A high-efficiency pump blade design method that can adapt to changing requirements of multiple working conditions

Abstract

The present invention discloses a method for designing efficient pump blades that can adapt to the requirements of multiple working conditions, including the following steps: S1, selecting a fish with a smooth surface, obtaining a cross-section along the length direction of the fish body to form an airfoil profile; S2, recording and fitting the points of the airfoil profile to obtain a bionic airfoil; S3, using the non-dominated solution sorting genetic algorithm NSGA-II to optimize the design of the bionic airfoil obtained in step S2 until the performance no longer improves with the increase in the number of iterations to obtain the Pareto optimal airfoil; S4, applying the airfoil obtained after the optimization to the water pump blade. Based on the formation of a bionic airfoil by fitting the airfoil profile of the turbot fish body, the present invention combines the genetic algorithm to optimize the lift and drag characteristics of the airfoil, so that the blade can maintain a higher performance in a wider working range, optimize the performance of the water pump, and enable the hydraulic machinery to adapt to more working environments, thereby achieving the purpose of optimizing the hydraulic machinery under multiple working conditions.

Description

Technical Field

[0001] The invention relates to the field of water pump blade design, and in particular to a high-efficiency pump blade design method applicable to changing requirements of multiple working conditions. Background Art

[0002] Water pumps are essential machines for transporting liquids. They transfer the mechanical energy of a motor to the liquid, pressurizing it and transporting it to a distant location. Vane pumps, in particular, utilize the interaction between rotating blades and water to transfer energy. The hydraulic performance of a water pump is generally determined by a variety of factors, with the blades being one of the most important. The lift-drag characteristics of the blades are a key determinant of pump performance, making optimizing the lift-drag design of pump blades a crucial approach to improving pump efficiency.

[0003] However, known water pump blade designs are usually designed for a specific flow rate condition, which means that the impeller of this type of water pump can only maintain high performance within a narrow flow rate range. When the actual operating point of the water pump deviates from the design operating point for some reason, its performance will drop significantly, resulting in energy waste and even reduced pump reliability, causing failures. Therefore, it is necessary to optimize the water pump blades under multiple operating conditions. The design flow rate of the pump is greatly affected by the design of the blade placement angle. Therefore, the performance of the water pump under multiple flow rates can be optimized by optimizing the blades at multiple angles of attack. In previous studies, designers have drawn inspiration from aquatic animals and used the shape of fish for the design of water pump blades, achieving better performance. However, how to use the shape of fish to maximize the lift and drag characteristics of the airfoil so that the blades can maintain high performance within a wider operating range has always been an urgent problem to be solved. Summary of the Invention

[0004] To avoid and overcome the technical problems of the prior art, the present invention provides a method for designing efficient pump blades that can adapt to changing operating conditions. Based on a bionic airfoil, the present invention combines a genetic algorithm to optimize the airfoil's lift-drag characteristics, enabling the blade to maintain high performance across a wider operating range.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A high-efficiency pump blade design method applicable to multiple operating conditions includes the following steps:

[0007] S1. Select a fish with a smooth surface and obtain a cross section along the length of the fish body to form an airfoil profile;

[0008] S2, record the points of the airfoil profile and fit it to obtain the bionic airfoil;

[0009] S3, optimizing the bionic airfoil obtained in step S2 using a non-dominated solution sorting genetic algorithm (NSGA-II) until a Pareto optimal airfoil is obtained when performance no longer improves with increasing iteration numbers;

[0010] S4. Apply the airfoil obtained after the optimization to the water pump blade.

[0011] As a further solution of the present invention: in step S2, the airfoil profile points are fitted using the following function:

[0012]

[0013]

[0014]

[0015] where e(k) = (ln0.5) / (lnx k ),(0≤x k ≤1), x (k) is the selected node on the airfoil chord line;

[0016] y up is the ordinate of the upper bone line of the original airfoil without fitting;

[0017] y low is the ordinate of the lower bone line of the original airfoil without fitting;

[0018] y Oup is the ordinate of the upper bone line of the new airfoil obtained by fitting;

[0019] y Olow is the ordinate of the lower bone line of the new airfoil obtained by fitting;

[0020] f k (x) is the shape function;

[0021] n is the shape function f k The number of (x);

[0022] c k are the coefficients of the shape function and the design variables of the optimization process.

[0023] As a further solution of the present invention: there are ten groups of shape functions, among which the design variables are:

[0024] c1∈[-0.006,0.006];

[0025] c2∈[-0.001,0.006];

[0026] c3∈[-0.006,0.008];

[0027] c4∈[-0.005,0.005];

[0028] c5∈[-0.005,0.010];

[0029] c6∈[-0.008,0.006];

[0030] c7∈[-0.010,0.010];

[0031] c8∈[-0.005,0.010];

[0032] c9∈[-0.005,0.005];

[0033] c 10 ∈[-0.005,0.015].

[0034] As a further solution of the present invention: when the non-dominated solution sorting genetic algorithm NSGA-II is used for optimization, the population size is set to 12, the iteration is 20 generations, the crossover probability is 0.9, and the mutation probability is 0.01;

[0035] The maximum lift coefficient C in the objective function l =F l / (0.5ρU ∞ 2 ×A);

[0036] Maximum lift-to-drag ratio J=F l / F d ;

[0037] F l is the lift of the airfoil;

[0038] F d is the drag of the airfoil;

[0039] ρ is the medium density;

[0040] U ∞ is the flow velocity at infinite distance from the hydrofoil;

[0041] A = chord length of airfoil × span.

[0042] As a further solution of the present invention: the airfoil obtained in step S2 is optimized at at least two angles of attack respectively, thereby obtaining a Pareto optimal airfoil;

[0043] The fitting points of the Pareto optimal airfoil at each angle of attack are statistically processed to obtain new control points, and the first-level optimized airfoil is obtained by fitting the new control points.

[0044] The node coordinates of the Pareto optimal airfoil at each angle of attack are statistically averaged, and the secondary optimized airfoil is obtained by fitting.

[0045] The hydraulic performance of the Pareto optimal airfoil, the first-level optimized airfoil and the second-level optimized airfoil are verified under the same working conditions, and the airfoil with the best hydraulic performance is selected and applied to the water pump blades.

[0046] As a further solution of the present invention: in step S2, the fitting points at the intersection of the fish tail, fish fin and fish body are deleted, and arc correction is applied to the non-smooth areas at the fish mouth and tail fin.

[0047] As a further solution of the present invention: in step S1, the smooth-surface fish is turbot, and at least five cross sections are obtained at intervals along the body of the fish to form at least five bionic wing shapes.

[0048] As a further solution of the present invention: the airfoil in step S4 is further optimized, and a convex fin arranged along the span direction is protruded at the leading edge of the airfoil body, and the convex fins are arranged in parallel and spaced apart along the chord length direction of the airfoil body, and there is an angle between the convex fin and the front end of the airfoil body.

[0049] As a further solution of the present invention: the curve at the leading edge of the airfoil body is: y = 2.02x 3 -2.058x 2 +0.662x+0.003957;

[0050] Take the tangent line of the curve at the leading edge of the airfoil at x = 0.04 (m-1) + 0.005 as the new X-axis, and use the tangent point as the coordinate origin to establish a new coordinate system corresponding to the mth row of convex fins. The new coordinates x', y' are:

[0051]

[0052] Where m is the row number of each convex fin from the leading edge to the trailing edge of the wing,

[0053] The curve of each convex fin in its corresponding coordinate system is:

[0054] y′=6.06x′ 2 –4.116x′+0.662–(m–1)h, h∈[0.0001,0.0004].

[0055] As a further solution of the present invention: there are seven rows of convex fins, h=0.0001; the angle between the convex fins and the front end of the airfoil body is 5° to 9°.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. Based on the bionic airfoil formed by fitting the turbot airfoil profile, the present invention combines genetic algorithms to optimize the lift-drag characteristics of the airfoil, enabling the blades to maintain high performance in a wider operating range, optimizing the performance of the water pump, and enabling the hydraulic machinery to adapt to more working environments, thereby achieving the purpose of optimizing the hydraulic machinery under multiple working conditions.

[0058] 2. The optimized hydrofoil of the present invention can enable the pump to maintain higher operating efficiency in a wider flow range. The extension of the high-efficiency section enables the water pump to adapt to more complex flow changes, thereby greatly expanding the application range of the water pump; and the water pump can maintain higher performance in a larger flow range, so that the working range of the pump is extended and the stability of the pump under complex flow changes is improved.

[0059] 3. The present invention effectively suppresses turbulent bursts and weakens the intensity of turbulent bursts by adding a convex fin structure to the optimized airfoil surface, increases the thickness of the fluid viscosity bottom layer, reduces the wall friction resistance, and has lower resistance during operation; and the present invention can also accelerate the evolution of unsteady cavitation flow, effectively improve the cavitation phenomenon of the hydrofoil and accelerate the cavitation cycle of the hydrofoil, greatly reducing the cavitation volume; the convex fin structure at the leading edge of the airfoil generates a second vortex group, thereby changing the cavitation evolution of the hydrofoil. The second vortex group acts like a "rolling bearing" to achieve a good drag reduction effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 Schematic diagram of the cross-sectional distribution of turbot when constructing the original airfoil.

[0061] Figure 2 for Figure 1 The outline of the hydrofoil at each cross section.

[0062] Figure 3 for Figure 2 The bionic hydrofoil obtained after the section S2 in the middle is modified.

[0063] Figure 4 It is the calculation area when the non-dominated solution sorting genetic algorithm NSGA-II is used for optimization.

[0064] Figure 5a Generate a graph of the lift coefficient variation of offspring for the non-dominated solution sorting genetic algorithm NSGA-II.

[0065] Figure 5b Generate a spatial distribution graph of offspring under the objective function for the non-dominated solution sorting genetic algorithm NSGA-II.

[0066] Figure 6a It is the Pareto optimal airfoil of the hydrofoil at S2 at an angle of attack of 0° obtained by optimizing the NSGA-II algorithm.

[0067] Figure 6b This is the Pareto optimal airfoil of the hydrofoil at S2 at an angle of attack of 4° obtained by optimizing the NSGA-II algorithm.

[0068] Figure 6c This is the Pareto optimal airfoil of the hydrofoil at S2 at an angle of attack of 8° obtained by optimizing the NSGA-II algorithm.

[0069] Figure 7a This is the first-level optimized airfoil diagram obtained after optimization of the hydrofoil at S1.

[0070] Figure 7b This is the first-level optimized airfoil diagram obtained after optimization of the hydrofoil at S2.

[0071] Figure 7c This is the first-level optimized airfoil diagram obtained after optimization of the hydrofoil at S3.

[0072] Figure 7d This is the first-level optimized airfoil diagram obtained after optimization of the hydrofoil at S4.

[0073] Figure 7e This is the first-level optimized airfoil diagram obtained after optimization of the hydrofoil at S5.

[0074] Figure 8 The lift-to-drag ratio comparison chart of the hydrofoil at each cross section at attack angles of 0°, 4°, and 8°.

[0075] Figure 9 The flow-head and flow-efficiency curves of the first-order optimized airfoil and the Pareto optimal airfoil.

[0076] Figure 10 Schematic diagram of the structure after adding convex fins to the optimal airfoil applied to the water pump.

[0077] Figure 11 for Figure 10 Top view of the wing.

[0078] Figure 12 for Figure 10 Distribution diagram of the convex fins at the leading edge of the wing.

[0079] Figure 13 Coordinate transformation diagram of each convex fin.

[0080] Figure 14 The vortex distribution comparison diagram of the optimal airfoil and the optimal airfoil after adding the convex fin structure.

[0081] Figure 15 The comparison chart of the cavitation volume curves of the optimal airfoil and the optimal airfoil after adding the convex fin structure in the same period.

[0082] Figure 16The comparison chart of the drag coefficient curves of the optimal airfoil and the optimal airfoil after adding the convex fin structure at the same period.

[0083] Figure 17 Comparison of cavitation evolution of the optimal airfoil and the optimal airfoil with added convex fin structure from 0 / 15T to 7 / 15T.

[0084] Figure 18 The following is a comparison of the cavitation evolution of the optimal airfoil and the optimal airfoil after adding the convex fin structure in the time from 8 / 15T to 15 / 15T.

[0085] In the figure: 1, airfoil body; 11, convex fin. DETAILED DESCRIPTION

[0086] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0087] See also Figures 1 to 18 In an embodiment of the present invention, a method for designing an efficient pump blade that can adapt to the requirements of multiple working conditions includes the following steps:

[0088] S1. Select a fish with a smooth surface and obtain a cross section along the length of the fish body to form an airfoil profile.

[0089] The smooth surface fish is preferably turbot, such as Figure 1 The airfoil profiles of five sections are obtained at even intervals along the length of the fish body, and the following are obtained: Figure 2 Five bionic airfoil shapes are shown.

[0090] S2. Record the points of the airfoil profile and fit them to obtain the bionic airfoil.

[0091] The following function is used for fitting:

[0092]

[0093]

[0094]

[0095] where e(k) = (ln0.5) / (lnx k ),(0≤x k ≤1);

[0096] k = 2 or 3 or 4 or 5 or 7 or 8 or 9 or 10;

[0097] x (k) is the node selected on the airfoil chord line, x (k) =0.2 or 0.4 or 0.6 or 0.8;

[0098] y up is the ordinate of the upper bone line of the original airfoil without fitting;

[0099] y low is the ordinate of the lower bone line of the original airfoil without fitting;

[0100] y Oup is the ordinate of the upper bone line of the new airfoil obtained by fitting;

[0101] y Olow is the ordinate of the lower bone line of the new airfoil obtained by fitting;

[0102] f k (x) is the shape function;

[0103] n is the shape function f k The number of (x) is usually 10;

[0104] c k are the coefficients of the shape function and the design variables of the optimization process;

[0105] c1∈[-0.006,0.006];

[0106] c2∈[-0.001,0.006];

[0107] c3∈[-0.006,0.008];

[0108] c4∈[-0.005,0.005];

[0109] c5∈[-0.005,0.010];

[0110] c6∈[-0.008,0.006];

[0111] c7∈[-0.010,0.010];

[0112] c8∈[-0.005,0.010];

[0113] c9∈[-0.005,0.005];

[0114] c 10 ∈[-0.005,0.015].

[0115] Before fitting, arcs are used to correct the rough areas at the fish mouth on the leading edge of the airfoil and the tail fin on the trailing edge; the fitting points at the intersection of the tail, fin and body are deleted, and the remaining fitting points are refitted.

[0116] Taking the cross section at S2 as an example, the cross section is fitted after correction as follows Figure 3 The bionic hydrofoil diagram is shown.

[0117] S3, optimizing the bionic airfoil fitted in step S2 using a non-dominated solution sorting genetic algorithm (NSGA-II) until a Pareto optimal airfoil is obtained when performance no longer improves with increasing iteration numbers;

[0118] When the non-dominated solution sorting genetic algorithm NSGA-II is used for optimization, the population size is set to 12, the iteration is 20 generations, the crossover probability is 0.9, and the mutation probability is 0.01;

[0119] The maximum lift coefficient C in the objective function l =F l / (0.5ρU ∞ 2 ×A);

[0120] Maximum lift-to-drag ratio J=F l / F d ;

[0121] F l is the lift of the airfoil;

[0122] F d is the drag of the airfoil;

[0123] ρ is the medium density;

[0124] U ∞ is the flow velocity at infinite distance from the hydrofoil;

[0125] A = chord length of airfoil × span.

[0126] When optimizing, the calculation area is as follows Figure 4 As shown, the chord length of the airfoil is L = 70 mm, the calculation area is 10L long, 2.7L wide, and 0.3L thick, and the distance from the airfoil to the inlet is 4L.

[0127] The inlet type is set to velocity inlet, the inlet flow velocity is 10 m / s, the Reynolds number is 7×105, the angle of attack α is set to 0°, 4° or 8°, the outlet type is set to pressure outlet, and the pressure is set to 43450 Pa; the airfoil wall adopts the no-slip wall condition, and the boundary conditions of the upper and lower surfaces of the calculation area are set to symmetric surfaces.

[0128] like Figure 5a and Figure 5bAs shown in Figure 2, the performance of the offspring generated by the NSGA-II algorithm is demonstrated. Figure 5a Generate a graph of the lift coefficient variation of the offspring for the NSGA-II algorithm. Figure 5b The spatial distribution of the offspring under the objective function generated by the NSGA-II algorithm shows that after optimization iterations, the offspring's performance has significantly improved, with the lift coefficient approaching 0.749 and the lift-to-drag ratio approaching 6.618. When the number of iterations reaches approximately 180, performance no longer improves with increasing iterations. This indicates that the Pareto optimal solution for the optimization problem has been reached, resulting in a Pareto optimal airfoil. i represents the airfoil section number.

[0129] The airfoil section at S2 is optimized at 0°, 4° and 8° angles of attack to obtain the following: Figure 6a to Figure 6c The three Pareto optimal airfoils are shown in S i-ORG mark.

[0130] For S under three attack angles 2-ORG The fitting points of the Pareto optimal airfoil are averaged and statistically processed to obtain new control points. The first-level optimized airfoil can be obtained by fitting them. 2-OPT Mark; Similarly, the optimization results of the airfoil at other sections can be obtained, using S i-OPT Mark, i is the airfoil section number. The first-level optimized airfoil of the five sections is as follows Figure 7a to Figure 7e shown.

[0131] The node coordinates of the three Pareto optimal airfoils are statistically averaged and fitted again to obtain the secondary optimized airfoil. i-AVG Mark, i is the airfoil section number.

[0132] like Figure 8 As shown in the figure, the Pareto optimal airfoil, the first-level optimized airfoil and the second-level optimized airfoil obtained by optimizing each section at the attack angles of 0°, 4° and 8° are compared in lift-to-drag ratio.

[0133] Taking the airfoil at section S2 as an example, at 0°, 4° and 8° angles of attack, S 2-AVG The lift-to-drag ratio lags behind S 2-OPT Airfoil 6.177%, 5.913%, 1.640%, while leading S 2-ORG The airfoil has 19.240%, 15.087%, and 2.819% values, and the airfoils at other sections show similar trends. It can be seen that S i-AVG The lift-to-drag ratio of the airfoil is slightly lower than S i-OPT The airfoil is significantly ahead of the original airfoil S i-ORG It can be seen that S i-AVG Airfoil and S i-OPTWhile retaining the advantages of the results obtained by genetic algorithm optimization, the airfoil can show lift-drag performance better than the Pareto optimal airfoil under multiple groups of attack angles, thereby enabling hydraulic machinery to adapt to more working environments and achieving the purpose of optimizing hydraulic machinery under multiple working conditions.

[0134] In order to verify the present invention i-OPT and S i-ORG The engineering feasibility of the airfoil is to apply the optimized hydrofoil to the blade of the jet pump. The corresponding scheme of each set of airfoils at each section is recorded as P i , the design parameters of the jet pump are: flow rate q des =456m 3 / h, design head H = 3.5m, speed n = 1600r / min. The geometric parameters of the impeller are: impeller outer diameter D2 = 200mm, hub diameter D h =80mm.

[0135] like Figure 9 From the flow-head curve at a in the middle left, we can see that S i-OPT The jet pump obtained by the airfoil has a significantly higher lift than the original airfoil S i-ORG The resulting jet pump head.

[0136] Under design conditions, relative to the hydrofoil S i-ORG The corresponding original solution, S i-OPT The lift of the hydrofoil scheme at the five sections was increased by 16.201%, 27.860%, 18.603%, 10.536% and 8.310% respectively.

[0137] Under large flow conditions (1.2Q), S i-OPT The lift of the hydrofoil scheme at the five sections was increased by 36.979%, 27.822%, 31.022%, 33.339% and 9.389% respectively.

[0138] like Figure 9 It can be seen from the flow-efficiency curve at point b in the middle right that the efficiency of the pump under different hydrofoil schemes shows a trend of first increasing and then decreasing as the flow rate continues to increase. i-OPT The efficiency of the obtained jet pump is higher than that of the hydrofoil S i-ORG The efficiency of the obtained jet pump is improved by 7.066%, 13.478%, 7.973%, 4.040% and 2.091% respectively under the design working conditions.

[0139] Under large flow conditions (1.2Q), the efficiency is increased by 55.729%, 39.885%, 60.991%, 65.812% and 21.714% respectively.

[0140] It can be seen that S in the present inventioni-OPT The hydrofoil solution can improve the hydraulic performance of the water pump. When the operating flow of the pump exceeds its rated flow, the solution effectively reduces the decline in water pump performance.

[0141] In order to better distinguish and describe, we define the area with efficiency higher than 60% as the high efficiency area of ​​the water pump, and use l i and l i ' indicates S i-ORG and S i-OPT The length of the high efficiency zone of the corresponding jet pump solution. i and l i 'It can reflect the performance of the pump under multiple flow conditions to a certain extent. The specific values ​​are shown in the following table:

[0142] i 1 2 3 4 5 <![CDATA[l i ]]> 0.322 0.257 0.327 0.315 0.232 <![CDATA[l i ’]]> 0.413 0.356 0.430 0.367 0.267 <![CDATA[Δ i ]]> 28.288％ 38.396％ 31.473％ 16.368％ 15.185％

[0143] where Δ i Representative i 'Relative to l i The growth rate is calculated as Δ i =(l i '-l i ) / l i ; In the table, 1, 2, 3, 4, and 5 represent five airfoil types respectively.

[0144] From the table above, we can see that after optimization, the length of the high-efficiency zone of each bionic hydrofoil has been significantly improved, among which S 2-OPT The largest improvement was 38.396%, which means that the optimized hydrofoils can enable the pump to maintain high operating efficiency in a wider flow range. The extension of the high-efficiency section allows the pump to adapt to more complex flow changes, thereby significantly expanding the application range of the pump.

[0145] It can be seen from this that the optimization scheme of the present invention can make the water pump show better performance in both head and efficiency, and the water pump can maintain high performance within a larger flow range, thereby extending the working range of the pump and improving the stability of the pump under complex flow changes.

[0146] S4. Apply the airfoil obtained after the optimization to the water pump blade.

[0147] It is preferred to select S in the embodiment 2-OPT The airfoil is applied to water pump blades.

[0148] In order to further reduce cavitation and lower resistance, S 2-OPT The airfoil is optimized as follows.

[0149] S 2-OPT The airfoil is an airfoil body 1, and the curve at the leading edge of the upper end face of the airfoil is:

[0150] y=2.02x 3 -2.058x 2 +0.662x+0.003957.

[0151] The leading edge of the wing is provided with convex fins 11 along the direction of extension. The number of convex fins 11 is set in 5 to 9 groups, preferably 7 groups. After adding the convex fins 11, the structure is as follows: Figure 10 shown.

[0152] Taking the number of convex fins 11 as 7 groups, each convex fin 11 is arranged parallel to the chord length direction of the airfoil body 1, and the convex fin 11 and the front end of the airfoil body 1 have an angle of 5° to 9°, preferably 7°. Figure 11 shown.

[0153] The tangent line of the curve at the leading edge of the wing at x = 0.04 (m-1) + 0.005 is taken as the new X-axis, and a new coordinate system is established with the tangent point as the coordinate origin, where m is the corresponding row number of each convex fin 11 from the leading edge to the trailing edge of the wing. There are seven groups of convex fins 11, and the seven groups of convex fins 11 correspond to seven coordinate systems.

[0154] Then, for the first row of convex fins to the seventh row of convex fins, the tangents at x = 0.005, x = 0.009, x = 0.013, x = 0.017, x = 0.021, x = 0.025, and x = 0.029 are used as the new X-axis, and the tangent point is the coordinate origin. The new coordinates x', y' can be obtained from the coordinate rotation formula as follows:

[0155]

[0156] In the above formula, θ is Figure 13 As shown, it is the angle between adjacent tangents.

[0157] After the seven coordinate systems are established, the curve of each convex fin 11 in its corresponding coordinate system is:

[0158] y′=6.06x′ 2 –4.116x′+0.662–(m–1)h, h∈[0.0001,0.0004];

[0159] Here h is a constant, which means that the function is shifted down by a height of h in the new coordinate system, and h is preferably 0.0001.

[0160] The distribution of the convex fins 11 at the leading edge of the wing is drawn using Matlab as shown below: Figure 12 shown.

[0161] from Figure 14From the velocity vector diagrams of the two airfoils, it can be seen that the velocity vector distribution in the area within the dotted line of the airfoil is very complex, which means that the flow in this area is very unstable and is the main area where cavitation occurs. 2-OPT Compared with the airfoil, S 2-OPT After the convex fin structure is added to the airfoil, the area within the dotted line is smaller, indicating that the cavitation phenomenon has been improved, which is consistent with the above conclusion. 2-OPT Compared with the airfoil, S 2-OPT The addition of a convex fin structure to an airfoil generates a second vortex group within the leading edge fin structure, which alters the airfoil's cavitation evolution. The number and stability of this second vortex group significantly influences the airfoil's cavitation performance and drag reduction. This second vortex group acts like a "rolling bearing," achieving excellent drag reduction.

[0162] Figure 16 The original S in the same period is given 2-OPT Airfoil (denoted as Scheme 1) and S after adding convex fin structure 2-OPT Comparison of the drag coefficients of the airfoils (denoted as Scheme 2). As can be seen from the figure, the drag curves of both airfoils change periodically over time, and the drag coefficient fluctuations of Scheme 2 are slightly smaller than those of Scheme 1. Judging from the time-averaged drag coefficients of the two airfoils in the figure, Scheme 2 has a smaller drag coefficient than Scheme 1. This is because the convex fin structure generates a secondary vortex group, which effectively suppresses turbulent bursts and weakens their intensity, increases the thickness of the fluid's viscous bottom layer, and reduces wall friction resistance.

[0163] Figures 17-18 The cavitation evolution diagram of Scheme 1 and Scheme 2 in a complete cycle from 0 / 15T to 15 / 15T is shown. The left side is the original S 2-OPT Airfoil (Scheme 1), the right side shows the S after adding the convex fin structure 2-OPT Airfoil (Scheme 2).

[0164] As can be seen from the figure, the cavitation shedding patterns of Scheme 1 and Scheme 2 are similar throughout the entire cycle. However, it can be seen that the sheet cavitation in Scheme 2 begins to break down at t = 1 / 15T, while in Scheme 1, it only begins to break down at t = 5 / 15T. This is because the non-smooth convex fin structure on the leading edge surface causes the reentry flow to occur earlier, resulting in premature cavitation. From t = 6 / 15T to t = 11 / 15T, the ruptured cloud cavitation gradually sheds from the leading edge to the trailing edge, and sheet cavitation begins to form again at the leading edge. It can be seen that during this stage, the sheet cavitation area formed at the leading edge of Scheme 2 is smaller. From t = 12 / 15T to t = 15 / 15T, the detached cloud cavitation continues to develop backwards, away from the hydrofoil, and gradually collapses, while the leading edge continues to form sheet cavitation to begin the next cycle. The sheet-like cavitation formed by Scheme 2 is more elongated, and the shed cloud cavitation is smaller and collapses earlier, indicating that the non-smooth convex fin structure of the leading edge can accelerate the evolution of unsteady cavitation flow to a certain extent. Numerical simulations ultimately determined the average cavitation cycle lengths for the two airfoils: 32.59ms for Scheme 1 and 30.87ms for Scheme 2.

[0165] Combine Figure 15 It can be seen that in the early stages of cavitation, when the sheet cavitation of the two airfoils breaks up and gradually falls off toward the trailing edge, the entire cavitation volume begins to decrease. When it reaches the middle stage of cavitation, when only cloud cavitation exists without sheet cavitation, the cavitation volume reaches its minimum. After that, attached cavitation begins to occur at the leading edge, and the cavitation volume gradually increases, and the volume decreases when it finally collapses. It can be seen that throughout the entire cycle, the cavity size of Scheme 2 is always smaller than that of Scheme 1. The non-smooth bionic hydrofoil leading edge of Scheme 2 can effectively improve the cavitation phenomenon of the hydrofoil and accelerate the cavitation cycle of the hydrofoil.

[0166] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0167] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0168] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0169] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0170] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A high-efficiency pump blade design method that can adapt to the requirements of multiple working conditions, characterized by: The steps include: S1. Select a fish with a smooth surface and obtain a cross section along the length of the fish body to form an airfoil profile; S2, record the points of the airfoil profile and fit it to obtain the bionic airfoil; S3, optimizing the bionic airfoil obtained in step S2 using a non-dominated solution sorting genetic algorithm (NSGA-II) until a Pareto optimal airfoil is obtained when performance no longer improves with increasing iteration numbers; S4, applying the airfoil obtained after the optimization to the water pump blade; The airfoil in step S4 is further optimized, and a convex fin (11) arranged along the span direction is provided at the leading edge of the airfoil body (1), and each convex fin (11) is arranged parallel and spaced along the chord length direction of the airfoil body (1), and an angle is formed between the convex fin (11) and the front end of the airfoil body (1); The curve at the leading edge of the airfoil body (1) is: y=2.02x 3 -2.058x 2 +0.662x+0.003957; Take the tangent line of the curve at the leading edge of the airfoil at x = 0.04 (m-1) + 0.005 as the new X-axis, and use the tangent point as the coordinate origin to establish a new coordinate system corresponding to the mth row of convex fins. The new coordinates x', y' are: Where m is the row number of each convex fin (11) from the leading edge to the trailing edge of the wing, The curve of each convex fin (11) in its corresponding coordinate system is: y′=6.06x′ 2 –4.116x′+0.662–(m–1)h,h∈[0.0001,0.0004]。 2. A high-efficiency pump blade design method that can adapt to multiple working conditions according to claim 1, characterized in that: In step S2, the following function is used to fit the airfoil profile points: where e(k) = (ln0.5) / (lnx k ),(0≤x k ≤1), x (k) is the selected node on the airfoil chord line; y up is the ordinate of the upper bone line of the original airfoil without fitting; y low is the ordinate of the lower bone line of the original airfoil without fitting; y Oup is the ordinate of the upper bone line of the new airfoil obtained by fitting; y Olow is the ordinate of the lower bone line of the new airfoil obtained by fitting; f k (x) is the shape function; n is the shape function f k The number of (x); c k are the coefficients of the shape function and the design variables of the optimization process.

3. The high-efficiency pump blade design method applicable to multiple working conditions according to claim 2 is characterized in that: There are ten groups of shape functions, including the design variables: c1∈[-0.006,0.006]； c2∈[-0.001,0.006]； c3∈[-0.006,0.008]； c4∈[-0.005,0.005]； c5∈[-0.005,0.010]； c6∈[-0.008,0.006]； c7∈[-0.010,0.010]； c8∈[-0.005,0.010]； c9∈[-0.005,0.005]； c 10 ∈[-0.005,0.015]。 4. The high-efficiency pump blade design method applicable to multiple working conditions according to claim 1 is characterized in that: When the non-dominated solution sorting genetic algorithm NSGA-II is used for optimization, the population size is set to 12, the iteration is 20 generations, the crossover probability is 0.9, and the mutation probability is 0.01; The maximum lift coefficient C in the objective function l =F l / (0.5ρU ∞ 2 ×A); Maximum lift-to-drag ratio J=F l / F d ; F l is the lift of the airfoil; F d is the drag of the airfoil; ρ is the medium density; U ∞ is the flow velocity at infinite distance from the hydrofoil; A = chord length of airfoil × span.

5. The high-efficiency pump blade design method applicable to multiple working conditions according to claim 2 is characterized in that: Optimizing the airfoil obtained in step S2 at at least two angles of attack to obtain a Pareto optimal airfoil; The fitting points of the Pareto optimal airfoil at each angle of attack are statistically processed to obtain new control points, and the first-level optimized airfoil is obtained by fitting the new control points. The node coordinates of the Pareto optimal airfoil at each angle of attack are statistically averaged, and the secondary optimized airfoil is obtained by fitting. The hydraulic performance of the Pareto optimal airfoil, the first-level optimized airfoil and the second-level optimized airfoil are verified under the same working conditions, and the airfoil with the best hydraulic performance is selected and applied to the water pump blades.

6. A high-efficiency pump blade design method that can adapt to multiple working conditions according to any one of claims 1 to 5, characterized in that: In step S2, the fitting points at the junction of the tail, fin and body are deleted, and arc correction is applied to the non-smooth areas at the mouth and tail fin.

7. A high-efficiency pump blade design method applicable to multiple operating conditions according to any one of claims 1 to 5, characterized in that: In step S1, the smooth-surface fish is turbot, and at least five cross sections are obtained at intervals along the body of the fish to form at least five types of bionic wing profiles.

8. The high-efficiency pump blade design method applicable to multiple working conditions according to claim 1 is characterized in that: The convex fins (11) have seven rows in total, and h=0.0001; the angle between the convex fins (11) and the front end of the airfoil body (1) is 5° to 9°.

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