A method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit

Abstract

A method for predicting cavitation intensity within the runner of a large-scale hydro-turbine unit includes the following steps: simplifying the gas-liquid two-phase flow within the entire runner channel under typical operating conditions; extracting a complete three-dimensional geometric model of the runner channel, generating a computational mesh, and verifying mesh independence; configuring the solver and computational mode, enabling the multiphase flow model and the turbulence model, and setting boundary conditions, flow field initialization methods, and computational convergence criteria; extracting impurity parameters and arranging discrete phase particles representing impurities in the water; simulating the movement of particles within the runner channel using the discrete phase model to obtain the particle distribution and mass concentration within the channel; calculating the partial pressure of insoluble gases to quantitatively characterize the influence of water quality; calculating the corrected vaporization pressure; obtaining the cavitation occurrence location within the channel and quantitatively evaluating the cavitation intensity; and comparing the cavitation intensity prediction results considering and not considering water quality. This application can improve the prediction accuracy of hydro-turbine cavitation intensity.

Description

Technical Field

[0001] This invention belongs to the technical field of turbine operation monitoring, and specifically relates to a method for predicting the cavitation intensity inside the runner of a large turbine unit. Background Technology

[0002] Cavitation is highly likely to occur within the runner of large hydro-generator units. Cavitation can lead to a series of problems, including vibration, noise, and cavitation erosion, posing a serious challenge to the safe and stable operation of the generator unit. Cavitation within the turbine runner can be broadly classified into four types: airfoil cavitation, gap cavitation, localized cavitation, and cavity cavitation. Airfoil cavitation mainly occurs on the lower half of the water outlet edge of the runner blades near the lower ring, and at the junction of the blade back and the upper and lower rings. Airfoil cavitation forms honeycomb-like holes on the blade surface, causing blade damage. Gap cavitation mainly occurs at various gaps in the turbine, causing damage to the surface near the gaps. Localized cavitation occurs at areas with uneven surfaces. Cavitation is the most prevalent type of cavitation. The water flow at the runner outlet has a certain circumferential velocity component. The rotating water flow converges to form a large, ribbon-like vortex. The vacuum at the center of this vortex is very high. When the pressure drops below the vaporization pressure of water, bubbles are generated at the center of the vortex, which is cavity cavitation. As these bubbles burst, they produce strong noise and cause unit vibration.

[0003] To effectively control the cavitation intensity within the runner, accurate prediction is essential. Current methods largely rely on computational fluid dynamics to numerically simulate the flow within the runner and predict the location and intensity of cavitation. However, due to limitations in computational resources, the influence of water quality conditions is rarely considered.

[0004] However, cavitation intensity is highly sensitive to water quality conditions. The worse the water quality, the more cavitation nuclei there are, the easier cavitation occurs, and the stronger the cavitation intensity. This is especially true for large turbine units in hydropower stations. On the one hand, the incoming flow contains a large amount of suspended impurities such as silt and dust, as well as dissolved impurities such as minerals. On the other hand, the large, ribbon-like eddies in the cavitation cavity have a strong entrainment effect on impurities in the water, resulting in a higher concentration of impurities in the cavitation cavity region. Summary of the Invention

[0005] This invention provides a method for predicting cavitation intensity inside the runner of a large hydro-turbine unit, in order to solve the problem that the predicted cavitation intensity is too low because the water quality conditions are not considered in the prediction of cavitation intensity of the hydro-turbine.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit includes the following steps: Step 1: The gas-liquid two-phase flow in the entire runner channel under typical working conditions is simplified using the CFD homogeneous flow numerical simulation method. Step 2: Extract the complete 3D geometric model of the impeller channel, generate a computational mesh, and verify mesh independence; Step 3: Configure the solver and calculation mode, enable the multiphase flow model and turbulence model, and set the boundary conditions, flow field initialization method and calculation convergence criteria; Step 4: Extract impurity parameters based on the actual incoming water quality conditions, and arrange discrete phase particles representing impurities in the water at the inlet of the runner; Step 5: Use a discrete phase model to simulate the motion of particles in the rotor channel to obtain the particle distribution and mass concentration in the channel; Step 6: Based on the ideal gas law, calculate the partial pressure of insoluble gases to quantitatively characterize the impact on water quality. p g ; Step 7: Based on the vapor phase mass transport equation, determine the partial pressure of insoluble gases. p g By incorporating the vaporization pressure, the corrected vaporization pressure is calculated. p vc ; Step 8: Track the particle motion process, obtain the location of cavitation in the flow channel, and quantitatively assess the cavitation intensity; Step 9: Compare the cavitation intensity prediction results with and without considering water quality, and record the range of severe cavitation conditions and the corresponding water quality conditions.

[0007] Furthermore, in step two, the three-dimensional geometric model of the runner channel includes fixed guide vanes, movable guide vanes, runner blades, and tailrace pipe; The computational mesh is set with a gradually denser mesh on the blade surface, hub and rim wall, and a polyhedral or hexahedral mesh is used in the core region.

[0008] Furthermore, in step three, a pressure-based coupled solver and an unsteady-state calculation mode are employed; the Mixture multiphase flow model is selected, with water as the main phase and water vapor as the second phase; and SST is used. k - ω Turbulence model.

[0009] Furthermore, in step three, the inlet is set as either a mass flow rate inlet or a total pressure inlet, the tailrace outlet is set as a static pressure outlet, and the solid wall is subjected to a no-slip wall condition; a hybrid initialization is used to obtain the initial flow field, and the convergence criterion is set to 10. -5 .

[0010] Furthermore, in step four, “Extracting Impurity Parameters”, the impurity parameters include impurity type, size, concentration, and distribution. The impurity size is described using Rosin-Rammler distribution or a custom particle size grouping. When arranging discrete phase particles representing impurities in the water, the discrete phase particles are arranged at the runner inlet section using either surface injection or lattice injection methods.

[0011] Furthermore, in step four, when the distribution of impurities in the incoming flow is uneven, a non-uniform particle release distribution is set in the inlet boundary conditions.

[0012] Furthermore, in step five, the continuous phase solves the Navier-Stokes equations within the Euler framework, while the discrete phase particles track their trajectories within the Lagrange framework, with the continuous and discrete phases coupled in one direction.

[0013] Furthermore, in step six, "partial pressure of insoluble gases" p g The formula for calculating " is: ; In the formula, p g0 Take it as far-field pressure, C g0 , ρ g These represent the mass concentration and density of impurities in the incoming stream, respectively. C g The mass concentration of local impurities. V v This represents the local steam volume fraction.

[0014] Furthermore, in step seven, the vaporization pressure is adjusted. p vc Calculate in two cases: when hour, ; when hour, ; in, R g , R v These represent the total radius of bubbles and vapor bubbles within the grid cell, respectively. p v It is the saturated vapor pressure. p g This represents the partial pressure of insoluble gases.

[0015] Furthermore, in step eight, the cavitation intensity is assessed by statistically analyzing the maximum gas phase volume fraction, the volume ratio of high-value regions, and the total cavitation volume, and monitoring points are set at key locations to extract pressure pulsation time-domain signals.

[0016] The present invention can achieve the following beneficial effects: This invention employs a cavitation prediction method that takes into account the impact of water quality. It can accurately predict the location and intensity of cavitation in the runner of large hydropower units, obtain the approximate operating conditions and incoming water quality when cavitation is severe, and avoid it as much as possible during the actual operation of the unit. This provides important support for the ultimate goal of controlling cavitation and effectively improves the operational reliability of hydropower station generator units. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of a method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit according to the present invention; Figure 2 This is a diagram illustrating the effect of arranging particles at the inlet of the rotor according to the present invention; Figure 3 The modified vaporization pressure is obtained by fusing bubbles and steam bubbles in this invention. p vc . Detailed Implementation

[0018] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0019] like Figure 1 As shown, a method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit includes the following steps: Step 1: The CFD homogeneous flow numerical simulation method is used to simplify the gas-liquid two-phase flow in the entire turbine flow channel under a typical working condition. The homogeneous flow model treats the liquid phase (water) and the gas phase (water vapor) as a uniform mixture, assuming no velocity slip between the two phases and that they are in local thermodynamic equilibrium. The physical properties of the mixed medium (such as density and viscosity) are used to describe the flow.

[0020] The homogeneous flow model simplifies the originally complex multiphase flow control equations into a single medium's continuity equation, momentum equation, and energy equation. It significantly reduces computational costs while ensuring engineering accuracy. It is especially suitable for high-speed, strong turbulent flow scenarios with insignificant phase separation, such as the analysis of fully wetted flow and cavitation characteristics in the runner channels of hydraulic machinery like mixed-flow turbines.

[0021] Step 2: Extract the complete 3D geometric model of the runner flow channel, including fixed guide vanes, movable guide vanes, runner blades, and tailrace pipes, etc.; generate a high-quality computational mesh using a meshing tool; to accurately capture the wall boundary layer flow, set a gradually denser mesh on the blade surface and the walls such as the hub and rim; use polyhedral or hexahedral meshes in the core region to balance computational accuracy and efficiency; perform mesh independence verification to ensure that the calculation results do not differ significantly with changes in mesh scale.

[0022] Step 3: Employ a pressure-based coupled solver, which exhibits good convergence when handling incompressible and weakly compressible flows; select the unsteady-state calculation mode; enable the Mixture multiphase flow model, designating water as the primary phase and water vapor as the secondary phase; and use SST (Self-Stress Transmission). k - ω Turbulence model, which combines k - ε Model in far-field flow and k - ω The model's advantage in near-wall treatment is that it can better capture the flow separation on the surface of the runner blades and the flow characteristics under adverse pressure gradients.

[0023] Based on typical operating conditions, a mass flow rate inlet or total pressure inlet is given, and the tailrace outlet section is set as a static pressure outlet. All solid walls (blades, hubs, rims, etc.) are subjected to no-slip wall conditions. A reasonable initial flow field is obtained using hybrid initialization; the convergence criterion is set to 10. -5 That is, the residuals of each component (continuity, velocity components, turbulent flow, etc.) decrease to 10. -5 The following is a simultaneous monitoring of key physical quantities (such as impeller torque, efficiency, and inlet / outlet flow difference) as they tend to stabilize.

[0024] Step 4: To accurately reflect the impact of incoming water quality on the internal flow and wear characteristics of the impeller, based on the actual incoming water quality conditions, including the type, size, concentration, and distribution of impurities in the water, discrete phase particles representing impurities in the water are arranged at the impeller inlet, such as... Figure 2 As shown, Figure 2This is for illustrative purposes only; particle size and distribution do not represent actual conditions. First, based on measured incoming water quality data, key parameters affecting particle movement and wear are extracted: impurity type, mainly referring to material density, such as silt, silica sand, and organic particles; impurity size, typically described using Rosin-Rammler distribution or custom particle size grouping to depict the particle size range from fine sand to coarse particles; impurity concentration, determining the mass concentration of impurities at the inlet, used to set the total number of particles released per unit time or the particle mass flow rate; and impurity distribution, analyzing the spatial distribution characteristics of impurities at the inlet cross-section. If the impurity distribution in the incoming flow is uneven, a non-uniform particle release distribution needs to be set in the inlet boundary conditions. After completing the water quality parameterization, surface injection or lattice injection is set at the runner inlet cross-section.

[0025] Step 5: Numerical simulation of the motion and migration of all particles within the rotor channel is performed based on the discrete phase model. The continuous phase (water) is solved using the Navier-Stokes equations within the Eulerian framework, while the discrete phase particles are tracked using the Lagrangian framework by integrating the particle motion equations. Bidirectional coupling of momentum, mass, and energy can be achieved between the particles and the continuous phase. Considering the generally low impurity concentration and to ensure computational efficiency and economy, unidirectional coupling can be adopted, meaning the continuous phase influences the particle trajectory, but the particles do not in turn influence the continuous flow field. Finally, the approximate distribution and mass concentration of discrete phase particles within the rotor channel are obtained, i.e., the water quality conditions within the rotor channel.

[0026] Step Six: Based on the distribution and mass concentration of discrete phase particles in the rotor channel, and according to the ideal gas law, calculate the partial pressure of insoluble gas. p g The quantitative impact on water quality is shown in the following formula. ; In the formula, p g0 The pressure is generally taken as the far-field pressure, that is, the pressure at the inlet of the guide vane or runner. p g0 = p ∞ ; C g0 , ρ g These represent the mass concentration and density of impurities in the incoming stream, respectively. C g The mass concentration of local impurities; V v This represents the local steam volume fraction, typically taken as 0.1.

[0027] Step Seven: As Figure 3Based on the vapor phase mass transport equation describing the liquid phase evaporation and vapor phase condensation processes in cavitation, this paper will use the partial pressure of insoluble gases as a basis for further research. p g The quantitative water quality impact considering the inlet vaporization pressure p v This forms a corrected vaporization pressure. p vc See the following formula.

[0028]

[0029] In the formula, p v It is the saturated vapor pressure. R g , R v These represent the total radius of the bubbles and vapor bubbles within each grid cell. p g This refers to the partial pressure of insoluble gases. p g The higher the value, the worse the water quality, and the easier cavitation is to occur.

[0030] Step 8: As discrete phase particles move with the water flow towards the runner outlet, the location and intensity of cavitation within the entire runner channel, taking into account water quality factors, are obtained. The maximum value of the gas phase volume fraction and the volume percentage of the high-value region within the channel are statistically analyzed; larger values ​​and wider ranges indicate more intense cavitation. The total cavitation volume is obtained by integrating the gas phase volume throughout the entire runner channel, serving as a macroscopic indicator for quantitatively assessing cavitation intensity. Monitoring points are set at key locations (such as the blade leading edge and tailrace inlet) to extract the time-domain signal of pressure pulsation; when cavitation is intense, the amplitude of pressure pulsation increases significantly.

[0031] Step Nine: Compare and analyze the cavitation intensity prediction results considering water quality with those not considering water quality. A larger difference indicates a more significant impact of water quality. Record the incoming water quality conditions; this can provide a reference for numerical simulation cavitation prediction under similar flow conditions in the future. If the difference is small, it indicates that the water quality has a negligible impact on cavitation. Record the approximate operating range and corresponding incoming water quality conditions during periods of intense cavitation. During actual unit operation, these operating ranges and incoming water quality conditions should be avoided as much as possible.

[0032] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit, characterized in that, Includes the following steps: Step 1: The gas-liquid two-phase flow in the entire runner channel under typical working conditions is simplified using the CFD homogeneous flow numerical simulation method. Step 2: Extract the complete 3D geometric model of the impeller channel, generate a computational mesh, and verify mesh independence; Step 3: Configure the solver and calculation mode, enable the multiphase flow model and turbulence model, and set the boundary conditions, flow field initialization method and calculation convergence criteria; Step 4: Extract impurity parameters based on the actual incoming water quality conditions, and arrange discrete phase particles representing impurities in the water at the inlet of the runner; Step 5: Use a discrete phase model to simulate the motion of particles in the rotor channel to obtain the particle distribution and mass concentration in the channel; Step 6: Based on the ideal gas law, calculate the partial pressure of insoluble gases to quantitatively characterize the impact on water quality. p g ; Step 7: Based on the vapor phase mass transport equation, determine the partial pressure of insoluble gases. p g By incorporating the vaporization pressure, the corrected vaporization pressure is calculated. p vc ; Step 8: Track the particle motion process, obtain the location of cavitation in the flow channel, and quantitatively assess the cavitation intensity; Step 9: Compare the cavitation intensity prediction results with and without considering water quality, and record the range of severe cavitation conditions and the corresponding water quality conditions.

2. The method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit according to claim 1, characterized in that: In step two, the three-dimensional geometric model of the runner channel includes fixed guide vanes, movable guide vanes, runner blades, and tailrace pipe; The computational mesh is set with a gradually denser mesh on the blade surface, hub and rim wall, and a polyhedral or hexahedral mesh is used in the core region.

3. The method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit according to claim 1, characterized in that: In step three, a pressure-based coupled solver and an unsteady-state calculation mode are employed; the Mixture multiphase flow model is selected, with water as the main phase and water vapor as the second phase; and SST is used. k - ω Turbulence model.

4. A method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit according to claim 1 or 3, characterized in that: In step three, the inlet is set as either a mass flow rate inlet or a total pressure inlet, the tailrace outlet is set as a static pressure outlet, and the solid wall is subjected to a no-slip wall condition; a hybrid initialization is used to obtain the initial flow field, and the convergence criterion is set to 10. -5 .

5. The method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit according to claim 1, characterized in that: In step four, "Extracting Impurity Parameters", the impurity parameters include impurity type, size, concentration, and distribution. The impurity size is described using Rosin-Rammler distribution or a custom particle size grouping. When arranging discrete phase particles representing impurities in the water, the discrete phase particles are arranged at the runner inlet section using either surface injection or lattice injection methods.

6. The method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit according to claim 5, characterized in that: In step four, when the distribution of impurities in the incoming flow is uneven, a non-uniform particle release distribution is set in the inlet boundary conditions.

7. The method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit according to claim 1, characterized in that: In step five, the continuous phase solves the Navier-Stokes equations within the Euler framework, while the discrete phase particles track their trajectories within the Lagrangian framework. The continuous and discrete phases are coupled in one direction.

8. The method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit according to claim 1, characterized in that: In step six, "partial pressure of insoluble gases" p g The formula for calculating " is: ； In the formula, p g0 Take it as far-field pressure, C g0 , ρ g These represent the mass concentration and density of impurities in the incoming stream, respectively. C g The mass concentration of local impurities. V v This represents the local steam volume fraction.

9. The method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit according to claim 1, characterized in that: In step seven, the vaporization pressure is adjusted. p vc Calculate in two cases: when hour, ; when hour, ; in, R g , R v These represent the total radius of bubbles and vapor bubbles within the grid cell, respectively. p v It is the saturated vapor pressure. p g This represents the partial pressure of insoluble gases.

10. The method for predicting cavitation intensity inside the runner of a large hydroelectric turbine unit according to claim 1, characterized in that: In step eight, the cavitation intensity is assessed by statistically analyzing the maximum gas volume fraction, the volume ratio of high-value regions, and the total cavitation volume, and monitoring points are set at key locations to extract the time-domain signal of pressure pulsation.

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