Shark scale groove resistance reduction method and system based on genetic algorithm

Abstract

The invention discloses a shark scale groove resistance reduction method and system based on a genetic algorithm, and belongs to the field of shark scale groove resistance reduction methods.The method comprises the steps that resistance reduction optimization variable parameters of a shark scale groove are determined; determining an objective function and a constraint condition which need to be optimized for the shark scale groove; encoding the resistance reduction optimization variable parameter oriented to a genetic algorithm; firstly, the fitness of the genetic algorithm is determined according to a target function, related operation parameters of the genetic algorithm are determined, then selection, crossover and variation are repeatedly and iteratively executed through the genetic algorithm, the fitness of the genetic algorithm is improved, individuals gradually approach an optimal solution, and finally a global optimal solution is output when an iteration exit condition is met. By means of the method, the scale resistance obtained through design can be small, a larger design space is provided for optimization of the shape of the drag reduction groove, and a new design method is provided for optimization design of the shape of the drag reduction groove.

Description

Technical Field

[0001] The present invention relates to the technical field of shark scale groove drag reduction design, and in particular to a shark scale groove drag reduction design method and system based on genetic algorithm. Background Art

[0002] As the global energy crisis intensifies, reducing energy consumption has always been a hot topic. In international trade, transportation is the most important link. Most of the fuel consumption of transportation is mainly used to overcome the friction resistance of the surface. Therefore, various methods of reducing resistance are needed. With the continuous development of bionics, bionic drag reduction surfaces inspired by biology have become a development trend.

[0003] In order to solve the problem of large amounts of fuel consumption to overcome surface friction resistance, the current approach is mainly active drag reduction methods such as flexible wall drag reduction, bubble drag reduction, and polymer drag reduction. These drag reduction technologies have good effects to a certain extent, but they are based on the idea of ​​active drag reduction, that is, giving certain substances or energy to the surface of the vehicle and the boundary layer, and there are problems such as high energy consumption, low efficiency, and environmental pollution. Summary of the invention

[0004] The technical problem to be solved by the present invention is: In view of the above technical problems existing in the prior art, the present invention provides a shark scale groove drag reduction design method and system based on genetic algorithm, and proposes a lateral drag reduction groove shape optimization method based on genetic algorithm. The method is used to parameterize and optimize the drag reduction of the triangular groove shape.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A shark scale groove drag reduction method based on genetic algorithm, comprising: 1) Determine the drag reduction optimization variable parameters of shark scale grooves; 2) Determine the objective function and constraints that need to be optimized for shark scale grooves; 3) Encoding the drag reduction optimization variable parameters for genetic algorithm; 4) First, determine the fitness of the genetic algorithm according to the objective function, determine the relevant operating parameters of the genetic algorithm, and then iterate the selection, crossover, and mutation through the genetic algorithm to improve the fitness of the genetic algorithm, and the individual gradually approaches the optimal solution. Finally, the global optimal solution is output when the iterative exit condition is met; 5) Decode the global optimal solution to obtain the optimal drag reduction optimization variable parameters.

[0006] The shape of the shark scale groove used is determined to be an inverted triangle geometric structure, and the determined shark scale groove drag reduction optimization variable parameters include the width L and depth H of the inverted triangle.

[0007] Before the operation, the following steps are also included to determine the variable parameters of the drag reduction optimization of the scale grooves: 1) Build a model of shark scale grooves; A( ), B( ), C( ) In the above formulas, A, B, and C are the three vertices of the inverted triangle of the scale groove, and t is a parameter. By using different t values, the coordinates of different points on the curve can be obtained. P(t) is the B-spline function; 2) The force analysis of the groove wall is performed to obtain the drag reduction function expression of the groove unit as shown in the following formula: In the above formula, R is the drag reduction rate of the groove unit, is the flow resistance of the smooth wall, is the flow resistance of the groove unit, and: In the above formula, x is the unit vector in the same direction as the flow direction, p is the pressure perpendicular to the micro-element segment, and f is the viscous shear stress tangent to the micro-element segment. is the pressure difference resistance of the groove unit, is the viscous resistance of the groove unit.

[0008] 3) According to the function expression of drag reduction rate, the factors affecting the drag of the scale groove are determined as: groove width L and depth H. Since the width and depth of the groove are controlled by the coordinate points of the inverted triangle, the coordinate points A, B, and C are used as the drag reduction optimization variable parameters of the shark scale groove; According to the shark scale groove drag reduction design method based on genetic algorithm according to claim 1, the function expression for determining the constraint conditions that need to be optimized for the scale groove is: In the above formula, L is the groove width, is the maximum groove width, H is the groove depth, is the maximum groove depth.

[0009] According to the shark scale groove drag reduction design method based on genetic algorithm as required, binary coding is used to represent the coordinates x and y of the inverted triangle coordinate points of the groove, and the lower vertex of the inverted triangle is moved as the control point to achieve the deformation of the groove shape. Then the global coordinates of the groove after deformation by the B-spline function method are calculated, and the flow field grid is reconstructed by batch processing. Finally, the flow field simulation is performed to calculate the wall resistance of the groove unit, and drag reduction optimization is performed with the minimum resistance as the optimization goal. Among the optimization variables, the upper limit of x is the initial width of the groove 20 μm, the lower limit is 0, the upper limit of y is -0.5 times the groove depth, -5 μm, and the lower limit is -5 times the groove depth, -50 μm. The genetic algorithm population size is 50, the maximum evolutionary generation is 50, and the optimization end condition is to reach the maximum evolutionary generation. .

[0010] According to the shark scale groove drag reduction design method based on genetic algorithm as required, the global optimal solution is decoded to obtain the optimal drag reduction optimization variable parameters.

[0011] According to the requirements, the function expression for determining the fitness of the genetic algorithm according to the objective function in the shark scale groove drag reduction design method based on the genetic algorithm is as follows: In the above formula, F is the fitness of the genetic algorithm, is the flow resistance.

[0012] In addition, the present invention also provides a shark scale groove drag reduction design system based on a genetic algorithm, using a computer and its related processor, which is configured to execute the steps of any one of the shark scale groove drag reduction design methods based on a genetic algorithm, or a computer program that executes any one of the shark scale groove drag reduction design methods based on a genetic algorithm.

[0013] Compared with the prior art, the present invention has the following advantages: the present invention includes determining the drag reduction optimization variable parameters of the shark scale groove; determining the objective function and constraint conditions that need to be optimized for the scale groove; encoding the drag reduction optimization variable parameters for the genetic algorithm; firstly determining the fitness of the genetic algorithm according to the objective function, determining the relevant operating parameters of the genetic algorithm, and then repeatedly iterating the selection, crossover, and mutation through the genetic algorithm to improve the fitness of the genetic algorithm, and the individual gradually approaches the optimal solution, and finally outputs the global optimal solution when the iterative exit condition is met; decoding the global optimal solution to obtain the optimal drag reduction optimization variable parameters. Through the above method, the present invention can simply and quickly complete the drag reduction design of the scale groove, so that the designed shark groove is subjected to small resistance and its drag reduction rate is greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a basic flow chart of the present invention.

[0015] Figure 2 It is a schematic diagram of the shark scale groove structure of the present invention.

[0016] Figure 3 It is a schematic diagram of the three-dimensional structure of the shark scale groove of the present invention.

[0017] Figure 4 It is a schematic diagram of the force on the wall surface of the shark scale groove of the present invention.

[0018] Figure 5 Schematic diagram comparing the initial shark scale groove and the optimized groove of the present invention.

[0019] Figure 6 This is a graph showing the change of the wall pressure with the number of iterations according to the present invention. DETAILED DESCRIPTION

[0020] like Figure 1 As shown, the shark scale groove drag reduction design method based on genetic algorithm in this embodiment includes: 1) Determine the drag reduction optimization variable parameters of shark scale grooves; 2) Determine the objective function and constraints that need to be optimized for shark scale grooves; 3) Encoding the drag reduction optimization variable parameters for genetic algorithm; 4) First, determine the fitness of the genetic algorithm according to the objective function, determine the relevant operating parameters of the genetic algorithm, and then iterate the selection, crossover, and mutation through the genetic algorithm to improve the fitness of the genetic algorithm, and the individual gradually approaches the optimal solution. Finally, the global optimal solution is output when the iterative exit condition is met; 5) Decode the global optimal solution to obtain the optimal drag reduction optimization variable parameters.

[0021] It should be noted that the shark scale groove drag reduction design method based on genetic algorithm of the present invention is not limited to a specific scale geometry, because the difference in groove geometry only leads to the difference in drag reduction optimization variable parameters, and it can also be applied to other types of scale groove drag reduction design methods based on genetic algorithms.

[0022] The following will be Figure 2 Taking the groove structure schematic diagram shown as an example, the shark scale groove drag reduction design method based on genetic algorithm of the present invention is further described in detail.

[0023] like Figure 2 The shark scale groove is an inverted triangle structure, and the determined drag reduction optimization variable parameters of the shark scale groove include the groove width L and the groove depth H.

[0024] The shark scale groove drag reduction design method based on genetic algorithm is used to determine the groove drag reduction optimization variable parameters: 1) Build a model of shark scale grooves; A( ), B( ), C( ) In the above formulas, A, B, and C are the three vertices of the inverted triangle of the scale groove, and t is a parameter. By using different t values, the coordinates of different points on the curve can be obtained. P(t) is the B-spline function; 2) The force analysis of the groove wall is performed to obtain the drag reduction function expression of the groove unit as shown in the following formula: In the above formula, R is the drag reduction rate of the groove unit, is the flow resistance of the smooth wall, is the flow resistance of the groove unit, and: In the above formula, x is the unit vector in the same direction as the flow direction, p is the pressure perpendicular to the micro-element segment, and f is the viscous shear stress tangent to the micro-element segment. is the pressure difference resistance of the groove unit, is the viscous resistance of the groove unit. 3) According to the function expression of the drag reduction rate, the factors affecting the drag of the scale groove are determined as follows: groove width L and depth H. Since the width and depth of the groove are controlled by the coordinate points of the inverted triangle, the coordinate points of A, B, and C are used as the drag reduction optimization variable parameters of the shark scale groove; The shark scale groove drag reduction design method based on genetic algorithm determines the function expression of the constraint conditions that need to be optimized for the scale groove.

[0025] In the above formula, L is the groove width, is the maximum groove width, H is the groove depth, is the maximum groove depth.

[0026] The shark scale groove drag reduction design method based on genetic algorithm adopts binary coding to represent the coordinates x and y of the inverted triangle coordinate points of the groove, and moves the lower vertex of the inverted triangle as the control point to realize the deformation of the groove shape. Then the global coordinates of the groove after deformation by the B-spline function method are calculated, and the flow field grid is reconstructed by batch processing. Finally, the flow field simulation is performed to calculate the wall resistance of the groove unit, and the drag reduction optimization is performed with the minimization of resistance as the optimization goal. Among the optimization variables, the upper limit of x is the initial width of the groove 20 μm, the lower limit is 0, the upper limit of y is -0.5 times the groove depth, -5 μm, and the lower limit is -5 times the groove depth, -50 μm. The genetic algorithm population size is 50, the maximum evolutionary generation is 50, and the optimization end condition is to reach the maximum evolutionary generation. .

[0027] The shark scale groove drag reduction design method based on genetic algorithm decodes the global optimal solution to obtain the optimal drag reduction optimization variable parameters.

[0028] The functional expression for determining the fitness of the genetic algorithm according to the objective function in the shark scale groove drag reduction design method based on the genetic algorithm is shown as follows: In the above formula, F is the fitness of the genetic algorithm, is the flow resistance.

[0029] In addition, the present invention also provides a shark scale groove drag reduction system based on a genetic algorithm, using a computer and its related processor, which is configured to execute the steps of any one of the shark scale groove drag reduction design methods based on a genetic algorithm, or execute a computer program of any one of the shark scale groove drag reduction design methods based on a genetic algorithm.

[0030] Compared with the prior art, the present invention has the following advantages: the present invention includes determining the drag reduction optimization variable parameters of the shark scale groove; determining the objective function and constraint conditions that need to be optimized for the scale groove; encoding the drag reduction optimization variable parameters for the genetic algorithm; firstly determining the fitness of the genetic algorithm according to the objective function, determining the relevant operating parameters of the genetic algorithm, and then repeatedly iterating the selection, crossover, and mutation through the genetic algorithm to improve the fitness of the genetic algorithm, and the individuals gradually approach the optimal solution, and finally outputting the global optimal solution when the iterative exit condition is met; decoding the global optimal solution to obtain the optimal drag reduction optimization variable parameters. Through the above method, the present invention can simply and quickly complete the drag reduction design of the scale groove, so that the designed groove is subjected to small resistance and its drag reduction rate is greatly improved.

[0031] The above is only a preferred embodiment of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A shark scale groove drag reduction method based on genetic algorithm, characterized in that: include: 1) Determine the drag reduction optimization variable parameters of shark scale grooves; 2) Determine the objective function and constraints that need to be optimized for shark scale grooves; 3) Encoding the drag reduction optimization variable parameters for genetic algorithm; 4) Determine the fitness of the genetic algorithm according to the objective function, determine the relevant operating parameters of the genetic algorithm, and then repeatedly perform selection, crossover, and mutation through the genetic algorithm to improve the fitness of the genetic algorithm, and the individual gradually approaches the optimal solution, and finally output the global optimal solution when the iterative exit conditions are met; 5) Decode the global optimal solution to obtain the optimal drag reduction optimization variable parameters.

2. The shark scale groove drag reduction method based on genetic algorithm according to claim 1, characterized in that: The scale groove is an inverted triangle structure, and the drag reduction optimization variable parameters of the shark scale groove include the groove width L and depth H.

3. The shark scale groove drag reduction method based on genetic algorithm according to claim 2 is characterized in that: The variable parameters of groove drag reduction optimization are determined as follows: 1) Model the grooves of shark scales: A(x1,y1), B(x2,y2), C(x3,y3) P(t) = (1 - t) 2 *A + 2(1 - t)t*B + t 2 C In the above formulas, A, B, C are the three vertices of the inverted triangle of the scale groove, t is a parameter, and the coordinates of different points on the curve can be obtained by different t values; P(t) is the B-spline function; 2) The force analysis of the groove wall is performed to obtain the drag reduction function expression of the groove unit as shown in the following formula: In the above formula, R is the drag reduction rate of the groove unit, F s is the flow resistance of the smooth wall, F l is the flow resistance of the groove unit, and: F1=∫dF=∫p·x·ds+∫f·x·ds In the above formula, x is a unit vector in the same direction as the flow direction, p is the pressure of the vertical micro-element segment, f is the viscous shear stress tangent to the micro-element segment, ∫p·x·ds is the pressure difference resistance of the groove unit, and ∫f·x·ds is the viscous resistance of the groove unit; 3) According to the function expression of the drag reduction rate, the factors affecting the drag of the scale groove are determined as follows: groove width L and depth H; since the width and depth of the groove are controlled by the coordinate points of the inverted triangle, the coordinate points of A, B, and C are used as the drag reduction optimization variable parameters of the shark scale groove.

4. The shark scale groove drag reduction method based on genetic algorithm according to claim 1, characterized in that: The function expression for determining the constraint conditions that need to be optimized for the scale groove is: In the above formula, L is the groove width, L max is the maximum groove width, H is the groove depth, H max is the maximum groove depth.

5. The shark scale groove drag reduction method based on genetic algorithm according to claim 2, characterized in that: The coordinates x and y of the inverted triangle coordinate points of the groove are represented by binary code, and the lower vertex of the inverted triangle is moved as a control point to achieve the deformation of the groove shape; then the global coordinates of the groove after deformation by the B-spline function method are calculated, and the flow field grid is reconstructed by batch processing; Finally, flow field simulation is performed to calculate the wall resistance of the groove unit, and drag reduction optimization is performed with the minimum resistance as the optimization goal; Among the optimization variables, the upper limit of x is the initial groove width of 20μm, the lower limit is 0, the upper limit of y is -0.5 times the groove depth, -5μm, and the lower limit is -5 times the groove depth, -50μm; the genetic algorithm population size is 50, the maximum evolutionary number is 50, and the optimization end condition is to reach the maximum evolutionary number.

6. The shark scale groove drag reduction method based on genetic algorithm according to claim 5, characterized in that: The global optimal solution is decoded to obtain the optimal drag reduction optimization variable parameters.

7. The shark scale groove drag reduction method based on genetic algorithm according to claim 4, characterized in that: The functional expression for determining the fitness of the genetic algorithm based on the objective function is shown as follows: In the above formula, F is the fitness of the genetic algorithm, F l is the flow resistance.

8. A shark scale groove drag reduction system based on a genetic algorithm, using a computer and its related processor, the processor is configured to execute the steps of the shark scale groove drag reduction method based on a genetic algorithm as described in any one of claims 1 to 7, or execute a computer program of the shark scale groove drag reduction method based on a genetic algorithm as described in any one of claims 1 to 7.

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