A method, system, equipment, and storage medium for cavitation prediction and assessment of podded propulsion systems based on Q-criterion vortex characteristics.

Abstract

This invention discloses a method, system, equipment, and storage medium for cavitation prediction and evaluation of podded propulsion systems based on Q-criterion vortex characteristics, relating to the field of marine propulsion technology. The method includes: obtaining the basic flow field and benchmark performance parameters through cavitation-free steady-state RANS calculations; reconstructing the spatial morphology of the vortex system based on the velocity gradient tensor Q-criterion; mapping the pressure field to the vortex system to predict the initial cavitation location and obtain the high-risk target area; focusing on the target area, performing unsteady cavitation calculations using VOF and Schnerr-Sauer models to capture the vortex-cavitation coupling evolution; extracting the transient hydrodynamic response, establishing a comprehensive attenuation evaluation index, and achieving a quantitative assessment of hydrodynamic performance attenuation. This invention can improve the accuracy of cavitation prediction, significantly reduce computational resource consumption, construct a closed-loop evaluation system for microscopic vortices and macroscopic performance attenuation, and achieve comprehensive evaluation under multiple operating conditions.

Description

Technical Field

[0001] This invention relates to the field of marine propulsion technology, and in particular to a method, system, device, and storage medium for predicting and evaluating cavitation in podded propulsion systems based on the Q-criterion vortex characteristics. Background Technology

[0002] In the design and operation of large ro-ro passenger ships and high-value-added vessels, podded propulsion systems are widely used due to their excellent maneuverability and hydrodynamic efficiency. However, the complex geometry of podded propulsion systems results in strong flow field interference between the propeller wake and the pod body and support structure behind it, which can easily induce unsteady cavitation. Cavitation not only leads to a decrease in propulsion efficiency, but its collapse process also generates strong pulsating pressure, seriously affecting the passenger comfort of ro-ro passenger ships.

[0003] Currently, existing technologies for assessing the cavitation performance and hydrodynamics of podded propulsion systems have the following drawbacks: conventional CFD (Computational Fluid Dynamics) numerical simulations largely rely on macroscopic threshold determinations of the absolute pressure of the flow field. However, in the complex wake field of podded propulsion systems, cavitation often first develops at the microscopic vortex core. Simply relying on the macroscopic pressure field distribution, without a detailed analysis of the internal rotational characteristics of the flow field, leads to significant errors in predicting the initial location and risk of tip vortex cavitation and gap cavitation. Existing technologies typically treat flow field calculation and cavitation multiphase flow calculation as two separate processes, failing to establish a direct mapping relationship between the evolution of vortex spatial morphology and the shedding and collapse of cavitation clusters, making it difficult to explain the rupture mechanism of the wake vortex structure under intense cavitation conditions. Existing evaluation methods mostly focus on the time history curve analysis of macroscopic thrust and torque coefficients, failing to map and correlate microscopic vortex core intensity decay and transient cavitation volume with macroscopic propulsion efficiency decline, making it difficult to provide multidimensional theoretical support for propeller profile optimization or pod operation attitude control during the design phase. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention provides a method, system, device and storage medium for predicting and evaluating cavitation of podded propulsion vehicles based on the Q-criterion vortex system characteristics.

[0005] Therefore, the technical problem solved by this invention is that the existing technology has difficulty in accurately predicting the initial cavitation location of the podded thruster, and lacks a quantitative evaluation method for vortex evolution and performance degradation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for predicting and evaluating cavitation in podded propulsion systems based on Q-criterion vortex characteristics, including: A three-dimensional computational flow domain including the geometric model of the podded thruster was established, and steady-state flow field calculations were performed using a steady-state RANS solver under cavitation-free conditions to obtain basic flow field data and benchmark hydrodynamic performance parameters. Based on the three-dimensional velocity vector field in the basic flow field data, the rotation-dominant region in the complex flow field is identified by calculating the velocity gradient tensor and the second invariant, and the unique vortex spatial morphology of the podded thruster is reconstructed. The absolute static pressure field in the basic flow field data is mapped to the reconstructed vortex spatial morphology. The rotation-dominated low-pressure region in the flow field is coupled and identified. The initial location of cavitation is predicted by combining spatial location criteria and pressure criteria, and high cavitation risk target area data is obtained. Based on the basic flow field data, with the high cavitation risk target area data as the key analysis area, the multiphase flow VOF model and the Schnerr-Sauer cavitation model are activated to carry out unsteady cavitation multiphase flow calculations. Based on the cloud map, the cavitation evolution process and the coupling changes with the vortex system structure are captured in the cavitation transient state to obtain unsteady cavitation evolution data. Based on unsteady cavitation evolution data and benchmark hydrodynamic performance parameters, transient hydrodynamic response changes under cavitation conditions are extracted, a comprehensive attenuation evaluation index is established, and a quantitative assessment of hydrodynamic performance attenuation caused by cavitation is completed.

[0007] As a preferred scheme for cavitation prediction and evaluation method of podded propulsion vehicles based on Q-criterion vortex system characteristics, wherein: The process involves establishing a three-dimensional computational flow domain including the geometric model of the podded propulsion device, and performing steady-state flow field calculations using a steady-state RANS solver under cavitation-free conditions to obtain basic flow field data and benchmark hydrodynamic performance parameters, including: The geometric model of the podded propulsion device is imported into the preprocessing platform to construct a three-dimensional computational flow domain that includes a rotating domain that envelops the propeller and a stationary domain that envelops the pod body and support. The sliding mesh method is used to transfer flow field information at the interface between the rotating and stationary domains to simulate the relative motion between the rotating propeller and the stationary pod body. The main region of the computational domain is discretized using a cut volume mesh, and a boundary layer prism mesh is laid along the normal direction on the surface of the propeller blade, the surface of the pod body, and the surface of the support. At the same time, the local volume refinement is carried out on the leading edge, trailing edge, blade tip of the propeller, and the wake region of the pod support to form a three-dimensional discrete computational mesh that can capture micro-eddies.

[0008] As a preferred scheme for cavitation prediction and evaluation method of podded propulsion vehicles based on Q-criterion vortex system characteristics, wherein: The process of establishing a three-dimensional computational flow domain including the geometric model of the podded thruster, and performing steady-state flow field calculations using a steady-state RANS solver under cavitation-free conditions to obtain basic flow field data and benchmark hydrodynamic performance parameters, also includes: Multiple sets of advance coefficient conditions are set, and a steady-state Reynolds-averaged Navier-Stokes solver is used for iterative solution until convergence. Extract the basic flow field data of each grid cell after convergence. The basic flow field data includes the pressure distribution on the blade surface, the velocity contour map of the propeller disk surface and the wake region, the three-dimensional velocity vector field, and the absolute static pressure field. Extract the thrust and torque of the podded thruster under cavitation-free conditions, and calculate parameters characterizing the baseline hydrodynamic performance based on the thrust and torque. These parameters include the advance coefficient, thrust coefficient, torque coefficient, and propulsion efficiency. Then, plot the performance curves.

[0009] As a preferred scheme for cavitation prediction and evaluation method of podded propulsion vehicles based on Q-criterion vortex system characteristics, wherein: The three-dimensional velocity vector field based on the basic flow field data, through the calculation of the velocity gradient tensor and the second invariant, identifies the rotation-dominant region in the complex flow field and reconstructs the unique vortex spatial morphology of the podded thruster, including: Based on the three-dimensional velocity vector field in the basic flow field data, the velocity gradient tensor is obtained by spatially differentiating the velocity components. The velocity gradient tensor is decomposed into a symmetric strain rate tensor and an antisymmetric rotation tensor, which are used to characterize the tensile shear deformation and rotational motion of the fluid micro-element, respectively. The Q value, which characterizes the degree to which the fluid rotation effect dominates relative to the deformation effect, is calculated based on the difference between the square of the rotation tensor norm and the square of the strain rate tensor norm. By setting a feature threshold, three-dimensional spatial isosurfaces with Q values ​​equal to the feature threshold are extracted. The vortex spatial morphology in the wake of the podded thruster is formed by different connected isosurface regions.

[0010] As a preferred scheme for cavitation prediction and evaluation method of podded propulsion vehicles based on Q-criterion vortex system characteristics, wherein: The process involves mapping the absolute static pressure field from the basic flow field data to the reconstructed vortex spatial morphology, coupling and identifying the rotation-dominated low-pressure region in the flow field, and predicting the initial cavitation location by combining spatial location criteria and pressure criteria to obtain high cavitation risk target area data, including: The absolute static pressure field is mapped as a spatial scalar attribute to the vortex spatial region corresponding to the reconstructed three-dimensional vortex isosurface, so that each spatial location is associated with local absolute static pressure. Set the saturated vapor pressure at the current water temperature; traverse the entire watershed grid and perform dual criteria screening for each spatial location: determine whether the location is located within the vortex system spatial region; determine whether the local absolute static pressure corresponding to the location is lower than the saturated vapor pressure; define the set of spatial grids that simultaneously meet the dual criteria as the high cavitation risk target area.

[0011] As a preferred scheme for cavitation prediction and evaluation method of podded propulsion vehicles based on Q-criterion vortex system characteristics, wherein: Based on fundamental flow field data, with high-cavitation risk target area data as the key analysis region, the multiphase flow VOF model and Schnerr-Sauer cavitation model are activated to conduct unsteady cavitation multiphase flow calculations. Based on contour maps, the cavitation evolution process and its coupling changes with the vortex system structure are captured transiently to obtain unsteady cavitation evolution data, including: With a steady flow field as the initial condition, the multiphase flow volume function model is activated to solve the volume fraction distribution of the liquid and vapor phases, and the cavitation model based on phase change mass transfer is activated. The cavitation model calculates the mass transfer rate of vaporization and condensation respectively based on the comparison of local pressure and saturated vapor pressure to update the vapor phase volume fraction distribution. Implicit unsteady time propagation is adopted, the time step is set, and steady flow field data is used as the initial value for iterative solution step by step. At the same time, the pre-identified high-cavitation risk target area is used as the key area for transient analysis of flow field and phase change process. Within the same transient time step, the morphology of the three-dimensional vortex system isosurface and the volume fraction distribution cloud map of cavitation water vapor are output synchronously. The output results of each time step are arranged in time sequence to record the corresponding transient processes of vortex system evolution and cavitation evolution within a single rotation cycle. By analyzing the volume fraction cloud map and the vortex system isosurface cloud map, the initial attachment, range expansion, detachment, migration and collapse processes of cavitation are identified. The elongation, contraction, fracture, dispersion and connectivity changes of the vortex system isosurface are observed to capture the destructive phenomenon of cavitation phase transition on the vortex system skeleton.

[0012] As a preferred scheme for cavitation prediction and evaluation method of podded propulsion vehicles based on Q-criterion vortex system characteristics, wherein: Based on unsteady cavitation evolution data and combined with baseline hydrodynamic performance parameters, the transient hydrodynamic response changes under cavitation conditions are extracted, a comprehensive attenuation evaluation index is established, and a quantitative assessment of hydrodynamic performance attenuation caused by cavitation is completed, including: Based on unsteady cavitation evolution data, a surface mechanical integral model is established to extract transient hydrodynamic loads in real time and convert them into dimensionless transient thrust coefficients, torque coefficients, and transient propulsion efficiency. Perform time-phase synchronization, output transient cavitation total volume curve and vortex system isosurface volume change curve, and align with transient hydrodynamic results on the time axis to establish the time-series correspondence between cavitation evolution, vortex system structure change and hydrodynamic response; The unsteady cavitation stabilization period is selected to calculate the time-averaged propulsion efficiency, and the hydrodynamic performance degradation rate is calculated by combining the non-cavitation benchmark propulsion efficiency. By integrating the characteristic parameters under multiple advance coefficient conditions, a multi-condition comprehensive attenuation evaluation matrix is ​​constructed. The columns of the multi-condition comprehensive attenuation evaluation matrix are subjected to dimensionless standardization to eliminate dimensional differences and obtain a standardized matrix. By setting weight vectors for each evaluation index and performing a weighted mapping between the standardized matrix and the weight vectors, the comprehensive attenuation evaluation index corresponding to each working condition is obtained.

[0013] Secondly, this invention provides a podded propulsion cavitation prediction and evaluation system based on Q-criterion vortex system characteristics, comprising: The steady flow field initialization and reference parameter extraction module is used to establish a three-dimensional computational flow domain including the geometric model of the podded thruster, and to perform steady-state flow field calculations using a steady-state RANS solver under cavitation-free conditions to obtain basic flow field data and reference hydrodynamic performance parameters. The Q-criterion vortex spatial morphology reconstruction module is used to identify the rotation-dominant region in a complex flow field based on the three-dimensional velocity vector field in the basic flow field data, by calculating the velocity gradient tensor and the second invariant, and to reconstruct the unique vortex spatial morphology of the podded thruster. The vortex-pressure coupled cavitation initiation prediction module is used to map the absolute static pressure field in the basic flow field data to the reconstructed vortex system spatial morphology, perform coupled identification of the rotation-dominated low-pressure region in the flow field, and predict the cavitation initiation location by combining spatial location criteria and pressure criteria to obtain high cavitation risk target area data. The unsteady vortex-cavitation coupled evolution capture module is used to perform unsteady cavitation multiphase flow calculations based on basic flow field data, with high cavitation risk target area data as the key analysis area. It activates the multiphase flow VOF model and the Schnerr-Sauer cavitation model, and captures the cavitation transients based on the cloud map to obtain unsteady cavitation evolution data. The comprehensive evaluation module for hydrodynamic performance degradation is used to extract transient hydrodynamic response changes under cavitation conditions based on unsteady cavitation evolution data and benchmark hydrodynamic performance parameters, establish a comprehensive degradation evaluation index, and complete the quantitative evaluation of hydrodynamic performance degradation caused by cavitation.

[0014] Thirdly, the present invention provides a computer device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the cavitation prediction and evaluation method for podded propulsion based on the Q-criterion vortex system characteristics.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of a method for predicting and evaluating cavitation of a podded propulsion system based on Q-criterion vortex characteristics.

[0016] The beneficial effects of this invention are as follows: This invention overcomes the errors caused by traditional CFD relying solely on pressure thresholds for macroscopic judgment. By calculating the velocity gradient tensor and utilizing the Q criterion, it accurately isolates the vortex cores dominated by rotation in complex wakes. By mapping low-pressure regions onto the vortex system framework, it achieves targeted localization of vortex cavitation at the blade tips and gap cavitation in passenger roll-on / roll-off ship pod propulsion systems, significantly improving the prediction accuracy of primary cavitation. Through the vortex core-pressure coupling screening mechanism in the steady flow field, high-risk areas can be pre-identified before initiating time-consuming unsteady multiphase flow calculations. This provides a clear target guide for subsequent adaptive mesh refinement and the setting of unsteady time steps, avoiding global errors. Blind calculations improve engineering evaluation efficiency. This invention correlates the transient vortex system fracture degree and transient cavitation volume characteristics extracted by the Q criterion with propeller thrust / torque pulsation in the time domain. It also constructs a multi-condition comprehensive attenuation evaluation matrix by incorporating indicators such as maximum transient cavitation volume, vortex system structure attenuation characteristics, hydrodynamic fluctuation characteristics, and propulsion efficiency attenuation rate. This enables a comprehensive evaluation of the cavitation-induced performance attenuation degree under different advance coefficient conditions. It can reveal the physical coupling mechanism of vortex-induced cavitation, cavitation evolution destroying the vortex system structure, and leading to a decrease in propulsion performance under complex interference conditions, providing a more complete analytical means for multi-condition hydrodynamic performance evaluation and optimization design of podded propulsion vehicles. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an overall flowchart of a podded thruster cavitation prediction and evaluation method based on Q-criterion vortex system characteristics provided by the present invention.

[0019] Figure 2 This is a schematic diagram of the reference computational domain for a podded thruster cavitation prediction and evaluation method based on Q-criterion vortex system characteristics provided by the present invention.

[0020] Figure 3 This is a schematic diagram of the mesh generation for a podded thruster cavitation prediction and evaluation method based on Q-criterion vortex system characteristics provided by the present invention; wherein: (A) is the overall mesh of the computational domain, (B) is the surface and near-wall mesh, and (C) is the local refinement.

[0021] Figure 4 This is a schematic diagram of the hydrodynamic performance curve of a podded propulsion cavitation prediction and evaluation method based on Q-criterion vortex system characteristics provided by the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for predicting and evaluating cavitation in podded propulsion vehicles based on Q-criterion vortex characteristics, including: S1: Establish a three-dimensional computational flow domain including the geometric model of the podded propulsion device, and perform steady-state flow field calculations using a steady-state RANS solver under cavitation-free conditions to obtain basic flow field data and benchmark hydrodynamic performance parameters; S2: Based on the three-dimensional velocity vector field in the basic flow field data, the rotation-dominant region in the complex flow field is identified by calculating the velocity gradient tensor and the second invariant, and the unique vortex spatial morphology of the podded thruster is reconstructed. S3: Map the absolute static pressure field in the basic flow field data to the reconstructed vortex spatial morphology, perform coupled identification of the rotation-dominated low-pressure region in the flow field, and predict the initial location of cavitation by combining spatial location criteria and pressure criteria to obtain high cavitation risk target area data. S4: Based on the basic flow field data, with the high cavitation risk target area data as the key analysis area, the multiphase flow VOF model and the Schnerr-Sauer cavitation model are activated to carry out unsteady cavitation multiphase flow calculations. Based on the cloud map, the cavitation evolution process and the coupling changes with the vortex system structure are captured in the cavitation transient state to obtain unsteady cavitation evolution data. S5: Based on unsteady cavitation evolution data and combined with benchmark hydrodynamic performance parameters, extract the transient hydrodynamic response changes under cavitation conditions, establish a comprehensive attenuation evaluation index, and complete the quantitative assessment of hydrodynamic performance attenuation caused by cavitation.

[0024] It should be noted that through steps S1-S5, cavitation initiation is targeted and predicted by reconstructing the vortex system of the steady flow field and pressure mapping. Then, the vortex-cavitation coupling evolution is captured by combining unsteady multiphase flow calculation, and finally a comprehensive attenuation evaluation index is established. This forms a complete technical path from prediction and capture to quantitative evaluation, providing an efficient and accurate solution for cavitation performance analysis and optimization design of podded propulsion vehicles.

[0025] Example 2, refer to Figures 1-4 As an embodiment of the present invention, based on the previous embodiment, a method for predicting and evaluating cavitation of podded propulsion vehicles based on Q-criterion vortex system characteristics is provided, including: In this embodiment, the three-dimensional computational flow domain including the geometric model of the podded thruster is established in step S1 above, and a steady-state flow field calculation is performed using a steady-state RANS solver under cavitation-free conditions to obtain basic flow field data and reference hydrodynamic performance parameters, including: The 3D geometric model of the pod is imported into a computational fluid dynamics preprocessing platform, such as STAR-CCM+ or Fluent, to establish a 3D computational domain containing a rotational domain and a stationary domain. The rotational domain encompasses the propeller, and the stationary domain encompasses the pod and its support structure. Figure 2 As shown.

[0026] The sliding mesh method (MRF) is used to handle relative motion. Specifically, in the rotational domain, a mesh is set with the propeller shaft as the center and the propeller speed as the axis of rotation. A rotating reference coordinate system is used, while a fixed reference coordinate system is set in the stationary domain. Flow field information is transferred at the interface between the rotating and stationary domains, thereby realizing the processing of the relative motion between the rotating propeller and the stationary pod, and obtaining a three-dimensional computational flow domain model that can be used for steady-state solutions.

[0027] The three-dimensional computational domain is discretized using a cut volume mesh and a boundary layer prism mesh, resulting in a three-dimensional discrete computational mesh composed of multiple mesh elements, such as... Figure 3 As shown.

[0028] Specifically, the main region of the computational watershed is divided using a cut volume mesh. Boundary layer prism meshes are laid along the normal direction on the surface of the propeller blades, the surface of the pod, and the surface of the support. Local volume densification regions are set for the propeller leading edge, trailing edge, blade tip, and the wake region of the pod support to ensure that the mesh resolution can capture microscopic eddies.

[0029] We set up multiple sets of typical advance coefficient operating conditions for passenger roll-on / roll-off ships and performed steady-state calculations based on a three-dimensional discrete computational grid.

[0030] Specifically, set the incoming flow rate. propeller speed Fluid density Boundary conditions and physical parameters such as fluid viscosity are solved iteratively using a steady-state RANS (Reynolds-averaged Navier-Stokes) solver until the residuals converge and the monitored quantities such as thrust and torque tend to stabilize.

[0031] Extract the basic flow field data of each grid cell in the converged computational domain, including the pressure distribution on the blade surface, velocity contour maps of the propeller disk surface and wake region, and three-dimensional velocity vectors. and absolute static pressure field data In the formula This represents the three-dimensional velocity vector at any grid cell. Indicates that the position is along The velocity component in the direction, Indicates that the position is along The velocity component in the direction, Indicates that the position is along The velocity component in the direction.

[0032] Based on steady-state calculations, the thrust and torque of the podded thruster under cavitation-free conditions are extracted, and the baseline hydrodynamic performance parameters under cavitation-free conditions are calculated: ; In the formula, Indicates the advance rate coefficient. Indicates the incoming flow velocity. Indicates the propeller speed. Indicates the propeller diameter. Indicates the reference thrust coefficient, Indicates the thrust of the podded propulsion system. Indicates fluid density, Indicates the reference torque coefficient. Indicates the torque of the podded thruster. This indicates the baseline propulsion efficiency.

[0033] The reference thrust coefficient, reference torque coefficient, and reference propulsion efficiency together constitute the reference hydrodynamic performance parameters required for subsequent steps, and performance curves are plotted, such as... Figure 4 As shown.

[0034] In this embodiment, step S2 above, based on the three-dimensional velocity vector field in the basic flow field data, identifies the rotation-dominant region in the complex flow field by calculating the velocity gradient tensor and the second invariant, and reconstructs the unique vortex spatial morphology of the podded thruster, including: Obtain the three-dimensional velocity vector at discrete grid points The three directional velocity components are obtained, and their spatial derivatives are taken to obtain the spatial velocity gradient tensor of each grid cell. , used to characterize the rate of change of the velocity vector in each direction in space, is expressed as: ; Decompose the velocity gradient tensor into a symmetric strain rate tensor. and antisymmetric rotation tensor : ; ; in, Using the transpose notation, the strain rate tensor is used to characterize the tensile and shear deformation of fluid micro-elements, while the rotation tensor is used to characterize the rotational motion of fluid micro-elements, thus separating the deformation effect from the rotational effect in complex flow fields.

[0035] Calculate the second invariant of the velocity gradient tensor of the mesh cells. value: ; The second invariant of the velocity gradient tensor of the mesh cells The Q value characterizes the degree to which the fluid rotation effect dominates the deformation effect at each spatial location: when the Q value in a certain spatial region is greater than the set characteristic threshold, it indicates that the fluid rotation effect dominates in that region, and the region can be determined to be a candidate region for vortex structure.

[0036] It should be noted that the feature threshold is set according to the discrete grid scale and the advance coefficient. The specific method includes: extracting vortex isosurfaces under several candidate values, and selecting the value with the highest matching degree between the vortex system morphology and empirical data or high-resolution simulation results.

[0037] Constructing scalar field functions in the post-processing platform ,in The set feature threshold is calibrated based on the discrete grid scale and the advance rate coefficient, for example... ,in In seconds.

[0038] Filter all data, extract and render The three-dimensional spatial isosurface is a spatial boundary formed by the continuous connection of multiple spatial locations that meet certain conditions. Different connected isosurface regions together constitute the vortex system spatial morphology in the wake of the podded propeller, representing the spatial tubular morphology of the blade tip vortex, propeller-cabin gap vortex, and support separation vortex of the podded propeller.

[0039] In this embodiment, step S3 above maps the absolute static pressure field in the basic flow field data to the reconstructed vortex spatial morphology, performs coupled identification of the rotation-dominated low-pressure region in the flow field, and predicts the initial cavitation location by combining spatial location criteria and pressure criteria, thus obtaining high cavitation risk target area data including: The absolute static pressure field data in the basic flow field data As a spatial scalar physical property, it is mapped onto the generated three-dimensional vortex system isosurface and its corresponding vortex system spatial region.

[0040] Through mapping, each discrete grid point or grid cell retains its vortex system spatial location information while associating it with the corresponding local absolute static pressure. ,in, This indicates the local absolute static pressure at that spatial location.

[0041] Set the saturated vapor pressure of the liquid under the current water temperature conditions. This represents the critical pressure at which the liquid vaporizes under the current water temperature conditions, for example... Take down When local absolute static pressure Below saturated vapor pressure This indicates that the location has the pressure conditions for cavitation to occur.

[0042] The entire watershed grid is traversed, and each discrete grid point or grid cell is screened using dual criteria: First, the current spatial location lies within the vortex system space region enclosed by the three-dimensional vortex system isosurface extracted by S2, indicating that the location is in a flow region dominated by rotational effects; Second, the local absolute static pressure corresponding to the current spatial location satisfies... This indicates that the location is in a low-pressure region that meets the conditions for cavitation to occur.

[0043] By using dual criteria, a set of spatial grids that simultaneously meets the conditions of being located within a vortex system spatial region and having a local absolute static pressure lower than the saturated vapor pressure is selected.

[0044] The set of spatial grids that meet the dual criteria is defined as the "high cavitation risk target area". This high cavitation risk target area is a cavitation initiation sensitive area that is pre-identified in the steady calculation results. Thus, the possible initiation source area of ​​unsteady cavitation can be located in advance without enabling the full-domain high-consumption unsteady cavitation multiphase flow calculation.

[0045] In this embodiment, in step S4 above, based on the basic flow field data, the high cavitation risk target area data is used as the key analysis region. The multiphase flow VOF model and the Schnerr-Sauer cavitation model are activated to perform unsteady cavitation multiphase flow calculations. Based on the cloud map, the cavitation evolution process and its coupling changes with the vortex system structure are captured in cavitation transients, and the unsteady cavitation evolution data obtained include: With a steady flow field as the initial condition, the multiphase flow volume function (VOF) model and the Schnerr-Sauer model phase change cavitation model are activated. The former is used to solve the volume fraction distribution of liquid and vapor phases in the flow field, while the latter is used to characterize the mass transfer process of liquid-to-vapor phase conversion and vapor-to-liquid phase recondensation caused by local pressure changes.

[0046] The source term expression for the Schnerr-Sauer cavitation model is: ; In the formula, This represents the overall phase change mass transfer rate. This indicates the vaporization mass transfer rate. Indicates the condensation mass transfer rate. Indicates the density of the vapor phase. Indicates the density of the liquid phase. This indicates the density of the vapor-liquid mixture. Indicates the volume fraction of the vapor phase. Indicates the equivalent bubble radius. Indicates local absolute static pressure. This represents the saturated vapor pressure of the liquid under the current water temperature conditions. The Schnerr-Sauer cavitation model updates the vapor phase volume fraction distribution by calculating the vaporization mass transfer rate and condensation mass transfer rate in each transient time step, thereby achieving an unsteady solution for the cavitation process.

[0047] The calculation mode is implicit unsteady time-progression. Based on the basic flow field data, the basic flow field data at the current moment is used as the initial value for the next time step, and the time step size is set. Furthermore, at each time step, the source terms corresponding to the Schnerr-Sauer phase change cavitation model in the computational domain of the pod thruster are iteratively solved.

[0048] Among them, time step The selection of time step should meet the time resolution requirements for propeller rotation and cavitation interface changes, ensuring that each time step can reflect the transient changes in the cavitation region. The value is less than the time corresponding to 1° of propeller rotation. Simultaneously, for the identified high-cavitation risk target area, key transient analyses are performed on the flow field and phase transition processes within the corresponding region to improve the accuracy of capturing the initial cavitation and its subsequent evolution.

[0049] Specifically, within the same transient time step, a three-dimensional vortex system is output synchronously. Isosurface morphology and cavitation water vapor volume fraction Distribution cloud map. Among them, the three-dimensional vortex isosurface morphology is used to characterize the spatial structure characteristics of the rotation-dominant region at the current moment, and the cavitation water vapor volume fraction distribution cloud map is used to characterize the spatial location, volume range, and vapor phase accumulation degree of the cavitation region at the current moment.

[0050] By arranging the output results at each time step in a time sequence, transient process cloud maps corresponding to the vortex system evolution and cavitation evolution within a single propeller rotation cycle are recorded. Analysis of distribution cloud maps and Q-isosurface morphological cloud maps identifies the initial attachment location of sheet cavitation, the expansion process of the attachment range, the detachment location, migration path, and collapse region of free-state cavitation clouds. Simultaneously, it observes the elongation, contraction, local fracturing, dispersion, and connectivity changes of the three-dimensional vortex isosurface within the corresponding region during cavitation development, capturing the impact of rapid cavitation phase transition on the original... The destruction phenomenon of the vortex system skeleton.

[0051] In this embodiment, step S5 above, based on unsteady cavitation evolution data and combined with baseline hydrodynamic performance parameters, extracts the transient hydrodynamic response changes under cavitation conditions, establishes a comprehensive attenuation evaluation index, and completes the quantitative assessment of hydrodynamic performance attenuation caused by cavitation, including: Based on unsteady cavitation evolution data, a surface mechanical integral model of the podded thruster is established to extract transient hydrodynamic loads on the surface of the podded thruster in real time, including thrust. With torque And simultaneously convert it into a dimensionless transient thrust coefficient. Torque coefficient and transient propulsion efficiency .

[0052] Specifically, transient thrust coefficient ; Torque coefficient ; Transient propulsion efficiency ; Perform time-phase synchronization: Based on the obtained cavitation evolution cloud map, output the transient cavitation water vapor total volume. Curves, three-dimensional vortex systems Criterion isosurface volume variation curve Furthermore, the time axis was aligned with the obtained transient hydrodynamic results to establish the temporal correspondence between cavitation evolution, vortex structure changes, and hydrodynamic response.

[0053] After the initial transient transition, the unsteady cavitation stabilization period, in which the main monitoring parameters such as cavitation volume, thrust, and torque exhibit periodic repetitive changes, was selected. Calculate the average propulsion efficiency within this period. : ; in, This indicates the end time of the selected stable time period. This indicates the start time of the selected stable time period. Indicates transient propulsion efficiency In time period Integrating over the curve yields the total area under the curve.

[0054] Call the vacuolation baseline propulsion efficiency extracted in step one Calculate the hydrodynamic performance degradation rate : ; Integrating multiple advance coefficients Based on the data below, a comprehensive attenuation assessment matrix for multiple operating conditions is constructed.

[0055] ; In the formula, Represents the maximum transient cavitation volume. This represents the amplitude of the total cavitation volume fluctuation. This represents the volume decay rate of the isosurface of the vortex system. This indicates the amplitude of thrust coefficient fluctuation. This indicates the amplitude of torque coefficient fluctuation. The matrix represents the rate of hydrodynamic performance degradation. Each row represents a set of comprehensive characteristic parameters under a given advance coefficient condition.

[0056] To eliminate the differences in dimensions and numerical ranges among different evaluation indicators, the matrix was modified. The columns are standardized without dimension to obtain the standardized matrix: ; In the formula: ; In the formula, The total number of operating conditions. The total number of evaluation indicators; subscript (1≤ ≤ () indicates the operating condition number, subscript (1≤ ≤ () indicates the index number; Representation matrix The Middle Operating condition, No. The original values ​​corresponding to each indicator This is the corresponding standardized value.

[0057] The weight vectors corresponding to each evaluation indicator are set as follows: ; ; In the formula, Indicates the first The weight of each evaluation indicator.

[0058] Based on this, the standardized matrix With weight vector By performing a weighted mapping, the comprehensive attenuation evaluation vector corresponding to each advance coefficient operating condition is obtained: ; In the formula, Indicates the first The comprehensive attenuation evaluation index under the given advance coefficient condition is expressed as follows: ; The above formula maps the cavitation volume characteristic parameters, vortex structure failure characteristic parameters, and hydrodynamic performance attenuation characteristic parameters under the same advance coefficient condition to a unified attenuation evaluation index. Above, multi-dimensional mapping and association are achieved.

[0059] Based on comprehensive attenuation evaluation vector The degree of unsteady cavitation performance degradation under different precession coefficients was evaluated. The larger the value, the larger the maximum transient cavitation volume, the more obvious the damage to the vortex system structure, the more severe the thrust and torque fluctuations, and the more serious the propulsion efficiency decline under this operating condition, resulting in a higher degree of overall performance degradation; conversely, the smaller the value, the greater the maximum transient cavitation volume, the more obvious the damage to the vortex system structure, the more intense the thrust and torque fluctuations, and the more severe the propulsion efficiency decline, resulting in a higher degree of overall performance degradation. The smaller the value, the lower the degree of cavitation-induced performance degradation of the podded thruster under that operating condition. This allows for a comprehensive assessment of the degree of unsteady cavitation performance degradation of the podded thruster under multiple advance coefficient conditions.

[0060] Example 3: The above is an illustrative scheme of a podded thruster cavitation prediction and evaluation method based on Q-criterion vortex system characteristics according to this embodiment. It should be noted that the technical solution of a podded thruster cavitation prediction and evaluation system based on Q-criterion vortex system characteristics and the above-described podded thruster cavitation prediction and evaluation method based on Q-criterion vortex system characteristics belong to the same concept. Details not described in detail in the technical solution of the podded thruster cavitation prediction and evaluation system based on Q-criterion vortex system characteristics in this embodiment can be found in the description of the above-described podded thruster cavitation prediction and evaluation method based on Q-criterion vortex system characteristics.

[0061] This embodiment also provides a podded propulsion cavitation prediction and evaluation system based on Q-criterion vortex system characteristics, including: The steady flow field initialization and reference parameter extraction module is used to establish a three-dimensional computational flow domain including the geometric model of the podded thruster, and to perform steady-state flow field calculations using a steady-state RANS solver under cavitation-free conditions to obtain basic flow field data and reference hydrodynamic performance parameters. The Q-criterion vortex spatial morphology reconstruction module is used to identify the rotation-dominant region in a complex flow field based on the three-dimensional velocity vector field in the basic flow field data, by calculating the velocity gradient tensor and the second invariant, and to reconstruct the unique vortex spatial morphology of the podded thruster. The vortex-pressure coupled cavitation initiation prediction module is used to map the absolute static pressure field in the basic flow field data to the reconstructed vortex system spatial morphology, perform coupled identification of the rotation-dominated low-pressure region in the flow field, and predict the cavitation initiation location by combining spatial location criteria and pressure criteria to obtain high cavitation risk target area data. The unsteady vortex-cavitation coupled evolution capture module is used to perform unsteady cavitation multiphase flow calculations based on basic flow field data, with high cavitation risk target area data as the key analysis area. It activates the multiphase flow VOF model and the Schnerr-Sauer cavitation model, and captures the cavitation transients based on the cloud map to obtain unsteady cavitation evolution data. The comprehensive evaluation module for hydrodynamic performance degradation is used to extract transient hydrodynamic response changes under cavitation conditions based on unsteady cavitation evolution data and benchmark hydrodynamic performance parameters, establish a comprehensive degradation evaluation index, and complete the quantitative evaluation of hydrodynamic performance degradation caused by cavitation.

[0062] This embodiment also provides an electronic device applicable to a method for predicting and evaluating cavitation in podded propulsion systems based on Q-criterion vortex characteristics, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a cavitation prediction and evaluation method for podded propulsion systems based on Q-criterion vortex characteristics, as proposed in the above embodiments.

[0063] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a cavitation prediction and evaluation method for podded propulsion based on Q-criterion vortex system characteristics as proposed in the above embodiment.

[0064] The storage medium proposed in this embodiment belongs to the same inventive concept as the cavitation prediction and evaluation method for podded propulsion based on Q-criterion vortex system characteristics proposed in the above embodiment. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0065] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for predicting and evaluating cavitation in podded propulsion systems based on Q-criterion vortex characteristics, characterized in that, include: A three-dimensional computational flow domain including the geometric model of the podded thruster was established, and steady-state flow field calculations were performed using a steady-state RANS solver under cavitation-free conditions to obtain basic flow field data and benchmark hydrodynamic performance parameters. Based on the three-dimensional velocity vector field in the basic flow field data, the rotation-dominant region in the complex flow field is identified by calculating the velocity gradient tensor and the second invariant, and the unique vortex spatial morphology of the podded thruster is reconstructed. The absolute static pressure field in the basic flow field data is mapped to the reconstructed vortex spatial morphology. The rotation-dominated low-pressure region in the flow field is coupled and identified. The initial location of cavitation is predicted by combining spatial location criteria and pressure criteria, and high cavitation risk target area data is obtained. Based on the basic flow field data, with the high cavitation risk target area data as the key analysis area, the multiphase flow VOF model and the Schnerr-Sauer cavitation model are activated to carry out unsteady cavitation multiphase flow calculations. Based on the cloud map, the cavitation evolution process and the coupling changes with the vortex system structure are captured in the cavitation transient state to obtain unsteady cavitation evolution data. Based on unsteady cavitation evolution data and benchmark hydrodynamic performance parameters, transient hydrodynamic response changes under cavitation conditions are extracted, a comprehensive attenuation evaluation index is established, and a quantitative assessment of hydrodynamic performance attenuation caused by cavitation is completed.

2. The method for predicting and evaluating cavitation in podded propulsion systems based on Q-criterion vortex characteristics as described in claim 1, characterized in that, The process involves establishing a three-dimensional computational flow domain including the geometric model of the podded propulsion device, and performing steady-state flow field calculations using a steady-state RANS solver under cavitation-free conditions to obtain basic flow field data and benchmark hydrodynamic performance parameters, including: The geometric model of the podded propulsion device is imported into the preprocessing platform to construct a three-dimensional computational flow domain that includes a rotating domain that envelops the propeller and a stationary domain that envelops the pod body and support. The sliding mesh method is used to transfer flow field information at the interface between the rotating and stationary domains to simulate the relative motion between the rotating propeller and the stationary pod body. The main region of the computational domain is discretized using a cut volume mesh, and a boundary layer prism mesh is laid along the normal direction on the surface of the propeller blade, the surface of the pod body, and the surface of the support. At the same time, the local volume refinement is carried out on the leading edge, trailing edge, blade tip of the propeller, and the wake region of the pod support to form a three-dimensional discrete computational mesh that can capture micro-eddies.

3. The method for predicting and evaluating cavitation in a podded propulsion system based on the Q-criterion vortex characteristics as described in claim 2, characterized in that, The process of establishing a three-dimensional computational flow domain including the geometric model of the podded thruster, and performing steady-state flow field calculations using a steady-state RANS solver under cavitation-free conditions to obtain basic flow field data and benchmark hydrodynamic performance parameters, also includes: Multiple sets of advance coefficient conditions are set, and a steady-state Reynolds-averaged Navier-Stokes solver is used for iterative solution until convergence. Extract the basic flow field data of each grid cell after convergence. The basic flow field data includes the pressure distribution on the blade surface, the velocity contour map of the propeller disk surface and the wake region, the three-dimensional velocity vector field, and the absolute static pressure field. Extract the thrust and torque of the podded thruster under cavitation-free conditions, and calculate parameters characterizing the baseline hydrodynamic performance based on the thrust and torque. These parameters include the advance coefficient, thrust coefficient, torque coefficient, and propulsion efficiency. Then, plot the performance curves.

4. The method for predicting and evaluating cavitation in a podded propulsion system based on the Q-criterion vortex characteristics as described in claim 3, characterized in that, The three-dimensional velocity vector field based on the basic flow field data, through the calculation of the velocity gradient tensor and the second invariant, identifies the rotation-dominant region in the complex flow field and reconstructs the unique vortex spatial morphology of the podded thruster, including: Based on the three-dimensional velocity vector field in the basic flow field data, the velocity gradient tensor is obtained by spatially differentiating the velocity components. The velocity gradient tensor is decomposed into a symmetric strain rate tensor and an antisymmetric rotation tensor, which are used to characterize the tensile shear deformation and rotational motion of the fluid micro-element, respectively. The Q value, which characterizes the degree to which the fluid rotation effect dominates relative to the deformation effect, is calculated based on the difference between the square of the rotation tensor norm and the square of the strain rate tensor norm. By setting a feature threshold, three-dimensional spatial isosurfaces with Q values ​​equal to the feature threshold are extracted. The vortex spatial morphology in the wake of the podded thruster is formed by different connected isosurface regions.

5. The method for predicting and evaluating cavitation in a podded propulsion system based on the Q-criterion vortex characteristics as described in claim 4, characterized in that, The process involves mapping the absolute static pressure field from the basic flow field data to the reconstructed vortex spatial morphology, coupling and identifying the rotation-dominated low-pressure region in the flow field, and predicting the initial cavitation location by combining spatial location criteria and pressure criteria to obtain high cavitation risk target area data, including: The absolute static pressure field is mapped as a spatial scalar attribute to the vortex spatial region corresponding to the reconstructed three-dimensional vortex isosurface, so that each spatial location is associated with local absolute static pressure. Set the saturated vapor pressure at the current water temperature; traverse the entire watershed grid and perform dual criteria screening for each spatial location: determine whether the location is located within the vortex system spatial region; determine whether the local absolute static pressure corresponding to the location is lower than the saturated vapor pressure; define the set of spatial grids that simultaneously meet the dual criteria as the high cavitation risk target area.

6. The method for predicting and evaluating cavitation in a podded propulsion system based on Q-criterion vortex characteristics as described in claim 5, characterized in that, Based on fundamental flow field data, with high-cavitation risk target area data as the key analysis region, the multiphase flow VOF model and Schnerr-Sauer cavitation model are activated to conduct unsteady cavitation multiphase flow calculations. Based on contour maps, the cavitation evolution process and its coupling changes with the vortex system structure are captured transiently to obtain unsteady cavitation evolution data, including: With a steady flow field as the initial condition, the multiphase flow volume function model is activated to solve the volume fraction distribution of the liquid and vapor phases, and the cavitation model based on phase change mass transfer is activated. The cavitation model calculates the mass transfer rate of vaporization and condensation respectively based on the comparison of local pressure and saturated vapor pressure to update the vapor phase volume fraction distribution. Implicit unsteady time propagation is adopted, the time step is set, and steady flow field data is used as the initial value for iterative solution step by step. At the same time, the pre-identified high-cavitation risk target area is used as the key area for transient analysis of flow field and phase change process. Within the same transient time step, the morphology of the three-dimensional vortex system isosurface and the volume fraction distribution cloud map of cavitation water vapor are output synchronously. The output results of each time step are arranged in time sequence to record the corresponding transient processes of vortex system evolution and cavitation evolution within a single rotation cycle. By analyzing the volume fraction cloud map and the vortex system isosurface cloud map, the initial attachment, range expansion, detachment, migration and collapse processes of cavitation are identified. The elongation, contraction, fracture, dispersion and connectivity changes of the vortex system isosurface are observed to capture the destructive phenomenon of cavitation phase transition on the vortex system skeleton.

7. The method for predicting and evaluating cavitation in a podded propulsion system based on the Q-criterion vortex characteristics as described in claim 6, characterized in that, Based on unsteady cavitation evolution data and combined with baseline hydrodynamic performance parameters, the transient hydrodynamic response changes under cavitation conditions are extracted, a comprehensive attenuation evaluation index is established, and a quantitative assessment of hydrodynamic performance attenuation caused by cavitation is completed, including: Based on unsteady cavitation evolution data, a surface mechanical integral model is established to extract transient hydrodynamic loads in real time and convert them into dimensionless transient thrust coefficients, torque coefficients, and transient propulsion efficiency. Perform time-phase synchronization, output transient cavitation total volume curve and vortex system isosurface volume change curve, and align with transient hydrodynamic results on the time axis to establish the time-series correspondence between cavitation evolution, vortex system structure change and hydrodynamic response; The unsteady cavitation stabilization period is selected to calculate the time-averaged propulsion efficiency, and the hydrodynamic performance degradation rate is calculated by combining the cavitation-free benchmark propulsion efficiency. By integrating the characteristic parameters under multiple advance coefficient conditions, a multi-condition comprehensive attenuation evaluation matrix is ​​constructed. The columns of the multi-condition comprehensive attenuation evaluation matrix are subjected to dimensionless standardization to eliminate dimensional differences and obtain a standardized matrix. By setting weight vectors for each evaluation index and performing a weighted mapping between the standardized matrix and the weight vectors, the comprehensive attenuation evaluation index corresponding to each working condition is obtained.

8. A podded propulsion cavitation prediction and evaluation system based on Q-criterion vortex system characteristics, employing the method described in any one of claims 1 to 7, characterized in that, include: The steady flow field initialization and reference parameter extraction module is used to establish a three-dimensional computational flow domain including the geometric model of the podded thruster, and to perform steady-state flow field calculations using a steady-state RANS solver under cavitation-free conditions to obtain basic flow field data and reference hydrodynamic performance parameters. The Q-criterion vortex spatial morphology reconstruction module is used to identify the rotation-dominant region in a complex flow field based on the three-dimensional velocity vector field in the basic flow field data, by calculating the velocity gradient tensor and the second invariant, and to reconstruct the unique vortex spatial morphology of the podded thruster. The vortex-pressure coupled cavitation initiation prediction module is used to map the absolute static pressure field in the basic flow field data to the reconstructed vortex system spatial morphology, perform coupled identification of the rotation-dominated low-pressure region in the flow field, and predict the cavitation initiation location by combining spatial location criteria and pressure criteria to obtain high cavitation risk target area data. The unsteady vortex-cavitation coupled evolution capture module is used to perform unsteady cavitation multiphase flow calculations based on basic flow field data, with high cavitation risk target area data as the key analysis area. It activates the multiphase flow VOF model and the Schnerr-Sauer cavitation model, and captures the cavitation transients based on the cloud map to obtain unsteady cavitation evolution data. The comprehensive evaluation module for hydrodynamic performance degradation is used to extract transient hydrodynamic response changes under cavitation conditions based on unsteady cavitation evolution data and benchmark hydrodynamic performance parameters, establish a comprehensive degradation evaluation index, and complete the quantitative evaluation of hydrodynamic performance degradation caused by cavitation.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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