Load simulation and load breaking method of submerged water jet flow field considering cavitation effect

Abstract

This invention relates to the field of submerged cavitation water jets, specifically to a method for simulating the flow field load and ice-breaking of submerged water jets considering cavitation effects. The method includes obtaining an initial flow field-ice plate topology model; adjusting the fractal dimension of the mesh through a fractal growth mechanism, fusing a probabilistic model, and embedding a viscoelastic network stress transfer mechanism to obtain a fractal characteristic load field; converting the load generated by fractal iterative loading into the stress and strain of the ice plate, extracting dynamic damage evolution data of the ice plate and chaotic characteristic parameters from the fractal characteristic load field, and obtaining optimized submerged water jet parameters. This invention constructs coupling and correlation rules, clarifying the core coupling relationship between the cavitation-sensitive region and the ice plate load action region, ensuring that the cavitation load is accurately transferred to key areas of the ice layer. By adjusting the fractal dimension of the mesh in real time through a fractal growth mechanism to adapt to the dynamic evolution stage of cavitation bubbles, it solves the problem that traditional models cannot simultaneously capture the cavitation flow field morphology and ice plate damage.

Description

Technical Field

[0001] This invention relates to the field of submerged cavitation water jets, and more specifically to a method for simulating the flow field load and ice-breaking load of submerged water jets that takes into account the cavitation effect. Background Technology

[0002] Traditional mechanical icebreaking methods rely on hull impact or propeller cutting, which suffer from drawbacks such as high structural impact, low icebreaking efficiency, high energy consumption, and easy damage to the hull, making it difficult to meet the development needs of large-scale and efficient polar engineering. High-pressure water jet icebreaking technology, with its advantages of being environmentally friendly, having concentrated energy, and causing less structural damage, has become a research hotspot for new icebreaking technologies. Its core principle is to use a high-pressure pump to pressurize water and then spray it through nozzles to form a high-speed jet that impacts the ice layer, combined with the cavitation effect to enhance icebreaking capability.

[0003] However, existing water jet ice-breaking technologies and numerical simulation methods still face numerous technical bottlenecks. The ice-breaking effect of submerged water jets is highly dependent on the cavitation effect, as the instantaneous high pressure released when cavitation bubbles collapse is the key driving force for ice layer destruction. However, existing methods often neglect the randomness and dynamic evolution characteristics of cavitation bubbles and fail to establish a precise correlation between cavitation load and ice plate damage, resulting in significant deviations between load simulation and the actual ice-breaking process. Furthermore, existing technologies often simplify the jet load to a uniform load, failing to consider the fractal characteristics of the cavitation flow field and the differences in the spatiotemporal distribution of the load, leading to inaccurate load transfer. Simultaneously, the optimization of ice-breaking parameters largely relies on experimental iterations, lacking quantitative optimization criteria based on the synchronous chaotic characteristics of the flow field and the ice plate, making it difficult to maximize ice-breaking efficiency. Moreover, as a brittle material, ice exhibits complex radial / circumferential crack propagation modes under high-speed jet impact. Existing models do not fully incorporate the JH-2 dynamic damage brittle constitutive characteristics of ice, failing to fully reproduce the dynamic process of ice layer from initial damage to failure and fragmentation, thus affecting the understanding of the ice-breaking mechanism and the optimization of the technology. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a method for simulating the load of a submerged water jet flow field and breaking ice under load, taking into account the cavitation effect.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: a method for simulating the load of a submerged water jet flow field and breaking ice under load, taking into account cavitation effects, comprising the following steps: The method includes: S101. Based on the principles of topology, and by coupling the Eulerian grid of the flow field with the Lagrange grid of the ice plate, the coupled grid is obtained and the topological association rule of the coupled grid is formed. The cavitation trigger threshold and the initial damage threshold of the ice plate are introduced to obtain the initial topological model of the flow field-ice plate. S102. Using the initial flow field-ice plate topology model as a carrier, the fractal dimension of the coupled mesh is adjusted in real time through the fractal growth mechanism to obtain a fractal mesh. Then, the probabilistic model is fused. Based on the fluid pressure parameters of the initial flow field-ice plate topology model, the cavitation triggering conditions of the fractal mesh nodes are corrected. Combined with the cavitation triggering threshold, the generation region of cavitation load and the action region of ice plate load are associated to form the generation region of cavitation load. Then, the viscoelastic network stress transfer mechanism is embedded to construct a viscoelastic network. The stress is converted into the load parameters of each fractal mesh node. The load parameters of all fractal mesh nodes are integrated and calibrated to obtain the fractal characteristic load field. S103. The macroscopic fractal primary load and microscopic fractal secondary load obtained by decomposing the fractal characteristic load field are both converted into stress and strain of the ice plate through the load generated by fractal iterative loading, thus completing the dynamic damage coupling of the ice plate and obtaining dynamic damage evolution data of the ice plate. The chaotic characteristic parameters in the dynamic damage evolution data of the ice plate and the fractal characteristic load field are extracted as inputs for icebreaking parameter optimization, and the optimized submerged water jet parameters are obtained, thus completing the load simulation and load icebreaking.

[0006] In a preferred embodiment, S101 further includes: dividing the Eulerian grid of the flow field and the Lagrange grid of the ice plate into grid nodes one by one, and establishing a mapping relationship between the grid cells of the Eulerian grid of the flow field and the Lagrange grid of the ice plate and the grid nodes one by one. The cavitation-sensitive region of the flow field and the load-bearing region of the ice plate are taken as the core regions of topological coupling. Specifically, the cavitation-sensitive region of the flow field is defined as the flow field region within the range from downstream of the nozzle outlet to the target distance. This region is the main area for the generation and collapse of cavitation bubbles and the core area for the generation of cavitation loads. The load-bearing region of the ice plate is defined as the central impact zone and crack propagation zone of the ice plate corresponding to the cavitation-sensitive region of the flow field. This region is the area where the ice plate is most concentrated under cavitation loads and is most prone to damage. Each cavitation-sensitive area grid node in the topological coupling core region corresponds to an ice plate load-acting area grid node, so that the grid cells of the cavitation-sensitive area and the grid cells of the ice plate load-acting area form a fixed coupling relationship, thereby ensuring that the cavitation load can be directly and accurately transmitted to the corresponding area of ​​the ice plate. In terms of physical morphology, the cavitation-sensitive area refers to the area downstream of the nozzle outlet and within the target distance range, while the ice plate load-acting area refers to the central impact zone and the crack propagation zone. The non-core region outside the topological coupling core region adopts a transitional topology. The non-core region outside the topological coupling core region refers to the stable flow field region outside the cavitation sensitive region and the non-stressed region outside the ice plate load region. The transitional topology refers to the grid nodes of the flow field excluding the cavitation sensitive region, which adopt a many-to-one mapping relationship with the grid nodes of the ice plate excluding the ice plate load region, forming the topological association rules of the coupled grid.

[0007] In a preferred embodiment, the method further includes, before S101, extracting from the non-submerged water jet ice-breaking simulation system core boundary parameters including inlet pressure range, nozzle structure parameters, target distance interval, fluid physical parameters including saturated vapor pressure, dynamic viscosity, and compressibility coefficient, and ice plate physical and constitutive parameters including density, elastic modulus, JH-2 model parameters, and fracture strain threshold. After constructing the topological association rules of the coupled mesh, S101 calls the core boundary parameters, fluid physics parameters, and ice plate physics and constitutive parameters to extract the construction parameters of the flow field Eulerian mesh and the ice plate Lagrange mesh, respectively generating independent flow field Eulerian mesh models and ice plate Lagrange mesh models. The topological association rules of the coupled mesh are embedded into the topological mapping algorithm. The topological mapping algorithm couples and binds the independent flow field Eulerian mesh and the ice plate Lagrange mesh, so that each flow field mesh node and mesh cell is mapped to the corresponding ice plate mesh node and mesh cell through the topological association rules. The coupled flow field Eulerian grid and ice plate Lagrangian grid are introduced into a cavitation trigger threshold set based on the difference between fluid pressure and saturated vapor pressure, and an initial damage threshold for the ice plate set based on the initial damage factor in the JH-2 model parameters. The flow field parameters including flow field pressure, fluid pressure, and velocity and the ice plate parameters are then bound together to obtain the initial flow field-ice plate topology model.

[0008] In a preferred embodiment, S102 extracts the flow field pressure distribution data of the initial flow field-ice plate topology model and divides the cavitation sensitive area, specifically: the area where the flow field pressure is lower than the saturated vapor pressure is divided into the cavitation bubble core area, the area where the flow field pressure is between the saturated vapor pressure and the ambient pressure is divided into the cavitation cloud transition area, and the area where the flow field pressure is equal to the ambient pressure is divided into the peripheral stable flow field area. Based on the fractal growth mechanism, according to the collapse frequency of cavitation bubbles in the flow field, a matching fractal growth iteration step size is set. One iteration step size corresponds to the generation and collapse of cavitation bubbles. The initial fractal dimension is also set to complete the initialization of the fractal growth mechanism. The fluid pressure of each coupled grid node in the flow field is collected in real time and compared with the cavitation trigger threshold set by the saturated vapor pressure difference to determine the dynamic evolution stage of cavitation bubbles. The dynamic evolution stage refers to the entire process including the bubble generation stage, bubble development stage, bubble collapse stage, and bubble dissipation stage. Based on the division of cavitation-sensitive regions, the following settings are made: When cavitation bubbles are collected in the initial and final stages of dynamic evolution in the core region, cavitation cloud transition region, and surrounding stable flow field region, the initial fractal dimension is adjusted upward by two iteration steps. When cavitation bubbles are collected in the middle stage of dynamic evolution in the core region, cavitation cloud transition region, and surrounding stable flow field region, the initial fractal dimension is adjusted downward by two iteration steps. The fractal generation density representing the flow field pressure gradient data in the coupled grid nodes is also densified to form a fractal grid.

[0009] In a preferred embodiment, after obtaining the fractal mesh, S102 calls the fluid pressure parameters bound to each fractal mesh node in the initial model of flow field-ice plate coupling, and at the same time extracts the flow field pressure gradient data corresponding to each fractal mesh node after adjustment by the fractal growth mechanism. Based on the difference between the fluid pressure parameter and the saturated vapor pressure parameter of each fractal grid node, the pressure barrier of each fractal grid node is obtained. This pressure barrier is the energy barrier that fluid molecules need to overcome to generate cavitation bubbles at the fractal grid node. Since the fluid pressure parameters of different fractal grid nodes are different, their corresponding pressure barriers also show a differentiated distribution, which is consistent with the pressure distribution state of the flow field. The product of the flow field pressure gradient data and the pressure barrier at each fractal grid node is exponentially calculated, and the ratio of dynamic viscosity to compressibility coefficient is introduced as a fitting coefficient. After multiplication, the breakthrough probability of fluid molecules overcoming the pressure barrier is obtained. After traversing each fractal grid node, the breakthrough probability of each fractal grid node is multiplied by the corresponding spatial density to obtain the preliminary probability density corresponding to each fractal grid node. The spatial density is the total number of nodes in the region where the node is located divided by the volume of the region where the node is located. The summation of all fractal grid nodes is averaged to obtain the average preliminary probability density of the entire flow field. The preliminary probability density corresponding to each fractal grid node is divided by the average preliminary probability density of the entire flow field to obtain the cavitation bubble generation probability density of each fractal grid node. Based on the calculated cavitation bubble generation probability density data of each fractal grid node, the randomness of cavitation bubble generation is incorporated into the triggering condition design. The cavitation triggering threshold is used to determine the cavitation bubble generation probability density of each fractal grid node one by one. All fractal grid nodes that meet the cavitation bubble generation conditions are marked to form the generation region of cavitation load. The initial intensity of the cavitation load of the fractal grid node is determined by combining the difference in cavitation bubble generation probability density.

[0010] In a preferred embodiment, S102 extracts the mesh nodes of the fractal mesh in the generated region as the network nodes of the viscoelastic network, and uses virtual springs as the connection units of the network nodes to transfer stress and energy. The distribution of virtual springs is specifically as follows: fractal mesh node distribution data is called, and virtual springs are set between each fractal mesh node and its adjacent fractal mesh nodes. The dynamic evolution of cavitation bubbles at each fractal grid node is monitored in real time. When any cavitation bubble at any fractal grid node is in the final stage of dynamic evolution, the viscoelastic network initiates an energy conversion operation. Specifically, the energy released by the cavitation bubble corresponding to the fractal grid node is directly converted into the stress of the corresponding viscoelastic network node. The calculated cavitation bubble generation probability density data at the fractal grid node is used to characterize the stress energy. Because the higher the cavitation bubble generation probability density, the more cavitation bubbles there are in a unit space, the greater the total energy released by collapse, and the greater the initial stress value of the corresponding viscoelastic network core node, ensuring that the initial stress magnitude is positively correlated with the cavitation bubble collapse energy. The stress is transferred along the stress transmission criterion, and the stress energy is stored through the elastic deformation of the virtual spring and / or dissipated through viscous dissipation. The stress of each viscoelastic network node is converted into corresponding load parameters including real-time pressure and velocity. The dynamic change data of the load parameters of each fractal grid node are recorded one by one in time series to obtain the spatiotemporal distribution data of the load parameters. Macroscopic principal loads and microscopic pulsating loads are extracted from the load parameters, and the fractal dimension of the fractal grid is called. The spatiotemporal data of load parameters of all fractal grid nodes are integrated to construct the spatiotemporal distribution matrix of the fractal characteristic load field, and finally form a spatiotemporally dynamic fractal characteristic load field.

[0011] In a preferred embodiment, S103 splits the fractal characteristic load field into macroscopic fractal primary load and microscopic fractal secondary load, and based on the load fractal dimension and fractal iteration step size of the fractal characteristic load field, applies the macroscopic fractal primary load to the topological coupling core region of the ice plate through a fractal iterative loading algorithm, and the loading direction is strictly set to be perpendicular to the surface of the ice plate. During the loading process, the magnitude and position of the macroscopic fractal primary load at each loading moment are recorded in real time. The micro-fractal secondary loads are applied synchronously with the macro-fractal primary loads using a superposition method. Specifically, the temporal characteristics of the final stage of cavitation bubbles in their dynamic evolution are obtained through the spatiotemporal distribution matrix of the fractal characteristic load field. The micro-fractal secondary loads are then superimposed on the macro-fractal primary loads using the same fractal iteration step size as the macro-fractal primary loads. The loading region of the micro-fractal secondary loads acts on the grid nodes corresponding to the cavitation-sensitive regions in the initial flow-ice plate coupled model. The loading timing is synchronized with the spatiotemporal distribution matrix of the fractal characteristic load field. Specifically, when the cavitation bubble is at its final collapse peak, the iteration gain coefficient of the micro-fractal secondary loads is increased to enhance the intensity of the micro-fractal secondary loads and adapt to the peak load characteristics during cavitation bubble collapse. When the cavitation is in a stable phase, the iteration gain coefficient of the micro-fractal secondary loads is decreased to reduce the intensity of the micro-fractal secondary loads and adapt to the load characteristics during stable cavitation.

[0012] In a preferred embodiment, S103 converts the force direction and force area corresponding to the ice plate unit that makes up the smallest unit of the ice plate into the stress and strain data of each ice plate unit, combined with the total load after the superposition of macroscopic fractal primary load and microscopic fractal secondary load. The transformed stress and strain data of the ice plate elements are substituted into the JH-2 model to calculate the initial damage factor of each ice plate element in real time. The initial damage factor is used to characterize the damage degree of the ice plate element. The damage factor of each ice plate element is compared with the initial damage threshold of the ice plate. If the damage factor of any ice plate element exceeds the initial damage threshold, the corresponding ice plate element is determined to be a failed ice plate element. All failed ice plate elements are summarized, and the mesh coordinates, failure time, and load magnitude at failure of the failed ice plate elements are recorded. The extension path of the ice plate crack along the direction of the maximum load gradient of the fractal characteristic load field is tracked, and complete dynamic damage evolution data of the ice plate is generated.

[0013] In a preferred embodiment, S103 fits the grid coordinates and failure time of the corresponding failed ice plate unit in the extended path of the ice plate dynamic damage evolution data, and extracts fractal characteristic load field chaotic characteristic parameters and ice plate damage chaotic characteristic parameters to characterize the degree of chaotic motion of the ice plate. These parameters are the maximum Lyapunov exponent, correlation dimension, and correlation dimension of Lyapunov exponent and number of failed ice plate units, respectively. The Lyapunov exponent characterizes the chaotic intensity of cavitation collapse pressure pulsation, and the Lyapunov exponent of ice plate crack propagation rate characterizes the chaotic intensity of ice plate damage evolution. The difference between the two directly guides the adjustment direction of the submerged water jet parameters. If the exponent difference is too large, the inlet pressure and target distance need to be adjusted to match the chaotic intensity. The correlation dimension of the fractal characteristic load field characterizes the spatial distribution complexity of the cavitation bubble in the initial and final stages. Combined with the fractal dimension, it fully characterizes the spatiotemporal chaotic characteristics of the cavitation load. The differences between the maximum Lyapunov exponent of the characteristic parameters of the fractal characteristic load field and the Lyapunov exponent of the chaotic characteristic parameters of ice plate damage, as well as the differences between the correlation dimension of the fractal characteristic load field and the correlation dimension of the number of failed ice plate units in the chaotic characteristic parameters of ice plate damage, are calculated separately. The comprehensive chaotic synchronization error is obtained by weighted summation.

[0014] In a preferred embodiment, step S103 forms continuous time-series data by sorting the comprehensive chaotic synchronization error along the time axis, establishes a chaotic synchronization error time-series sequence, and calculates the first derivative point by point on the chaotic synchronization error time-series sequence to obtain the rate of change of the comprehensive chaotic synchronization error. A positive derivative indicates that the comprehensive chaotic synchronization error is increasing, the synchronization between the flow field and the chaotic characteristics of the ice plate is continuously deteriorating, and the energy transfer efficiency is continuously decreasing. A negative derivative indicates that the error is decreasing, and the synchronization is continuously improving. When the derivative approaches 0, it indicates that the error is stabilizing, the synchronization reaches a dynamic equilibrium, and the error time-series sequence is subjected to definite integral operation within a set iteration period. The integration interval is the iteration period of a single parameter optimization to obtain the cumulative chaotic synchronization error.

[0015] The beneficial effects of this invention are: based on the principle of topology, the coupling and association rules of the flow field Eulerian grid and the ice plate Lagrange grid are constructed, the one-to-one core coupling relationship between the cavitation sensitive area and the ice plate load area is clarified, and the transition topology structure of the non-core area is combined to ensure that the cavitation load is accurately transferred to the key area of ​​the ice layer. The fractal dimension of the grid is adjusted in real time through the fractal growth mechanism to adapt to the dynamic evolution stage of cavitation bubbles, thus solving the problem that traditional models cannot simultaneously capture the cavitation flow field morphology and ice plate damage. Furthermore, the probabilistic model quantifies the randomness of cavitation bubble generation, calculates the probability density of cavitation bubble generation through pressure barrier and flow field pressure gradient, and accurately defines the cavitation load generation region and initial intensity. An embedded viscoelastic network stress transfer mechanism transforms the cavitation bubble collapse energy into real-time load parameters of the grid nodes, fully reproducing the spatiotemporal distribution characteristics of macroscopic stable stagnant pressure and microscopic cavitation pulsating load, filling the gap in existing methods' insufficient characterization of the dynamic characteristics of cavitation loads. Combined with the JH-2 dynamic damage brittle constitutive model of ice, fractal loads are transformed into stress-strain data of ice plate elements, damage factors are calculated in real time, and element failure is determined. The propagation path of cracks along the direction of maximum load gradient is accurately tracked. By extracting the fractal characteristic load field and chaotic characteristic parameters of ice plate damage, and calculating the comprehensive chaotic synchronization error, a synchronous optimization criterion for flow field and ice plate damage is established, enabling dynamic adjustment of key parameters such as inlet pressure and target distance. Attached Figure Description

[0016] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] As attached Figure 1 As shown, this embodiment provides a method for simulating the load on a submerged water jet flow field and performing ice-breaking under cavitation effects, including the following steps: The method includes: S101. Based on the principles of topology, and by coupling the Eulerian grid of the flow field with the Lagrange grid of the ice plate, the coupled grid is obtained and the topological association rule of the coupled grid is formed. The cavitation trigger threshold and the initial damage threshold of the ice plate are introduced to obtain the initial topological model of the flow field-ice plate. S101 also includes: dividing the Eulerian grid of the flow field and the Lagrange grid of the ice plate into grid nodes one by one, and establishing a mapping relationship between the grid elements of the Eulerian grid of the flow field and the Lagrange grid of the ice plate and the grid nodes one by one; The cavitation-sensitive region of the flow field and the load-bearing region of the ice plate are taken as the core regions of topological coupling. Specifically, the cavitation-sensitive region of the flow field is defined as the flow field region within the range from downstream of the nozzle outlet to the target distance. This region is the main area for the generation and collapse of cavitation bubbles and the core area for the generation of cavitation loads. The load-bearing region of the ice plate is defined as the central impact zone and crack propagation zone of the ice plate corresponding to the cavitation-sensitive region of the flow field. This region is the area where the ice plate is most concentrated under cavitation loads and is most prone to damage. Each cavitation-sensitive area grid node in the topological coupling core region corresponds to an ice plate load-acting area grid node, so that the grid cells of the cavitation-sensitive area and the grid cells of the ice plate load-acting area form a fixed coupling relationship, thereby ensuring that the cavitation load can be directly and accurately transmitted to the corresponding area of ​​the ice plate. In terms of physical morphology, the cavitation-sensitive area refers to the area downstream of the nozzle outlet and within the target distance range, while the ice plate load-acting area refers to the central impact zone and the crack propagation zone. The non-core region outside the topological coupling core region adopts a transitional topology. The non-core region outside the topological coupling core region refers to the stable flow field region outside the cavitation sensitive region and the non-stressed region outside the ice plate load region. The transitional topology refers to the grid nodes of the flow field excluding the cavitation sensitive region, which adopt a many-to-one mapping relationship with the grid nodes of the ice plate excluding the ice plate load region, forming the topological association rules of the coupled grid.

[0019] The method also includes extracting, before S101, the core boundary parameters of the non-submerged water jet ice-breaking simulation system, including inlet pressure range, nozzle structure parameters, target distance range, fluid physical parameters including saturated vapor pressure, dynamic viscosity, compressibility coefficient, and ice plate physical and constitutive parameters including density, elastic modulus, JH-2 model parameters, and fracture strain threshold. After constructing the topological association rules of the coupled mesh, S101 calls the core boundary parameters, fluid physics parameters, and ice plate physics and constitutive parameters to extract the construction parameters of the flow field Eulerian mesh and the ice plate Lagrange mesh, respectively generating independent flow field Eulerian mesh models and ice plate Lagrange mesh models. The topological association rules of the coupled mesh are embedded into the topological mapping algorithm. The topological mapping algorithm couples and binds the independent flow field Eulerian mesh and ice plate Lagrange mesh, so that each flow field mesh node and mesh cell is mapped to the corresponding ice plate mesh node and mesh cell through the topological association rules. The coupled flow field Eulerian grid and ice plate Lagrangian grid are introduced into a cavitation trigger threshold set based on the difference between fluid pressure and saturated vapor pressure, and an initial damage threshold for the ice plate set based on the initial damage factor in the JH-2 model parameters. The flow field parameters including flow field pressure, fluid pressure, and velocity and the ice plate parameters are then bound together to obtain the initial flow field-ice plate topology model.

[0020] In some other specific implementations, the construction parameters for extracting the flow field Eulerian mesh and the ice plate Lagrange mesh by calling the core boundary parameters, fluid physics parameters, and ice plate physics and constitutive parameters specifically refer to: The mesh spatial boundary and basic size parameters are extracted from the core boundary parameters, namely the nozzle structure parameters (nozzle outlet diameter, outlet shape, and nozzle length), as the construction parameters for the inlet boundary of the flow field Eulerian mesh. The nozzle outlet shape determines the mesh inlet cross-sectional profile, the outlet diameter determines the initial size of the inlet mesh, and the nozzle length helps to determine the mesh extension range of the flow field inlet region to avoid misalignment between the mesh and the nozzle structure. The target distance interval, i.e., the distance from the jet outlet to the ice plate surface, is extracted. Combined with the nozzle outlet size, the axial spatial range of the flow field Eulerian mesh is determined, extending from the nozzle inlet to 10%-15% outside the target distance end to reserve space for flow field diffusion. At the same time, the radial spatial range of the mesh is determined, with the nozzle outlet center as the center, extending radially to 1.2 times the maximum diffusion radius of the jet to cover the complete cavitation flow field region.

[0021] The grid density gradient and fluid adaptation parameters are extracted from the fluid physics parameters, namely, dynamic viscosity and compressibility coefficient, as the core basis for dividing the flow field into Eulerian grid density gradients. Correspondingly, the greater the fluid dynamic viscosity, the smaller the flow field velocity gradient, and the grid density in the corresponding region can be appropriately reduced. The compressibility coefficient determines whether the flow field is compressible, and thus determines the time step adaptation parameter of the grid.

[0022] The mesh boundary adaptation parameters are extracted from the physical and constitutive parameters of the ice plate, i.e., the ice plate density is extracted. The size and shape of the Eulerian grid exit boundary of the flow field are determined. The ice plate surface is the solid wall boundary of the flow field. The grid exit boundary must be completely consistent with the geometry of the ice plate surface. The ice plate density helps to calibrate the mesh stress transfer parameters in the contact area between the flow field and the ice plate to ensure that the mesh boundary is adapted to the ice plate.

[0023] The cavitation triggering threshold disclosed above is set based on the difference between fluid pressure and saturated vapor pressure, and is used to determine whether cavitation bubbles are triggered in the flow field. The initial damage threshold of the ice plate characterizes the deformation cracks that appear in the grid cells of the ice plate under the minimum stress threshold. This is clearly defined in the field and will not be elaborated further.

[0024] S102. Using the initial flow field-ice plate topology model as a carrier, the fractal dimension of the coupled mesh is adjusted in real time through the fractal growth mechanism to obtain a fractal mesh. Then, the probabilistic model is fused. Based on the fluid pressure parameters of the initial flow field-ice plate topology model, the cavitation triggering conditions of the fractal mesh nodes are corrected. Combined with the cavitation triggering threshold, the generation region of cavitation load and the action region of ice plate load are associated to form the generation region of cavitation load. Then, the viscoelastic network stress transfer mechanism is embedded to construct a viscoelastic network. The stress is converted into the load parameters of each fractal mesh node. The load parameters of all fractal mesh nodes are integrated and calibrated to obtain the fractal characteristic load field. S102 extracts the flow field pressure distribution data of the initial flow field-ice plate topology model and divides the cavitation sensitive area. Specifically, the area where the flow field pressure is lower than the saturated vapor pressure is divided into the cavitation bubble core area, the area where the flow field pressure is between the saturated vapor pressure and the ambient pressure is divided into the cavitation cloud transition area, and the area where the flow field pressure is equal to the ambient pressure is divided into the peripheral stable flow field area. Based on the fractal growth mechanism, according to the collapse frequency of cavitation bubbles in the flow field, a matching fractal growth iteration step size is set. One iteration step size corresponds to the generation and collapse of cavitation bubbles. This application provides a preferred correspondence, that is, when the cavitation bubble collapses at 100Hz, the iteration step size is set to 0.01, and the initial fractal dimension is set to complete the initialization of the fractal growth mechanism. The fluid pressure of each coupled grid node in the flow field is collected in real time and compared with the cavitation trigger threshold set by the saturated vapor pressure difference to determine the dynamic evolution stage of cavitation bubbles. The dynamic evolution stage refers to the entire process including the bubble generation stage, bubble development stage, bubble collapse stage, and bubble dissipation stage. Based on the division of cavitation sensitive areas, the following settings are made: When cavitation bubbles are collected in the core area of ​​cavitation bubbles, the transition area of ​​cavitation clouds, and the surrounding stable flow field area at the beginning and final stages of dynamic evolution, the initial fractal dimension is adjusted upward by two iteration steps. When cavitation bubbles are collected in the core area of ​​cavitation bubbles, the transition area of ​​cavitation clouds, and the surrounding stable flow field area at the intermediate stage of dynamic evolution, the initial fractal dimension is adjusted downward by two iteration steps. The fractal generation density representing the flow field pressure gradient data in the coupled grid nodes is also densified to form a fractal grid. Furthermore, in this technical field, the core region of cavitation bubbles is the area where cavitation bubble generation and collapse are most intense. Therefore, cavitation bubbles are often in the generation and dissipation stages of dynamic evolution. This application provides a preferred fractal dimension of 1.8-2.0 for this region. For the cavitation cloud transition region, the cavitation bubble density is moderate and the pressure gradient is gentle. Therefore, cavitation bubbles are often in the generation and dissipation stages of dynamic evolution. This application provides a preferred fractal dimension of 1.5-1.8 for this region. Furthermore, for the peripheral stable flow field region where there is no obvious cavitation phenomenon and the flow field state is stable, the fractal generation mechanism preferably sets the initial fractal dimension to 1.2-1.5.

[0025] After obtaining the fractal mesh, S102 calls the fluid pressure parameters bound to each fractal mesh node in the initial model of flow field-ice plate coupling, and extracts the flow field pressure gradient data corresponding to each fractal mesh node after adjustment by the fractal growth mechanism. Based on the difference between the fluid pressure parameter and the saturated vapor pressure parameter of each fractal grid node, the pressure barrier of each fractal grid node is obtained. This pressure barrier is the energy barrier that fluid molecules need to overcome to generate cavitation bubbles at the fractal grid node. Since the fluid pressure parameters of different fractal grid nodes are different, their corresponding pressure barriers also show a differentiated distribution, which is consistent with the pressure distribution state of the flow field. The product of the flow field pressure gradient data and the pressure barrier at each fractal grid node is exponentially calculated, and the ratio of dynamic viscosity to compressibility coefficient is introduced as a fitting coefficient. After multiplication, the breakthrough probability of fluid molecules overcoming the pressure barrier is obtained. After traversing each fractal grid node, the breakthrough probability of each fractal grid node is multiplied by the corresponding spatial density to obtain the preliminary probability density corresponding to each fractal grid node. The spatial density is the total number of nodes in the region where the node is located divided by the volume of the region where the node is located. The summation of all fractal grid nodes is averaged to obtain the average preliminary probability density of the entire flow field. The preliminary probability density corresponding to each fractal grid node is divided by the average preliminary probability density of the entire flow field to obtain the cavitation bubble generation probability density of each fractal grid node. Based on the calculated cavitation bubble generation probability density data of each fractal grid node, the randomness of cavitation bubble generation is incorporated into the triggering condition design. The cavitation triggering threshold is used to determine the cavitation bubble generation probability density of each fractal grid node one by one. All fractal grid nodes that meet the cavitation bubble generation conditions are marked to form the generation region of cavitation load. The initial intensity of the cavitation load of the fractal grid node is determined by combining the difference in cavitation bubble generation probability density.

[0026] It should be noted that in this step, the pressure barrier and flow field pressure gradient of each fractal grid node are used as core input parameters to calculate the probability density of cavitation bubble generation at that node. The calculation process follows the correlation that the lower the pressure barrier and the more suitable the flow field pressure gradient, the higher the probability of fluid molecules breaking through the barrier, and the higher the probability density of cavitation bubble generation, i.e., exponential operation. The core logic of this application in this step is that the cavitation bubble generation process in this scheme is defined as the tunneling process of fluid molecules breaking through the pressure barrier. The formation of the pressure barrier is directly related to the flow field pressure and saturated vapor pressure. That is, the difference between the flow field pressure and the saturated vapor pressure constitutes the pressure barrier that fluid molecules need to break through to generate cavitation bubbles. The flow field pressure gradient directly affects the distribution and energy level of the pressure barrier. The difference in the flow field pressure gradient will lead to different characteristics of the pressure barrier at different fractal grid nodes, thus affecting the probability of fluid molecules breaking through the pressure barrier, i.e., the probability of cavitation bubble generation. Based on this core correlation logic, the probability density of cavitation bubble generation under different flow field pressure gradient conditions is calculated node by node.

[0027] S102 extracts the fractal mesh nodes in the generated region as network nodes of the viscoelastic network, and uses virtual springs as connection units of the network nodes to transfer stress and energy. The distribution of virtual springs is as follows: call the fractal mesh node distribution data, and set virtual springs between each fractal mesh node and its adjacent fractal mesh nodes. The dynamic evolution of cavitation bubbles at each fractal grid node is monitored in real time. When any cavitation bubble at any fractal grid node is in the final stage of dynamic evolution, the viscoelastic network initiates an energy conversion operation. Specifically, the energy released by the cavitation bubble corresponding to the fractal grid node is directly converted into the stress of the corresponding viscoelastic network node. The calculated cavitation bubble generation probability density data at the fractal grid node is used to characterize the stress energy. Because the higher the cavitation bubble generation probability density, the more cavitation bubbles there are in a unit space, the greater the total energy released by collapse, and the greater the initial stress value of the corresponding viscoelastic network core node, ensuring that the initial stress magnitude is positively correlated with the cavitation bubble collapse energy. The stress is transferred along the stress transmission criterion, and the stress energy is stored through the elastic deformation of the virtual spring and / or dissipated through viscous dissipation. The stress of each viscoelastic network node is converted into corresponding load parameters including real-time pressure and velocity. The dynamic change data of the load parameters of each fractal grid node are recorded one by one in time series to obtain the spatiotemporal distribution data of the load parameters. Macroscopic principal loads and microscopic pulsating loads are extracted from the load parameters, and the fractal dimension of the fractal grid is called. The spatiotemporal data of the load parameters of all fractal grid nodes are integrated to construct the spatiotemporal distribution matrix of the fractal characteristic load field, and finally a spatiotemporally dynamic fractal characteristic load field is formed. The macroscopic principal load refers to the stable stagnation pressure in the jet core region, which corresponds to the region with gentle cavitation activity and stable flow field. The microscopic pulsating load refers to the pressure peak generated by cavitation collapse, which corresponds to the core region of cavitation bubbles. The spatiotemporal distribution matrix is ​​used to record the magnitude of load parameters at different times and spatial locations. In some other specific implementations, while transmitting stress, the fractal dimensions of the fractal mesh can be combined to make targeted adjustments to the virtual spring stiffness coefficients of different flow field regions. Specifically, this includes calling the fractal dimension data of each region after the fractal mesh has been adjusted by the fractal growth mechanism to clarify the differences in fractal dimensions between the cavitation bubble core region, the cavitation cloud transition region, and the peripheral stable flow field region; for the cavitation bubble core region, the virtual spring stiffness coefficient of this region is incrementally adjusted based on the characteristic of the fractal dimension adapting to the intense cavitation activity. During the adjustment process, the compatibility between the stiffness coefficient and the fractal dimension is verified in real time to ensure that stress can be transmitted quickly and accurately in the cavitation bubble core region, thereby capturing the stress during cavitation collapse. Peak stress; For the cavitation cloud transition zone, considering the fractal dimension adaptation to the smooth cavitation activity in this region, the virtual spring stiffness coefficient of this region is maintained at a fixed level without increment or decrement adjustment, balancing the accuracy of stress transfer and the rationality of energy dissipation simulation; For the outer stable flow field region, considering the fractal dimension adaptation to the stable flow field in this region, the virtual spring stiffness coefficient of this region is decremented to reduce stress transfer redundancy in non-core areas and reduce mesh computation redundancy; Through the above regional adjustment operations, the non-uniform distribution and attenuation calculation of stress inside the cavitation cloud are realized, so that the stress distribution characteristics are fully adapted to the dynamic characteristics of cavitation bubble generation-collapse and the structural characteristics of the fractal mesh.

[0028] S103. The macroscopic fractal primary load and microscopic fractal secondary load obtained by decomposing the fractal characteristic load field are both converted into stress and strain of the ice plate through the load generated by fractal iterative loading, thus completing the dynamic damage coupling of the ice plate and obtaining dynamic damage evolution data of the ice plate. The chaotic characteristic parameters in the dynamic damage evolution data of the ice plate and the fractal characteristic load field are extracted as inputs for icebreaking parameter optimization, and the optimized submerged water jet parameters are obtained, thus completing the load simulation and load icebreaking.

[0029] S103 decomposes the fractal characteristic load field into macroscopic fractal primary load and microscopic fractal secondary load. Based on the load fractal dimension and fractal iteration step size of the fractal characteristic load field, the macroscopic fractal primary load is applied to the topological coupling core region of the ice plate through a fractal iterative loading algorithm. The loading direction is strictly set to be perpendicular to the surface of the ice plate. During the loading process, the magnitude and position of the macroscopic fractal primary load at each loading moment are recorded in real time. The micro-fractal secondary loads are applied synchronously with the macro-fractal primary loads using a superposition method. Specifically, the temporal characteristics of the final stage of cavitation bubbles in their dynamic evolution are obtained through the spatiotemporal distribution matrix of the fractal characteristic load field. The micro-fractal secondary loads are then superimposed on the macro-fractal primary loads using the same fractal iteration step size as the macro-fractal primary loads. The loading region of the micro-fractal secondary loads acts on the grid nodes corresponding to the cavitation-sensitive regions in the initial flow-ice plate coupled model. The loading timing is synchronized with the spatiotemporal distribution matrix of the fractal characteristic load field. Specifically, when the cavitation bubble is at its final collapse peak, the iteration gain coefficient of the micro-fractal secondary loads is increased to enhance the intensity of the micro-fractal secondary loads and adapt to the peak load characteristics during cavitation bubble collapse. When the cavitation is in a stable phase, the iteration gain coefficient of the micro-fractal secondary loads is decreased to reduce the intensity of the micro-fractal secondary loads and adapt to the load characteristics during stable cavitation.

[0030] S103 converts the force direction and force area corresponding to the ice plate unit that makes up the smallest unit of the ice plate into the stress and strain data of each ice plate unit by combining the total load after the superposition of macroscopic fractal principal load and microscopic fractal secondary load. After the transformed stress and strain data of the ice plate elements are substituted into the JH-2 model, the initial damage factor of each ice plate element is calculated in real time. The initial damage factor is used to characterize the damage degree of the ice plate element. The damage factor of each ice plate element is compared with the initial damage threshold of the ice plate. If the damage factor of any ice plate element exceeds the initial damage threshold, the corresponding ice plate element is determined to be a failed ice plate element. All failed ice plate elements are summarized, and the mesh coordinates, failure time, and load magnitude at the time of failure of the failed ice plate elements are recorded. The extension path of the ice plate crack along the direction of the maximum load gradient of the fractal characteristic load field is tracked, and complete dynamic damage evolution data of the ice plate is generated. In some other specific implementations, by calling the mesh topology mapping relationship in the initial model of flow field-ice plate coupling, the relationship between ice plate mesh nodes and elements is clarified. Taking the failed ice plate element as the core, the propagation direction of ice plate crack is tracked—the ice plate crack extends along the direction of the maximum load gradient of the fractal characteristic load field. The coordinate changes, propagation rate, and propagation time during the crack propagation process are recorded in real time, thereby generating complete dynamic damage evolution data of the ice plate.

[0031] S103 fits the grid coordinates and failure time of the corresponding failed ice plate unit in the extended path of the dynamic damage evolution data of the ice plate. It extracts the fractal characteristic load field chaotic characteristic parameters and ice plate damage chaotic characteristic parameters to characterize the degree of chaotic motion of the ice plate. These are the maximum Lyapunov exponent, correlation dimension, and correlation dimension of Lyapunov exponent and number of failed ice plate units, respectively. The Lyapunov exponent characterizes the chaotic intensity of cavitation collapse pressure pulsation, and the Lyapunov exponent of ice plate crack propagation rate characterizes the chaotic intensity of ice plate damage evolution. The difference between the two directly guides the adjustment direction of the submerged water jet parameters. If the exponent difference is too large, the inlet pressure and target distance need to be adjusted to match the chaotic intensity. The correlation dimension of the fractal characteristic load field characterizes the spatial distribution complexity of the cavitation bubble in the initial and final stages. Combined with the fractal dimension, it fully characterizes the spatiotemporal chaotic characteristics of the cavitation load. The differences between the maximum Lyapunov exponent of the characteristic parameters of the fractal characteristic load field and the Lyapunov exponent of the chaotic characteristic parameters of ice plate damage, as well as the differences between the correlation dimension of the fractal characteristic load field and the correlation dimension of the number of failed ice plate units in the chaotic characteristic parameters of ice plate damage, are calculated separately. The comprehensive chaotic synchronization error is obtained by weighted summation. In conventional applications, the weighting coefficients for the difference in Lyapunov exponent and the difference in correlation dimension are 0.6 and 0.4, respectively.

[0032] S103, based on the comprehensive chaotic synchronization error, sorts the data along the time axis to form continuous time-series data, establishes a chaotic synchronization error time-series sequence, and calculates the first derivative point by point on the chaotic synchronization error time-series sequence to obtain the rate of change of the comprehensive chaotic synchronization error. A positive derivative indicates that the comprehensive chaotic synchronization error is increasing, the synchronization between the flow field and the chaotic characteristics of the ice plate is continuously deteriorating, and the energy transfer efficiency is continuously decreasing; a negative derivative indicates that the error is decreasing, and the synchronization is continuously improving; a derivative approaching 0 indicates that the error is stabilizing, and the synchronization has reached a dynamic equilibrium. Furthermore, the error time-series sequence is subjected to definite integral operation within a set iteration period, with the integration interval being the iteration period of a single parameter optimization, to obtain the cumulative chaotic synchronization error. When the rate of change of the comprehensive chaotic synchronization error is less than zero, the dynamic optimization process for icebreaking parameters is initiated. Constraints are set on the error rate of change approaching zero and the cumulative error converging to a minimum. If the maximum Lyapunov exponent is greater than the Lyapunov exponent, a linear proportional control algorithm is used to reduce the submerged water jet inlet pressure and / or increase the submerged water jet target distance. If the maximum Lyapunov exponent is less than the Lyapunov exponent, a linear proportional control algorithm is used to increase the submerged water jet inlet pressure and / or decrease the submerged water jet target distance, obtaining optimized submerged water jet parameters and completing load simulation and load icebreaking. A maximum Lyapunov exponent greater than the Lyapunov exponent indicates excessive cavitation load concentration, which can easily lead to localized overload damage to the ice plate and increased energy dissipation. A maximum Lyapunov exponent less than the Lyapunov exponent indicates insufficient cavitation load, failing to achieve efficient ice plate damage. The comprehensive chaotic synchronization error is then recalculated, and the error rate of change and cumulative chaotic synchronization error are updated accordingly.

Claims

1. A load simulation and ice-breaking method for a submerged water jet flow field considering cavitation effects, characterized in that, The method includes: S101. Based on the principles of topology, and by coupling the Eulerian grid of the flow field with the Lagrange grid of the ice plate, the coupled grid is obtained and the topological association rule of the coupled grid is formed. The cavitation trigger threshold and the initial damage threshold of the ice plate are introduced to obtain the initial topological model of the flow field-ice plate. S102. Using the initial flow field-ice plate topology model as a carrier, the fractal dimension of the coupled mesh is adjusted in real time through the fractal growth mechanism to obtain a fractal mesh. Then, the probabilistic model is fused. Based on the fluid pressure parameters of the initial flow field-ice plate topology model, the cavitation triggering conditions of the fractal mesh nodes are corrected. Combined with the cavitation triggering threshold, the generation region of cavitation load and the action region of ice plate load are associated to form the generation region of cavitation load. Then, the viscoelastic network stress transfer mechanism is embedded to construct a viscoelastic network. The stress is converted into the load parameters of each fractal mesh node. The load parameters of all fractal mesh nodes are integrated and calibrated to obtain the fractal characteristic load field. S103. The macroscopic fractal primary load and microscopic fractal secondary load obtained by decomposing the fractal characteristic load field are both converted into stress and strain of the ice plate through the load generated by fractal iterative loading, thus completing the dynamic damage coupling of the ice plate and obtaining dynamic damage evolution data of the ice plate. The chaotic characteristic parameters in the dynamic damage evolution data of the ice plate and the fractal characteristic load field are extracted as inputs for icebreaking parameter optimization, and the optimized submerged water jet parameters are obtained, thus completing the load simulation and load icebreaking.

2. The method for simulating the load of a submerged water jet flow field and performing ice-breaking under cavitation effect as described in claim 1, characterized in that, S101 further includes: dividing the Eulerian grid of the flow field and the Lagrange grid of the ice plate into grid nodes one by one, and establishing a mapping relationship between the grid cells of the Eulerian grid of the flow field and the Lagrange grid of the ice plate and the grid nodes one by one. The cavitation-sensitive region of the flow field and the load-bearing region of the ice plate are taken as the core region of topological coupling. Specifically, the cavitation-sensitive region of the flow field is defined as the flow field region within the range from downstream of the nozzle outlet to the target distance, and the load-bearing region of the ice plate is defined as the central impact zone and crack propagation zone of the ice plate corresponding to the cavitation-sensitive region of the flow field. Each cavitation-sensitive region grid node in the topological coupling core region corresponds to an ice plate load-acting region grid node, thus forming a fixed coupling relationship between the grid cells of the cavitation-sensitive region and the grid cells of the ice plate load-acting region. The non-core regions outside the core region of the topological coupling adopt a transitional topological structure to form the topological association rules of the coupled mesh.

3. The method for simulating the load of a submerged water jet flow field and performing ice-breaking under cavitation effect as described in claim 2, characterized in that, The method further includes, before S101, extracting the core boundary parameters of the non-submerged water jet ice-breaking simulation system, including inlet pressure range, nozzle structure parameters, target distance interval core boundary parameters, fluid physical parameters including saturated vapor pressure, dynamic viscosity, compressibility coefficient, and ice plate physical and constitutive parameters including density, elastic modulus, JH-2 model parameters, and fracture strain threshold. After constructing the topological association rules of the coupled mesh, S101 calls the core boundary parameters, fluid physics parameters, and ice plate physics and constitutive parameters to extract the construction parameters of the flow field Eulerian mesh and the ice plate Lagrange mesh, respectively generating independent flow field Eulerian mesh models and ice plate Lagrange mesh models. The topological association rules of the coupled mesh are embedded into the topological mapping algorithm. The topological mapping algorithm couples and binds the independent flow field Eulerian mesh and the ice plate Lagrange mesh, so that each flow field mesh node and mesh cell is mapped to the corresponding ice plate mesh node and mesh cell through the topological association rules. The coupled flow field Eulerian grid and ice plate Lagrangian grid are introduced into a cavitation trigger threshold set based on the difference between fluid pressure and saturated vapor pressure, and an initial damage threshold for the ice plate set based on the initial damage factor in the JH-2 model parameters. The flow field parameters including flow field pressure, fluid pressure, and velocity and the ice plate parameters are then bound together to obtain the initial flow field-ice plate topology model.

4. The method for simulating the load on the submerged water jet flow field and breaking ice under load, taking into account cavitation effects, as described in claim 1, is characterized in that... S102 extracts the flow field pressure distribution data of the initial flow field-ice plate topology model and divides the cavitation sensitive area. Specifically, the area where the flow field pressure is lower than the saturated vapor pressure is divided into the cavitation bubble core area, the area where the flow field pressure is between the saturated vapor pressure and the ambient pressure is divided into the cavitation cloud transition area, and the area where the flow field pressure is equal to the ambient pressure is divided into the peripheral stable flow field area. Based on the fractal growth mechanism, the matching fractal growth iteration step size is set according to the collapse frequency of cavitation bubbles in the flow field, and the initial fractal dimension is set to complete the initialization of the fractal growth mechanism. The fluid pressure of each coupled grid node in the flow field is collected in real time and compared with the cavitation trigger threshold set by the saturated vapor pressure difference to determine the dynamic evolution stage of cavitation bubbles. Based on the division of cavitation-sensitive regions, the following settings are made: When cavitation bubbles are collected in the initial and final stages of dynamic evolution in the core region, cavitation cloud transition region, and surrounding stable flow field region, the initial fractal dimension is adjusted upward by two iteration steps. When cavitation bubbles are collected in the middle stage of dynamic evolution in the core region, cavitation cloud transition region, and surrounding stable flow field region, the initial fractal dimension is adjusted downward by two iteration steps. The fractal generation density representing the flow field pressure gradient data in the coupled grid nodes is also densified to form a fractal grid.

5. The method for simulating the load of a submerged water jet flow field and performing ice-breaking under cavitation effect as described in claim 4, characterized in that, After obtaining the fractal mesh, S102 calls the fluid pressure parameters bound to each fractal mesh node in the initial model of flow field-ice plate coupling, and at the same time extracts the flow field pressure gradient data corresponding to each fractal mesh node after adjustment by the fractal growth mechanism. The pressure barrier of each fractal grid node is obtained based on the difference between the fluid pressure parameter and the saturated vapor pressure parameter of each fractal grid node. The product of the flow field pressure gradient data and the pressure barrier at each fractal grid node is exponentially calculated, and the ratio of dynamic viscosity to compressibility coefficient is introduced as a fitting coefficient. After multiplication, the breakthrough probability of fluid molecules breaking through the pressure barrier is obtained. After traversing each fractal grid node, the breakthrough probability of each fractal grid node is multiplied by the corresponding spatial density to obtain the preliminary probability density corresponding to each fractal grid node. The summation of all fractal grid nodes is averaged to obtain the average preliminary probability density of the entire flow field. The preliminary probability density corresponding to each fractal grid node is divided by the average preliminary probability density of the entire flow field to obtain the cavitation bubble generation probability density of each fractal grid node. Based on the calculated cavitation bubble generation probability density data of each fractal grid node, the randomness of cavitation bubble generation is incorporated into the triggering condition design. The cavitation triggering threshold is used to determine the cavitation bubble generation probability density of each fractal grid node one by one. All fractal grid nodes that meet the cavitation bubble generation conditions are marked to form the generation region of cavitation load. The initial intensity of the cavitation load of the fractal grid node is determined by combining the difference in cavitation bubble generation probability density.

6. The method for simulating the load of a submerged water jet flow field and performing ice-breaking under cavitation effect as described in claim 1, characterized in that, S102 extracts the fractal mesh nodes in the generated region as network nodes of the viscoelastic network, and uses virtual springs as connection units of the network nodes to transfer stress and energy. The distribution of virtual springs is as follows: fractal mesh node distribution data is called, and virtual springs are set between each fractal mesh node and its adjacent fractal mesh nodes. The dynamic evolution state of cavitation bubbles at each fractal grid node is monitored in real time. When the cavitation bubble at any fractal grid node is in the final stage of dynamic evolution, the viscoelastic network initiates an energy conversion operation. Specifically, the energy released by the cavitation bubble corresponding to the fractal grid node is directly converted into the stress of the corresponding viscoelastic network node, and the calculated cavitation bubble generation probability density data at the fractal grid node is called to characterize the energy of the stress. The stress is transferred along the stress transmission criterion, and the stress energy is stored through the elastic deformation of the virtual spring and / or dissipated through viscous dissipation. The stress of each viscoelastic network node is converted into corresponding load parameters including real-time pressure and velocity. The dynamic change data of the load parameters of each fractal grid node are recorded one by one in time series to obtain the spatiotemporal distribution data of the load parameters. Macroscopic principal loads and microscopic pulsating loads are extracted from the load parameters, and the fractal dimension of the fractal grid is called. The spatiotemporal data of load parameters of all fractal grid nodes are integrated to construct the spatiotemporal distribution matrix of the fractal characteristic load field, and finally form a spatiotemporally dynamic fractal characteristic load field.

7. The method for simulating the load of a submerged water jet flow field and performing ice-breaking under load, taking into account cavitation effects, as described in claim 1, is characterized in that... S103 splits the fractal characteristic load field into macroscopic fractal primary load and microscopic fractal secondary load, and based on the load fractal dimension and fractal iteration step size of the fractal characteristic load field, applies the macroscopic fractal primary load to the topological coupling core region of the ice plate through the fractal iterative loading algorithm, and the loading direction is strictly set to be perpendicular to the surface of the ice plate. The micro-fractal secondary loads are applied synchronously with the macro-fractal primary loads using a superposition method. Specifically, the temporal characteristics of the final stage of the dynamic evolution of cavitation bubbles are obtained through the spatiotemporal distribution matrix of the fractal characteristic load field. The micro-fractal secondary loads are superimposed on the macro-fractal primary loads using the same fractal iteration step size as the macro-fractal primary loads. The loading region of the micro-fractal secondary loads acts on the grid nodes corresponding to the cavitation sensitive region in the initial model of the flow field-ice plate coupling. The loading timing is synchronized with the spatiotemporal distribution matrix of the fractal characteristic load field.

8. The method for simulating the load of a submerged water jet flow field and breaking ice under load, taking into account cavitation effects, as described in claim 7, is characterized in that... S103 converts the force direction and force area corresponding to the ice plate unit that makes up the smallest unit of the ice plate into the stress and strain data of each ice plate unit by combining the total load after the superposition of macroscopic fractal main load and microscopic fractal secondary load. After the transformed stress and strain data of the ice plate elements are substituted into the JH-2 model, the initial damage factor of each ice plate element is calculated in real time. Then, the damage factor of each ice plate element is compared with the initial damage threshold of the ice plate. If the damage factor of any ice plate element exceeds the initial damage threshold, the corresponding ice plate element is determined to be a failed ice plate element. All failed ice plate elements are summarized, and the mesh coordinates, failure time, and load magnitude at failure of the failed ice plate elements are recorded. The extension path of the ice plate crack along the direction of the maximum load gradient of the fractal characteristic load field is tracked, and complete dynamic damage evolution data of the ice plate is generated.

9. The method for simulating the load of a submerged water jet flow field and performing ice-breaking under load, taking into account cavitation effects, as described in claim 8, is characterized in that... The S103 fits the grid coordinates and failure time of the corresponding failed ice plate unit in the extended path of the ice plate dynamic damage evolution data, and extracts the fractal characteristic load field chaotic characteristic parameters and ice plate damage chaotic characteristic parameters to characterize the degree of chaotic motion of the ice plate. The differences between the maximum Lyapunov exponent of the characteristic parameters of the fractal characteristic load field and the Lyapunov exponent of the chaotic characteristic parameters of ice plate damage, as well as the differences between the correlation dimension of the fractal characteristic load field and the correlation dimension of the number of failed ice plate units in the chaotic characteristic parameters of ice plate damage, are calculated separately. The comprehensive chaotic synchronization error is obtained by weighted summation.

10. The method for simulating the load of a submerged water jet flow field and performing ice-breaking under load, taking into account cavitation effects, as described in claim 8, is characterized in that... S103 forms continuous time-series data by sorting the comprehensive chaotic synchronization error according to the time axis, establishes a chaotic synchronization error time-series sequence, calculates the first derivative of the chaotic synchronization error time-series sequence point by point to obtain the comprehensive chaotic synchronization error change rate, and performs definite integral operation on the error time-series sequence within the set iteration period, with the integration interval being the single parameter optimization iteration period, to obtain the cumulative chaotic synchronization error.

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