A numerical simulation method and apparatus for polar ship icing under wind, rain and fog conditions.

Abstract

A numerical simulation method for polar ship icing under wind, rain, and fog conditions is disclosed, belonging to the field of ship icing numerical simulation technology. To address the challenge of simultaneously considering rain impact, fog diffraction, boundary layer humidity effects, and low Mach number flow field characteristics in polar ship icing simulation, this invention improves the accuracy of spatial distribution simulation of surface icing on polar ships under wind, rain, and fog coupling conditions by generating a surface mesh model, constructing an environmental condition parameter set, solving the incompressible aerodynamic flow field, generating a comprehensive water collection coefficient matrix, calculating the dynamic icing rate, and reconstructing a three-dimensional asymmetric ice shape. This method is applicable to fields such as polar ship icing risk assessment, anti-icing and de-icing design, and prediction of ship surface icing morphology.

Description

Technical Field

[0001] This invention belongs to the field of numerical simulation technology for ship icing, specifically involving a multiphysics numerical simulation technology for the spatial distribution of surface icing on polar ships under coupled wind, rain and fog conditions. Background Technology

[0002] Polar vessels are prone to surface icing under the combined effects of low temperatures, strong winds, precipitation, and sea fog. The distribution of icing affects the load conditions of the ship's superstructure, deck equipment, and local components, and also influences the design of anti-icing and de-icing schemes. With the development of computational fluid dynamics and multiphysics simulation technologies, predicting the surface icing morphology of ships through three-dimensional geometric models, surface mesh discretization, flow field calculations, droplet impact analysis, and thermodynamic phase transition calculations has become an important technical direction in the icing risk assessment and anti-icing design of polar vessels.

[0003] In existing technologies, common approaches include establishing icing prediction models based on meteorological parameters such as ambient temperature, wind speed, and liquid water content, or using numerical simulations to calculate the impact, collection, and freezing processes of supercooled water droplets on structural surfaces. Some solutions also draw on droplet trajectory tracking and ice growth methods from the field of aviation icing to perform icing analysis on ship hulls or local ship structures. While these solutions can predict droplet impact and ice growth in windward areas under certain operating conditions, their environmental input and phase change calculations typically focus on a single precipitation phase or a single droplet attachment process.

[0004] In the actual service environment of polar ships, icing is often not simply a matter of raindrop impact or fog droplet adhesion, but rather the result of the combined effects of wind, rain, and fog. Rain with high inertia is more likely to directly impact the windward side, while fog with strong flow characteristics may diffract with the airflow to the leeward region, altering the local humidity conditions within the boundary layer. If the simulation fails to distinguish the motion characteristics of the rain and fog phases, and does not consider the impact of high humidity on evaporative cooling and the release of latent heat of phase change, it will be difficult to accurately reflect the integrated water collection process, the liquid water film flow process, and the formation process of asymmetric icing morphology.

[0005] Furthermore, the flow field solution logic of some icing simulation methods is more suitable for high-speed aerodynamic icing scenarios, while the aerodynamic flow resulting from the synthesis of polar ship speed and natural wind speed is typically a low Mach number flow. Under such conditions, if the fluid dynamics assumptions, boundary layer mesh processing, and wall shear stress calculations are not adapted to the ship icing scenario, it can easily affect the transport of liquid water film across surface mesh cells, secondary boundary freezing, and subsequent ice shape reconstruction results. Therefore, a numerical simulation method for polar ship icing that can take into account rain impact, fog diffraction, boundary layer humidity effects, and low Mach number flow field characteristics is needed. Summary of the Invention

[0006] To address the problem that existing polar ship icing simulations struggle to simultaneously account for rain impact, fog diffraction, boundary layer humidity effects, and low Mach number flow field characteristics, this invention proposes the following solution: A numerical simulation method for polar ship icing under wind, rain, and fog conditions, the method comprising: S1. Obtain the three-dimensional geometric model of the polar vessel, and perform surface unstructured mesh discretization and boundary layer mesh refinement on the three-dimensional geometric model to generate a surface mesh model composed of surface mesh elements and surface mesh nodes. S2. Based on the physical mechanism of wind, rain and fog synergistic coupling, construct an environmental condition parameter set including air flow velocity, ambient temperature, initial relative humidity and dual-modal phase distribution of rain and fog; S3. Based on the surface mesh model and the environmental condition parameter set, the aerodynamic flow field of the polar ship is solved using the incompressible fluid assumption to obtain the wall pressure field and boundary layer viscous shear stress corresponding to each surface mesh unit. S4. Based on the aerodynamic flow field, track the motion trajectories of the large inertial rain phase and the strong flow-dependent fog phase to obtain the comprehensive water collection coefficient matrix corresponding to each surface grid unit; S5. Based on the wall pressure field, boundary layer viscous shear stress, comprehensive water collection coefficient matrix and environmental condition parameter set, solve the control volume energy balance equation including the latent heat effect of high humidity retardation phase change in the boundary layer, and obtain the dynamic icing rate of each surface grid unit. S6. Based on the dynamic icing rate of each surface grid unit, determine the ice thickness data of each surface grid node, and use the grid node displacement algorithm to reconstruct the three-dimensional asymmetric ice shape, outputting the spatial distribution data of polar ship surface icing under the coupled environment of wind, rain and fog.

[0007] Furthermore, the surface unstructured mesh discretization and boundary layer mesh refinement of the three-dimensional geometric model described in S1 includes: performing topological cleanup on the wetted surface exposed to the aerodynamic flow field in the three-dimensional geometric model, performing local surface mesh refinement in the curvature abrupt change region and streamline separation region, dividing a multi-layer prism boundary layer mesh at the normal wall, and constraining the height of the first layer mesh to meet the fluid dynamics bottom layer solution requirement of dimensionless wall distance y+≈1.

[0008] Furthermore, S2 describes the construction of an environmental condition parameter set that includes airflow velocity, ambient temperature, initial relative humidity, and dual-mode phase distribution of rain and fog. This includes: setting the liquid water content and average volume diameter of freezing rain in the high-inertia rain phase to define the direct impact boundary on the windward side; setting the liquid water content and microscopic droplet size distribution of sea fog in the strong-flow-dependent fog phase to define the flow-dependent diffraction and boundary layer humidification boundary; and coupling and superimposing the rain phase parameters and fog phase parameters at the entrance of the spatial computational domain.

[0009] Furthermore, the method described in S3 for solving the aerodynamic flow field of polar ships using the incompressible fluid assumption includes: solving the steady-state incompressible Navier-Stokes equations and the corresponding turbulent closed model based on the aerodynamic Mach number characteristics of the ship's speed and natural wind speed to obtain the aerodynamic flow field that satisfies the mass conservation constraint; and obtaining the wall pressure field and boundary layer viscous shear stress corresponding to each surface grid cell by solving the velocity gradient distribution tensor of the viscous sublayer within the multi-layer prism boundary layer.

[0010] Furthermore, S4 describes tracking the motion trajectories of the large-inertia rain phase and the strong-following-flow fog phase based on the aerodynamic flow field to obtain the comprehensive water collection coefficient matrix corresponding to each surface grid unit. This includes: reading the velocity vector data of the aerodynamic flow field, substituting it into the momentum equations of the rain phase particles and the fog phase particles respectively to optimize the motion trajectory, calculating the local impact limit of the two phase particles at each surface grid unit, and using a linear or nonlinear superposition algorithm to combine the impact efficiencies of the two phases to obtain the comprehensive water collection coefficient matrix reflecting the windward impact of the rain phase and the leeward diffraction characteristics of the fog phase.

[0011] Furthermore, the solution of the control volume energy balance equation, which includes the latent heat of phase change hindered by high humidity in the boundary layer, to obtain the dynamic freezing rate of each surface grid cell, as described in S5, includes: establishing a set of coupled control volume mass conservation and energy conservation equations that include the mass of impact water, inflow water, evaporated water, freezing water, and outflow water; when solving the convective heat transfer term and the latent heat of evaporation term in the energy conservation equation, introducing the fog phase parameter from the environmental condition parameter set, and adaptively correcting the evaporative heat dissipation coefficient through local relative humidity to simulate the physical process of the latent heat of phase change of liquid water film being hindered under high humidity conditions; calculating the mass transfer and secondary boundary freezing of the unfrozen liquid water film across the surface grid cell under the viscous shear stress of the boundary layer to obtain the dynamic freezing rate of each surface grid cell.

[0012] Furthermore, S6, which determines the ice thickness data of each surface grid node based on the dynamic icing rate of each surface grid unit and reconstructs the three-dimensional asymmetric ice shape using a grid node displacement algorithm, includes: weighting the dynamic icing rates of surface grid units adjacent to the same surface grid node to obtain the ice thickness data of each surface grid node; applying a spatial displacement vector corresponding to the ice thickness data along the outward normal direction of each surface grid node; performing grid topology smoothing and interference checks; and outputting a three-dimensional ice shape grid file containing geometric deformation features.

[0013] Based on the same inventive concept, this invention also proposes a numerical simulation device for polar ship icing under wind, rain, and fog conditions, comprising: The surface mesh model generation module is used to obtain the three-dimensional geometric model of polar ships, perform surface unstructured mesh discretization and boundary layer mesh refinement on the three-dimensional geometric model, and generate a surface mesh model composed of surface mesh elements and surface mesh nodes. The environmental condition parameter set construction module is used to construct an environmental condition parameter set that includes airflow velocity, ambient temperature, initial relative humidity, and dual-modal phase distribution of rain and fog based on the physical mechanism of wind, rain and fog synergistic coupling. The aerodynamic flow field solution module is used to solve the aerodynamic flow field of polar ships based on the surface mesh model and the environmental condition parameter set, using the incompressible fluid assumption, to obtain the wall pressure field and boundary layer viscous shear stress corresponding to each surface mesh unit. The integrated water collection coefficient matrix generation module is used to track the motion trajectory of the large inertial rain phase and the strong flow-dependent fog phase based on the aerodynamic flow field, and obtain the integrated water collection coefficient matrix corresponding to each surface grid unit. The dynamic icing rate calculation module is used to solve the control volume energy balance equation, which includes the latent heat effect of high humidity phase change in the boundary layer, based on the wall pressure field, boundary layer viscous shear stress, comprehensive water collection coefficient matrix and environmental condition parameter set, to obtain the dynamic icing rate of each surface grid cell. The ice shape reconstruction output module is used to determine the ice thickness data of each surface grid node based on the dynamic icing rate of each surface grid unit, and to reconstruct the three-dimensional asymmetric ice shape using the grid node displacement algorithm, outputting the spatial distribution data of surface icing of polar ships under the coupled environment of wind, rain and fog.

[0014] Based on the same inventive concept, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to realize the above-mentioned numerical simulation method for polar ship icing under windy, rainy and foggy environmental conditions.

[0015] Based on the same inventive concept, the present invention also proposes a computer storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned numerical simulation method for polar ship icing under wind, rain, and fog conditions.

[0016] Compared with the prior art, the present invention has the following beneficial effects: By performing surface unstructured mesh discretization and boundary layer mesh refinement on the three-dimensional geometric model of polar ships, a surface mesh model composed of surface mesh elements and surface mesh nodes is generated. This provides a unified spatial computing carrier for flow field solution, icing rate calculation, and ice thickness mapping, thereby improving the continuity and reconfigurability of subsequent icing spatial distribution output.

[0017] By constructing an environmental condition parameter set that includes airflow velocity, ambient temperature, initial relative humidity, and dual-modal phase distribution of rain and fog based on the physical mechanism of wind, rain and fog synergy, the simulation process can simultaneously describe the environmental input characteristics of large-inertia rain phase and strong-flow-dependent fog phase, thereby improving the problem that a single precipitation phase or a single fog droplet attachment process is difficult to reflect the actual icing conditions of polar ships.

[0018] By utilizing the incompressible fluid assumption to solve the aerodynamic flow field of polar ships, and obtaining the wall pressure field and boundary layer viscous shear stress corresponding to each surface grid element, the flow field solution is better adapted to the low Mach number flow characteristics of ships, thereby improving the reliability of the boundary layer parameters required for liquid water film transport and subsequent icing rate calculation. Compared with existing technologies, this process can reduce the adaptation deviation caused when high-speed aerodynamic icing solution logic is directly applied to ship icing scenarios.

[0019] By tracking the motion trajectories of the high-inertia rain phase and the strong-flow-dependent fog phase, and obtaining the comprehensive water collection coefficient matrix corresponding to each surface grid unit, the windward impact of the rain phase and the leeward diffraction of the fog phase can be comprehensively represented in the same surface grid model, thereby improving the completeness of water collection distribution calculation in complex wind, rain and fog environments.

[0020] By solving the control volume energy balance equation that includes the latent heat of phase change due to high humidity in the boundary layer, based on the wall pressure field, boundary layer viscous shear stress, comprehensive water collection coefficient matrix and environmental condition parameter set, the icing rate calculation can take into account the influence of boundary layer humidity on evaporative cooling and latent heat release of phase change, thereby improving the problem of inaccurate prediction of icing thickness and icing area when high humidity environment is not considered.

[0021] By determining the ice thickness data of each surface grid node based on the dynamic icing rate of each surface grid unit, and reconstructing the three-dimensional asymmetric ice shape using the grid node displacement algorithm, the surface icing calculation results can be converted into a three-dimensional ice shape grid file, thereby intuitively outputting the spatial distribution data of surface icing of polar ships under the coupled environment of wind, rain and fog.

[0022] This invention has the ability to simultaneously characterize the windward impact of rain phase, the leeward diffraction of fog phase, the influence of boundary layer humidity, and the characteristics of low Mach number flow fields. It can improve the accuracy of numerical simulation of the spatial distribution of surface icing on polar ships under coupled wind, rain, and fog conditions. It is applicable to the fields of polar ship icing risk assessment, anti-icing and de-icing design, and prediction of ship surface icing morphology. Attached Figure Description

[0023] Figure 1 This is an overall flowchart of the numerical simulation method for polar ship icing under wind, rain, and fog conditions as described in the implementation method. Figure 2 This is a schematic diagram of the surface boundary layer mesh encryption described in the implementation method; Figure 3 This is a trend chart of boundary layer viscous shear stress distribution data as described in the implementation method; Figure 4 This is a schematic diagram of the dual-modal particle motion trajectory in rain and fog as described in the implementation method; Figure 5 This is a schematic diagram of the three-dimensional asymmetric ice shape reconstruction described in the implementation method; Figure 6 This is a block diagram of the numerical simulation device module for polar ship icing under wind, rain, and fog conditions as described in the implementation method. Figure 7 This is a schematic diagram of the hardware structure of the computer device described in the implementation method. Detailed Implementation

[0024] The technical solutions in the embodiments of the present invention will now be clearly and completely described in conjunction with the accompanying drawings.

[0025] Implementation Method 1 like Figure 1 As shown, a numerical simulation method for polar ship icing under wind, rain, and fog conditions is presented. The method includes: S1. Obtain the three-dimensional geometric model of the polar vessel, and perform surface unstructured mesh discretization and boundary layer mesh refinement on the three-dimensional geometric model to generate a surface mesh model composed of surface mesh elements and surface mesh nodes. S2. Based on the physical mechanism of wind, rain and fog synergistic coupling, construct an environmental condition parameter set including air flow velocity, ambient temperature, initial relative humidity and dual-modal phase distribution of rain and fog; S3. Based on the surface mesh model and the environmental condition parameter set, the aerodynamic flow field of the polar ship is solved using the incompressible fluid assumption to obtain the wall pressure field and boundary layer viscous shear stress corresponding to each surface mesh unit. S4. Based on the aerodynamic flow field, track the motion trajectories of the large inertial rain phase and the strong flow-dependent fog phase to obtain the comprehensive water collection coefficient matrix corresponding to each surface grid unit; S5. Based on the wall pressure field, boundary layer viscous shear stress, comprehensive water collection coefficient matrix and environmental condition parameter set, solve the control volume energy balance equation including the latent heat effect of high humidity retardation phase change in the boundary layer, and obtain the dynamic icing rate of each surface grid unit. S6. Based on the dynamic icing rate of each surface grid unit, determine the ice thickness data of each surface grid node, and use the grid node displacement algorithm to reconstruct the three-dimensional asymmetric ice shape, outputting the spatial distribution data of polar ship surface icing under the coupled environment of wind, rain and fog.

[0026] Preferably, the surface mesh elements are used to carry the wall pressure field, boundary layer viscous shear stress, comprehensive water collection coefficient matrix and dynamic freezing rate, and the surface mesh nodes are used to carry ice layer thickness data and node displacement results.

[0027] By using the above method, environmental condition parameters, aerodynamic flow field, multiphase water collection, thermodynamic phase change and ice shape reconstruction are associated in the same surface mesh model, so as to achieve continuous simulation output of the spatial distribution of ice formation on the surface of polar ships under coupled wind, rain and fog conditions.

[0028] Furthermore, the surface unstructured mesh discretization and boundary layer mesh refinement of the three-dimensional geometric model described in S1 includes: performing topological cleanup on the wetted surface exposed to the aerodynamic flow field in the three-dimensional geometric model, performing local surface mesh refinement in the curvature abrupt change region and streamline separation region, dividing a multi-layer prism boundary layer mesh at the normal wall, and constraining the height of the first layer mesh to meet the fluid dynamics bottom layer solution requirement of dimensionless wall distance y+≈1.

[0029] By performing topology cleanup, local mesh refinement, and boundary layer mesh refinement on the wet surface, the surface mesh model achieves a refined representation of curvature abrupt change regions, streamline separation regions, and near-wall flow regions, providing a stable mesh basis for subsequent calculations of boundary layer viscous shear stress and icing rate.

[0030] Furthermore, S2 describes the construction of an environmental condition parameter set that includes airflow velocity, ambient temperature, initial relative humidity, and dual-mode phase distribution of rain and fog. This includes: setting the liquid water content and average volume diameter of freezing rain in the high-inertia rain phase to define the direct impact boundary on the windward side; setting the liquid water content and microscopic droplet size distribution of sea fog in the strong-flow-dependent fog phase to define the flow-dependent diffraction and boundary layer humidification boundary; and coupling and superimposing the rain phase parameters and fog phase parameters at the entrance of the spatial computational domain.

[0031] By setting phase parameters for the high-inertia rain phase and the strong-flow-dependent fog phase respectively, and coupling and superimposing them at the entrance of the spatial computational domain, a unified description of the input conditions for the windward impact of the rain phase and the flow-dependent diffraction of the fog phase is achieved, so that the environmental condition parameter set can reflect the icing boundary under the combined action of wind, rain and fog.

[0032] Furthermore, the method described in S3 for solving the aerodynamic flow field of polar ships using the incompressible fluid assumption includes: solving the steady-state incompressible Navier-Stokes equations and the corresponding turbulent closed model based on the aerodynamic Mach number characteristics of the ship's speed and natural wind speed to obtain the aerodynamic flow field that satisfies the mass conservation constraint; and obtaining the wall pressure field and boundary layer viscous shear stress corresponding to each surface grid cell by solving the velocity gradient distribution tensor of the viscous sublayer within the multi-layer prism boundary layer.

[0033] By solving the aerodynamic flow field based on the incompressible fluid assumption and obtaining the wall pressure field and boundary layer viscous shear stress from the viscous sublayer velocity gradient distribution tensor, the calculation of low Mach number flow and near-wall flow parameters is achieved, providing a boundary layer parameter basis for subsequent calculations of liquid water film transport and dynamic freezing rate.

[0034] Furthermore, S4 describes tracking the motion trajectories of the large-inertia rain phase and the strong-following-flow fog phase based on the aerodynamic flow field to obtain the comprehensive water collection coefficient matrix corresponding to each surface grid unit. This includes: reading the velocity vector data of the aerodynamic flow field, substituting it into the momentum equations of the rain phase particles and the fog phase particles respectively to optimize the motion trajectory, calculating the local impact limit of the two phase particles at each surface grid unit, and using a linear or nonlinear superposition algorithm to combine the impact efficiencies of the two phases to obtain the comprehensive water collection coefficient matrix reflecting the windward impact of the rain phase and the leeward diffraction characteristics of the fog phase.

[0035] By tracking the trajectories of rain particles and fog particles separately and merging the collision efficiencies of the two phases into a comprehensive water collection coefficient matrix, the difference in water collection of each surface grid unit is calculated, enabling the windward impact of rain particles and the leeward diffraction of fog particles to be characterized in the same simulation process.

[0036] Furthermore, the solution of the control volume energy balance equation, which includes the latent heat of phase change hindered by high humidity in the boundary layer, to obtain the dynamic freezing rate of each surface grid cell, as described in S5, includes: establishing a set of coupled control volume mass conservation and energy conservation equations that include the mass of impact water, inflow water, evaporated water, freezing water, and outflow water; when solving the convective heat transfer term and the latent heat of evaporation term in the energy conservation equation, introducing the fog phase parameter from the environmental condition parameter set, and adaptively correcting the evaporative heat dissipation coefficient through local relative humidity to simulate the physical process of the latent heat of phase change of liquid water film being hindered under high humidity conditions; calculating the mass transfer and secondary boundary freezing of the unfrozen liquid water film across the surface grid cell under the viscous shear stress of the boundary layer to obtain the dynamic freezing rate of each surface grid cell.

[0037] By establishing a set of coupled equations for the conservation of mass and energy in the control volume, and by combining the fog phase parameters to correct the evaporation heat dissipation coefficient, the latent heat effect of the high humidity barrier phase change in the boundary layer is calculated. By introducing the viscous shear stress of the boundary layer to drive the mass transfer of the unfrozen liquid water film across the surface grid cells and the secondary boundary freezing, the dynamic freezing rate of each surface grid cell is solved iteratively.

[0038] Furthermore, S6, which determines the ice thickness data of each surface grid node based on the dynamic icing rate of each surface grid unit and reconstructs the three-dimensional asymmetric ice shape using a grid node displacement algorithm, includes: weighting the dynamic icing rates of surface grid units adjacent to the same surface grid node to obtain the ice thickness data of each surface grid node; applying a spatial displacement vector corresponding to the ice thickness data along the outward normal direction of each surface grid node; performing grid topology smoothing and interference checks; and outputting a three-dimensional ice shape grid file containing geometric deformation features.

[0039] By weighting and mapping the dynamic icing rate of surface mesh cells to surface mesh nodes and applying a spatial displacement vector along the outward normal direction, the conversion from icing rate data to a three-dimensional asymmetric ice shape mesh file is achieved, enabling the spatial distribution data of surface icing on polar ships to be output in the form of geometric deformation.

[0040] The method described in this embodiment can be executed by a processor calling a computer program, which can be stored in a computer storage medium. When the computer program is executed by the processor, the numerical simulation method for polar ship icing under windy, rainy, and foggy environmental conditions can be implemented.

[0041] Implementation Method 2 In this embodiment, the numerical simulation method for polar ship icing under wind, rain, and fog conditions is executed in the following order: processing the three-dimensional geometric model of the polar ship, constructing the environmental condition parameter set, solving the aerodynamic flow field, tracking rain and fog dual-mode particles, calculating the thermodynamic phase transition, and reconstructing the three-dimensional asymmetric ice shape. The overall process is as follows: Figure 1 As shown.

[0042] First, the CAD data of the polar icebreaker's exterior was read to obtain a 3D geometric model of the polar vessel. To meet the requirements of solving the viscous sublayer of the incompressible flow field, a mesh preprocessing algorithm was used to unstructure the wetted surfaces such as the bow and superstructure, generating a surface mesh model. When generating the volume mesh, at least 15 layers of prismatic mesh were extruded upwards along the wall normal, and an adaptive layer height algorithm was used to control the height of the first layer of mesh within the range of y+≈1, thus forming a boundary layer mesh refinement structure. Figure 2 As shown, the surface mesh model includes a boundary layer enrichment region near the ship's surface and an outer unstructured far-field mesh. The surface mesh model is composed of surface mesh cells and surface mesh nodes.

[0043] Then, the velocity inlet boundary of the aerodynamic flow field is defined as the relative composite vector of the ship's speed and the natural storm flow field. Considering the characteristics of Arctic autumn, the precipitation boundary at the computational domain inlet is defined as a dual-modal matrix input: matrix A represents the rain phase, defining a large-inertial rain phase with a liquid water content of 1.2 g / m³ and a characteristic particle size of 800 μm; matrix B represents the fog phase, defining a strongly flow-dependent fog phase with a liquid water content of 0.3 g / m³ and a particle size of 15 μm. An environmental condition parameter set, including airflow velocity, ambient temperature, initial relative humidity, and the dual-modal phase distribution of rain and fog, is constructed using the above method. Figure 4 As shown, rain particles exhibit linear impact characteristics, while fog particles exhibit flow-diffraction characteristics.

[0044] Next, the program calls an incompressible solver based on a pressure basis to solve the mass and momentum conservation equations based on the surface mesh model and environmental condition parameter set, obtaining the aerodynamic flow field of the polar vessel. After the aerodynamic flow field converges, the wall pressure field and boundary layer viscous shear stress τ corresponding to each surface mesh element are extracted. For example... Figure 3 As shown, the boundary layer viscous shear stress is distributed along the distance of the ship's surface, and forms a high shear stress concentration zone in local areas.

[0045] Subsequently, the trajectories of rain and fog particles were tracked based on the aerodynamic flow field. The algorithm superimposes the rain impact amount and fog collection amount on the surface grid cells to obtain the comprehensive water collection coefficient matrix corresponding to each surface grid cell. The comprehensive water collection coefficient matrix is ​​arranged according to... To represent, where Indicates the overall water collection coefficient. Indicates the rain phase collection coefficient. Indicates the fog collection coefficient. and This represents the corresponding weighting coefficient.

[0046] In the thermodynamic phase transition calculation, based on the wall pressure field, boundary layer viscous shear stress, comprehensive water collection coefficient matrix, and environmental condition parameter set, the control volume energy balance equation, which includes the latent heat effect of high boundary layer humidity hindering phase transition, is solved to obtain the dynamic freezing rate of each surface grid cell. In the thermodynamic iteration, a local high relative humidity correction is introduced to attenuate the evaporative heat dissipation term and simulate the latent heat inhibition effect. For the unfrozen liquid water film, the mass transport across surface grid cells and the secondary boundary freezing process are calculated in conjunction with the boundary layer viscous shear stress.

[0047] After time-step iterations, the solver outputs the ice thickness of each surface mesh element. The mesh reconstruction process is then invoked to map the dynamic icing rate of each surface mesh element to the ice thickness data of each surface mesh node, and the surface mesh nodes are driven to displace along the outward normal direction, resulting in a three-dimensional asymmetric ice shape mesh file. For example... Figure 5 As shown, the three-dimensional asymmetric ice mesh file can represent the heavily iced area, asymmetric ice load, and center of gravity offset vector results on the ship's cross section. Here, CG represents the center of gravity position before icing, CG' represents the center of gravity position after the formation of asymmetric ice load, and ΔCG represents the center of gravity offset vector from CG to CG'. The three-dimensional asymmetric ice mesh file serves as the output data for the spatial distribution of surface icing on polar ships under a coupled wind, rain, and fog environment.

[0048] In this embodiment, a numerical simulation device for polar ship icing under wind, rain, and fog conditions is used to execute the above method. For example... Figure 6 As shown, the device includes a surface mesh model generation module, an environmental condition parameter set construction module, an aerodynamic flow field solution module, a comprehensive water collection coefficient matrix generation module, a dynamic icing rate calculation module, and an ice shape reconstruction output module. The surface mesh model generation module is used to obtain a three-dimensional geometric model of the polar vessel, and performs surface unstructured mesh discretization and boundary layer mesh refinement on the three-dimensional geometric model to generate a surface mesh model composed of surface mesh elements and surface mesh nodes. The environmental condition parameter set construction module is used to construct an environmental condition parameter set containing airflow velocity, ambient temperature, initial relative humidity, and rain / fog dual-mode phase distribution based on the physical mechanism of wind, rain, and fog synergistic coupling. The aerodynamic flow field solution module is used to solve the aerodynamic flow field of the polar vessel based on the surface mesh model and the environmental condition parameter set, using the incompressible fluid assumption, to obtain the wall pressure field and boundary layer viscous shear stress corresponding to each surface mesh element. The comprehensive water collection coefficient matrix generation module... The array generation module is used to track the motion trajectory of the large-inertia rain phase and the strong-flow-dependent fog phase based on the aerodynamic flow field, and obtain the comprehensive water collection coefficient matrix corresponding to each surface grid unit; the dynamic icing rate calculation module is used to solve the control volume energy balance equation including the latent heat effect of the high humidity retardation phase change in the boundary layer based on the wall pressure field, boundary layer viscous shear stress, comprehensive water collection coefficient matrix and environmental condition parameter set, and obtain the dynamic icing rate of each surface grid unit; the ice shape reconstruction output module is used to determine the ice layer thickness data of each surface grid node based on the dynamic icing rate of each surface grid unit, and reconstruct the three-dimensional asymmetric ice shape using the grid node displacement algorithm, and output the spatial distribution data of surface icing of polar ships under the coupled wind, rain and fog environment.

[0049] like Figure 7As shown, the computer device provided in this embodiment includes a processor and a memory. The processor can be a computing core such as a CPU, DSP, or ASIC, responsible for loading and executing instructions stored in the memory to implement the numerical simulation method described above. The memory includes built-in RAM / ROM and extended memory, used to store the operating system, numerical simulation control program, and generated mesh data. The method described in this embodiment can be used as a computer program product, running on a general-purpose or special-purpose computer cluster, and executed by calling the computer program through the processor.

Claims

1. A numerical simulation method for ice accretion on a polar ship in a wind, rain and fog environment, characterized in that, The method includes: S1. Obtain the three-dimensional geometric model of the polar vessel, and perform surface unstructured mesh discretization and boundary layer mesh refinement on the three-dimensional geometric model to generate a surface mesh model composed of surface mesh elements and surface mesh nodes. S2. Based on the physical mechanism of wind, rain and fog synergistic coupling, construct an environmental condition parameter set including air flow velocity, ambient temperature, initial relative humidity and dual-modal phase distribution of rain and fog; S3. Based on the surface mesh model and the environmental condition parameter set, the aerodynamic flow field of the polar ship is solved using the incompressible fluid assumption to obtain the wall pressure field and boundary layer viscous shear stress corresponding to each surface mesh unit. S4. Based on the aerodynamic flow field, track the motion trajectories of the large inertial rain phase and the strong flow-dependent fog phase to obtain the comprehensive water collection coefficient matrix corresponding to each surface grid unit; S5. Based on the wall pressure field, boundary layer viscous shear stress, comprehensive water collection coefficient matrix and environmental condition parameter set, solve the control volume energy balance equation including the latent heat effect of high humidity retardation phase change in the boundary layer to obtain the dynamic icing rate of each surface grid unit. S6. Based on the dynamic icing rate of each surface grid unit, determine the ice thickness data of each surface grid node, and use the grid node displacement algorithm to reconstruct the three-dimensional asymmetric ice shape, outputting the spatial distribution data of polar ship surface icing under the coupled environment of wind, rain and fog.

2. The method of claim 1, wherein, S1 describes performing surface unstructured mesh discretization and boundary layer mesh refinement on the three-dimensional geometric model, including: performing topological cleanup on the wetted surfaces exposed to the aerodynamic flow field in the three-dimensional geometric model, performing local surface mesh refinement in curvature abrupt change regions and streamline separation regions, dividing multiple prism boundary layer meshes at the normal wall, and constraining the height of the first layer mesh to meet the fluid dynamics bottom layer solution requirement of dimensionless wall distance y+≈1.

3. The method of claim 1, wherein, The environmental condition parameter set described in S2, which includes airflow velocity, ambient temperature, initial relative humidity, and dual-mode phase distribution of rain and fog, includes: setting the liquid water content and average volume diameter of freezing rain in the high-inertia rain phase to define the direct impact boundary on the windward side; setting the liquid water content and microscopic droplet size distribution of sea fog in the strong-flow-dependent fog phase to define the flow-dependent diffraction and boundary layer humidification boundary; and coupling and superimposing the rain phase parameters and fog phase parameters at the entrance of the spatial computational domain.

4. The method of claim 1, wherein, S3 describes the use of the incompressible fluid assumption to solve the aerodynamic flow field of polar ships, including: solving the steady-state incompressible Navier-Stokes equations and the corresponding turbulent closed model based on the aerodynamic Mach number characteristics of the ship's speed and natural wind speed, to obtain the aerodynamic flow field that satisfies the mass conservation constraint; and obtaining the wall pressure field and boundary layer viscous shear stress corresponding to each surface grid cell by solving the velocity gradient distribution tensor of the viscous sublayer within the multi-layer prism boundary layer.

5. The method of claim 1, wherein, S4 describes the process of tracking the motion trajectories of the large-inertia rain phase and the strong-following-flow fog phase based on the aerodynamic flow field to obtain the comprehensive water collection coefficient matrix corresponding to each surface grid unit. This includes: reading the velocity vector data of the aerodynamic flow field, substituting it into the momentum equations of the rain phase particles and the fog phase particles respectively to optimize the motion trajectory, calculating the local impact limit of the two phase particles at each surface grid unit, and using a linear or nonlinear superposition algorithm to combine the impact efficiencies of the two phases to obtain the comprehensive water collection coefficient matrix that reflects the windward impact of the rain phase and the leeward diffraction characteristics of the fog phase.

6. The method according to claim 1, characterized in that, S5 describes solving the control volume energy balance equation, which includes the latent heat of phase change hindered by high humidity in the boundary layer, to obtain the dynamic freezing rate of each surface grid cell. This includes: establishing a set of coupled control volume mass conservation and energy conservation equations that include the mass of impact water, inflow water, evaporated water, freezing water, and outflow water; when solving the convective heat transfer term and the latent heat of evaporation term in the energy conservation equation, introducing fog phase parameters from the environmental condition parameter set, and adaptively correcting the evaporative heat dissipation coefficient through local relative humidity to simulate the physical process of the latent heat of phase change of liquid water film being hindered under high humidity conditions; calculating the mass transfer and secondary boundary freezing of the unfrozen liquid water film across surface grid cells driven by the viscous shear stress of the boundary layer to obtain the dynamic freezing rate of each surface grid cell.

7. The method according to claim 1, characterized in that, S6 describes determining the ice thickness data of each surface mesh node based on the dynamic icing rate of each surface mesh unit, and reconstructing the three-dimensional asymmetric ice shape using a mesh node displacement algorithm. This includes: weighting the dynamic icing rates of surface mesh units adjacent to the same surface mesh node to obtain the ice thickness data of each surface mesh node; applying a spatial displacement vector corresponding to the ice thickness data along the outward normal direction of each surface mesh node; performing mesh topology smoothing and interference checks; and outputting a three-dimensional ice shape mesh file containing geometric deformation features.

8. A numerical simulation device for polar ship icing under wind, rain, and fog conditions, characterized in that, include: The surface mesh model generation module is used to obtain the three-dimensional geometric model of polar ships, perform surface unstructured mesh discretization and boundary layer mesh refinement on the three-dimensional geometric model, and generate a surface mesh model composed of surface mesh elements and surface mesh nodes. The environmental condition parameter set construction module is used to construct an environmental condition parameter set that includes airflow velocity, ambient temperature, initial relative humidity, and dual-modal phase distribution of rain and fog based on the physical mechanism of wind, rain and fog synergistic coupling. The aerodynamic flow field solution module is used to solve the aerodynamic flow field of polar ships based on the surface mesh model and the environmental condition parameter set, using the incompressible fluid assumption, to obtain the wall pressure field and boundary layer viscous shear stress corresponding to each surface mesh unit. The integrated water collection coefficient matrix generation module is used to track the motion trajectory of the large inertial rain phase and the strong flow-dependent fog phase based on the aerodynamic flow field, and obtain the integrated water collection coefficient matrix corresponding to each surface grid unit. The dynamic icing rate calculation module is used to solve the control volume energy balance equation, which includes the latent heat effect of high humidity phase change in the boundary layer, based on the wall pressure field, boundary layer viscous shear stress, comprehensive water collection coefficient matrix and environmental condition parameter set, to obtain the dynamic icing rate of each surface grid cell. The ice shape reconstruction output module is used to determine the ice thickness data of each surface grid node based on the dynamic icing rate of each surface grid unit, and to reconstruct the three-dimensional asymmetric ice shape using the grid node displacement algorithm, outputting the spatial distribution data of surface icing of polar ships under the coupled environment of wind, rain and fog.

9. A computer device, characterized in that, The method includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1 to 7.

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