A water hammer prediction method, device, equipment and medium for filling slurry pipeline

Abstract

This invention relates to the field of mine backfilling and discloses a method, apparatus, equipment, and medium for predicting water hammer in backfill slurry pipelines. The method includes: constructing a non-Newtonian slurry water hammer propagation model for the backfill slurry pipeline; based on the non-Newtonian slurry water hammer propagation model, obtaining key water hammer parameters, including maximum water hammer pressure and pressure wave propagation distance, through a fluid dynamics simulation process; acquiring observed pressure monitoring data and historical pressure monitoring data through pressure sensors; constructing water hammer occurrence criteria, including primary criteria and statistical criteria, based on the maximum water hammer pressure, pressure wave propagation distance, and historical pressure monitoring data; and analyzing the maximum water hammer pressure, pressure wave propagation distance, and observed pressure monitoring data based on the primary criteria and statistical criteria to predict whether water hammer will occur in the backfill slurry pipeline. This application has positive effects such as high computational efficiency, clear criteria, and strong adaptability, and can provide strong protection for the safe and stable operation of mine backfill pipeline transportation processes.

Description

Technical Field

[0001] This invention relates to the field of mine backfilling, and in particular to a method, apparatus, equipment and medium for predicting water hammer in backfill slurry pipelines. Background Technology

[0002] In tailings cemented backfilling systems, the backfill slurry possesses rheological properties characterized by high concentration, strong shear thinning, and a certain degree of compressibility. During pipeline transportation, it is highly susceptible to unsteady-state disturbances such as start-up and shutdown, empty pipe grouting, backfill head switching, and local blockage removal, causing sudden changes in slurry flow velocity and pressure fluctuations, resulting in a phenomenon similar to "water hammer." This type of water hammer can lead to local pipeline pressure exceeding limits, connection detachment, abnormal equipment vibration, or slurry backflow, seriously threatening the system's operational stability and the safety of backfilling operations.

[0003] Traditional water hammer analysis methods, such as the Joukowsky equation and one-dimensional transient flow models, are commonly used in existing engineering projects to assess pressure changes. However, these models are based on the Newtonian fluid assumption and fail to consider the significant impact of parameters such as the slurry's yield stress, plastic viscosity, and particle concentration on the pressure propagation velocity and amplitude, resulting in insufficient simulation accuracy under high-concentration conditions. On the other hand, although CFD simulation technology can be used to simulate transient slurry flow, its modeling is complex, the simulation cycle is long, and it requires highly skilled operators, making it unsuitable for rapid on-site assessment and comparison of multiple operating conditions. Therefore, it is currently difficult to achieve rapid identification and response to water hammer risks in backfilling projects. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a method, apparatus, computer equipment and readable storage medium for predicting water hammer in a filled slurry pipeline.

[0005] This invention provides the following technical solution:

[0006] In a first aspect, this disclosure provides a method for predicting water hammer in a slurry-filled pipeline, the method comprising:

[0007] Construct a non-Newtonian slurry water hammer propagation model for filled slurry pipes;

[0008] Based on the non-Newtonian slurry water hammer propagation model, key water hammer parameters are obtained through a fluid dynamics simulation process. These key water hammer parameters include the maximum water hammer pressure and the pressure wave propagation distance.

[0009] The observed pressure monitoring data and historical pressure monitoring data are obtained by pressure sensors. Based on the maximum water hammer pressure, the pressure wave propagation distance and the historical pressure monitoring data, a water hammer occurrence criterion is constructed. The water hammer occurrence criterion includes a main criterion and a statistical criterion.

[0010] Based on the main criterion and the statistical criterion, the maximum water hammer pressure, the pressure wave propagation distance, and the observed pressure monitoring data are analyzed to predict whether water hammer will occur in the filling slurry pipeline.

[0011] In an optional implementation, the non-Newtonian slurry water hammer propagation model for constructing the filled slurry conduit includes:

[0012] Determine the density of the solid particles and the density of the liquid water in the non-Newtonian slurry, and calculate the pressure wave velocity when water hammer occurs in the non-Newtonian slurry by combining the elastic modulus of the slurry, the elastic modulus of the solid particles, the volume concentration, the elastic modulus of the pipe, the pipe diameter and the pipe wall thickness.

[0013] Based on the density of the solid particles, the density of the liquid water, and the pressure wave velocity when the non-Newtonian slurry water hammer occurs, the initial impact pressure when the non-Newtonian slurry water hammer occurs is derived.

[0014] The particle drag coefficient, the flow velocity of solid particles, and the flow velocity of liquid water in the non-Newtonian slurry are determined. Based on the particle drag coefficient, the flow velocity of solid particles, and the flow velocity of liquid water, the secondary impact pressure when water hammer occurs in the non-Newtonian slurry is derived.

[0015] The initial impact pressure and the secondary impact pressure are superimposed to calculate the total pressure of the non-Newtonian slurry water hammer. Combined with the pressure wave velocity when the non-Newtonian slurry water hammer occurs, the propagation model of the non-Newtonian slurry water hammer is constructed.

[0016] In an optional implementation, obtaining key water hammer parameters through computational fluid dynamics simulation based on the non-Newtonian slurry water hammer propagation model includes:

[0017] Based on the non-Newtonian slurry water hammer propagation model, the pressure wave peak position, pressure wave timing and initial pressure are calculated to obtain semi-analytical pre-solution results;

[0018] Based on the structural characteristics of the filling slurry pipe, a local computational domain is generated, and a high-precision mesh is generated based on the local computational domain to obtain parametric geometry and mesh template;

[0019] The local computational domain is solved in a steady state to obtain the steady-state flow field, which is then used as the initial condition for the transient simulation.

[0020] The semi-analytical pre-solution results are loaded as transient boundary conditions into the computational fluid dynamics simulation to carry out transient simulation, and pressure data are collected in real time at preset monitoring points.

[0021] The pressure data is analyzed to extract the maximum water hammer pressure. The pressure attenuation function along the pressure path is obtained by fitting the pressure data. The propagation distance of the pressure wave is calculated based on the pressure attenuation function along the pressure path.

[0022] In an optional implementation, the step of constructing a water hammer occurrence criterion based on the maximum water hammer pressure, the pressure wave propagation distance, and the historical pressure monitoring data includes:

[0023] The maximum water hammer pressure is multiplied by a preset proportional coefficient to obtain the prediction threshold;

[0024] The maximum observed pressure of multiple pressure sensors within a preset sampling window is obtained, wherein the pressure sensors include a first sensor and a second sensor adjacent to the first sensor;

[0025] Determine whether the maximum observed pressure of the first sensor is greater than the prediction threshold;

[0026] When the maximum observed pressure of the first sensor is greater than the prediction threshold, determine whether the maximum observed pressure of the second sensor is greater than the prediction threshold within a preset propagation time.

[0027] If the maximum observed pressure of the second sensor is greater than the prediction threshold within the preset propagation time, then the main criterion is determined to be completed. The main criterion is: when there is a pressure sensor whose maximum observed pressure is greater than the prediction threshold, and the maximum observed pressure of its adjacent pressure sensor is also greater than the prediction threshold within the preset propagation time, the main criterion is determined to be satisfied.

[0028] In an optional implementation, the step of constructing a water hammer occurrence criterion based on the maximum water hammer pressure, the pressure wave propagation distance, and the historical pressure monitoring data further includes:

[0029] Acquire historical pressure monitoring data from multiple pressure sensors, and calculate the mean and standard deviation of the historical pressure monitoring data;

[0030] The statistical threshold is obtained by adding the product of the mean, the preset statistical threshold coefficient, and the standard deviation.

[0031] Real-time acquisition of instantaneous pressure monitoring data from the pressure sensor, calculation of the instantaneous pressure rise rate of the instantaneous pressure monitoring data, and determination of whether the instantaneous pressure monitoring data is greater than the statistical threshold;

[0032] If the instantaneous pressure monitoring data is greater than the statistical threshold, determine whether the instantaneous pressure rise rate is greater than the preset initial judgment ratio;

[0033] If the instantaneous pressure rise rate is greater than the preset initial judgment ratio, then the statistical criterion is determined to be completed. The statistical criterion is: when there is an instantaneous pressure monitoring data of a pressure sensor that is greater than the statistical threshold, and the instantaneous pressure rise rate of the instantaneous pressure monitoring data is greater than the preset initial judgment ratio, the statistical criterion is determined to be satisfied.

[0034] In an optional implementation, the step of analyzing the maximum water hammer pressure, the pressure wave propagation distance, and the observed pressure monitoring data based on the primary criterion and the statistical criterion to predict whether water hammer will occur in the filling slurry pipeline includes:

[0035] Acquire observed pressure monitoring data from multiple pressure sensors, including real-time maximum observed pressure and real-time instantaneous pressure rise rate;

[0036] Substitute the real-time maximum observed pressure into the main criterion to determine whether the main criterion is met. If the main criterion is met, it is determined that water hammer has occurred in the filling slurry pipeline.

[0037] If the main criterion is not met, the real-time instantaneous pressure and the real-time instantaneous pressure rise rate are substituted into the statistical criterion to determine whether the statistical criterion is met. If the statistical criterion is met, it is determined that water hammer has occurred in the filling slurry pipeline. If the statistical criterion is not met, it is determined that water hammer has not occurred in the filling slurry pipeline.

[0038] Output water hammer determination results, which include whether water hammer has occurred or not.

[0039] In an optional implementation, the step of performing a steady-state solution on the local computational domain to obtain a steady-state flow field, and using the steady-state flow field as the initial condition for the transient simulation, includes:

[0040] In the computational fluid dynamics simulation, the steady-state simulation parameters of the local computational domain are set, including the slurry yield stress, plastic viscosity, and shear thinning law;

[0041] Based on the steady-state simulation parameters, steady-state boundary conditions corresponding to the local computational domain are loaded, and the steady-state solver is used to perform steady-state solution on the local computational domain according to the steady-state boundary conditions to obtain the initial flow field data;

[0042] Determine whether the initial flow field data meets the preset convergence condition. If the initial flow field data meets the preset convergence condition, determine the flow field corresponding to the initial flow field data as the steady-state flow field, and use the steady-state flow field as the initial condition for the transient simulation.

[0043] Secondly, this disclosure provides a water hammer prediction device for a filled slurry pipeline, the device comprising:

[0044] The first building module is used to construct a non-Newtonian slurry water hammer propagation model for filled slurry pipes;

[0045] The simulation module is used to obtain key water hammer parameters through a fluid dynamics simulation process based on the non-Newtonian slurry water hammer propagation model. The key water hammer parameters include the maximum water hammer pressure and the pressure wave propagation distance.

[0046] The second construction module is used to acquire observed pressure monitoring data and historical pressure monitoring data through pressure sensors, and to construct water hammer occurrence criteria based on the maximum water hammer pressure, the pressure wave propagation distance and the historical pressure monitoring data. The water hammer occurrence criteria include a main criterion and a statistical criterion.

[0047] The prediction module is used to analyze the maximum water hammer pressure, the pressure wave propagation distance and the observed pressure monitoring data based on the main criteria and the statistical criteria, and to predict whether water hammer will occur in the filling slurry pipeline.

[0048] Thirdly, this disclosure provides a computer device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the water hammer prediction method for the filling slurry pipeline described in the first aspect.

[0049] Fourthly, this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the water hammer prediction method for the filled slurry pipeline described in the first aspect.

[0050] The beneficial effects of this application are:

[0051] The water hammer prediction method for slurry-filled pipelines provided in this application overcomes the limitations of traditional water hammer models, which are based on Newtonian fluid assumptions and cannot accurately reflect the rheological properties of high-concentration slurries, by constructing a water hammer propagation model suitable for non-Newtonian slurries. This model better reflects the transient pressure evolution and pressure wave propagation characteristics of the slurry under actual working conditions, solving the problem of insufficient prediction accuracy in traditional models. Furthermore, by using a computational fluid dynamics simulation process based on this non-Newtonian slurry water hammer propagation model, key water hammer parameters such as maximum water hammer pressure and pressure wave propagation distance are obtained, providing reliable quantitative data for water hammer assessment. Supported by data analysis, this paper constructs primary and statistical criteria by combining key water hammer parameters with observed pressure monitoring data from pressure sensors and historical pressure monitoring data. This enables quantitative judgment of water hammer occurrence, overcoming the shortcomings of existing technologies that rely on manual experience and lack clear judgment criteria. Finally, based on the dual criteria, the analysis of key parameters and monitoring data predicts water hammer, enabling rapid and accurate identification of water hammer risks in filling slurry pipelines. This effectively avoids problems such as pipeline pressure exceeding limits and equipment damage caused by water hammer, significantly improving the operational safety and risk management capabilities of filling slurry pipeline transportation systems. This application is applicable to various filling slurry pipeline systems, possessing good versatility and scalability. It can be promoted and applied under different mining and engineering conditions, providing a practical and reliable technical solution for filling mining and related slurry transportation projects.

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the various drawings, similar components are numbered similarly.

[0054] Figure 1 A flowchart of a water hammer prediction method for a filled slurry pipe provided in an embodiment of this application is shown;

[0055] Figure 2 A schematic diagram of a mesh partitioning diagram provided in an embodiment of this application is shown;

[0056] Figure 3 A schematic diagram of a steady-state transport diagram provided in an embodiment of this application is shown;

[0057] Figure 4This diagram illustrates the pressure cloud distribution of a Newtonian slurry water hammer propagation model according to an embodiment of this application.

[0058] Figure 5 This diagram illustrates the pressure cloud distribution of a non-Newtonian slurry water hammer propagation model provided in an embodiment of this application.

[0059] Figure 6 A schematic diagram of a water hammer pressure curve provided in an embodiment of this application is shown;

[0060] Figure 7 This paper shows a schematic diagram of the structure of a water hammer prediction device for a filled slurry pipeline provided in an embodiment of this application;

[0061] Figure 8 A schematic diagram of the structure of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0062] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0063] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the template description is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0065] Example 1

[0066] Existing technologies have three main objective drawbacks in addressing the risk of water hammer in filled grout pipelines:

[0067] (1) Lack of a water hammer theory model applicable to non-Newtonian slurries. Traditional water hammer analysis methods are usually based on the Joukowsky equation or a one-dimensional transient flow model. These models assume that the transport medium is a Newtonian fluid and ignore the non-Newtonian rheological properties exhibited by high-concentration filling slurries during actual transport, such as yield stress, plastic viscosity, and shear thinning behavior, resulting in a large deviation between the predicted results and the actual pressure fluctuations. This invention improves the traditional model, corrects the wave velocity and pressure formulas, and constructs a water hammer propagation model that better fits the characteristics of the slurry, thus making up for the shortcomings of this theory.

[0068] (2) There is no clear and practical criterion for water hammer that can be used for engineering judgment. Most existing methods rely on qualitative experience or manual analysis results after complex simulations, lacking a quantitative indicator that can quickly determine whether water hammer may occur, which limits the response capability of on-site engineering. This invention extracts the maximum pressure and propagation distance as core indicators and combines them with local historical monitoring data to construct a set of quantitative "water hammer occurrence criteria", which improves the convenience and reliability of engineering applications.

[0069] (3) Existing CFD simulation processes are complex and computationally inefficient. Although some studies have attempted to simulate transient slurry flow using CFD, the modeling process involves manual mesh generation and complex boundary condition settings, and the solution time is long and the convergence is poor, making it difficult to apply to rapid engineering analysis. This invention constructs a rapid simulation process through modular modeling and automatic boundary initialization technology, which significantly improves simulation efficiency and reduces the operational threshold for engineers.

[0070] To address the aforementioned problems, this application proposes a method for predicting water hammer in slurry-filled pipes, such as... Figure 1 The diagram shown is a flowchart of a water hammer prediction method for a slurry-filled pipeline according to an embodiment of this application. The water hammer prediction method for a slurry-filled pipeline provided in this application includes the following steps:

[0071] Step S110: Construct a non-Newtonian slurry water hammer propagation model for the filled slurry pipe.

[0072] Traditional water hammer theory is based on Newtonian fluids. It assumes that when the slurry in a filled pipe is a Newtonian slurry, it can be treated as a single-phase flow. However, in pipe sections where pressure increases, the pipe cross-section changes from... Become slurry density Become Flow rate Become At this point, the pressure can be simplified and derived from the momentum equation:

[0073]

[0074] in, Pressure wave velocity:

[0075]

[0076] in, It is the elastic modulus of the slurry, in Pa; It is the elastic modulus of solid particles, in Pa; It is volume concentration; It is the elastic modulus of the pipe, in Pa; It is the pipe diameter, in meters (m). It is the pipe wall thickness, in meters (m).

[0077] The above methods cannot accurately reflect the rheological properties (such as yield stress, plastic viscosity, and particle concentration) and actual flow state of high-concentration filling slurries. Therefore, this application constructs a water hammer propagation model applicable to non-Newtonian slurries.

[0078] Specifically, the density of solid particles in a non-Newtonian slurry is first determined. Density of liquid water Simultaneously, by combining the elastic modulus of the slurry, the elastic modulus of the solid particles, the volume concentration, the elastic modulus of the pipe, the pipe diameter, and the pipe wall thickness, the pressure wave velocity during water hammer in non-Newtonian slurry is calculated. The formula is as follows:

[0079]

[0080] Based on the density of the solid particles, the density of the liquid water, and the pressure wave velocity during non-Newtonian slurry water hammer, the initial impact pressure during non-Newtonian slurry water hammer can be derived. (This pressure is generated in the initial stage of water hammer due to the density difference between the solid and liquid components in the slurry.) The formula is as follows:

[0081]

[0082] in, It is the initial flow velocity of a non-Newtonian slurry.

[0083] Next, the particle drag coefficient, solid particle velocity, and liquid water velocity of the non-Newtonian slurry are determined. Because the particles in a non-Newtonian slurry have high inertia, their velocity lags behind the liquid water velocity, resulting in a secondary impact during water hammer. Therefore, based on the particle drag coefficient, solid particle velocity, and liquid water velocity, the secondary impact pressure during water hammer in a non-Newtonian slurry is derived using the following formula:

[0084]

[0085] in, It is the particle drag coefficient of the group of particles. It is the flow rate of the solid particles. It is the flow rate of liquid water.

[0086] Finally, by superimposing the derived initial impact pressure and the secondary impact pressure, the total pressure of the non-Newtonian slurry water hammer is calculated, as shown in the following formula:

[0087]

[0088] By combining the pressure wave velocity of the non-Newtonian slurry water hammer calculated above, the propagation model of the non-Newtonian slurry water hammer was finally constructed.

[0089] To accurately reflect the non-Newtonian rheological properties of high-concentration filling slurries and overcome the problem that traditional water hammer theory is only applicable to Newtonian fluids and thus leads to large deviations in predicted pressure, the above process constructs a water hammer propagation model applicable to non-Newtonian slurries. By considering the velocity lag of solid particles and liquid water and the secondary impact effect respectively, the initial impact pressure, secondary impact pressure and total pressure are derived, thereby more accurately predicting the maximum water hammer pressure and pressure wave propagation velocity, providing a reliable theoretical basis for water hammer criteria.

[0090] Step S120: Based on the non-Newtonian slurry water hammer propagation model, key water hammer parameters are obtained through a fluid dynamics simulation process. The key water hammer parameters include the maximum water hammer pressure and the pressure wave propagation distance.

[0091] First, based on the established non-Newtonian slurry water hammer propagation model, the pressure wave peak position, pressure wave timing, and initial pressure are calculated to obtain semi-analytical preliminary solutions. These results can be used to guide the setting of boundary conditions in subsequent computational fluid dynamics simulations, improving simulation efficiency and accuracy.

[0092] Secondly, based on the circular structure of the filling slurry pipeline, a local computational domain is generated through template-based or script-based methods (avoiding the complexity and errors of manual modeling), and a high-precision mesh is generated based on this local computational domain to obtain parametric geometry and mesh templates, which facilitates subsequent multi-condition reuse and rapid modeling.

[0093] Next, a steady-state solution is performed on the local computational domain to obtain the steady-state flow field, which is used as the initial condition for the transient simulation. The specific process is as follows: In the computational fluid dynamics simulation, steady-state simulation parameters (including slurry yield stress, plastic viscosity, and shear thinning law, etc., to match the real rheological characteristics of non-Newtonian slurries) are set for the local computational domain. Based on these steady-state simulation parameters, steady-state boundary conditions (such as pipe inlet velocity and outlet pressure) are applied to the local computational domain. The steady-state solver is then used to perform a steady-state solution on the local computational domain according to the above steady-state boundary conditions to obtain the initial flow field data. It is then determined whether the initial flow field data meets the preset convergence condition (i.e., the change in flow field parameters is less than the preset change threshold; flow field parameters include slurry pressure, velocity, and shear rate). When the preset convergence condition is met, the flow field corresponding to the initial flow field data is determined as the steady-state flow field, thereby avoiding numerical oscillations in the transient simulation and improving convergence efficiency.

[0094] Subsequently, the semi-analytical pre-solution results obtained above were loaded as transient boundary conditions into the computational fluid dynamics simulation to conduct transient simulation, and pressure data were collected in real time at preset monitoring points (such as key pressure-bearing sections of the pipeline and equipment connection sections). Finally, the collected pressure data were analyzed to extract the maximum water hammer pressure; at the same time, a pressure friction loss function (reflecting the change of pressure with pipeline distance) was fitted based on the pressure data, and the pressure wave propagation distance was calculated based on the pressure friction loss function, ultimately obtaining the key water hammer parameters.

[0095] To meet the needs of rapid on-site analysis, multi-condition comparison, and engineering applications, and to overcome the problems of complex traditional fluid dynamics simulation processes, time-consuming modeling, and long solution cycles, the above process constructs a fluid dynamics simulation process suitable for non-Newtonian slurries. It generates the initial pressure distribution through a semi-analytical model pre-solution, and uses parametric geometry and mesh templates, steady-state initialization, and automated transient simulation to monitor key parameters, achieving rapid output of maximum pressure and propagation distance without changing the steady-state solver kernel. This significantly shortens the calculation time and lowers the operational threshold, thus providing efficient and quantifiable parameter support for criterion construction.

[0096] Step S130: Obtain observed pressure monitoring data and historical pressure monitoring data through pressure sensors. Based on the maximum water hammer pressure, the pressure wave propagation distance, and the historical pressure monitoring data, construct a water hammer occurrence criterion. The water hammer occurrence criterion includes a main criterion and a statistical criterion.

[0097] Specifically, when constructing the main criterion, the maximum water hammer pressure obtained in step S120 is first multiplied by a preset proportional coefficient (with a value of 85%, which is determined based on engineering experience; when the observed pressure reaches 85% of the maximum water hammer pressure and the propagation conditions are met, the risk of water hammer can be determined with a high probability) to obtain the prediction threshold; the maximum observed pressure of multiple pressure sensors within a preset sampling window (a type of observed pressure monitoring data; the preset sampling window is determined based on the pipeline transportation cycle and water hammer response time) is obtained, and the pressure sensors include a first sensor and a second sensor adjacent to the first sensor.

[0098] Determine whether the maximum observed pressure of the first sensor is greater than the prediction threshold. If the maximum observed pressure of the first sensor is greater than the prediction threshold, further determine whether the maximum observed pressure of the second sensor is greater than the prediction threshold within a preset propagation time (taken as 6s, calculated based on the pressure wave propagation distance and slurry flow velocity). If the maximum observed pressure of the second sensor is greater than the prediction threshold within the preset propagation time, then the main criterion is determined to be complete. The main criterion is: when there is a pressure sensor whose maximum observed pressure is greater than the prediction threshold, and the maximum observed pressure of its adjacent pressure sensor is also greater than the prediction threshold within the preset propagation time, the main criterion is satisfied.

[0099] Furthermore, when constructing statistical criteria, firstly, historical pressure monitoring data from multiple pressure sensors (long-term monitoring data under normal operating conditions) are acquired, and the mean and standard deviation of this historical pressure monitoring data are calculated. The mean is then multiplied by a preset statistical threshold coefficient (valued at 3) and the standard deviation to obtain the statistical threshold (based on the statistical 3σ principle, which can effectively distinguish between normal fluctuations and abnormal pressure). Real-time instantaneous pressure monitoring data from the pressure sensors (another type of observed pressure monitoring data) is then collected, and the instantaneous pressure rise rate (the change in instantaneous pressure per unit time) of this instantaneous pressure monitoring data is calculated. The system then determines whether the instantaneous pressure monitoring data is greater than a statistical threshold. If the instantaneous pressure monitoring data is greater than the statistical threshold, it determines whether the instantaneous pressure rise rate is greater than a preset initial judgment ratio (set to 20%, used to distinguish between instantaneous pressure surges caused by water hammer and chronic pressure changes). If the instantaneous pressure rise rate is greater than the preset initial judgment ratio, the statistical criterion is determined to be complete. The statistical criterion is: when there is an instantaneous pressure monitoring data of a pressure sensor that is greater than the statistical threshold, and the instantaneous pressure rise rate of the instantaneous pressure monitoring data is greater than the preset initial judgment ratio, the statistical criterion is determined to be satisfied.

[0100] To improve the engineering applicability and on-site judgment capability of water hammer prediction and overcome the limitations of existing methods that rely on experience or complex analysis, the above process is based on the maximum pressure and propagation distance predicted by the model, combined with historical data from pressure sensors, to construct a dual criterion system, including a primary criterion and a statistical criterion, to achieve rapid, quantitative, and visualized water hammer occurrence determination, thereby improving the reliability and robustness of engineering decisions.

[0101] Step S140: Based on the main criterion and the statistical criterion, analyze the maximum water hammer pressure, the pressure wave propagation distance and the observed pressure monitoring data to predict whether water hammer will occur in the filling slurry pipeline.

[0102] First, pressure monitoring data from multiple pressure sensors are acquired through pressure sensors. The pressure monitoring data includes the real-time maximum observed pressure (corresponding to the judgment requirements of the main criterion) and the real-time instantaneous pressure rise rate (corresponding to the judgment requirements of the statistical criterion).

[0103] Secondly, the real-time maximum observed pressure is substituted into the established primary criterion to determine whether the primary criterion is met. If the primary criterion is met, water hammer is directly determined to have occurred in the filling slurry pipeline. If the primary criterion is not met, the real-time instantaneous pressure (the value corresponding to the real-time instantaneous pressure monitoring data) and the real-time instantaneous pressure rise rate are substituted into the established statistical criterion to determine whether the statistical criterion is met. If the statistical criterion is met, water hammer is determined to have occurred in the filling slurry pipeline; if the statistical criterion is not met, water hammer is determined not to have occurred in the filling slurry pipeline.

[0104] Finally, the system outputs water hammer determination results, including whether water hammer occurred or not, providing a basis for taking protective measures at the engineering site (such as adjusting pump speed or closing valves).

[0105] The above process allows for the rapid detection of water hammer, enabling proactive protective or control measures to be taken in advance. This reduces the risk of damage to pipelines and pumping station equipment, while also improving the efficiency and accuracy of data utilization in the monitoring system, lowering on-site maintenance costs, and enhancing the overall system safety and stability.

[0106] In one alternative implementation, it further includes:

[0107] Taking a copper mine backfilling site as an application scenario, the mine uses pipelines to transport cemented backfill slurry made from tailings. The total length of the pipeline is about 5km, mainly consisting of vertical shaft sections, horizontal sections, and inclined sections. The diameter of the horizontal section is 150mm. In actual operation, due to pipe blockage or pump station start-up and shutdown during the transportation process, transient conditions often occur, which can easily induce water hammer and pose a threat to pipeline safety. Based on the water hammer prediction method provided in the embodiments of this application, a specific application is carried out.

[0108] First, the non-Newtonian slurry water hammer propagation model constructed in this application is imported into CFD simulation software (such as Fluent, CFX or Comsol). In the simulation, the yield stress, plastic viscosity and shear thinning law of the slurry are explicitly defined to ensure that the simulation can accurately reflect the transient response characteristics of the slurry, thereby automatically solving the pressure and pressure wave velocity when water hammer occurs.

[0109] Secondly, calculations are performed according to the fluid dynamics simulation process proposed in this application: circular pipe geometry and mesh templates are quickly generated using modular modeling scripts (such as...). Figure 2 As shown in the diagram, the mesh is generated, and approximate initial values ​​are provided by a non-Newtonian slurry water hammer propagation model to initialize the transient boundary conditions; the simulation software first runs steady-state calculations to obtain the pressure distribution of the slurry under normal transport conditions (e.g., Figure 3 As shown in the steady-state transport diagram, this step provides reasonable initial conditions for transient simulation, avoids numerical oscillations, and improves convergence efficiency. Subsequently, water hammer model boundary conditions are loaded in the transient solution, and key water hammer parameters (including maximum water hammer pressure and pressure attenuation function along the pressure path) are output in real time at preset monitoring points. In this embodiment, "rapid closure of the outlet valve" is used as a typical transient condition to simulate the water hammer phenomenon that may occur during the transport of filling slurry.

[0110] To verify the effectiveness of the method proposed in this application, the traditional water hammer model (based on the Newtonian fluid assumption) and the non-Newtonian slurry water hammer propagation model of this application were respectively imported into a CFD platform for simulation under the same working conditions, and the pressure cloud distribution of the two models during the pressurization and depressurization stages were compared:

[0111] Results obtained from traditional models (such as) Figure 4 As shown, the spatial distribution of the pressure field (including the valve-closing pressure increase stage and the valve-closing pressure decrease stage) is relatively smooth, and the transition process of the pressure field appears natural, exhibiting impact propagation characteristics similar to an ideal fluid; the results obtained from the improved model in this application (such as...) Figure 5 As shown, including the valve-closing pressurization stage and the valve-closing depressurization stage, the pressure fluctuations in the local area are more significant due to the consideration of the rheological properties of non-Newtonian slurry (especially the viscoelastic effect and the difference in the response of particles to the liquid phase). The contour map shows more color bands, and the transition characteristics are not as uniform as those of traditional models. It can better reveal the complex evolution process of slurry under transient impact and is closer to the non-uniform response characteristics in engineering practice.

[0112] According to the water hammer pressure curve (e.g.) Figure 6As shown in the figure, the water hammer pressure curves under different inlet flow velocities are analyzed. It can be seen that the maximum water hammer pressure predicted by the model of this application is 5.07 MPa. Combined with the pressure wave propagation distance calculation, the preset propagation time is determined to be 6s. According to the main criterion construction logic, the prediction threshold is 5.07 MPa × 85% = 4.31 MPa. That is, if the pressure of a certain sensor is greater than 4.31 MPa, and an adjacent sensor exceeds the threshold within 6s, the main criterion is considered to be valid.

[0113] Meanwhile, during the filling and transportation of the copper mine, the average monitoring pressure (mean of historical pressure monitoring data) of a certain pressure sensor was 3.2 MPa, with a standard deviation of 0.25 MPa. According to the logic of constructing statistical criteria, the statistical threshold is 3.2 MPa + 3 × 0.25 MPa = 3.95 MPa. That is, if the pressure of the sensor is greater than 3.95 MPa and the instantaneous pressure rise rate is greater than 20%, then the statistical criteria are considered to be valid.

[0114] Verification through the above embodiments shows that the water hammer prediction method for filling slurry pipelines provided in this application can accurately capture the water hammer characteristics of non-Newtonian slurry, and through the combination of main criteria and statistical criteria, it can quickly and reliably predict the occurrence of water hammer, providing effective protection for the safety of mine filling pipeline transportation.

[0115] The water hammer prediction method for slurry-filled pipelines provided in this application overcomes the limitations of traditional water hammer models, which are based on Newtonian fluid assumptions and cannot accurately reflect the rheological properties of high-concentration slurries, by constructing a water hammer propagation model suitable for non-Newtonian slurries. This model better reflects the transient pressure evolution and pressure wave propagation characteristics of the slurry under actual working conditions, solving the problem of insufficient prediction accuracy in traditional models. Furthermore, by using a computational fluid dynamics simulation process based on this non-Newtonian slurry water hammer propagation model, key water hammer parameters such as maximum water hammer pressure and pressure wave propagation distance are obtained, providing reliable quantitative data for water hammer assessment. Supported by data analysis, this paper constructs primary and statistical criteria by combining key water hammer parameters with observed pressure monitoring data from pressure sensors and historical pressure monitoring data. This enables quantitative judgment of water hammer occurrence, overcoming the shortcomings of existing technologies that rely on manual experience and lack clear judgment criteria. Finally, based on the dual criteria, the analysis of key parameters and monitoring data predicts water hammer, enabling rapid and accurate identification of water hammer risks in filling slurry pipelines. This effectively avoids problems such as pipeline pressure exceeding limits and equipment damage caused by water hammer, significantly improving the operational safety and risk management capabilities of filling slurry pipeline transportation systems. This application is applicable to various filling slurry pipeline systems, possessing good versatility and scalability. It can be promoted and applied under different mining and engineering conditions, providing a practical and reliable technical solution for filling mining and related slurry transportation projects.

[0116] Example 2

[0117] like Figure 7The diagram shown is a structural schematic of a water hammer prediction device 700 for a slurry-filled pipeline according to an embodiment of this application. The device includes:

[0118] The first construction module 710 is used to construct a non-Newtonian slurry water hammer propagation model for a filled slurry pipe.

[0119] Simulation module 720 is used to obtain key water hammer parameters through a fluid dynamics simulation process based on the non-Newtonian slurry water hammer propagation model. The key water hammer parameters include the maximum water hammer pressure and the pressure wave propagation distance.

[0120] The second construction module 730 is used to acquire observed pressure monitoring data and historical pressure monitoring data through pressure sensors, and to construct a water hammer occurrence criterion based on the maximum water hammer pressure, the pressure wave propagation distance and the historical pressure monitoring data. The water hammer occurrence criterion includes a main criterion and a statistical criterion.

[0121] The prediction module 740 is used to analyze the maximum water hammer pressure, the pressure wave propagation distance and the observed pressure monitoring data based on the main criterion and the statistical criterion to predict whether water hammer will occur in the filling slurry pipeline.

[0122] The water hammer prediction device for the filling slurry pipeline provided in this application embodiment can realize each process of the water hammer prediction method for the filling slurry pipeline corresponding to Embodiment 1, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0123] Example 3

[0124] This application also provides a computer device. Please refer to the following for details. Figure 8 , Figure 8 This is a basic structural block diagram of the computer device in this embodiment.

[0125] The computer device 8 includes a memory 81, a processor 82, and a network interface 83 that are interconnected via a system bus. It should be noted that only a computer device 8 with a memory 81, processor 82, and network interface 83 is shown in the figure; however, it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0126] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0127] The memory 81 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or D slot compatibility test memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 81 may be an internal storage unit of the computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the memory 81 may also be an external storage device of the computer device 8, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device 8. Of course, the memory 81 may also include both the internal storage unit and its external storage device of the computer device 8. In this embodiment, the memory 81 is typically used to store the operating system and various application software installed on the computer device 8, such as computer-readable instructions for slot compatibility testing methods. In addition, the memory 81 can also be used to temporarily store various types of data that have been output or will be output.

[0128] In some embodiments, the processor 82 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other water hammer prediction chip for filling slurry pipes. The processor 82 is typically used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to execute computer-readable instructions stored in the memory 81 or to process data, such as computer-readable instructions for executing the slot compatibility testing method.

[0129] The network interface 83 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 8 and other electronic devices.

[0130] The computer device provided in this embodiment can execute the water hammer prediction method for the filled slurry pipeline described above. The water hammer prediction method for the filled slurry pipeline described here can be any of the water hammer prediction methods for the filled slurry pipelines described in the various embodiments above.

[0131] Example 4

[0132] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the water hammer prediction method for the filling slurry pipeline in this embodiment.

[0133] In this embodiment, the computer-readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the computer-readable storage medium can be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium can also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device. Of course, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the computer-readable storage medium is typically used to store the operating system and various application software installed on the computer device. In addition, the computer-readable storage medium can also be used to temporarily store various types of data that have been output or will be output.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, as an alternative implementation, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0135] In addition, the functional modules or units in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0136] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium can be a non-volatile storage medium or a volatile storage medium. For example, the storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other medium capable of storing program code.

[0137] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting water hammer in a slurry-filled pipeline, characterized in that, The method includes: A non-Newtonian slurry water hammer propagation model is constructed for filled slurry pipes; among other things, the density of solid particles in the non-Newtonian slurry is determined. Density of liquid water Simultaneously, by combining the elastic modulus of the slurry, the elastic modulus of the solid particles, the volume concentration, the elastic modulus of the pipe, the pipe diameter, and the pipe wall thickness, the pressure wave velocity during water hammer in non-Newtonian slurry is calculated. The formula is as follows: in, It is the elastic modulus of the slurry. It is the elastic modulus of solid particles. It is volume concentration. It is the elastic modulus of the pipe. It is the pipe diameter. It refers to the pipe wall thickness; Based on the density of the solid particles, the density of the liquid water, and the pressure wave velocity at the time of water hammer in the non-Newtonian slurry, the initial impact pressure at the time of water hammer in the non-Newtonian slurry is obtained. The formula is as follows: in, It is the initial flow velocity of the non-Newtonian slurry; Based on the particle drag coefficient of the group of particles in the non-Newtonian slurry, the flow velocity of the solid particles, and the flow velocity of the liquid water, the secondary impact pressure during water hammer in the non-Newtonian slurry is obtained using the following formula: in, It is the particle drag coefficient of the group of particles. It is the flow rate of the solid particles. It is the flow rate of the liquid water; The total pressure of the non-Newtonian slurry water hammer is calculated by superimposing the initial impact pressure and the secondary impact pressure, as shown in the following formula: Based on the pressure wave velocity during the occurrence of non-Newtonian slurry water hammer, the propagation model of non-Newtonian slurry water hammer is finally constructed. Based on the non-Newtonian slurry water hammer propagation model, key water hammer parameters are obtained through a fluid dynamics simulation process. These key water hammer parameters include the maximum water hammer pressure and the pressure wave propagation distance. The observed pressure monitoring data and historical pressure monitoring data are obtained by pressure sensors. Based on the maximum water hammer pressure, the pressure wave propagation distance and the historical pressure monitoring data, a water hammer occurrence criterion is constructed. The water hammer occurrence criterion includes a main criterion and a statistical criterion. Based on the main criterion and the statistical criterion, the maximum water hammer pressure, the pressure wave propagation distance, and the observed pressure monitoring data are analyzed to predict whether water hammer will occur in the filling slurry pipeline.

2. The water hammer prediction method for filled grout pipes according to claim 1, characterized in that, The key water hammer parameters, obtained through computational fluid dynamics simulation based on the non-Newtonian slurry water hammer propagation model, include: Based on the non-Newtonian slurry water hammer propagation model, the pressure wave peak position, pressure wave timing and initial pressure are calculated to obtain semi-analytical pre-solution results; Based on the structural characteristics of the filling slurry pipe, a local computational domain is generated, and a high-precision mesh is generated based on the local computational domain to obtain parametric geometry and mesh template; The local computational domain is solved in a steady state to obtain the steady-state flow field, which is then used as the initial condition for the transient simulation. The semi-analytical pre-solution results are loaded as transient boundary conditions into the computational fluid dynamics simulation to carry out transient simulation, and pressure data are collected in real time at preset monitoring points. The pressure data is analyzed to extract the maximum water hammer pressure. The pressure attenuation function along the pressure path is obtained by fitting the pressure data. The propagation distance of the pressure wave is calculated based on the pressure attenuation function along the pressure path.

3. The water hammer prediction method for filled slurry pipelines according to claim 1, characterized in that, The step of constructing a water hammer occurrence criterion based on the maximum water hammer pressure, the pressure wave propagation distance, and the historical pressure monitoring data includes: The maximum water hammer pressure is multiplied by a preset proportional coefficient to obtain the prediction threshold; The maximum observed pressure of multiple pressure sensors within a preset sampling window is obtained, wherein the pressure sensors include a first sensor and a second sensor adjacent to the first sensor; Determine whether the maximum observed pressure of the first sensor is greater than the prediction threshold; When the maximum observed pressure of the first sensor is greater than the prediction threshold, determine whether the maximum observed pressure of the second sensor is greater than the prediction threshold within a preset propagation time. If the maximum observed pressure of the second sensor is greater than the prediction threshold within the preset propagation time, then the main criterion is determined to be completed. The main criterion is: when there is a pressure sensor whose maximum observed pressure is greater than the prediction threshold, and the maximum observed pressure of its adjacent pressure sensor is also greater than the prediction threshold within the preset propagation time, the main criterion is determined to be satisfied.

4. The water hammer prediction method for filled slurry pipelines according to claim 3, characterized in that, The step of constructing a water hammer occurrence criterion based on the maximum water hammer pressure, the pressure wave propagation distance, and the historical pressure monitoring data further includes: Acquire historical pressure monitoring data from multiple pressure sensors, and calculate the mean and standard deviation of the historical pressure monitoring data; The statistical threshold is obtained by adding the product of the mean, the preset statistical threshold coefficient, and the standard deviation. Real-time acquisition of instantaneous pressure monitoring data from the pressure sensor, calculation of the instantaneous pressure rise rate of the instantaneous pressure monitoring data, and determination of whether the instantaneous pressure monitoring data is greater than the statistical threshold; If the instantaneous pressure monitoring data is greater than the statistical threshold, determine whether the instantaneous pressure rise rate is greater than the preset initial judgment ratio; If the instantaneous pressure rise rate is greater than the preset initial judgment ratio, then the statistical criterion is determined to be completed. The statistical criterion is: when there is an instantaneous pressure monitoring data of a pressure sensor that is greater than the statistical threshold, and the instantaneous pressure rise rate of the instantaneous pressure monitoring data is greater than the preset initial judgment ratio, the statistical criterion is determined to be satisfied.

5. The water hammer prediction method for filled grout pipelines according to claim 4, characterized in that, The step of analyzing the maximum water hammer pressure, the pressure wave propagation distance, and the observed pressure monitoring data based on the main criterion and the statistical criterion to predict whether water hammer will occur in the filling slurry pipeline includes: Acquire observed pressure monitoring data from multiple pressure sensors, including real-time maximum observed pressure and real-time instantaneous pressure rise rate; Substitute the real-time maximum observed pressure into the main criterion to determine whether the main criterion is met. If the main criterion is met, it is determined that water hammer has occurred in the filling slurry pipeline. If the main criterion is not met, the real-time instantaneous pressure and the real-time instantaneous pressure rise rate are substituted into the statistical criterion to determine whether the statistical criterion is met. If the statistical criterion is met, it is determined that water hammer has occurred in the filling slurry pipeline. If the statistical criterion is not met, it is determined that water hammer has not occurred in the filling slurry pipeline. Output water hammer determination results, which include whether water hammer has occurred or not.

6. The water hammer prediction method for filled slurry pipelines according to claim 2, characterized in that, The step of performing a steady-state solution on the local computational domain to obtain the steady-state flow field, and using the steady-state flow field as the initial condition for the transient simulation, includes: In the computational fluid dynamics simulation, the steady-state simulation parameters of the local computational domain are set, including the slurry yield stress, plastic viscosity, and shear thinning law; Based on the steady-state simulation parameters, steady-state boundary conditions corresponding to the local computational domain are loaded, and the steady-state solver is used to perform steady-state solution on the local computational domain according to the steady-state boundary conditions to obtain the initial flow field data; Determine whether the initial flow field data meets the preset convergence condition. If the initial flow field data meets the preset convergence condition, determine the flow field corresponding to the initial flow field data as the steady-state flow field, and use the steady-state flow field as the initial condition for the transient simulation.

7. A water hammer prediction device for a filled slurry pipeline, characterized in that, The device includes: The first construction module is used to construct a non-Newtonian slurry water hammer propagation model for filled slurry pipes; wherein, the density of solid particles in the non-Newtonian slurry is determined. Density of liquid water Simultaneously, by combining the elastic modulus of the slurry, the elastic modulus of the solid particles, the volume concentration, the elastic modulus of the pipe, the pipe diameter, and the pipe wall thickness, the pressure wave velocity during water hammer in non-Newtonian slurry is calculated. The formula is as follows: in, It is the elastic modulus of the slurry. It is the elastic modulus of solid particles. It is volume concentration. It is the elastic modulus of the pipe. It is the pipe diameter. It refers to the pipe wall thickness; Based on the density of the solid particles, the density of the liquid water, and the pressure wave velocity at the time of water hammer in the non-Newtonian slurry, the initial impact pressure at the time of water hammer in the non-Newtonian slurry is obtained. The formula is as follows: in, It is the initial flow velocity of the non-Newtonian slurry; Based on the particle drag coefficient of the group of particles in the non-Newtonian slurry, the flow velocity of the solid particles, and the flow velocity of the liquid water, the secondary impact pressure during water hammer in the non-Newtonian slurry is obtained using the following formula: in, It is the particle drag coefficient of the group of particles. It is the flow rate of the solid particles. It is the flow rate of the liquid water; The total pressure of the non-Newtonian slurry water hammer is calculated by superimposing the initial impact pressure and the secondary impact pressure, as shown in the following formula: Based on the pressure wave velocity during the occurrence of non-Newtonian slurry water hammer, the propagation model of non-Newtonian slurry water hammer is finally constructed. The simulation module is used to obtain key water hammer parameters through a fluid dynamics simulation process based on the non-Newtonian slurry water hammer propagation model. The key water hammer parameters include the maximum water hammer pressure and the pressure wave propagation distance. The second construction module is used to acquire observed pressure monitoring data and historical pressure monitoring data through pressure sensors, and to construct water hammer occurrence criteria based on the maximum water hammer pressure, the pressure wave propagation distance and the historical pressure monitoring data. The water hammer occurrence criteria include a main criterion and a statistical criterion. The prediction module is used to analyze the maximum water hammer pressure, the pressure wave propagation distance and the observed pressure monitoring data based on the main criteria and the statistical criteria, and to predict whether water hammer will occur in the filling slurry pipeline.

8. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the water hammer prediction method for the filling slurry pipe according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the water hammer prediction method for the filled slurry pipeline according to any one of claims 1-6.

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