A cavitation risk prediction method based on cavitation collapse potential energy

Abstract

The present invention discloses a cavitation risk prediction method based on cavitation collapse potential energy, comprising the following steps: establishing a cavitation risk assessment method based on the potential energy contained in the cavitation collapse jet; S2, using modeling software to model the calculation domain and perform grid division on it; selecting corresponding turbulence models and cavitation models to solve the unsteady cavitation flow in the selected calculation domain; performing cavitation calculations, and when a more accurate cavitation morphology is obtained, starting to perform time averaging on the pressure to solve its average pressure field. Beneficial effects: The present invention predicts the cavitation distribution on the surface of a material based on the means of computational fluid dynamics, avoids a large number of tedious and difficult experimental explorations through numerical analysis, and saves experimental costs and time; quantitatively performing cavitation analysis on the model through the means of computational fluid dynamics, can more accurately and comprehensively predict the degree of cavitation damage in various areas of the model surface.

Description

Technical Field

[0001] The present invention relates to the field of fluid machinery and cavitation technology, and in particular to a cavitation risk prediction method based on cavitation collapse potential energy. Background Art

[0002] When the internal pressure of a liquid falls below its saturated vapor pressure, it vaporizes locally, forming cavitation bubbles. The pressure waves generated during the collapse of these bubbles cause localized erosion of the wall surface. As the bubbles continue to collapse, the erosion spreads further and spreads to the entire wall surface. This material damage caused by cavitation is called cavitation erosion. When a fluid moves through a hydraulic machine, its flow conditions (such as pressure and temperature) can easily change, leading to the formation of cavitation bubbles and cavitation erosion. Consequently, cavitation erosion is a common phenomenon in various hydraulic machines, such as centrifugal pumps, marine propellers, and high-speed inducers. Cavitation erosion severely damages the inherent strength of the material, compromising its durability and significantly reducing its service life. Cavitation erosion is also a major factor in inducing vibration and noise. The collapse of cavitation bubbles radiates intense acoustic pressure pulses. Cavitation erosion reduces the efficiency and performance of hydraulic machines; it also causes significant vibration and noise, significantly shortening their service life. In more serious cases, it can affect the proper functioning of the system and even cause safety incidents.

[0003] Currently, research on cavitation erosion, both domestically and internationally, relies heavily on visualization techniques, using high-speed video to capture the cavitation and erosion process. However, visualization techniques are not only costly but also require specialized equipment and personnel. Furthermore, cavitation flows are multiphase, the bubble structure is complex, and the microjets generated by cavitation are extremely high in velocity, making observation extremely difficult. Therefore, establishing accurate methods for calculating cavitation flows and predicting cavitation erosion is of great significance.

[0004] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0005] In response to the problems in the related art, the present invention proposes a cavitation risk prediction method based on cavitation collapse potential energy to overcome the above-mentioned technical problems existing in the existing related art.

[0006] To this end, the specific technical solutions adopted in the present invention are as follows:

[0007] A cavitation erosion risk prediction method based on cavitation collapse potential energy comprises the following steps:

[0008] S1. Establish a cavitation risk assessment method based on the potential energy contained in the cavitation collapse jet;

[0009] S2. Use modeling software to model the computational domain and divide it into grids;

[0010] S3, select the corresponding turbulence model and cavitation model to solve the unsteady cavitation flow in the selected computational domain;

[0011] S4. Perform cavitation calculations, and when a relatively accurate cavitation morphology is obtained, start time averaging the pressure to solve its average pressure field;

[0012] S5. The obtained average pressure field is used as a parameter in the cavitation erosion model, and the cavitation erosion calculation program is loaded in the calculation domain to obtain the cavitation erosion risk index and distribution of each point in the calculation domain.

[0013] Preferably, in step S1, the cavitation model is based on the following assumption: cavitation is caused by the potential energy contained in the large-scale cavitation vortex structure of the cavitation bubbles near the material surface when they collapse, that is, the pressure wave generated when the cavitation bubbles collapse is the main factor causing cavitation, so the cavitation damage caused by the cavitation bubbles near the material surface area is much greater than that in other areas.

[0014] Preferably, in step S1, the formula used in the cavitation model is as follows:

[0015] (a1) Use formula 1 to obtain the pressure wave P when the cavitation structure collapses pot :

[0016] Formula 1:

[0017] Where, ΔP=(P d -P v ), P d represents the general unknown pressure field driving the cavitation collapse, P v Indicates saturation pressure, V v represents the cavitation volume;

[0018] (a2) Calculate the potential power density according to Formula 2 and Formula 3:

[0019] Formula 2:

[0020] Among them, α v =V ν / V cell , α v represents the volume fraction of cavitation, V cell α v represents the volume of the cell where the vacuoles are located; at the same time, α v It can also be expressed as α v =(ρ-ρ l ) / (ρ ν -ρl ), ρ ν is the steam density, ρ l is the liquid density, ρ l is the density of the mixture.

[0021] Formula 3:

[0022] (a3) Calculate the volume fraction α according to the transport equation of formula 4 v :

[0023] Formula 4:

[0024] Where u is the fluid velocity vector and S is the mass source term (which can be calculated using the cavitation model)

[0025] (a4) Calculate S according to Formula 5, the mixture phase continuity equation, and Formula 6, the momentum equation:

[0026] Formula 5;

[0027] Formula 6:

[0028] Where τ is the shear stress, for incompressible fluid (ignoring the volume expansion rate);

[0029] Formula 7: τ=μ(▽u+▽u T )

[0030] Where μ is viscosity, which is a basic property of fluid, and ▽u is

[0031] Formula 8:

[0032] Through the above momentum equation, the flow field velocity and pressure parameters can be calculated.

[0033] The model assumes that energy changes only with the total cavitation volume. Therefore, the cavitation structure releases energy gradually as it condenses, rather than immediately upon collapse. The second term in Equation 3 describes the change in potential energy of the cavitation structure as it moves toward a positive (negative) pressure gradient. However, as long as the cavitation does not undergo any volume change, this change in potential energy has no effect on the energy balance at collapse.

[0034] (a6) Calculate the local instantaneous cavitation rate in the unit grid according to formula 4

[0035] Formula 9:

[0036] Wherein is the average pressure distribution of the transient flow field calculated considering cavitation, which is obtained by accumulating each time step and dividing it by the total calculation time. Preferably, the average pressure calculation time at least ensures that the cavitation generation and collapse exceed 10 cycles.

[0037] Formula 10:

[0038] (a7) Calculate the time-accumulated cavitation rate according to Formula 11

[0039] Formula 11:

[0040] As a preference, the cavitation rate e s As a signal input, the impact strength index n is introduced to calculate the cavitation rate e S Perform different standardization processes to obtain the cavitation risk index E f .

[0041] As a preferred embodiment, the cavitation risk indicator E f The formula 12 used is as follows:

[0042] Formula 12:

[0043] Among them, E f is the total cavitation rate e S About the cumulative cavitation rate and the total impact time T are normalized parameters.

[0044] Preferably, the impact strength index n used to amplify the cavitation rate is n=1, 2, 3, ...; the value of n determines the accuracy of the predicted cavitation area.

[0045] The beneficial effects of the present invention are as follows: the present invention predicts the cavitation distribution on the material surface based on computational fluid dynamics, avoids a large number of tedious and difficult experimental investigations through numerical analysis, and saves experimental costs and time;

[0046] By quantitatively analyzing the cavitation of the model using computational fluid dynamics, the degree of cavitation damage in various areas of the model surface can be predicted more accurately and comprehensively.

[0047] Using computational fluid dynamics to predict the cavitation distribution on the material surface reduces the computational cost and can further shorten the computational cycle through subsequent algorithm optimization, thus providing assistance for the cavitation optimization design of the model.

[0048] More physical details of the cavitation process can be observed, such as the internal pressure field, temperature field, etc., thereby providing technical ideas and theoretical support for further optimization design. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 1 is a flow chart of a method for predicting cavitation risk based on cavitation collapse potential energy according to an embodiment of the present invention;

[0051] Figure 2 1 is a grid diagram of a cooling water pump in a cavitation risk prediction method based on cavitation collapse potential energy according to an embodiment of the present invention;

[0052] Figure 3 This is one of the cavitation distribution diagrams in the pump represented by the cavitation risk index in a cavitation risk prediction method based on cavitation collapse potential energy according to an embodiment of the present invention;

[0053] Figure 4 This is the second cavitation distribution diagram in a pump represented by a cavitation risk indicator in a cavitation risk prediction method based on cavitation collapse potential energy according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention. They are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. By referring to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0055] According to an embodiment of the present invention, a cavitation risk prediction method based on cavitation collapse potential energy is provided.

[0056] like Figure 1-4 As shown, the cavitation risk prediction method based on cavitation collapse potential energy according to an embodiment of the present invention includes the following steps:

[0057] S1. Establish a cavitation risk assessment method based on the potential energy contained in the cavitation collapse jet;

[0058] S2. Use modeling software to model the computational domain and divide it into grids;

[0059] S3, select the corresponding turbulence model and cavitation model to solve the unsteady cavitation flow in the selected computational domain;

[0060] S4. Perform cavitation calculations, and when a relatively accurate cavitation morphology is obtained, start time averaging the pressure to solve its average pressure field;

[0061] S5. The obtained average pressure field is used as a parameter in the cavitation erosion model, and the cavitation erosion calculation program is loaded in the calculation domain to obtain the cavitation erosion risk index and distribution of each point in the calculation domain.

[0062] The cavitation calculation process for cooling water pump impeller blades is as follows: Step 1: Create a three-dimensional model of the centrifugal pump computational domain, including modeling the inlet and outlet extensions, front chamber, impeller, volute, and rear chamber. Step 2: Mesh the computational domain and locally refine the impeller fluid domain (the impeller mesh is shown in Figure 2). Step 3: Import the mesh into a solver for solution, using an appropriate turbulence model. Preferably, the LES large eddy simulation method can be used to simulate the cavitation generation and collapse characteristics. The boundary conditions are velocity inlet boundary conditions, total pressure outlet boundary conditions, and a no-slip wall function. Step 4: Select the MeshMotion model for the rotating region, and the interface between the rotating region and the stationary region as the Interface surface. Step 5: First, perform a single-phase steady-state calculation. Then, using the stable single-phase steady-state calculation results as input, perform an unsteady cavitation flow calculation using the Schnerr-Sauer cavitation model. Step 6: Perform the cavitation calculation. Once a relatively accurate cavitation morphology is obtained, begin time-averaging the pressure to solve for the mean pressure field. Step 7: Use the obtained average pressure field as a parameter in the cavitation model, load the cavitation calculation program in the calculation domain, and obtain the cavitation risk index and distribution of each point in the calculation domain. The distribution of cavitation rate after numerical simulation is as follows Figure 3-4 shown.

[0063] In one embodiment, the cavitation model in step S1 is based on the following assumption: cavitation is caused by the potential energy contained in the large-scale cavitation vortex structure released when cavitation bubbles near the material surface collapse. Cavitation damage near the material surface is far greater than damage in other areas. In step S1, the cavitation model uses the following formula:

[0064] (a1) Use formula 1 to obtain the pressure wave P when the cavitation structure collapses pot :

[0065] Formula 1:

[0066] Where, ΔP=(P d -P v ), P d represents the general unknown pressure field driving the cavitation collapse, P v Indicates saturation pressure, Vv represents the cavitation volume;

[0067] (a2) Calculate the potential power density according to Formula 2 and Formula 3:

[0068] Formula 2:

[0069] Among them, α v =V ν / V cell , α v represents the volume fraction of cavitation, V cell represents the volume of the cell where the vacuoles are located; at the same time, α v It can also be expressed as α v =(ρ-ρ l ) / (ρ ν -ρ l ), ρ ν is the steam density, ρ l is the density of the liquid, and ρ is the density of the mixture.

[0070] Formula 3:

[0071] (a3) Calculate the volume fraction α according to the transport equation of formula 4 v :

[0072] Formula 4:

[0073] Where u is the fluid velocity vector and S is the mass source term (which can be calculated using the cavitation model)

[0074] (a4) Calculate u based on the mixture phase continuity equation (Equation 5) and the momentum equation (Equation 6):

[0075] Formula 5;

[0076] Formula 6:

[0077] Where τ is the shear stress, for incompressible fluid (ignoring the volume expansion rate);

[0078] Formula 7: τ=μ(▽u+▽u T )

[0079] Where, is viscosity, which is a basic property of fluid, and is ▽u

[0080] Formula 8:

[0081] Through the above momentum equation, the flow field velocity and pressure parameters can be calculated.

[0082] In addition, in one embodiment, the cavitation model in step S3 can be calculated using a simplified cavitation mass transfer model based on the Rayleigh-Plesset equation (assuming that all bubbles in the liquid region are spherical and there is no interaction between bubbles). Preferably, the Schnerr-Sauer model can be used. The model assumes that energy changes only with the total cavitation volume. Therefore, the cavitation structure gradually releases energy during condensation, rather than immediately releasing energy after collapse.

[0083] (a5) Calculate the mass source term according to Formula 9:

[0084] By combining the transport equation of formula 4 and the continuous phase equation of formula 5, we can obtain:

[0085] Formula 9:

[0086] The relationship between volume fraction, cavitation number density and cavitation radius is:

[0087] Formula 10:

[0088] Combining Formula 9 and Formula 10, we can get;

[0089] Formula 11:

[0090] The model assumes that energy changes only with the total cavitation volume. Therefore, the cavitation structure releases energy gradually as it condenses, rather than immediately upon collapse. The second term in Equation 3 describes the change in potential energy of the cavitation structure as it moves toward a positive (negative) pressure gradient. However, as long as the cavitation does not undergo any volume change, this change in potential energy has no effect on the energy balance at collapse.

[0091] (a6) Calculate the local instantaneous cavitation rate in the unit grid according to formula 4:

[0092] Formula 12:

[0093] Wherein is the average pressure distribution of the transient flow field calculated considering cavitation, which is obtained by accumulating each time step and dividing it by the total calculation time. Preferably, the average pressure calculation time at least ensures that the cavitation generation and collapse exceed 10 cycles.

[0094] Formula 13:

[0095] (a7) Calculate the time-accumulated cavitation rate according to Formula 13:

[0096] Formula 14:

[0097] The cavitation rate e sAs a signal input, the impact strength index n is introduced to calculate the cavitation rate e S Perform different standardization processes to obtain the cavitation risk index E f Characterizing the cavitation risk index E f The formula 15 used is as follows:

[0098] Formula 15:

[0099] Wherein, is the parameter of total cavitation rate normalized with respect to cumulative cavitation rate and total impact time. Furthermore, this parameter can be modified through experiments to obtain the actual cavitation damage amount.

[0100] In one embodiment, the cavitation rate is used as a signal input in step S5, and an impact strength index n is introduced to perform different normalization operations on the cavitation rate to obtain a cavitation risk index. The impact strength index n, used to amplify the cavitation rate, can be 1, 2, 3, etc. The value of n determines the accuracy of the predicted cavitation rate. A larger value of n results in more consistent results. However, considering computer efficiency, n should not be too large. Preferably, a value of 3 is recommended for n.

[0101] To sum up, with the help of the above-mentioned technical scheme of the present invention, the cavitation distribution on the material surface is predicted by means of computational fluid dynamics, and a large number of tedious and difficult experimental explorations are avoided through numerical analysis, saving experimental costs and time; the cavitation analysis of the model is quantitatively performed by means of computational fluid dynamics, which can more accurately and comprehensively predict the degree of cavitation damage in various areas of the model surface; with the help of computational fluid dynamics to predict the cavitation distribution on the material surface, the calculation cost is reduced, and the calculation cycle can be further shortened through later algorithm optimization, which provides assistance for the cavitation optimization design of the model; more physical details in the cavitation process can be observed, such as the internal pressure field, temperature field, etc., thereby providing technical ideas and theoretical support for further optimization design.

[0102] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A cavitation risk prediction method based on cavitation collapse potential energy, characterized in that: The following steps are involved: S1. Establish a cavitation risk assessment method based on the potential energy contained in the cavitation collapse jet; S2. Use modeling software to model the computational domain and divide it into grids; S3, select the corresponding turbulence model and cavitation model to solve the unsteady cavitation flow in the selected computational domain; S4. Perform cavitation calculations, and when a relatively accurate cavitation morphology is obtained, start time averaging the pressure to solve its average pressure field; S5. Using the obtained average pressure field as a parameter in the cavitation erosion model, loading the cavitation erosion calculation program in the calculation domain, thereby obtaining the cavitation erosion risk index and distribution of each point in the calculation domain; The formula used in the cavitation model is as follows: (a1) Use formula 1 to obtain the pressure wave P when the cavitation structure collapses pot : Formula 1: Where, ΔP=(P d -P v ), P d represents the general unknown pressure field driving the cavitation collapse, P v Indicates saturation pressure, V v represents the cavitation volume; (a2) Calculate the potential power density according to Formula 2 and Formula 3: Formula 2: Among them, α v =V ν / V cell , α v represents the volume fraction of cavitation, V cell represents the volume of the cell where the vacuoles are located; at the same time, α v It can also be expressed as α v =(ρ-ρ l ) / (ρ ν -ρ l ), ρ ν is the steam density, ρ l is the liquid density, ρ l is the density of the mixture; Formula 3: (a3) Calculate the volume fraction α according to the transport equation of formula 4 v : Formula 4: Where u is the fluid velocity vector and S is the mass source term (a4) Calculate S according to Formula 5, the mixture phase continuity equation, and Formula 6, the momentum equation: Formula 5; Formula 6: Where τ is the shear stress, for incompressible fluid; Formula 7: Where μ is viscosity, a basic property of fluids, for Formula 8: Through the above momentum equation, the flow field velocity and pressure parameters can be calculated; The model assumes that energy changes only with the total cavitation volume; therefore, the cavitation structure releases energy gradually as it condenses, rather than immediately upon collapse. The second term in Equation 3 describes the change in potential energy of the cavitation structure as it moves toward a positive pressure gradient; however, as long as the cavitation does not undergo any volume change, this change in potential energy has no effect on the energy balance at collapse. (a6) Calculate the local instantaneous cavitation rate in the unit grid according to formula 4 Formula 9: Where is the average pressure distribution of the transient flow field calculated considering cavitation, which is obtained by accumulating each time step and dividing it by the total calculation time. Preferably, the average pressure calculation time is at least enough to ensure that the cavitation generation and collapse exceed 10 cycles; Formula 10: (a7) Calculate the time-accumulated cavitation rate according to Formula 11 Formula 11:

2. The cavitation risk prediction method based on cavitation collapse potential energy according to claim 1, characterized in that: In step S1, the cavitation model is based on the following assumption: cavitation is caused by the potential energy contained in the large-scale cavitation vortex structure of the cavitation bubbles near the material surface when they collapse, that is, the pressure wave generated when the cavitation bubbles collapse is the main factor causing cavitation, so the cavitation damage caused by the cavitation bubbles near the material surface area is much greater than that in other areas.

3. The cavitation risk prediction method based on cavitation collapse potential energy according to claim 2, characterized in that: The cavitation rate e s As a signal input, the impact strength index n is introduced to calculate the cavitation rate e S Perform different standardization processes to obtain the cavitation risk index E f .

4. The method for predicting cavitation risk based on cavitation collapse potential energy according to claim 3, characterized in that: The formula twelve used to characterize the cavitation risk index is as follows: Formula Twelve: Among them, E f is the total cavitation rate e S About the cumulative cavitation rate and the total impact time T are normalized parameters.

5. The method for predicting cavitation risk based on cavitation collapse potential energy according to claim 4, characterized in that: The impact strength index n=1, 2, 3, ...; the value of n determines the accuracy of the predicted cavitation area.

Images