Simulation method for researching pulsation of movable guide vane of water turbine

Abstract

The invention relates to a simulation method for researching pulsation of a movable guide vane of a water turbine. The simulation method comprises the following steps: discretizing a three-dimensional fluid domain of the movable guide vane of the water turbine to form grids; constructing a basic flow simulation model of the pulsation of the movable guide vane of the water turbine, and inputting model parameters into the discretized grid to obtain a grid simulation model of the water turbine; a turbulence model is added into the water turbine grid simulation model, and a turbulence phenomenon in the operation process of the water turbine is modeled; and based on the simulation model added with the turbulence model, adopting a UDF custom form to control the movement of the movable guide vane, and using a COUPLE coupling solution mode to carry out three-dimensional simulation transient solution on the simulation model to obtain transient simulation results of the flow velocity and the pressure of the water turbine. The problem that the angle of the movable guide vane cannot be changed in the traditional simulation process can be solved; the control-based change of the movable guide vane in the simulation process and the influence of the change of the movable guide vane on the simulation result can be considered bidirectionally at the same time.

Description

Technical Field

[0001] This invention relates to the field of computer software technology, and in particular to a simulation method for studying the pulsation of moving guide vanes in a water turbine. Background Technology

[0002] The pump-turbine is the core component of pumped storage technology. The economic level and efficiency of a power station or power system largely depend on the performance of the turbine.

[0003] Simulation technology can perform detailed analysis of the internal flow field of water pump turbines, and can predict and optimize the performance of water pump turbines during the design phase, thereby improving turbine efficiency and reducing the cost and time of actual manufacturing and testing.

[0004] Practice has proven that high efficiency alone is insufficient for pump-turbines; operational stability must also be guaranteed. Pressure pulsation is one of the main factors affecting the operational stability of pump-turbines. Research on pressure pulsation helps improve the efficiency and stability of pump-turbines, especially the pressure pulsation of the moving guide vanes.

[0005] Chinese Patent CN 112528805 A, entitled "A Method, Apparatus, Equipment and Storage Medium for Analyzing Pressure Pulsations in a Hydropower Turbine," provides a pressure pulsation analysis method for extracting the amplitude and frequency characteristics of hydropower turbine pressure pulsation signals. This method addresses the limitations of traditional signal processing methods in analyzing the operational characteristics of hydropower turbine pressure pulsations. However, it does not provide a method for simulating and analyzing pressure pulsations.

[0006] Chinese Patent CN 118965738 A, entitled "A Simulation System and Method for an Impulse Turbine," provides a simulation system and method for an impulse turbine. This system and method integrates a simulation input module, an impulse turbine simulation module, a unit speed simulation module, and a simulation output module. It has a simple structure, is easy to operate, and can reflect the turbine's operating status and performance in real time, providing immediate feedback for the control and operation of hydropower stations. However, this method is a signal-based system simulation method and does not obtain the pressure pulsation values ​​at the specific locations of the guide vanes. Summary of the Invention

[0007] The purpose of this invention is to propose a simulation method for studying the pulsation of movable guide vanes in hydraulic turbines, thereby addressing the problems existing in the prior art. This invention solves the problem that the movable guide vanes cannot change their angle during traditional simulations. It simultaneously considers both the control-based changes in the movable guide vanes during the simulation process and the impact of these changes on the simulation results.

[0008] To achieve the above objectives, the present invention provides the following solution:

[0009] A simulation method for studying the pulsation of moving guide vanes in a water turbine includes:

[0010] The three-dimensional fluid domain of the moving guide vanes of the water turbine is discretized to form a mesh;

[0011] A basic flow simulation model of the pulsating guide vanes of a water turbine is constructed, and the model parameters are input into a discretized mesh to obtain a mesh simulation model of the water turbine.

[0012] A turbulence model is added to the turbine mesh simulation model to model the turbulence phenomenon during turbine operation;

[0013] Based on the simulation model with the turbulence model added, the motion of the guide vane is controlled by a custom UDF, and the three-dimensional transient simulation of the simulation model is performed using the coupled solution method of COUPLE to obtain the transient simulation results of the flow velocity and pressure of the turbine.

[0014] Optionally, the basic flow simulation model is:

[0015] ;

[0016] Where ρ is the fluid density, t is time, u is the fluid velocity vector field, ∇⋅ is the divergence operator, ∇ is the gradient operator, p is the pressure field inside the fluid, τ is the viscous stress tensor, g is the gravitational acceleration vector, and (u⋅∇)u is the convective acceleration term.

[0017] Optionally, the turbulence model includes:

[0018] Turbulent kinetic energy quantum model:

[0019] ;

[0020] Specific dissipation rate sub-model:

[0021] ;

[0022] Where ∂(ρ) / ∂t is the unsteady-state term, ∇⋅(ρu⋅) is the convection term, Pk is the turbulent kinetic energy generation term, and Dk is the turbulent kinetic energy dissipation term. For the turbulent kinetic energy diffusion term, P ω D is a term that generates ω. ω For ω, the dissipation term, ω is the diffusion term, CD is the cross-diffusion term, ρ is the fluid density, u is the fluid velocity vector field, k is the turbulent kinetic energy, and ω is the turbulent dissipation rate.

[0023] Optionally, controlling the movement of the guide vane using a UDF (User-Defined Function) customization method includes:

[0024] Define the motion of the guide vane;

[0025] Set the grid adaptive strategy;

[0026] Automated control of guide vane motion is achieved through UDF.

[0027] Optionally, defining the motion of the guide vane includes:

[0028] The rotating region is set as a rotating sliding grid, whose rotational motion is defined by absolute angular velocity;

[0029] The active guide vane area is set as a moving mesh, and its motion is a rigid body rotation about a specified axis.

[0030] Optionally, setting the mesh adaptive strategy includes:

[0031] Several monitoring points were set up in the area where the vortex evolution was intense between the impeller and the movable guide vane to record the pressure pulsation in real time;

[0032] After every few time steps of iteration, a mesh adaptive loop is triggered to refine / coarse the mesh cells that meet the above criteria.

[0033] Optionally, automated control of guide vane motion via UDF includes:

[0034] S1. Perform simulation calculations and extract key performance indicators from the simulation results;

[0035] S2. Construct an objective function and constraints using key performance indicators, establish a penalty mechanism using the objective function and constraints, and find the angle result from the minimum penalty score;

[0036] S3. Substitute the obtained angle result into the calculation for the next time step;

[0037] S4. Repeat the process of S1-S3 during the transient simulation time step growth.

[0038] Optionally, key performance indicators include: runner torque, hydraulic efficiency, pressure pulsation amplitude, and vortex belt strength;

[0039] Objective function:

[0040] ;

[0041] constraint:

[0042] ;

[0043] Where Θ is the decision variable – the angle of the active guide vane. The objective function is the turbine efficiency. This is a function of the pressure pulsation amplitude. The vortex zone intensity function. , These are the maximum allowable values ​​for pressure pulsation and vortex intensity, respectively;

[0044] The penalty function method is as follows: by adding the amount of constraint violation to the original objective function in the form of a penalty term, a new unconstrained objective function is constructed. The more severe the constraint violation, the larger the value of the penalty term, thus making the value function smaller.

[0045] Using the external penalty function method, the value function is constructed as follows:

[0046] ;

[0047] Where Θ is the value function, i.e., the new objective to be maximized; and It serves as a punishment factor, used to determine the severity of the punishment; and The penalty term is defined as a function of the constraint violation amount;

[0048] Explanation of penalty items:

[0049] When constraints are satisfied, the equality term is 0, the penalty term is 0, and the value function... = ;

[0050] The term is positive when a constraint is violated, and the more severe the violation, the larger the value of the term.

[0051] The optimization algorithm uses the gradient ascent method.

[0052] Optionally, using a coupled solution method with a COUPLE to perform transient 3D simulation of the simulation model includes:

[0053] Given the transient time step number and time step size, the time required for each impeller rotation is used as the time step size. First, a preset time step number is given. During the calculation process, the convergence status is judged by checking the values ​​of residual monitoring and custom monitoring points. Then, the time step number is adjusted until the calculation converges. Pressure monitoring points are set at key locations such as the guide vane trailing edge and impeller inlet. Iterative calculations are performed until the pressure pulsation reaches a statistical steady state.

[0054] The beneficial effects of this invention are as follows:

[0055] This invention proposes an innovative simulation modeling method for the motion of hydraulic turbine guide vanes, which combines custom code written using UDFs to control the motion of the guide vanes with mesh adaptation and sliding mesh technology.

[0056] Normal simulation processes cannot account for the dynamic changes of the moving guide vanes over time. The dynamic changes of the guide vanes depend on the results indicators during the turbine simulation process, and the simulation data affects the geometric angle changes of the moving guide vanes. This is a two-way influence process, and normal simulation modeling methods struggle to perform continuous transient simulations. This invention is the first to adopt and realize the autonomous adjustment and change process of the moving guide vanes in the simulation of variable-speed pumped-storage units. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of the automated control process implemented by the UDF in an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram illustrating the process of calling the decision module for each control step in an embodiment of the present invention;

[0060] Figure 3 This is a schematic flowchart of a simulation method for studying the pulsation of a water turbine's moving guide vanes, according to an embodiment of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0063] like Figure 3 As shown in the figure, this embodiment proposes a simulation method for studying the pulsation of moving guide vanes in a water turbine, including:

[0064] The three-dimensional fluid domain of the moving guide vanes of the water turbine is discretized to form a mesh;

[0065] A basic flow simulation model of the pulsating guide vanes of a water turbine is constructed, and the model parameters are input into a discretized mesh to obtain a mesh simulation model of the water turbine.

[0066] A turbulence model is added to the turbine mesh simulation model to model the turbulence phenomenon during turbine operation;

[0067] Based on the simulation model with the turbulence model added, the motion of the guide vane is controlled by a custom UDF, and the three-dimensional transient simulation of the simulation model is performed using the coupled solution method of COUPLE to obtain the transient simulation results of the flow velocity and pressure of the turbine.

[0068] Specifically, in this embodiment, step 1 involves using the CFD software PERASIM Fluid to generate a mesh based on the three-dimensional fluid domain geometry of the turbine's movable guide vanes. This mesh discretizes the fluid region containing the geometry of the turbine blades and the guide vanes to form a computational mesh. This mesh is then used to input the physical model and computational parameters for simulation.

[0069] Step 2: Establish the basic flow simulation model for the pulsation simulation of the turbine's moving guide vanes. The established model and parameters will be input into the discretized mesh for subsequent simulation calculations.

[0070] The equation is as follows:

[0071] ;

[0072] Where ρ is the fluid density, t is time, u is the fluid velocity vector field, ∇⋅ is the divergence operator, ∇ is the gradient operator, p is the pressure field inside the fluid, τ is the viscous stress tensor, ∇² is the Laplace operator, μ is the fluid dynamic viscosity, g is the gravitational acceleration vector, and (u⋅∇)u is the convective acceleration term.

[0073] Step 3: Add turbulence equations to the basic flow simulation model to model the turbulence phenomena during turbine operation, in order to capture pressure fluctuations in the simulation. The turbulence equations are as follows:

[0074] Turbulent kinetic energy (k) equation:

[0075] ;

[0076] Equation for specific dissipation rate (ω):

[0077] ;

[0078] Where ∂(ρ) / ∂t is the unsteady-state term, ∇⋅(ρu⋅) is the convection term, Pk is the turbulent kinetic energy generation term, and Dk is the turbulent kinetic energy dissipation term. For the turbulent kinetic energy diffusion term, P ω D is a term that generates ω. ω For ω, the dissipation term, Let ω be the diffusion term, CD be the cross-diffusion term, ρ be the fluid density, and u be the fluid velocity vector field.

[0079] Step 4: Controlling the movement of the guide vane using a UDF (User-Defined Function) includes:

[0080] Define the motion of the guide vane;

[0081] Set the grid adaptive strategy;

[0082] Automated control of guide vane motion is achieved through UDF.

[0083] Specifically, step 4 uses custom code written in a UDF (User-Defined Function) to control the motion of the guide vane, and combines mesh adaptation and sliding mesh techniques for transient simulation analysis. This establishes a method for motion control of the constructed guide vane mesh during the simulation calculation process.

[0084] 4.1 Definition of motion: The sliding mesh is coupled with the moving mesh;

[0085] Computational domain settings: Set the rotating region as a rotating sliding mesh. Its rotational motion is defined by the absolute angular velocity, which can be constant or given by the operating conditions, as shown in the following formula:

[0086] ;

[0087] Where N is the rotational speed of the impeller.

[0088] Guide vane movement: The active guide vane region is set as a dynamic mesh, and its motion is a rigid body rotation about a specified axis. Its transient angular displacement is given in real time by the control system.

[0089] 4.2 Mesh Adaptation: Gradient-Based Dynamic Encryption

[0090] To ensure the accuracy of the flow field data upon which control decisions depend, mesh adaptation is enabled in key areas.

[0091] ;

[0092] Monitoring points: Ten monitoring points are set up in the area where the vortex evolution is strong between the impeller and the moving guide vane to record pressure pulsation in real time;

[0093] Adaptive frequency: After every 20 time steps, a mesh adaptive loop is triggered to refine / coarse the mesh cells that meet the above criteria.

[0094] 4.3 Intelligent Control System: UDF Implementation:

[0095] The method achieves automated control through UDFs, and the algorithm flow is as follows: Figure 1 As shown;

[0096] Sensing Module - Physical Quantity Monitoring:

[0097] This module executes automatically after each decision step, reading and calculating the following key performance indicators (KPIs):

[0098] Rotor torque: obtained through macrointegration of Compute_Force_And_Moment.

[0099] ;

[0100] Where r is the face-centered vector, T is the viscous stress tensor, P is the pressure, and n is the face unit normal vector.

[0101] Hydraulic efficiency (approximate assessment):

[0102] ;

[0103] Where ρ is the fluid density, g is the gravitational acceleration, Q is the volumetric flow rate, and H is the head.

[0104] Pressure pulsation amplitude: Root mean square (RMS) or amplitude of pressure at the monitoring point.

[0105] ;

[0106] Vortex intensity: Statistically analyzed in a specified area using vortex identification methods such as the Q criterion.

[0107] ;

[0108] Where Ω and S are the antisymmetric and symmetric parts of the velocity gradient tensor, respectively. The fluid volume or maximum value within the computational domain can be statistically calculated.

[0109] Decision-making module - Intelligent regulation:

[0110] Objective function and constraints:

[0111] The goal of the decision module is to find an optimal active guide vane angle to maximize turbine efficiency while satisfying hard constraints on stability and safety. As a typical constrained nonlinear optimization problem, it can be mathematically expressed as follows:

[0112] ;

[0113] Where Θ is the decision variable – the angle of the active guide vane. The objective function is the turbine efficiency. This is a function of the pressure pulsation amplitude. The vortex zone intensity function. , These are the maximum allowable values ​​for pressure pulsation and vortex intensity, respectively;

[0114] Penalty function method:

[0115] The penalty function method constructs a new unconstrained objective function (called the value function $J(\theta)$) by adding the constraint violation as a penalty term to the original objective function. The more severe the constraint violation, the larger the penalty term, thus making the value function smaller.

[0116] Using the external penalty function method, the value function is constructed as follows:

[0117] ;

[0118] Where Θ is the value function, i.e., the new objective to be maximized; and It serves as a punishment factor, used to determine the severity of the punishment; and The penalty term is defined as a function of the constraint violation.

[0119] Explanation of penalty items:

[0120] When the constraint is satisfied (e.g.) The terms are 0, the penalty term is 0, and the value function is... = At this point, the system only prioritizes high efficiency.

[0121] When constraints are violated (e.g.) > The terms are positive, and the more severe the violation, the larger the value of the term. Because this term has a negative sign and a large coefficient λ, it leads to problems in the value function. A sharp decline. The optimization algorithm aims to maximize... This will force the search direction to move away from the constraint violation area.

[0122] Penalty factor selection: λ must be large enough to ensure that the optimal solution satisfies the constraints. Typically, a small value can be started and gradually increased during the optimization iterations (sequential penalty function method) until the solution converges into the feasible region.

[0123] Optimization Algorithm: Gradient Ascent Method

[0124] Because CFD simulation is a computationally expensive "black box" function, it cannot be directly obtained... , , The analytical gradient is obtained. Therefore, the finite difference method is used to estimate the gradient, and the gradient ascent method is used for iterative optimization. At each control step, the decision module is called to execute the following process, such as... Figure 2 As shown;

[0125] Its mathematical expression is:

[0126] a. Gradient estimation:

[0127] ;

[0128] in It's a very small angular perturbation. This requires an additional CFD simulation after the perturbation to calculate... (Note: To reduce computation, historical information from the previous step can be used to construct an approximate gradient.)

[0129] b. Parameter update:

[0130] ;

[0131] in The learning rate (step size) needs to be carefully chosen to ensure stable convergence of the optimization process.

[0132] Coupled simulation and startup transient calculation.

[0133] a. Perform simulation calculations and extract key performance indicators from the simulation results;

[0134] b. Construct objective functions and constraints using key performance indicators, establish penalty mechanisms using objective functions and constraints, and find the optimal result from the minimum penalty score;

[0135] c. Substitute the obtained angle result into the calculation for the next time step;

[0136] d. Repeat the S1-S3 process during the transient simulation time step increase. Repeat this cycle until the set total time or convergence target is reached.

[0137] The angle value is obtained by: calculating the initial angle w of an active guide vane geometry, and then continuously changing the angle parameter through an optimization algorithm until the angle reaches the minimum penalty score required for optimization.

[0138] Step 5: Solve the iterative calculation:

[0139] In PERASIM Fluid software, combined with custom dynamic guidance control code, the COUPLE coupled solution method is used to perform three-dimensional simulation transient solution of the turbine mesh, and obtain transient simulation results of the turbine's flow velocity and pressure.

[0140] Given a transient time step number and time step size, the time required for the impeller to rotate Δα = 2° is used as the time step size. Initially, 1000 time steps are given. During the calculation, the convergence is assessed by checking the values ​​of residual monitoring and custom monitoring points. The time step number is then adjusted until convergence is achieved. Pressure monitoring points are set at key locations such as the guide vane trailing edge and the impeller inlet, and iterative calculations are performed until the pressure pulsation reaches a statistical steady state.

[0141] Statistical convergence criterion:

[0142] ;

[0143] in, The standard deviation of pressure pulsation is given by ΔT = 0.1 s.

[0144] Step 6: Post-process the calculation results to obtain the simulation results:

[0145] Post-processing can extract and display point, line, and surface data, and has various flow field result visualization functions such as streamline diagrams, vector diagrams, and curve diagrams. It also provides user-defined functions such as field variable calculation, dynamic result preview, and animation production. In Perasim Fluid software, flow field changes and pressure pulsation data during the operation of a water turbine are extracted, and cross-sections and surface cloud maps are used to visualize the flow velocity and pressure data.

[0146] This embodiment presents an innovative simulation modeling method for the motion of a hydro turbine's motorized guide vane, which uses custom code written in UDFs to control the motion of the guide vane, combined with mesh adaptation and sliding mesh technology.

[0147] Normal simulation processes cannot account for the dynamic changes of the moving guide vanes over time. The dynamic changes of the guide vanes depend on the results indicators in the turbine simulation process, and the results data in the simulation process affect the geometric angle changes of the moving guide vanes. It is a two-way influence process, and normal simulation modeling methods are difficult to perform continuous transient simulations. This embodiment is the first to adopt and realize the autonomous adjustment and change process of the moving guide vanes in the simulation of variable speed pumped storage units.

[0148] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A simulation method for studying the pulsation of moving guide vanes in a water turbine, characterized in that, include: The three-dimensional fluid domain of the moving guide vanes of the water turbine is discretized to form a mesh; A basic flow simulation model of the pulsating guide vanes of a water turbine is constructed, and the model parameters are input into a discretized mesh to obtain a mesh simulation model of the water turbine. A turbulence model is added to the turbine mesh simulation model to model the turbulence phenomenon during turbine operation; Based on the simulation model with the turbulence model added, the motion of the guide vane is controlled by a custom UDF, and the three-dimensional transient simulation of the simulation model is performed using the coupled solution method of COUPLE to obtain the transient simulation results of the flow velocity and pressure of the turbine.

2. The simulation method for studying the pulsation of moving guide vanes in a water turbine according to claim 1, characterized in that, The basic flow simulation model is as follows: ； Where ρ is the fluid density, t is time, u is the fluid velocity vector field, ∇⋅ is the divergence operator, ∇ is the gradient operator, p is the pressure field inside the fluid, τ is the viscous stress tensor, g is the gravitational acceleration vector, and (u⋅∇)u is the convective acceleration term.

3. The simulation method for studying the pulsation of moving guide vanes in a water turbine according to claim 1, characterized in that, The turbulence model includes: Turbulent kinetic energy quantum model: ； Specific dissipation rate sub-model: ； Where ∂(ρ) / ∂t is the unsteady-state term, ∇⋅(ρu⋅) is the convection term, and P k D is the term for turbulent kinetic energy generation. k For turbulent kinetic energy dissipation, For the turbulent kinetic energy diffusion term, P ω D is a term that generates ω. ω For ω, the dissipation term, ω is the diffusion term, CD is the cross-diffusion term, ρ is the fluid density, u is the fluid velocity vector field, k is the turbulent kinetic energy, and ω is the turbulent dissipation rate.

4. The simulation method for studying the pulsation of moving guide vanes in a water turbine according to claim 1, characterized in that, Using UDF (User-Defined Function) to control the movement of the guide vane includes: Define the motion of the guide vane; Set the grid adaptive strategy; Automated control of guide vane motion is achieved through UDF.

5. The simulation method for studying the pulsation of moving guide vanes in a water turbine according to claim 4, characterized in that, Defining the motion of the guide vane includes: The rotating region is set as a rotating sliding grid, whose rotational motion is defined by absolute angular velocity; The active guide vane area is set as a moving mesh, and its motion is a rigid body rotation about a specified axis.

6. The simulation method for studying the pulsation of moving guide vanes in a water turbine according to claim 4, characterized in that, Setting a mesh adaptive strategy includes: Several monitoring points were set up in the area where the vortex evolution was intense between the impeller and the movable guide vane to record the pressure pulsation in real time; After every certain number of time steps, a mesh adaptive loop is triggered to refine / coarse the mesh cells that meet the criteria.

7. The simulation method for studying the pulsation of moving guide vanes in a water turbine according to claim 4, characterized in that, Automated control of guide vane motion achieved through UDF includes: S1. Perform simulation calculations and extract key performance indicators from the simulation results; S2. Construct an objective function and constraints using key performance indicators, establish a penalty mechanism using the objective function and constraints, and find the angle result from the minimum penalty score; S3. Substitute the obtained angle result into the calculation for the next time step; S4. Repeat the process of S1-S3 during the transient simulation time step growth.

8. The simulation method for studying the pulsation of moving guide vanes in a water turbine according to claim 7, characterized in that, Key performance indicators include: runner torque, hydraulic efficiency, pressure pulsation amplitude, and vortex strength; Objective function: ； constraint: ； Where Θ is the decision variable – the angle of the active guide vane. The objective function is the turbine efficiency. This is a function of the pressure pulsation amplitude. The vortex zone intensity function. , These are the maximum allowable values ​​for pressure pulsation and vortex intensity, respectively; The penalty function is as follows: a new unconstrained objective function is constructed by adding the amount of constraint violation as a penalty term to the original objective function. The more severe the constraint violation, the larger the value of the penalty term, thus making the value function smaller. Using the external penalty function method, the value function is constructed as follows: ； Where Θ is the value function, i.e., the new objective to be maximized; and It serves as a punishment factor, used to determine the severity of the punishment; and The penalty term is defined as a function of the constraint violation amount; Explanation of penalty items: When constraints are satisfied, the equality term is 0, the penalty term is 0, and the value function... = ; The term is positive when a constraint is violated, and the more severe the violation, the larger the value of the term. The optimization algorithm uses the gradient ascent method.

9. The simulation method for studying the pulsation of moving guide vanes in a water turbine according to claim 1, characterized in that, Using the COUPLE coupled solution method to perform 3D simulation transient solution of the simulation model includes: Given the transient time step number and time step size, the time required for each impeller rotation is used as the time step size. First, a preset time step number is given. During the calculation process, the convergence status is judged by checking the values ​​of residual monitoring and custom monitoring points. Then, the time step number is adjusted until the calculation converges. Pressure monitoring points are set at key locations such as the guide vane trailing edge and impeller inlet. Iterative calculations are performed until the pressure pulsation reaches a statistical steady state.

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