A method and system for predicting cavitation erosion based on solid material strength domain

Abstract

The application provides a cavitation erosion prediction method and system based on solid material strength domain, comprising the following steps: step 1, obtaining a table file and a DAT file on a target solid surface, wherein the table file contains instantaneous pressure, average pressure, instantaneous vapor volume fraction and average vapor volume fraction; step 2, reading and storing the table file and the DAT file; step 3, calculating an instantaneous erosion index of each node in each time interval; step 4, calculating an instantaneous cavitation erosion index; step 5, superimposing the erosion indexes of each node at different times and considering the material accumulation effect to obtain a final cavitation erosion risk index; and step 6, based on point-surface structure data of a geometric model, interpolating the cavitation erosion risk index into color data, and overlaying the data as a fourth dimension on a reconstructed model. After the numerical simulation result is connected to MATLAB for calculation and processing, the water wing cavitation erosion prediction result can be obtained more accurately and conveniently.

Description

Technical Field

[0001] This invention mainly relates to the fields of fluid mechanics and cavitation, and in particular to a method and system for predicting cavitation erosion based on the strength domain of solid materials. Background Technology

[0002] Computational Fluid Dynamics is an interdisciplinary field that lies between mathematics, fluid mechanics, and computer science. Its main research content is to solve the governing equations of fluid mechanics using computers and numerical methods, and to simulate and analyze fluid mechanics problems.

[0003] Cavitation is a fluid dynamics phenomenon that occurs when a liquid, under certain temperature conditions, undergoes a series of vaporization and condensation processes due to changes in local pressure within the liquid or at the liquid-solid interface. In engineering applications, cavitation often brings many adverse effects: generating significant noise, causing strong vibrations, reducing mechanical operating efficiency, and producing microparticles that contaminate the fluid. Among these adverse effects, cavitation erosion has the most severe impact.

[0004] Cavitation erosion refers to the formation of gas-liquid bubbles on a material surface due to localized pressure reduction caused by the impact of high-speed airflow or liquid flow. These cavitation bubbles move with the main flow, and when they enter a pressure recovery zone, the surrounding pressure rapidly increases, causing the cavitation bubbles to shrink rapidly, condense, and collapse violently. This process generates microjets and huge shock waves, producing high-frequency pressure pulses. The main hazards of cavitation erosion include, but are not limited to: reduced material strength, stiffness, and toughness; changes in surface morphology and decreased dimensional accuracy; accelerated material fatigue and crack propagation; and, in severe cases, equipment failure and accidents. Therefore, solving the problem of cavitation erosion is of great significance for improving the safety, reliability, and service life of equipment.

[0005] Currently, the theories regarding the cavitation erosion mechanism are mainly divided into two types: microjets and shock waves. Microjets theory mainly posits that the microjets generated by the collapse of cavitation bubbles near the solid wall are the primary cause of material damage; shock waves theory suggests that the intense pressure pulses generated during cavitation collapse compress the surrounding medium, forming pressure shock waves that propagate to the material surface, causing impact and erosion.

[0006] Based on different theories of cavitation erosion mechanisms, several theoretical models for predicting cavitation erosion have been proposed: Dular et al. believe that the shock wave released by the collapse of cloud cavitation will propagate to the vicinity of the solid surface, causing the cavitation near the solid surface to collapse and form microjets, which in turn impact the solid surface and cause erosion damage; Kato et al. believe that the shock wave caused by the collapse of cavitation bubbles detached from sheet cavitation is the main cause of erosion damage to the material surface; Fortes-Patella et al., based on the concept of energy cascade, believe that the potential energy contained in macroscopic vapor clouds will be converted into acoustic energy through the shock wave generated when cavitation bubbles collapse, and this shock wave will propagate to the solid surface and impact the material, thus forming cavitation erosion.

[0007] Existing typical Euler-Euler cavitation risk prediction methods mainly include the Intensity Function Method (IFM), Time-Averaged Erosion Indicators (TAIs), and Gray Scale Method (GLM). In the Intensity Function Method, the rate of change of instantaneous local pressure at a certain point with respect to time is used as the criterion. When the value exceeds a certain threshold, cavitation erosion risk can be considered present. The time-averaged erosion index assessment method proposes three assumptions: ① Compared to cavitation bubbles near the solid wall, those far from the solid wall pose a significantly lower risk of cavitation erosion on the material surface; ② When cavitation bubbles gradually move to the downstream high-pressure zone, they will undergo violent collapse and may cause some erosion damage to the wall; ③ The intensity of cavitation erosion is related to the number of collapsing bubbles in the flow field. Based on these three assumptions, an expression is defined to represent the instantaneous local cavitation erosion of the material surface. The grayscale method is developed based on the research results of Fortes-Patella et al., which found that the initial potential energy contained in the cavitation structure in the cavitation flow field is correlated with the volume of the vapor cloud and changes in the surrounding environmental pressure. These three methods typically show significant differences between the predicted and experimental results, failing to accurately simulate and predict the results. Furthermore, existing cavitation erosion calculation methods only consider the external conditions of the solid material and do not take into account the strength and other conditions of the solid material itself.

[0008] This application, based on the theory of cavitation erosion mechanism, compares numerical simulation and cavitation erosion risk prediction results to propose a cavitation erosion prediction model based on the strength domain of solid materials. Compared with existing methods, this method can more accurately predict the cavitation erosion risk locations on solid surfaces. Furthermore, by integrating the calculations of numerical simulation results and cavitation erosion risk prediction results into MATLAB, the post-processing workflow after data output is greatly reduced, significantly decreasing workload, and enabling data interoperability between existing computational software and MATLAB. Summary of the Invention

[0009] This application provides a method for calculating and predicting the erosion of solid material surfaces. By integrating the numerical simulation results into MATLAB for post-processing, the prediction results of hydrofoil cavitation can be obtained more accurately and conveniently.

[0010] Based on the above problems, the present invention adopts the following technical solution:

[0011] A cavitation erosion prediction method based on the strength domain of solid materials includes the following steps:

[0012] Step 1: Obtain a table file and a DAT file containing instantaneous pressure, average pressure, instantaneous vapor volume fraction, and average vapor volume fraction on the target solid surface required for calculation.

[0013] Step 2: Read and store the table file and DAT file;

[0014] Step 3: The instantaneous terms are calculated using the first-order backward difference method. Then, the instantaneous erosion index of each node in each time interval is calculated based on the instantaneous pressure, average pressure, instantaneous steam volume fraction, time step, and hydrofoil coordinate data.

[0015] Step 4: Calculate the instantaneous cavitation erosion index according to the core formula, and superimpose the erosion indexes at different times at each node and consider the material accumulation effect to calculate the final cavitation erosion risk index.

[0016] Step 5: Based on the point-surface structure data of the geometric model, reconstruct the geometry by sequentially connecting each node, and interpolate the cavitation risk index into the RGB color data. Use this data as the fourth dimension to overlay the reconstructed model, and use color changes to represent its numerical value.

[0017] Furthermore, in step 2, for DAT files, regular expressions are used to match the data header and read the subsequent data; for table files, MATLAB's built-in functions such as csvread are used for reading and storage.

[0018] Furthermore, the method for calculating the instantaneous cavitation erosion index is as follows:

[0019]

[0020] Where Metrics is the instantaneous erosion index, and σ represents the instantaneous erosion load; σ I denoted by ; k is the linear fatigue cumulative damage effect function; n1 is the fatigue damage erosion index, n2 is the instantaneous high impact erosion index; q is a hybrid function used to smoothly bridge the instantaneous high impact erosion function and the fatigue erosion function.

[0021] Furthermore, the final expression for the cavitation risk index is as follows:

[0022]

[0023] Φ represents the final cavitation risk index after considering the cumulative load effect of materials, and N represents the total number of time points; Metrics i This represents the instantaneous erosion index at time i.

[0024] Furthermore, the expression for the linear fatigue cumulative damage effect function k is as follows:

[0025]

[0026] Where λ is a parameter used to adjust k as a function of k. The rate of change.

[0027] Furthermore, the expression for the instantaneous erosion load σ is as follows:

[0028]

[0029] in, For the average pressure, p v Let t be the saturated vapor pressure, D be the instantaneous gas volume fraction, and t be the time.

[0030] Furthermore,

[0031]

[0032] Where q is about The function is a hybrid function used to smoothly bridge the instantaneous high-impact erosion function and the fatigue erosion function, where c is a parameter used to adjust q as a function of time. Rate of change.

[0033] Furthermore, in step 6, the geometric construction process involves extracting the node coordinates, arrangement order, and connection relationships of the midpoints of the face from the table file and DAT file, and then reconnecting each node according to the rules to form a face to complete the reproduction of the geometric model in MATLAB.

[0034] Furthermore, step 6 includes the following sub-steps:

[0035] Step 6.1: Set the time step and saturated vapor pressure value;

[0036] Step 6.2: Select the path where the table file and DAT file are stored;

[0037] Step 6.3: Select the type of file to read;

[0038] Step 6.4: Calculate the cavitation risk forecast results, draw a cloud map, and display the results.

[0039] This invention also provides a cavitation erosion prediction system based on the strength domain of solid materials, comprising:

[0040] The instantaneous term acquisition module obtains a table file and a DAT file containing instantaneous pressure, average pressure, instantaneous vapor volume fraction, and average vapor volume fraction on the target solid surface required for calculation.

[0041] The read and store module is used to read and store table files and DAT files;

[0042] The instantaneous erosion index calculation module is used to calculate the instantaneous terms using the first-order backward difference method, and then calculates the instantaneous erosion index for each node in each time interval based on instantaneous pressure, average pressure, instantaneous steam volume fraction, time step, and hydrofoil coordinate data.

[0043] The instantaneous cavitation erosion index calculation module is used to calculate the instantaneous cavitation erosion index according to the core formula, and then to calculate the final cavitation erosion risk index by superimposing the erosion index at different times at each node and considering the material accumulation effect.

[0044] The result acquisition module is used to reconstruct the geometry by sequentially connecting each node based on the point-surface structure data of the geometric model, and interpolating the cavitation risk index into the RGB color data. This data is then overlaid on the reconstructed model as the fourth dimension, and its numerical value is represented by color changes.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The method described in this invention can better predict the erosion range of solid material surfaces;

[0047] 2. The method described in this invention is the first to incorporate the strength and properties of solid materials themselves into the calculation of cavitation erosion prediction;

[0048] 3. This invention integrates numerical simulation results and cavitation risk prediction results into MATLAB's AppDesigner program for processing, realizing data docking between existing computing software and MATLAB, and greatly reducing the workload of subsequent data processing. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the operation process of this method.

[0050] Figure 2 Design the interface for the software.

[0051] Figure 3 This is a presentation of the final results of the cavitation risk prediction calculation. Detailed Implementation

[0052] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0053] Example 1

[0054] like Figure 2 As shown, this embodiment provides a cavitation erosion prediction method based on the strength domain of solid materials, including the following steps:

[0055] Step 1: Obtain a table file and a DAT file containing instantaneous pressure, average pressure, instantaneous vapor volume fraction, and average vapor volume fraction on the target solid surface required for calculation.

[0056] Step 2: Input the saturated vapor pressure of 3169 Pa and the set time step of 0.00001 s, and read and store the table file and DAT file. For the DAT file, use regular expressions to match the data header and read the subsequent data; for the table file, use built-in MATLAB functions such as csvread to read and store the data.

[0057] Step 3: The instantaneous term is calculated using the first-order backward difference method. Then, the instantaneous erosion index of each node in each time interval is calculated based on the saturated vapor pressure, instantaneous pressure, average pressure, instantaneous vapor volume fraction, time step, and hydrofoil coordinate data.

[0058] Step 4: Calculate the instantaneous cavitation erosion index according to the core formula, and superimpose the erosion indexes at different times on each node and consider the material accumulation effect to calculate the final cavitation erosion risk index corresponding to the coordinates of each node.

[0059] Step 5: Based on the point-surface structure data of the geometric model, reconstruct the geometry by sequentially connecting each node, and interpolate the cavitation risk index into the RGB color data. This data is then used as the fourth dimension overlaid in the reconstructed model, with color changes representing its numerical magnitude, ultimately yielding the following result: Figure 3 The results shown Figure 3 The colors from dark to light indicate that the risk of cavitation erosion increases from low to high. In the calculation results, the risk of cavitation erosion is mainly concentrated in the center of the hydrofoil, near the leading edge, and in the U-shaped vortex-related area at the trailing edge, which is in good agreement with the experimental results.

[0060] In the above embodiments, the potential energy contained in the cavitation structure of the cavitation flow field is related to the vapor volume and the ambient pressure. Therefore, the initial potential energy per unit volume is expressed as follows:

[0061] e = (p d -p v D,

[0062] In the above formula, p d Due to environmental pressures, p is the average pressure on the solid surface. v Where is the saturated vapor pressure, and D is the instantaneous vapor volume fraction.

[0063] The instantaneous potential power can be obtained by differentiating the initial potential energy over time. Of the two terms in the instantaneous power, the first term has a much greater impact than the second. To simplify the calculation, the instantaneous power value is approximated by the first term, as shown in the following expression:

[0064]

[0065] In complex cavitation flows, the ambient pressure is usually unknown, and also highly unsteady and non-uniform. To obtain an approximation of the conditions experienced by cavitation structures of arbitrary shapes, the ambient pressure p... d Approximately taken as mean pressure

[0066] Compared with existing technologies, the method provided in this application is the first to incorporate the strength and properties of the solid material itself into the calculation of cavitation erosion prediction. Impact loads exhibit a large range of change within an extremely short time; this rapid change in force manifests as an instantaneous wave-like impact. In this method, when the instantaneous erosion load σ is greater than the critical impact load σ... I At this time, the material is mainly damaged by instantaneous impact; when the instantaneous erosion load σ is less than the critical impact load σ... I At that time, it was assumed that solid materials were primarily subjected to a continuous load with smaller variations, leading to fatigue failure. This method, by setting... The function is used to define parameters n1 and n2 for two impact scenarios to describe and correct for the cavitation risk at each instant. When fatigue damage occurs, i.e., the instantaneous erosion load σ is less than the critical impact load σ... I Considering the difference in contribution of different impact sizes to fatigue damage, i.e., smaller impacts require more impact cycles to achieve the same degree of erosion as larger impacts with fewer impact cycles, a function k is introduced as an adjustment factor based on the cumulative fatigue damage theory. Combining numerical simulation results and experimental data, k is set to 1 when ε≥0.8 and gradually reduced to 0 when ε<0.8.

[0067] Therefore, the method for calculating the instantaneous cavitation erosion index is as follows:

[0068]

[0069] Where Metrics is the instantaneous erosion index, and σ represents the instantaneous erosion load; σ Idenoted by ; k is the linear fatigue cumulative damage effect function; n1 is the fatigue damage erosion index, n2 is the instantaneous high impact erosion index; q is a hybrid function used to smoothly bridge the instantaneous high impact erosion function and the fatigue erosion function.

[0070] The final expression for the cavitation risk index is as follows:

[0071]

[0072] Φ represents the final cavitation risk index after considering the cumulative load effect of materials, and N represents the total number of time points; Metrics i This represents the instantaneous erosion index at time i.

[0073] The expression for the linear fatigue cumulative damage effect function k is as follows:

[0074]

[0075] Where λ is a parameter used to adjust k as a function of k. The rate of change is usually taken as 30 to ensure that... When k approaches 1 with an error order of 10, the time can be approximated. -4 .

[0076] The expression for the instantaneous erosion load σ is as follows:

[0077]

[0078] in, For the average pressure, p v Let t be the saturated vapor pressure, D be the instantaneous gas volume fraction, and t be the time.

[0079]

[0080] Where q is about The function is a hybrid function used to smoothly bridge the instantaneous high-impact erosion function and the fatigue erosion function, where c is a parameter used to adjust q as a function of time. The rate of change is typically set to 60, allowing for rapid switching between the two erosion calculation methods for the instantaneous erosion metrics. To improve the relatively complex post-processing, this embodiment uses AppDesigner to create cavitation erosion risk prediction calculation software. It extracts geometric data and calculation results from DAT files output from Fluent and tabular files output from CFD-Post, and uses the first-order backward difference method to calculate the time term. The geometry is then reproduced in the software, and the cavitation erosion risk prediction results are reflected on the drawn geometry through color interpolation.

[0081] The specific process is as follows:

[0082] The method proposed in this embodiment is mainly based on the geometry drawing of ICEM software in ANSYS in the early stage, and the calculation settings and calculation output are performed in Fluent. It should be noted that the output is performed once at each time step.

[0083] Data processing: ① DAT file: Match the data header of the corresponding data in the file using regular expressions and extract the subsequent data; ② Table file: Output the data in CFD-Post by writing macros, and then read and store it using MATLAB's built-in functions such as csvread.

[0084] Geometric processing: Based on the ICEM mesh drawing principle and the point-plane relationship storage in Fluent, the patch function is used to redistribute and connect point-plane data to form patches.

[0085] Cavitation risk calculation: The reciprocal of time is calculated based on the first-order backward difference method.

[0086]

[0087]

[0088] Results: By using the allocated grid node data and surface data, the patch function is used to sequentially connect the nodes to form a patch; the cavitation risk calculation results are interpolated into the RGB data matrix of JET, so that different cavitation risk values ​​correspond to different colors, and the colors are interpolated into the drawn patch geometry to obtain the final result.

[0089] like Figure 2 As shown, the specific usage of the software will then be introduced:

[0090] Step 1: Set the time step and saturated vapor pressure value;

[0091] Step 2: Select the path where the table file or DAT file is stored;

[0092] Step 3: Select the type of file to read (table file, DAT file);

[0093] Step 4: Calculate the cavitation risk forecast results, draw a cloud map, and display the results.

[0094] Example 2

[0095] A cavitation erosion prediction system based on the strength domain of solid materials includes:

[0096] The instantaneous term acquisition module obtains a table file and a DAT file containing instantaneous pressure, average pressure, instantaneous vapor volume fraction, and average vapor volume fraction on the target solid surface required for calculation.

[0097] The read and store module is used to read and store table files and DAT files.

[0098] The instantaneous erosion index calculation module is used to calculate the instantaneous terms using the first-order backward difference method, and then calculates the instantaneous erosion index for each node in each time interval based on instantaneous pressure, average pressure, instantaneous steam volume fraction, time step, and hydrofoil coordinate data.

[0099] The instantaneous cavitation erosion index calculation module is used to calculate the instantaneous cavitation erosion index according to the core formula. Then, the erosion index at different times at each node is superimposed and the material accumulation effect is considered to calculate the final cavitation erosion risk index.

[0100] The result acquisition module is used to reconstruct the geometry by sequentially connecting each node based on the point-surface structure data of the geometric model, and interpolating the cavitation risk index into the RGB color data. This data is then overlaid on the reconstructed model as the fourth dimension, and its numerical value is represented by color changes.

[0101] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made based on the content of this specification should be included within the protection scope of the present invention.

Claims

1. A method for predicting cavitation erosion based on the strength domain of solid materials, characterized in that, Includes the following steps: Step 1: Obtain a table file and a DAT file containing instantaneous pressure, average pressure, instantaneous vapor volume fraction, and average vapor volume fraction on the target solid surface required for calculation. Step 2: Read and store the table file and DAT file; Step 3: The instantaneous term is calculated using the first-order backward difference method. Then, based on the instantaneous pressure, average pressure, instantaneous steam volume fraction, time step, and hydrofoil coordinate data, the instantaneous erosion index for each node in each time interval is calculated. The method for calculating the instantaneous cavitation erosion index is as follows: （1.1） in, As an indicator of instantaneous erosion, Indicates instantaneous erosion load; Represents the critical impact load of the material; k is the linear fatigue cumulative damage effect function; For fatigue damage erosion index, The instantaneous high impact erosion index; q It is a hybrid function used to smoothly bridge the instantaneous high-impact erosion function and the fatigue erosion function; Step 4: Calculate the instantaneous cavitation erosion index at each node on the hydrofoil at different times according to the core formula, and calculate the final cavitation erosion risk index considering the material accumulation effect; the final cavitation erosion risk index expression is as follows: （1.2） The final cavitation risk index is obtained after taking into account the cumulative load effect of materials, where N is the total number of time points; express i Instantaneous erosion indicators; Step 5: Based on the point-surface structure data of the geometric model, reconstruct the geometry by sequentially connecting each node, and interpolate the cavitation risk index into the RGB color data. Use this data as the fourth dimension to overlay the reconstructed model, and use color changes to represent its numerical value.

2. The cavitation erosion prediction method based on the strength domain of solid materials according to claim 1, characterized in that: In step 2, for DAT files, regular expressions are used to match the data header and read the subsequent data; for table files, MATLAB's built-in functions such as csvread are used to read and store the data.

3. The cavitation erosion prediction method based on the strength domain of solid materials according to claim 2, characterized in that, Linear fatigue cumulative damage effect function The expression is as follows: （1.3） in, These are parameters used for adjustment. k Follow The rate of change.

4. The cavitation erosion prediction method based on the strength domain of solid materials according to claim 3, characterized in that, Instantaneous erosion load The expression is as follows: （1.4） in, For average pressure, Let D be the saturated vapor pressure and D be the instantaneous gas volume fraction. For time.

5. The cavitation erosion prediction method based on the strength domain of solid materials according to claim 3, characterized in that, （1.5） in, q For about The function is a hybrid function used to smoothly bridge the instantaneous high-impact erosion function and the fatigue erosion function, where c is a parameter used to adjust... q Follow Rate of change.

6. The cavitation erosion prediction method based on the strength domain of solid materials according to claim 1, characterized in that, In step 5, the geometric construction process involves extracting the node coordinates, arrangement order, and connection relationships of the midpoints of the face from the table file and DAT file, and then reconnecting each node according to the rules to form a face to reproduce the geometric model in MATLAB.

7. The cavitation erosion prediction method based on the strength domain of solid materials according to claim 6, characterized in that, Step 5 includes the following sub-steps: Step 5.1: Set the time step and saturated vapor pressure value; Step 5.2: Select the path where the table file and DAT file are stored; Step 5.3: Select the type of file to read; Step 5.4: Calculate the cavitation risk forecast results, draw a cloud map, and display the results.

8. A cavitation erosion prediction system based on the strength domain of solid materials, used to perform the steps in the cavitation erosion prediction method based on the strength domain of solid materials according to any one of claims 1-7, characterized in that, include: instantaneous The item acquisition module obtains a table file and a DAT file containing instantaneous pressure, average pressure, instantaneous vapor volume fraction, and average vapor volume fraction on the target solid surface required for calculation. The read and store module is used to read and store table files and DAT files; The instantaneous erosion index calculation module is used to calculate the instantaneous terms using the first-order backward difference method, and then calculates the instantaneous erosion index for each node in each time interval based on instantaneous pressure, average pressure, instantaneous steam volume fraction, time step, and hydrofoil coordinate data. The instantaneous cavitation index calculation module is used to calculate the instantaneous cavitation index of each node on the hydrofoil at different times according to the core formula, and to calculate the final cavitation risk index by considering the material accumulation effect. The result acquisition module is used to reconstruct the geometry by sequentially connecting each node based on the point-surface structure data of the geometric model, and interpolating the cavitation risk index into the RGB color data. This data is then overlaid on the reconstructed model as the fourth dimension, and its numerical value is represented by color changes.

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