Automatic modeling and optimizing method for runner bucket blade of impulse turbine

Abstract

The invention relates to an automatic modeling and optimizing method for a runner bucket blade of a water turbine, in particular to an automatic modeling and optimizing method for the runner bucket blade of the water turbine. The method aims at solving the problems that in an existing parameterized model, the number of geometric variable parameters is large, the optimization design time cost is high, or control parameters are single, and the requirement for obtaining an optimal model cannot be met. The method specifically comprises the following steps: step 1, establishing a runner bucket model; 2, parameterizing the runner bucket model; 3, automatically dividing a rotating wheel model grid; 4, calculating the numerical value of the impact basin; and 5, iteratively optimizing the genetic algorithm. The invention belongs to the technical field of turbine runner blade design.

Description

Technical Field

[0001] The invention relates to an automatic modeling and optimization method for a water turbine runner bucket blade, and belongs to the technical field of water turbine runner blade design. Background Art

[0002] A Pelton turbine is a prime mover that utilizes the kinetic energy of a high-pressure water jet to rotate a runner, converting hydraulic energy into mechanical energy. Due to its intuitive design and suitability for high to very high head applications, it has been the preferred choice for power plants for many years. Its primary flow components are the water distribution ring, nozzles, and runner / buoy. The bucket is the core working element of the runner and plays a key role in the unit's performance.

[0003] Blades are one of the most critical components of a turbine, and their shape directly influences the fluid flow and, consequently, the impeller's efficiency. However, there is no established theory for the design of Pelton lines. Computational domain modeling for CFD simulation of Pelton turbines relies heavily on design experience and extensive model testing. Existing parametric models also suffer from numerous geometric variables, resulting in high optimization time and cost, or a single set of control parameters, making it difficult to obtain an optimal model. Summary of the Invention

[0004] In order to solve the problems in existing parametric models such as many geometric variable parameters, high optimization design time cost, or single control parameters that cannot meet the requirements for obtaining the optimal model, the present invention proposes an automatic modeling and optimization method for the impulsive turbine runner bucket blade.

[0005] The technical solution adopted by the present invention to solve the above problems is: the present invention specifically includes:

[0006] Step 1: Establish a runner bucket model;

[0007] Step 2: parameterize the runner bucket model;

[0008] Step 3: Automatically divide the mesh of the runner model;

[0009] Step 4, calculate the impact basin value;

[0010] Step 5: Iterate and optimize the genetic algorithm.

[0011] Furthermore, the geometric parameters of the runner bucket model in step 1 include depth H, width B, length L, notch size R, inlet angle a and outlet angle β; the above parameters are subjected to three-dimensional rotation, cutting and mirroring to obtain the bucket entity, and the impact runner flow field is obtained through Boolean operation.

[0012] Furthermore, step 2 specifically includes using multiple segments of B-spline curves to define and control the inner surface of the bucket, including the flow section line and the longitudinal guide line;

[0013] The flow section line is used to control the bucket depth H, the water divider inflow angle α, and the water outlet angle β. The bucket width varies with the position of the section and is limited by the depth of the bucket guide line at the corresponding position. Based on these parameters, the bucket parameterized model is automatically updated.

[0014] The B-spline curve includes 8 flow section lines and 1 longitudinal guide line;

[0015] The guide line is formed by splicing two segments of B-spline curves, ensuring equal curvature and smooth transition at the connection between adjacent points.

[0016] Furthermore, in step 3, a runner model design sample library is generated based on optimized Latin square sampling, and automatic unstructured grid division is performed for different runner flow domain models. The runner torque curve Tt is extracted through numerical simulation calculation.

[0017] The wheel meshing can realize automatic local encryption and the addition of boundary layers and boundary naming.

[0018] Furthermore, in step 4, the steady-state full-basin numerical calculation of the impulse turbine unit is performed, the RANS RNGk-ε model is adopted, and the convergence accuracy and iteration number are set, and the full-basin numerical simulation of the unit is performed through the CFL expression.

[0019] Furthermore, in step 5, a multi-island genetic algorithm is used to perform single-objective optimization on the runner hydraulic efficiency η;

[0020] Set the population size to 40, the number of islands to 10, the evolutionary generations to 200, the crossover probability to 0.9, and the mutation probability to 0.01. Generate a new generation through crossover and mutation operations, and iterate continuously until the termination condition is met.

[0021] The beneficial effects of the present invention are as follows: the present invention adopts B-spline fitting curve to construct correlation relationship, finely controls its three-dimensional shape, and obtains optimized runner model by multi-island genetic algorithm;

[0022] The present invention realizes the parametric design of the impulse turbine runner model through the B-spline curve, which will greatly improve the design efficiency, shorten the development cycle and reduce the design cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of the main parameters of the runner bucket of an impulse turbine according to the present invention;

[0024] Figure 2 2. It is a schematic diagram of the flow cross-section line of the runner bucket of the impulse turbine according to the present invention;

[0025] Figure 3This is a schematic diagram of the guide line of the runner bucket of the impulse turbine according to the present invention;

[0026] Figure 4 2. Schematic diagram of torque variation curve of the same bucket runner before and after optimization according to the present invention;

[0027] Figure 5 It is a schematic diagram comparing the runner pressure at the same moment before and after the optimization described in the present invention. DETAILED DESCRIPTION

[0028] Specific implementation method 1: Figures 1 to 5 As shown in FIG, a method for automatically modeling and optimizing the bucket blades of an impulse turbine runner comprises the following specific steps:

[0029] Step 1: Establish a runner bucket model. The runner bucket geometric parameters, such as depth H, width B, length L, notch size R, inlet angle a, and outlet angle β, are used to obtain the bucket entity through three-dimensional rotation, cutting, and mirroring. The impact runner flow domain is obtained through Boolean operations.

[0030] Step 2: Parameterize the runner bucket model. Use multiple B-spline curves to define and control the bucket's inner surface, including eight flow section lines and one longitudinal guide line. Similar to the flow section line, the guide line is constructed by splicing two B-spline curves together to ensure constant curvature and smooth transitions between adjacent points.

[0031] like Figure 2 As shown, B F 、C F 、C F '、D F The flow cross-section line is used to control the bucket depth H, the water-dividing blade inflow angle α, and the water-outlet angle β. Based on the above six parameters, the bucket parameterized model can be automatically updated.

[0032] The width of the bucket varies with the position of the cross section and is limited by the depth of the bucket guide line at the corresponding position. The relationship between them is expressed by the proportional coefficient M ij Determine the length of the section line of each section, by M ij Get the bucket depth of different cross sections, L AE and L h like Figure 2 As shown, the relationship is as follows:

[0033]

[0034] In formula (1), i and j represent the contour control lines of different sections, and the bucket depth of the cross-section contour line is controlled by the height of the guide line of different sections;

[0035] Based on the above parameters, the automatic update model of the runner bucket was constructed using Inventor 3D software with the yz plane as the symmetry plane.

[0036] Step 3: Generate a runner model sample library based on optimized Latin square sampling, and perform automatic unstructured grid division for different runner watershed models; perform 56 sets of scheme designs for the six geometric parameters of the above-mentioned bucket, and the bucket parameter design table is shown in Table 1;

[0037] Step 4: Perform numerical simulations, use the VOF model to simulate the flow characteristics, and select the RNG k-ε turbulence model; set the wall boundary to a no-slip state, and at the initial moment, both the nozzle flow area and the rotor flow area are filled with air;

[0038] The homogeneous flow model assumes that the transport volume is the same at each stage in the process, as shown in formula (2):

[0039]

[0040] In formula (2), N P Indicates the total number of phases;

[0041] The transport capacity is shared in uniform multiphase flow, and the shared flow field is solved by an overall transport equation, which is given by summing the transport equations of each single phase. As an example, the overall transport equation is shown in formula (3):

[0042]

[0043] In formula (3), ρ represents the mixing density, γ α represents the volume fraction of α phase; U represents the average velocity, Γ represents the average mass transfer between phases,

[0044] A given nozzle inlet is defined as a mass flow inlet, and the mass flow rate of each inlet is 13.7 kg / s.

[0045] The boundary of the rotor flow domain is an open outlet at atmospheric pressure. Different regions are connected by interfaces; the speed is set to 638.238 r / min; the convergence target of the residual is set to 10 -4 , the time step is 2×10 -4 s, each time step is iterated for a maximum of 20 times, and data is recorded every 2 time steps; the extracted wheel torque curve Tt is obtained by numerical simulation;

[0046] The wheel meshing can realize automatic local encryption and the addition of boundary layers and boundary naming;

[0047] Table 1

[0048]

[0049] Step 5: Use Kriging as a proxy model to perform high-precision interpolation optimization design, and use the improved multi-island genetic algorithm to perform single-objective optimization of the runner hydraulic efficiency η;

[0050] Set the population size to 40, the number of islands to 10, and the number of generations to 200. Generate a new generation through crossover and mutation operations, and iterate continuously until the termination condition is met.

[0051] M t+1 =M t +α×(var(F t )-var(F t-1 )) (4),

[0052] In formula (4), M t is the migration interval at time t, M0 = 5 (the initial migration interval is 5 generations), α is the adjustment coefficient, α = 0.1, var(F r ) is the variance of population fitness;

[0053] p c，t+1 =p c，t +β×(std(F t )-std(F t-1 )) (5),

[0054] In formula (5), p c,t is the crossover probability at time t, β is the adjustment coefficient, β = 0.05, std(F t ) is the standard deviation of population fitness;

[0055] p m，t+1 =p m，t +γ×(mean(F t )-mean(F t-1 )) (6),

[0056] In formula (6), p m,t is the probability of mutation at time t. γ is the adjustment coefficient, γ=0.02, mean(F t ) is the average fitness of the population;

[0057]

[0058] In formula (7), F i is the original fitness of individual i, sh(i) is the set of individuals similar to individual i, sim(x i , xj ) is the similarity between individuals i and j;

[0059] Finally, the optimized runner bucket solution was obtained. The torque of the optimized runner was greater than that of the prototype runner, and the total torque of the runner was significantly improved, increasing by 6.69% compared with the prototype runner.

[0060] The hydraulic efficiency of the runner is improved by 4.88% compared with the prototype. The complete 3D model of the impulse turbine runner is obtained by rotating and copying the single-channel runner prt format file in the 3D modeling software.

[0061] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with the present profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical content disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement of the above embodiments made according to the technical essence of the present invention, within the spirit and principles of the present invention, without departing from the content of the technical solution of the present invention, shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An automatic modeling and optimization method for an impulse turbine runner bucket blade, characterized in that: Specifically include: Step 1, establish a runner bucket model; Step 2, parameterizing the runner bucket model; Step 3: Automatically divide the mesh of the wheel model; Step 4, calculate the impact basin value; Step 5: Iterate and optimize the genetic algorithm.

2. The method for automatic modeling and optimization of impingement turbine runner bucket blades according to claim 1, characterized in that: The geometric parameters of the runner bucket model in step 1 include depth H, width B, length L, notch size R, inlet angle a and outlet angle β; the above parameters are subjected to three-dimensional rotation excision and mirroring to obtain the bucket entity, and the impact runner flow field is obtained through Boolean operation.

3. The method for automatic modeling and optimization of impingement turbine runner bucket blades according to claim 1, characterized in that: Step 2 specifically includes using a multi-segment B-spline curve to define and control the inner surface of the bucket, including the flow section line and the longitudinal guide line; The flow section line is used to control the bucket depth H, the water-dividing blade inflow angle α and the water-outlet angle β; the width of the bucket changes with the position of the section and is limited by the depth of the bucket guide line at the corresponding position; based on the above parameters, the bucket parameterized model is automatically updated; The B-spline curve includes 8 flow section lines and 1 longitudinal guide line; The guide line is formed by splicing two sections of B-spline curves, ensuring equal curvature and smooth transition at the connection of adjacent points.

4. The method for automatic modeling and optimization of impingement turbine runner bucket blades according to claim 1, characterized in that: In step 3, a runner model design sample library is generated based on optimized Latin square sampling, and automatic unstructured grid division is performed for different runner flow basin models, and the runner torque curve Tt is extracted through numerical simulation calculation; The wheel meshing can realize automatic local encryption and the addition of boundary layers and boundary naming.

5. The method for automatic modeling and optimization of impingement turbine runner bucket blades according to claim 1, characterized in that: In step 4, the steady-state full-basin numerical calculation of the impulse turbine unit is performed, the RANS RNG k-ε model is adopted, and the convergence accuracy and iteration number are set, and the full-basin numerical simulation of the unit is performed through the CFL expression.

6. The method for automatic modeling and optimization of impingement turbine runner bucket blades according to claim 1, characterized in that: In step 5, a multi-island genetic algorithm is used to perform single-objective optimization on the runner hydraulic efficiency η; Set the population size to 40, the number of islands to 10, the number of evolution generations to 200, the crossover probability to 0.9, and the mutation probability to 0.

01. Generate a new generation through crossover and mutation operations, and iterate continuously until the termination condition is met.

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