefficient meets the target drag reduction requirement; if not, repeat steps 1 to 4 until the target drag reduction requirement is met, output the fairing design parameters, drag coefficient and drag reduction rate, and obtain the optimized design of the ship fairing shape.

[0063] The present invention establishes a functional relationship between the drag coefficient and the fairing shape parameters through RBF interpolation, and uses a GA algorithm to search the function space to find the fairing shape parameters corresponding to the optimal ship drag reduction.

[0064] In another preferred embodiment of the present invention, Figure 1 As shown, the present invention discloses a ship drag reduction optimization design method based on CFD-GA, including ship fairing parametric modeling, CFD-based ship wind resistance numerical analysis and RBF-GA optimization design. During operation, the result after each optimization can also be added to the optimization system, and iterated until a fairing design that meets the expected drag reduction effect is obtained.

[0065] The first part introduces the parametric modeling of the fairing, the second part introduces the ship windage analysis based on CFD, the third part introduces the RBF-GA optimization method, and the fourth part analyzes the drag reduction effect obtained by this method.

[0066] 1. Parametric modeling of fairing

[0067] like Figure 2a and Figure 2b As shown in the figure, the fairing is located on the bow bulwark of the ship, and the front vertex of the bulwark is also the front vertex of the fairing. The fairing is composed of the bulwark top line, the front curve, two symmetrical terminal curves, and the spline generated by the vertices of the front curve and the two terminal curves using multiple sections. Figure 3As shown, in the middle section of the fairing, the front curve is set as a cubic curve, the front vertex of the curve is fixed as the starting endpoint, and a vertical line is drawn through the starting endpoint to be tangent to the curve, and the tangent point is the starting endpoint. The height of the end point of the curve is fixed, but it can be moved horizontally to control the horizontal distance from the starting endpoint. At the same time, the straight line extending backward from the end point of the curve is used as the tangent line of the end of the curve. The angle between the end straight line and the horizontal line can be adjusted. The curvature of the curve is designed to be 0.5 to reduce the variables during the optimization of the fairing. In this way, the fairing has two variable parameters, namely the horizontal distance between the starting and end points of the front curve of the fairing and the angle between the end straight line and the horizontal line. The shape of the fairing can be quickly changed by changing the parameters. In this example, the point with a horizontal distance x from the starting endpoint and a longitudinal distance of 9.14m is taken as the end of the front curve of the bulwark. The vertical line through the starting endpoint and the straight line with an angle of θ between the end point and the horizontal line are tangent to the front curve of the fairing. The tangent points are the starting endpoint and the end point respectively. The front curve of the fairing is generated by two tangents, and the curvature is set to 0.5. The fairing is controlled by the horizontal distance x between the start and end points and the angle θ between the end straight line and the horizontal line.

[0068] 2. Ship wind resistance analysis based on CFD

[0069] This example aims at the optimization of wind resistance of container ships. First, the ship model is imported, and the length, width and height of the calculation domain are established, which are 7L, 1.5L and 1.5L respectively, where L is the length of the ship, the velocity inlet is located at 1.5L of the bow, and the pressure outlet is 4.5L from the stern. Considering the symmetry of the ship used in the calculation, the half-ship flow field is adopted, the mid-section and the width direction boundary 1.5L from the mid-section are set as symmetric boundary conditions, the bottom and the top 1.5L from the waterline of the ship are set as sliding walls, and the hull part is set as non-slip walls.

[0070] The present invention relates to the turbulent flow of viscous incompressible fluid. The k-ε turbulence model is used to simulate the problem of wind field around a ship, the drag coefficient of a container ship is calculated, and the flow field change and drag reduction rate of a fairing container ship are compared and analyzed. The calculation formulas of the drag coefficient and the drag reduction rate are as follows:

[0071]

[0072] Among them C d0 represents the drag coefficient of the ship without fairing, C di represents the drag coefficient of the i-th fairing.

[0073] 3. RBF-GA Method

[0074] The present invention relates to RBF and GA methods, and establishes a functional relationship between fairing design parameters and drag coefficient through RBF interpolation. The RBF calculation formula is as follows:

[0075]

[0076] Among them, xi is the fairing design parameter, y i Output drag coefficient for CFD calculation, λ i is the weight coefficient, and φ(r) is the basis function. After obtaining the functional relationship between the fairing design parameters and the drag coefficient through RBF interpolation, any number of fairing design parameter-drag coefficient data pairs can be obtained as the initial data for subsequent GA optimization.

[0077] When using the GA method for optimization, n individuals are randomly selected, such as n = 200, and one individual is a set of fairing design parameters. Each sample is represented by a binary array (composed of multiple columns, each column value is 0 or 1). The binary array is called the gene of the individual. The gene can be converted into the fairing design parameter x through the following formula. This process is called decoding. Assume that the gene of an individual in the sample is b = [b0, b1, ..., b n-1 ],but:

[0078]

[0079] Among them, x is the fairing design parameter, x max ,x min are the maximum and minimum values ​​of the fairing design parameters;

[0080] Based on RBF interpolation, the functional relationship between x (fairing design parameters) and y (drag coefficient) is obtained, and n pairs of data points (x i ,y i ), x i is the design parameter of the ith fairing, y i To correspond to the drag coefficient and to carry out the optimization design, firstly, n pairs of data points are screened by individual retention probability P:

[0081]

[0082] Among them, pop i =y max -y i is the fitness, y max is the maximum value of the resistance coefficient among n pairs of data points. max is the maximum fitness, which is the difference between the calculated maximum drag coefficient and the minimum drag coefficient (pop max =y max -y min ). i The smaller the fitness, the pop i The larger the retention probability p i The larger the value, the easier it is to retain the data.

[0083] The screening result is called the parent generation (i.e. the screened fairing parameter-drag coefficient data pair, the binary code corresponding to the fairing parameter is the parent gene), and a child gene is generated by combining two parent genes. This process is called gene inheritance. During the gene inheritance process, part of the binary code will mutate randomly (also called gene mutation), that is, the binary code changes from 0 to 1 or from 1 to 0. A new generation of genes (i.e. the binary code corresponding to the fairing design parameters) is generated by combining gene inheritance and gene mutation. The drag coefficient corresponding to the new generation of genes is smaller, which is an iteration. After multiple iterations, the fairing design parameters corresponding to the minimum drag coefficient corresponding to this optimization are found.

[0084] 4. Analysis of the drag reduction effect of container ship fairing

[0085] This example takes the KCS model as an example, and the specific parameters are shown in the following table.

[0086]

[0087]

[0088] Table 1: Main parameters of container ship numerical simulation

[0089] The fairing has a significant effect on reducing the drag of ships. Figure 4a The pressure distribution shows that when a container ship without a fairing is subjected to positive wind, a pressure concentration area is formed on the surface of the front container. Figure 4a Streamline and Figure 4b The flow field diagram shows that when facing the wind, the fluid hits the first container of the container ship and either rises or falls at the bow to form a vortex, and there is obvious flow separation above the container. Figure 5a and Figure 5b It can be seen that after the fairing is installed, the pressure concentration area appears at the front end of the fairing. The airflow passes through the front container after being rectified by the fairing, and the rear airflow also rises and passes over the top of the container, eliminating the vortex in the middle of the container. At the same time, the installation of the fairing avoids the occurrence of vortices on the bow deck of the ship, the formation of local vortices above the front container, and the occurrence of breakage. Through calculation, in the initial fairing design, the horizontal distance between the beginning and end of the front curve of the optimal fairing is 10.000m, the angle between the end straight line and the horizontal line is 30.000°, and the drag reduction rate is 15.97%. Based on the preliminary design parameters and results, after 5 optimizations, the optimal container ship fairing design was selected: the horizontal distance between the beginning and end of the front curve of the fairing is 9.785m, the angle between the end straight line and the horizontal line is 20.615°, and the drag reduction rate is 17.02%. The optimized surface obtained based on RBF-GA is as follows Figure 6 As shown in the figure, the optimal fairing is as follows Figure 7aAs shown, the overall shape is streamlined, and the fairing allows the incoming air to bypass the front container, effectively reducing wind resistance.

[0090] Optimization comparison result analysis: comparison Figure 7b and Figure 5b It can be seen from the flow field in the figure that after optimization, the height of the flow field around the first container is lower, and the fluid can flow more smoothly along the surface of the container, reducing the resistance caused by separation. The optimized fairing controls the flow field separation more accurately, which significantly reduces the drag coefficient.

[0091] In this example, the ship drag reduction optimization design method based on CFD-GA achieves a drag reduction rate of 17.02% for container ships, and verifies the drag reduction optimization effect through numerical simulation. The ship drag reduction optimization design method based on CFD-GA proposed in the present invention can efficiently design ship drag reduction schemes and reduce navigation fuel consumption and greenhouse gas emissions.

[0092] The above shows and describes the basic principles, research methods and drag reduction optimization design effects of the present invention. Those skilled in the art should understand that the present invention is not limited by the above examples. The above examples and descriptions are only for explaining the principles of the present invention. Without departing from the essential content of the present invention, the present invention will have various optimizations and improvements for specific engineering application scenarios. These optimizations and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

[0093] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0094] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A ship drag reduction optimization design method based on CFD-GA, characterized in that: The method comprises the following steps: Step 1, determine the initial design parameters of the fairing, perform parametric modeling of the fairing, and generate a parametric model of the fairing; Step 2, CFD numerical simulation is performed on the ship without fairing and multiple groups of ships with fairing (the fairing model is established based on the initial design parameters), the drag coefficient of the ship without fairing and the ship with fairing is calculated, and the drag reduction rate of the ship with fairing is calculated; Step 3: Construct a GA ship drag reduction optimization model based on RBF interpolation, input the initial design parameters of the fairing and the CFD calculated drag coefficient into the RBF-GA ship drag reduction optimization model, and obtain the optimized fairing design parameters; Step 4: Generate a new fairing model according to the fairing design parameters optimized in step 3, and perform CFD numerical simulation to calculate the ship resistance coefficient; Step 5: Determine whether the optimized ship resistance coefficient meets the target drag reduction requirement; if not, repeat steps 1 to 4 until the target drag reduction requirement is met, output the fairing design parameters, drag coefficient and drag reduction rate, and obtain the optimized design of the ship fairing shape.

2. The ship drag reduction optimization design method based on CFD-GA according to claim 1 is characterized in that: The method for performing parametric modeling of the fairing in step 1 includes: The fairing is set on the bow bulwark of the ship, and the front end vertex of the bulwark is used as the front end vertex of the fairing. The fairing includes a curved surface composed of the following lines: a bulwark top curve, a front end curve, two symmetrical terminal curves, and an endpoint connection spline generated by the front end curve and the vertices of the two terminal curves; the fairing is adjusted by the following two variable parameters: the horizontal distance between the start and end points of the front end curve of the fairing and the angle between the terminal straight line and the horizontal line.

3. The ship drag reduction optimization design method based on CFD-GA according to claim 2 is characterized in that: In step 1, the front end curve is set as a cubic curve, the front end vertex of the front end curve is fixed as the starting endpoint, a vertical line is drawn through the starting endpoint to be tangent to the front end curve, and the tangent point is the starting endpoint; the height of the end point of the front end curve is fixed and can be moved horizontally to control the horizontal distance from the starting endpoint; at the same time, the tangent line extending backward through the end point of the front end curve is used as the end straight line, and the angle between the end straight line and the horizontal line can be adjusted.

4. The ship drag reduction optimization design method based on CFD-GA according to claim 1 is characterized in that: The method for performing CFD numerical simulation in step 2 includes: Import the fairing parameterized model of the ship established in step 1, and establish a calculation domain with a length, width, and height of 7L, 1.5L, and 1.5L, respectively, where L is the length of the ship, the velocity inlet is located at 1.5L from the bow, and the pressure outlet is 4.5L from the stern; considering the symmetry of the ship used in the calculation, the half-ship flow field is adopted, the middle section and the width direction boundary 1.5L away from the middle section are set as symmetric boundary conditions, the bottom and the top 1.5L away from the waterline of the ship are set as sliding walls, and the hull part is set as a non-slip wall; The wind resistance problem of container ships involves the turbulent flow of viscous incompressible fluids. The k-ε turbulence model is used to simulate the flow field, calculate the drag coefficient of the container ship superstructure, and compare and analyze the flow field changes and drag reduction rate of the fairing container ship. The drag coefficient and drag reduction rate are defined as follows: Fairing drag reduction rate: Among them, C d0 represents the drag coefficient of the ship without fairing, C di represents the drag coefficient of the ith fairing.

5. The ship drag reduction optimization design method based on CFD-GA according to claim 1 is characterized in that: The RBF interpolation method in step 3 for constructing the RBF-GA ship drag reduction optimization model includes: The functional relationship between the fairing design parameters and the drag coefficient is established through RBF interpolation. The RBF calculation formula is as follows: Among them, x i is the fairing design parameter, y i Output drag coefficient for CFD calculation, λ i is the weight coefficient, φ(r) is the basis function; after obtaining the functional relationship between the fairing design parameters and the drag coefficient through RBF interpolation, any multiple sets of fairing design parameter-drag coefficient data pairs can be obtained as the initial data for subsequent GA optimization.

6. The ship drag reduction optimization design method based on CFD-GA according to claim 5 is characterized in that: The GA method in step 3 of constructing the RBF-GA ship drag reduction optimization model includes: When using the GA method for optimization, n individuals are randomly selected, and one individual is a set of fairing design parameters; each sample is represented by a binary array, which consists of multiple columns, and each column value is 0 or 1. The binary array is called the gene of the individual, and the gene is converted into the fairing design parameter x through the following formula. This process is called decoding; assuming that the gene of an individual is b = [b0, b1, ..., b n-1 ],but: Among them, x is the fairing design parameter, x max ,x min are the maximum and minimum values ​​of the fairing design parameters; Based on RBF interpolation, the functional relationship between the x fairing design parameters and the y drag coefficient is obtained, and n pairs of data points (x i ,y i ), x i is the design parameter of the ith fairing, y i To correspond to the drag coefficient and to carry out the optimization design, firstly, n pairs of data points are screened by individual retention probability P: Among them, pop i =y max -y i is the fitness, y max is the maximum value of the resistance coefficient among n pairs of data points; pop max is the maximum fitness, which is the difference between the calculated maximum drag coefficient and the minimum drag coefficient (pop max =y max -y min );y i The smaller the fitness, the pop i The larger the retention probability p i The larger the value is, the easier it is to retain the data. The screening result is called the parent generation, that is, the screened fairing parameter-drag coefficient data pair, the binary code corresponding to the fairing parameter is the parent gene, and a child gene is generated by combining two parent genes. This process is called gene inheritance; during the gene inheritance process, part of the binary code will mutate randomly, that is, the binary code changes from 0 to 1 or from 1 to 0; a new generation of genes is generated by combining gene inheritance and gene mutation, that is, the binary code corresponding to the fairing design parameters, and the drag coefficient corresponding to the new generation of genes is smaller, which is an iteration; after multiple iterations, the fairing design parameters corresponding to the minimum drag coefficient corresponding to this optimization are found.

7. The ship drag reduction optimization design method based on CFD-GA according to claim 6 is characterized in that: Step 3 obtains the optimized fairing design parameters including: The GA algorithm randomly samples n pairs of fairing design parameter-drag coefficient data from the functional relationship established by RBF, retains the design parameter genes that are closer to the optimization target after screening, iterates the genetic and mutation operations on the genes to generate new design parameters, which is one iteration. After multiple iterations, the best design parameters and the minimum drag coefficient for this optimization are found and output.

8. The ship drag reduction optimization design method based on CFD-GA according to claim 1, characterized in that: The method for determining whether the optimized ship resistance coefficient meets the target drag reduction requirement in step 5 includes: The new fairing was established using the optimized design parameters and CFD calculations were performed. The relative error between the calculated drag coefficient and the optimized predicted drag coefficient result was within 3%; the calculated fairing drag reduction rate reached the expected result.

9. A ship drag reduction optimization design system based on CFD-GA, characterized in that: include: a memory for storing executable computer programs; A processor, used to implement the ship drag reduction optimization design method based on CFD-GA as described in any one of claims 1 to 8 when executing an executable computer program stored in a memory.

10. A computer-readable storage medium, characterized in that: A computer program is stored, which is used to implement the ship drag reduction optimization design method based on CFD-GA as described in any one of claims 1 to 8 when executed by a processor.

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