Water ring vacuum pump blade cavitation impact energy evolution and cavitation prediction method

Abstract

The embodiment of the invention provides a water ring vacuum pump blade cavitation impact energy evolution and cavitation prediction method. The method comprises the steps that a water ring vacuum pump blade cavitation bubble Rayleigh-Plesset equation and a water ring vacuum pump blade cavitation bubble dimensionless equation considering random pulsation and time-varying load pressure are constructed; solving the water ring vacuum pump blade cavitation bubble dimensionless equation to obtain the evolution process of the bubble radius along with the time change; constructing a water ring vacuum pump blade cavitation impact energy evolution equation to obtain a water ring vacuum pump blade cavitation impact energy evolution process; and a water ring vacuum pump blade cavitation impact cavitation area judgment equation is constructed under the time-varying working condition, the effective time-varying cavitation head of the blade and the cavitation area change range are obtained, and the maximum cavitation area occurrence time is predicted. According to the method, the cavitation impact energy evolution of the water ring vacuum pump under the random time-varying pulsation working condition is considered, and the blade cavitation damage and the space distribution characteristics of the cavitation area under the complex flow working condition can be effectively judged.

Description

Technical Field

[0001] This application relates to the field of hydraulic pump condition monitoring and prediction technology, specifically to a method for predicting the evolution of cavitation impact energy and cavitation in water ring vacuum pump blades. Background Technology

[0002] High-pressure water ring vacuum pumps are widely used in flammable and explosive industrial applications such as submarine vacuum systems, vacuum distillation, and dust and exhaust gas treatment due to their compact structure, resistance to complex working conditions, stable operation, and variable frequency speed regulation according to vacuum requirements. However, factors such as unreasonable pump geometry design (e.g., mismatch between blade inlet angle and attack angle), flow channel pressure fluctuations, turbulent pulsation, and corrosive media (e.g., acidic solutions, seawater) can easily cause cavitation pits, local perforation, or breakage in the blades of high-pressure water ring vacuum pumps. Therefore, clarifying the evolution law of cavitation impact energy in high-pressure water ring vacuum pump blades and predicting cavitation defects is of significant engineering importance for real-time monitoring and intelligent operation and maintenance of complex hydraulic systems.

[0003] Currently, methods for predicting hydraulic pump impact energy evolution and cavitation mainly include computational fluid dynamics finite element simulation, high-speed photography and schlieren imaging, cavitation noise acoustic measurement, and end-to-end deep learning data-driven methods. However, these methods cannot capture the evolution process and degradation mechanism of hydraulic pump impact energy, nor can they explain the mapping relationship between hydraulic pump impact energy evolution and cavitation prediction. Existing theoretical-physical models rarely consider the impact of different external operating conditions (such as pressure fluctuations, random variable loads, and pump speed variations) on hydraulic pump impact energy evolution and cavitation prediction. Summary of the Invention

[0004] The purpose of this application is to provide a method for predicting the evolution of cavitation impact energy and cavitation in water ring vacuum pump blades, so as to at least solve the technical problem that existing hydraulic pump impact energy evolution and cavitation prediction technologies cannot capture the hydraulic pump impact energy evolution process and evolution degradation mechanism, nor can they explain the mapping relationship between hydraulic pump impact energy evolution and cavitation prediction.

[0005] To achieve the above objectives, the embodiments of this application provide the following technical solutions.

[0006] According to one embodiment of this application, a method for predicting the cavitation impact energy evolution and cavitation of water ring vacuum pump blades is provided, including the following steps:

[0007] S1: Construct the Rayleigh-Plesset equation for cavitation bubbles in the blades of a water ring vacuum pump that considers random pulsation and time-varying load pressure;

[0008] S2: Based on the Rayleigh-Plesset equation, construct a dimensionless equation for the cavitation bubbles of the water ring vacuum pump blades considering random pulsation and time-varying load pressure;

[0009] S3: The dimensionless equation of cavitation bubbles in the blades of a water ring vacuum pump considering random pulsation and time-varying load pressure is solved using the variable step size Runge-Kutta-Fehlberg method, and the evolution of bubble radius with time under random pulsation and time-varying load pressure is obtained.

[0010] S4: Construct the evolution equation of cavitation impact energy of water ring vacuum pump blades to obtain the evolution process of cavitation impact energy of water ring vacuum pump blades under random pulsating conditions.

[0011] S5: Construct a cavitation impact cavitation zone determination equation for water ring vacuum pump blades under time-varying operating conditions, and obtain the effective time-varying cavitation margin of the blades, the range of cavitation zone variation, and the predicted time of occurrence of the maximum cavitation zone.

[0012] S6: Based on the cavitation evolution process of the water ring vacuum pump blades under time-varying load, the operating parameters and mechanical structure of the water ring vacuum pump are optimized and controlled.

[0013] Preferably, in step S1, the constructed Rayleigh-Plesset equation is expressed as:

[0014]

[0015] In the formula, R represents the bubble radius; and Let represent the first and second derivatives of the bubble radius, respectively; Indicates the density of the liquid; Indicates the pressure inside the bubble; σ is the liquid vapor pressure; σ is the surface tension coefficient. The dynamic viscosity of the liquid. For random pulsation terms; For far-field environmental pressure, ; Indicates static pressure; For random time-varying load pressure, the pressure inside the bubble It can be calculated using thermodynamic adiabatic equations. get, The initial internal pressure of the bubble, Let the initial radius of the bubble be 1. This is the specific heat capacity ratio.

[0016] Preferably, the process of constructing the Rayleigh-Plesset equation includes the following steps:

[0017] Step S11: Random Pulsation Term The construction process is represented as follows:

[0018] ;

[0019] ;

[0020] Step S21: The time-varying load pressure model is expressed as:

[0021] ;

[0022] In the formula, Let A be a constant, and w be the pulsating angular frequency. For phase shift, This represents the proportion of random pulsation amplitude. This represents the load amplitude ratio.

[0023] Preferably, in step S2, the dimensionless equation constructed is expressed as:

[0024]

[0025] In the formula, the dimensionless radius ;time ,pressure viscosity coefficient Surface tension random pulsation term .

[0026] Preferably, in step S3, let , ,but:

[0027]

[0028] According to the first-order system of equations The iterative calculation of the variable step-size fourth-order Runge-Kutta-Fehlberg method is expressed as follows:

[0029]

[0030]

[0031] In the formula, For time t n A random pulsation sequence at a given location.

[0032] Preferably, in step S4, the overall impact energy equation of the bubble is constructed as follows:

[0033]

[0034] In the formula, kinetic energy for Pressure energy for .

[0035] Preferably, step S5 specifically includes:

[0036] Step S51: Construct the equations for the synthesized time-varying pulsating flow rate, synthesized time-varying rotational speed, and synthesized time-varying inlet pressure parameters, expressed as:

[0037] Synthetic time-varying pulsating flow rate: ;

[0038] Synthetic time-varying speed: ;

[0039] Synthesis time-varying inlet pressure: ;

[0040] In the formula, random pulsating flow rate Traffic under periodic load Random pulsating speed Rotational speed under periodic load Random pulsating inlet pressure Inlet pressure under cyclic load ; These are random numbers distributed according to a standard normal distribution. This is the average flow rate. This is the average rotational speed. This represents the average import pressure. and These represent the pulsation amplitude and the load amplitude coefficient, respectively, with w being the pulsation angular frequency. For phase;

[0041] Step S52: Assuming that the axial velocity, blade angle, and width vary linearly along the radius, construct a time-varying relative speed versus absolute speed relationship model, expressed as:

[0042]

[0043] In the formula, the vacuum pump blade angle The radial linear variation is ; Time-varying circular velocity ; Time-varying uniformly distributed axial velocity Vacuum pump blade width The radial linear variation is ; Where is the impeller inlet radius. The impeller outlet radius; For the width of the inlet, For the width of the outlet; For the blade inlet angle, This refers to the blade exit angle;

[0044] Step S53: According to Bernoulli's equation, the time-varying pressure distribution on the surface of the water ring vacuum pump blades can be constructed as follows:

[0045]

[0046] In the formula, the absolute velocity of the import is The absolute velocity of the blade surface is , The absolute velocity component in the circumferential direction. Radial velocity component;

[0047] Step S54: The time-varying cavitation prediction model for water ring vacuum pump blades is expressed as follows:

[0048]

[0049] like If cavitation occurs, then cavitation will occur; otherwise, cavitation will not occur.

[0050] The time-varying effective net positive suction head (NPSH) of a water ring vacuum pump blade is expressed as:

[0051]

[0052] Where g is the gravitational acceleration constant, g = 9.80665 m / s².

[0053] According to another embodiment of this application, a non-volatile storage medium is also provided, the storage medium including a stored program, wherein the program, when running, controls the device where the storage medium is located to execute the above-mentioned method for the evolution of cavitation impact energy of water ring vacuum pump blades and prediction of cavitation.

[0054] According to another embodiment of this application, a computer terminal is also provided, the computer terminal comprising:

[0055] Memory;

[0056] A processor is configured to run a program stored in the memory, wherein the program executes the method for predicting the cavitation impact energy evolution and cavitation of water ring vacuum pump blades provided in the above embodiments.

[0057] Compared with the prior art, the technical advantages of the method for predicting the cavitation impact energy evolution and cavitation of water ring vacuum pump blades in this application are as follows:

[0058] First, the energy evolution and cavitation prediction method provided in this application takes into account the cavitation impact energy evolution of a water ring vacuum pump under random time-varying pulsating conditions, which can effectively reveal the cavitation impact energy evolution process and cavitation evolution mechanism of the water ring vacuum pump.

[0059] Secondly, compared with traditional computational fluid dynamics finite element simulation technology and data-driven prediction methods, the method proposed in this application can effectively determine whether blade cavitation damage occurs under complex flow conditions (random time-varying load, fluid pulsation), the spatial distribution characteristics of cavitation areas on the blade surface, predict the effective time-varying cavitation margin of the blade, the proportion of cavitation areas, and the time of occurrence of the maximum cavitation area, etc. It provides intuitive visualization and theoretical reference for the anti-cavitation research of water ring vacuum pump blades, the precise positioning of cavitation risk engineering applications, and the efficient and stable operation of equipment, and has good industrial application value.

[0060] In summary, the method for predicting the cavitation impact energy evolution and cavitation of water ring vacuum pump blades provided in this application embodiment can predict blade cavitation under random time-varying operating conditions, providing a theoretical basis for real-time monitoring and intelligent operation and maintenance of complex hydraulic systems. Attached Figure Description

[0061] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0062] In the attached diagram:

[0063] Figure 1 This is a diagram illustrating the architecture of the water ring vacuum pump blade cavitation impact energy evolution and cavitation prediction method provided in an embodiment of the present invention.

[0064] Figure 2 The flow rate of the water ring vacuum pump in this embodiment of the invention is shown as a curve of change over time.

[0065] Figure 3 The rotational speed of the water ring vacuum pump in this embodiment of the invention is shown as a function of time.

[0066] Figure 4 The curve showing the inlet pressure of the water ring vacuum pump as a function of time in an embodiment of the present invention;

[0067] Figure 5 This is a graph showing the change of bubble radius over time in an embodiment of the present invention;

[0068] Figure 6 This is a histogram showing the statistical distribution of bubble radii in an embodiment of the present invention.

[0069] Figure 7 This is a diagram illustrating the evolution of bubble energy according to an embodiment of the present invention.

[0070] Figure 8 The curves showing the effective time-varying cavitation margin and cavitation region ratio as a function of time in an embodiment of the present invention are shown.

[0071] Figure 9 This is a velocity distribution diagram on the surface of the water ring vacuum pump blades according to an embodiment of the present invention;

[0072] Figure 10 This is a pressure distribution diagram on the surface of the water ring vacuum pump blades according to an embodiment of the present invention;

[0073] Figure 11 This is the predicted cavitation region of the water ring vacuum pump in this embodiment of the invention;

[0074] Figure 12 This is a hardware block diagram of a computer terminal for a method of predicting the evolution of cavitation impact energy and cavitation of water ring vacuum pump blades according to an embodiment of the present invention. Detailed Implementation

[0075] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0076] According to the embodiments of this application, a method embodiment for predicting the evolution of cavitation impact energy of water ring vacuum pump blades and cavitation is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0077] To overcome the aforementioned problems of cavitation impact energy evolution and cavitation prediction in water ring vacuum pumps, especially for the cavitation impact energy evolution and cavitation prediction under random time-varying pulsating conditions, this invention proposes a method for predicting the cavitation impact energy evolution and cavitation prediction of water ring vacuum pump blades. This prediction method can realize blade cavitation prediction under random time-varying conditions, providing a theoretical basis for real-time monitoring and intelligent operation and maintenance of complex hydraulic systems.

[0078] The prediction method in this embodiment uses a certain type of water ring vacuum pump. The specific geometric parameters of this type of water ring vacuum pump are as follows:

[0079] Impeller inlet radius r1=0.05m, impeller outlet radius r2=0.15m, blade inlet angle β1=30°, blade outlet angle β2=20°, vacuum pump inlet width b1=0.02m, vacuum pump outlet width b2=0.01m;

[0080] The fluid characteristic parameters of the water ring vacuum pump are as follows:

[0081] The fluid density is ρ l =1000kg / m³, the speed of sound in the liquid is 1500m / s, the surface tension is 0.072N / m, and the hydrodynamic viscosity is μ. l =0.001 Pa·s, temperature T=20℃, vapor pressure of liquid at 20℃ p v =2339Pa, the initial conditions for the bubbles are R0=0.001m;

[0082] The operating parameters of the water ring vacuum pump are as follows: average flow rate Q mean =0.1m³ / s, average rotational speed n mean =1500rpm, average inlet pressure p in_mean =101325Pa, pulsation amplitude 0.1, pulsation frequency 10Hz, time-varying load change amplitude 0.2, time-varying load change frequency 100Hz, phase offset π / 4.

[0083] In addition, the filter used in the time-varying load stress model is a fourth-order Butterworth low-pass filter.

[0084] Figure 2 , Figure 3 and Figure 4 The flow rate, rotational speed, and inlet pressure of the water ring vacuum pump according to an embodiment of the present invention are given as curves over time. It can be seen that the flow rate of the water ring vacuum pump is approximately 0.08 m³ / s. 3 / s-0.13m 3 The speed of the water ring vacuum pump fluctuates randomly within the range of 1182 rpm to 1945 rpm, and the inlet pressure of the water ring vacuum pump fluctuates randomly within the range of 61843 Pa to 116976 Pa.

[0085] Specifically, such as Figure 1 As shown, according to one embodiment of this application, a method for predicting the evolution of cavitation impact energy and cavitation in water ring vacuum pump blades is provided, including the following steps:

[0086] S1: Construct the Rayleigh-Plesset equation for cavitation bubbles in the blades of a water ring vacuum pump that considers random pulsation and time-varying load pressure;

[0087] S2: Based on the Rayleigh-Plesset equation, construct a dimensionless equation for the cavitation bubbles of the water ring vacuum pump blades considering random pulsation and time-varying load pressure;

[0088] S3: The dimensionless equation of cavitation bubbles in the blades of a water ring vacuum pump considering random pulsation and time-varying load pressure is solved using the variable step size Runge-Kutta-Fehlberg method, and the evolution of bubble radius with time under random pulsation and time-varying load pressure is obtained.

[0089] like Figure 5 As shown, the bubble radius changes with time. It can be seen that within the time range of 0-5ms, the bubble radius increases in a basically linear trend with the increase of time.

[0090] Figure 6 This is a histogram of the probability density statistical distribution of bubble radius, used to present the probability density distribution of occurrence corresponding to different bubble radii;

[0091] Combination Figure 5 and Figure 6 It can be seen that: ① The bubble radius varies within the range of 0-43 mm. When the bubble radius is in the range of 0-5 mm, the probability density has a significant peak, indicating that the probability of small-radius bubbles (especially those close to 0 mm) is relatively high. However, it then drops rapidly, and the probability density drops to a low level (approximately 0.02-0.022) around 5 mm. ② In the range of 5-10 mm, the probability density shows a fluctuation of first rising and then falling, indicating that the probability of certain specific radii of bubbles in this range is relatively prominent, and there may be a certain number of concentrated bubble sizes. ③ In the range of approximately 10-45 mm, the probability density is relatively stable, maintaining a level of approximately 0.02-0.025, indicating that the probability density difference of bubbles with different radii in this range is not significant, and the distribution of bubble radii is relatively uniform.

[0092] S4: Construct the evolution equation of cavitation impact energy of water ring vacuum pump blades to obtain the evolution process of cavitation impact energy of water ring vacuum pump blades under random pulsating conditions.

[0093] Figure 7 This is a diagram illustrating the energy evolution of bubbles according to an embodiment of the present invention, including the changes in bubble kinetic energy, pressure energy, and total energy. It can be seen that:

[0094] ① In the initial stage of bubble energy evolution (around 0-2ms): During this stage, the changes in bubble kinetic energy, pressure energy and total energy are small and close to 0, indicating that the system has little initial energy and is in a relatively calm state;

[0095] ② During the bubble energy evolution and development stage (around 2-4ms): During this stage, the bubble kinetic energy, pressure energy and total energy curves all show an upward trend, indicating that the pump system begins to absorb energy, internal interactions are enhanced, and total energy gradually accumulates.

[0096] ③ In the later stage of bubble energy evolution (about 4-5ms): During this stage, the bubble kinetic energy, pressure energy and total energy continue to increase at an accelerated rate, and the total energy increases sharply at the end, indicating that the energy inside the pump system is changing drastically and may be entering the bubble bursting stage.

[0097] S5: Construct a cavitation impact cavitation zone determination equation for water ring vacuum pump blades under time-varying operating conditions, and obtain the effective time-varying cavitation margin of the blades, the range of cavitation zone variation, and the predicted time of occurrence of the maximum cavitation zone.

[0098] S6: Based on the cavitation evolution process of the water ring vacuum pump blades under time-varying load, the operating parameters and mechanical structure of the water ring vacuum pump are optimized and controlled.

[0099] Preferably, in step S1, the constructed Rayleigh-Plesset equation is expressed as:

[0100]

[0101] In the formula, R represents the bubble radius; and Let represent the first and second derivatives of the bubble radius, respectively; Indicates the density of the liquid; Indicates the pressure inside the bubble; σ is the liquid vapor pressure; σ is the surface tension coefficient. The dynamic viscosity of the liquid. For random pulsation terms; For far-field environmental pressure, ; Indicates static pressure; For random time-varying load pressure, the pressure inside the bubble It can be calculated using thermodynamic adiabatic equations. get, The initial internal pressure of the bubble, Let the initial radius of the bubble be 1. This is the specific heat capacity ratio.

[0102] Preferably, the process of constructing the Rayleigh-Plesset equation includes the following steps:

[0103] Step S11: Random Pulsation Term The construction process is represented as follows:

[0104] ;

[0105] ;

[0106] In the formula, It indicates the intensity of random pulsations, reflecting the magnitude of random excitation; This indicates the computation time step, used to control computational accuracy and speed; Denotes the Dirac function, when Time indicates relevance. Indicates an instantaneous pulse. The time indicates no correlation, meaning that white noise has no memory; Indicates the time value;

[0107] Furthermore, it also includes step S21: the time-varying load pressure model is expressed as:

[0108] ;

[0109] In the formula, Let A be a constant, and w be the pulsating angular frequency. For phase shift, This represents the proportion of random pulsation amplitude. The value represents the load amplitude ratio; filter indicates a low-pass filter. This represents the angular frequency of the time-varying load.

[0110] Preferably, in step S2 of this embodiment, the constructed dimensionless equation is expressed as:

[0111]

[0112] In the formula, the dimensionless radius ;time ,pressure viscosity coefficient Surface tension random pulsation term .

[0113] Furthermore, in this embodiment of the application, in step S3, let , ,but:

[0114]

[0115] According to the first-order system of equations The iterative calculation of the variable step-size fourth-order Runge-Kutta-Fehlberg method is expressed as follows:

[0116]

[0117]

[0118] In the formula, For time t n The random pulsation sequence at the location, This represents the state variables at step n, such as bubble radius R and the rate of change of bubble radius. The state variables that make up the state.

[0119] Preferably, in step S4, the overall impact energy equation of the bubble is constructed as follows:

[0120]

[0121] In the formula, kinetic energy for Pressure energy for .

[0122] Preferably, step S5 specifically includes:

[0123] Step S51: Construct the equations for the synthesized time-varying pulsating flow rate, synthesized time-varying rotational speed, and synthesized time-varying inlet pressure parameters, expressed as:

[0124] Synthetic time-varying pulsating flow rate: ;

[0125] Synthetic time-varying speed: ;

[0126] Synthesis time-varying inlet pressure: ;

[0127] In the formula, random pulsating flow rate ;

[0128] Traffic under periodic load ;

[0129] Random pulsating speed ;

[0130] Speed ​​under periodic load ;

[0131] Random pulsating inlet pressure ;

[0132] Inlet pressure under cyclic load ;

[0133] These are random numbers distributed according to a standard normal distribution.

[0134] This is the average flow rate. This is the average rotational speed. This represents the average import pressure.

[0135] and These represent the pulsation amplitude and the load amplitude coefficient, respectively, with w being the pulsation angular frequency. For phase;

[0136] Step S52: Assuming that the axial velocity, blade angle, and width vary linearly along the radius, construct a time-varying relative speed versus absolute speed relationship model, expressed as:

[0137]

[0138] In the formula, the vacuum pump blade angle The radial linear variation is ; Time-varying circular velocity ; Time-varying uniformly distributed axial velocity Vacuum pump blade width The radial linear variation is ; Where is the impeller inlet radius. The impeller outlet radius; For the width of the inlet, For the width of the outlet; For the blade inlet angle, This refers to the blade exit angle;

[0139] Step S53: According to Bernoulli's equation, the time-varying pressure distribution on the surface of the water ring vacuum pump blades can be constructed as follows:

[0140]

[0141] In the formula, the absolute velocity of the import is The absolute velocity of the blade surface is , The absolute velocity component in the circumferential direction. Radial velocity component;

[0142] Step S54: The time-varying cavitation prediction model for water ring vacuum pump blades is expressed as follows:

[0143]

[0144] like If cavitation occurs, then cavitation will occur; otherwise, cavitation will not occur.

[0145] The time-varying effective net positive suction head (NPSH) of a water ring vacuum pump blade is expressed as:

[0146]

[0147] Where g is the gravitational acceleration constant, g = 9.80665 m / s².

[0148] Specifically, in this embodiment of the invention, under random pulsating conditions, the key performance parameters of the water ring vacuum pump are obtained as curves of change over time by utilizing the constructed time-varying relative speed and absolute speed relationship model, the time-varying pressure distribution model on the surface of the water ring vacuum pump blade, and the time-varying cavitation prediction model of the water ring vacuum pump blade.

[0149] Figure 8 The curves showing the effective time-varying NPSH and cavitation region ratio of the water ring vacuum pump over time reveal that the NPSH generally fluctuates within the range of 20-40 μm, indicating frequent sudden increases in the cavitation region ratio during the operation of the water ring vacuum pump system. High-frequency, small-amplitude fluctuations are observed in the 0-0.1 s and 0.2-0.3 s intervals, reflecting the dynamic influence of factors such as the suction side pressure and fluid velocity on the NPSH. Furthermore, sharp peaks in the NPSH curve, such as near 0.1 s, 0.2 s, and 0.3 s, indicate a sudden and significant increase in the cavitation region ratio, suggesting a severe cavitation risk to the system.

[0150] Figure 9 The diagram shows the velocity distribution on the surface of a water ring vacuum pump blade. It visually presents the velocity magnitude at different locations on the blade surface. It can be seen that the velocity distribution is concentrically distributed around the central axis, indicating that the velocity on the blade surface is uniform along the circumferential direction. This conforms to the actual blade design / flow near axisymmetric assumption, reflecting that when the water ring vacuum pump blade rotates, the flow in the circumferential direction of the blade is uniform and the motion characteristics in the circumferential direction are relatively consistent.

[0151] Figure 10 This is a pressure distribution diagram on the surface of a water ring vacuum pump blade, showing the pressure magnitude and distribution pattern at different locations on the blade surface. According to Bernoulli's equation and the characteristics of rotating flow fields, the larger the radius (the closer to the blade edge), the higher the fluid pressure. It can be seen that the pressure distribution diagram on the blade surface is a concentric ring with the central axis as the center, and it also shows a radial distribution of "low pressure in the center and high pressure at the edge". This indicates that the pressure on the blade surface is uniform along the circumference, which is consistent with the flow assumption of ideal rotating machinery.

[0152] Finally, based on the time-varying cavitation prediction and judgment model for water ring vacuum pump blades, it is determined whether cavitation has occurred in the water ring vacuum pump blades. If cavitation has occurred, the time-varying effective net positive suction head (NPSH) of the water ring vacuum pump blades is calculated.

[0153] The results of the embodiments of the present invention show that: in the time range of 0-1 s, the NPSH variation range is 21.80 to 36.78 m, the cavitation region ratio variation range is 0.00% to 78.00%, and the time of occurrence of the maximum cavitation region is 0.85 s.

[0154] Figure 11The diagram shows the predicted cavitation zone for a water ring vacuum pump. The shaded annular area represents the predicted cavitation occurrence zone, while the white area represents the non-cavitation zone. The diagram clearly delineates the spatial range between the "cavitation zone" and the "non-cavitation zone." This cavitation zone is annular and distributed around the blade center / axis, indicating that within a specific radial region on the blade surface, the non-central area (low pressure, where flow velocity and bubble formation conditions do not reach the cavitation criticality) and the non-edge area (high pressure, less prone to cavitation) are the locations where cavitation is likely to occur.

[0155] In summary, the predicted cavitation region of the water ring vacuum pump determined by the method of this invention conforms to the flow assumptions and velocity distribution assumptions of ideal rotating machinery. It provides the spatial distribution characteristics of the cavitation region on the blade surface, offering intuitive visualization and theoretical reference for the anti-cavitation research of water ring vacuum pump blades, precise positioning of cavitation risk engineering applications, and ensuring efficient and stable operation of equipment. It has good industrial application value.

[0156] Figure 12 A hardware block diagram of a computer terminal for implementing a method for predicting the cavitation impact energy evolution and cavitation of water ring vacuum pump blades is shown.

[0157] like Figure 12 As shown, a computer terminal may include one or more processors (processors may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), a memory for storing data, and a transmission module for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 12 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device.

[0158] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits can be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits can be a single, independent processing module, or wholly or partially integrated into any other element in a computer terminal. As involved in the embodiments of this application, the data processing circuit serves as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0159] It should be noted here that, in some optional embodiments, the above... Figure 12The computer terminal shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 12 This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned computer terminal.

[0160] It should be noted that, Figure 12 The computer terminal shown is used to execute Figure 1 The method for predicting the cavitation impact energy of the water ring vacuum pump blades shown above is also applicable to this electronic device, and will not be repeated here.

[0161] This application also provides a non-volatile storage medium, which includes a stored program. When the program runs, it controls the device where the storage medium is located to execute the above-mentioned method for predicting the evolution of cavitation impact energy of water ring vacuum pump blades.

[0162] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for predicting the cavitation impact energy evolution and cavitation cavitation of water ring vacuum pump blades, characterized in that, Includes the following steps: S1: Construct the Rayleigh-Plesset equation for cavitation bubbles in the blades of a water ring vacuum pump that considers random pulsation and time-varying load pressure; S2: Based on the Rayleigh-Plesset equation, construct a dimensionless equation for the cavitation bubbles of the water ring vacuum pump blades considering random pulsation and time-varying load pressure; S3: The dimensionless equation of cavitation bubbles in the blades of a water ring vacuum pump considering random pulsation and time-varying load pressure is solved using the variable step size Runge-Kutta-Fehlberg method, and the evolution of bubble radius with time under random pulsation and time-varying load pressure is obtained. S4: Construct the evolution equation of cavitation impact energy of water ring vacuum pump blades to obtain the evolution process of cavitation impact energy of water ring vacuum pump blades under random pulsating conditions. S5: Construct a cavitation impact cavitation zone determination equation for water ring vacuum pump blades under time-varying operating conditions, and obtain the effective time-varying cavitation margin of the blades, the range of cavitation zone variation, and the predicted time of occurrence of the maximum cavitation zone. S6: Based on the cavitation evolution process of the water ring vacuum pump blades under time-varying load, the operating parameters and mechanical structure of the water ring vacuum pump are optimized and controlled.

2. The method for predicting the cavitation impact energy evolution and cavitation prediction of water ring vacuum pump blades according to claim 1, characterized in that, In step S1, the constructed Rayleigh-Plesset equation is expressed as: In the formula, R represents the bubble radius; and Let represent the first and second derivatives of the bubble radius, respectively; Indicates the density of the liquid; Indicates the pressure inside the bubble; σ is the liquid vapor pressure; σ is the surface tension coefficient. The dynamic viscosity of the liquid. For random pulsation terms; For far-field environmental pressure, ; Indicates static pressure; For random time-varying load pressure, calculations are performed using thermodynamic adiabatic equations. Obtain the internal pressure of the bubble , The initial internal pressure of the bubble, Let the initial radius of the bubble be 1. This is the specific heat capacity ratio.

3. The method for predicting the cavitation impact energy evolution and cavitation prediction of water ring vacuum pump blades according to claim 2, characterized in that, The process of constructing the Rayleigh-Plesset equations includes the following steps: Step S11: Random Pulsation Term The construction process is shown as follows: In the formula, It indicates the intensity of random pulsations, reflecting the magnitude of random excitation; This indicates the computation time step, used to control computational accuracy and speed; Denotes the Dirac function, when Time indicates relevance. Indicates an instantaneous pulse. The time indicates no correlation, meaning that white noise has no memory; Indicates the time value; Step S21: The time-varying load pressure model is expressed as: In the formula, Let A be a constant, and w be the pulsating angular frequency. For phase shift, This represents the proportion of random pulsation amplitude. The value represents the load amplitude ratio; filter indicates a low-pass filter. This represents the angular frequency of the time-varying load.

4. The method for predicting the cavitation impact energy evolution and cavitation prediction of water ring vacuum pump blades according to claim 3, characterized in that, In step S2, the constructed dimensionless equation is expressed as: In the formula, the dimensionless radius ;time ,pressure viscosity coefficient Surface tension random pulsation term .

5. The method for predicting the cavitation impact energy evolution and cavitation prediction of water ring vacuum pump blades according to claim 4, characterized in that, In step S3, let , ,but: According to the first-order system of equations The iterative calculation of the variable step-size fourth-order Runge-Kutta-Fehlberg method is expressed as follows: In the formula, For time t n A random pulsation sequence at a location; This represents the state variable at step n.

6. The method for predicting the cavitation impact energy evolution and cavitation prediction of water ring vacuum pump blades according to claim 5, characterized in that, In step S4, the overall impact energy equation for the bubble is constructed as follows: In the formula, kinetic energy for ; Pressure energy for .

7. The method for predicting the cavitation impact energy evolution and cavitation prediction of water ring vacuum pump blades according to claim 6, characterized in that, Step S5 specifically includes: Step S51: Construct the equations for the synthesized time-varying pulsating flow rate, synthesized time-varying rotational speed, and synthesized time-varying inlet pressure parameters, expressed as: Synthetic time-varying pulsating flow rate: ; Synthetic time-varying speed: ; Synthesis time-varying inlet pressure: ; In the formula, random pulsating flow rate ; Traffic under periodic load ; Random pulsating speed ; Speed ​​under periodic load ; Random pulsating inlet pressure ; Inlet pressure under cyclic load ; in, These are random numbers distributed according to a standard normal distribution. This is the average flow rate. This is the average rotational speed. This represents the average import pressure. and These represent the pulsation amplitude and the load amplitude coefficient, respectively, with w being the pulsation angular frequency. For phase; Step S52: Assuming that the axial velocity, blade angle, and width vary linearly along the radius, construct a time-varying relative speed versus absolute speed relationship model, expressed as: In the formula, the vacuum pump blade angle The radial linear variation is ; Time-varying circular velocity ; Time-varying uniformly distributed axial velocity ; Vacuum pump blade width The radial linear variation is ; in, Where is the impeller inlet radius. The impeller outlet radius; For the width of the inlet, For the width of the outlet; For the blade inlet angle, This refers to the blade exit angle; Step S53: According to Bernoulli's equation, the time-varying pressure distribution on the surface of the water ring vacuum pump blades can be constructed as follows: In the formula, the absolute velocity of the import is The absolute velocity of the blade surface is , The absolute velocity component in the circumferential direction. Radial velocity component; Step S54: The time-varying cavitation prediction model for water ring vacuum pump blades is expressed as follows: like If cavitation occurs, then cavitation will occur; otherwise, cavitation will not occur. The time-varying effective net positive suction head (NPSH) of a water ring vacuum pump blade is expressed as: Where g is the gravitational acceleration constant, g = 9.80665 m / s².

8. A non-volatile storage medium, characterized in that, Storage media includes stored programs; During program execution, the device containing the storage medium executes the method for predicting the evolution of cavitation impact energy of water ring vacuum pump blades as described in any one of claims 1 to 7.

9. A computer terminal, characterized in that, Computer terminals include: Memory; A processor is configured to run a program stored in the memory, wherein the program executes the method for predicting the cavitation impact energy evolution and cavitation prediction of water ring vacuum pump blades as described in any one of claims 1 to 7.

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