Ablation simulation and grid processing method and system based on gaussian weighted smoothing processing

Abstract

The application discloses an ablation simulation and grid processing method and system based on Gaussian weighting smoothing processing, belongs to the numerical simulation field intersected by computational fluid dynamics and computational solid mechanics, and comprises the following steps: S1, calculation and collection of original ablation rate; S2, after the original ablation rate of a boundary unit is obtained, spatial smoothing processing is carried out based on a Gaussian weighting function; S3, after the smoothing processing is completed, ablation depth accumulation is carried out, and a unit mark is deleted by using a criterion; and S4, unit deletion and grid topology updating are carried out. The application improves the physical authenticity of an ablation surface, enhances the stability of numerical calculation and reduces the grid dependency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of numerical simulation of the intersection of computational fluid dynamics and computational solid mechanics, and more particularly, to an ablation simulation and grid processing method and system based on Gaussian weighted smoothing processing. BACKGROUND

[0002] In the numerical simulation of material ablation, the ablation rate is usually calculated at the integral points of the grid elements. However, due to grid division, numerical error or the characteristics of the physical model itself, the directly calculated ablation rate may have large, non-physical jumps or oscillations between adjacent elements. This spatial discontinuity can cause the following problems:

[0003] 1) Ablation topography distortion: the simulated ablation surface may exhibit unrealistic "sawtooth" or "checkerboard" topography related to the grid shape, rather than the physically expected smooth transition.

[0004] 2) Numerical calculation instability: the sharp jumps in ablation rate can cause convergence difficulties for the solver, especially in thermal-structural strong coupling analysis, which can lead to a sharp decrease in time step size, or even computational divergence.

[0005] 3) Strong grid dependence: the simulation results are highly sensitive to the initial grid division method and quality, and replacing a set of grids may result in completely different ablation results, reducing the reliability of the simulation results.

[0006] Existing methods usually use element averaging or maximum value to process the ablation rate within the element, but this does not solve the problem of rate jumps between elements. Therefore, a new method is needed that can effectively smooth the ablation rate, eliminate non-physical oscillations, improve the stability of the calculation and the authenticity of the results. SUMMARY

[0007] The purpose of the present application is to overcome the shortcomings of the prior art and provide an ablation simulation and grid processing method and system based on Gaussian weighted smoothing processing, which improves the physical authenticity of the ablation surface, enhances the stability of the numerical calculation and reduces the grid dependence.

[0008] The purpose of the present application is achieved by the following scheme:

[0009] An ablation simulation and grid processing method based on Gaussian weighted smoothing processing, comprising the following steps:

[0010] S1, calculation and collection of original ablation rate;

[0011] S2, after obtaining the original ablation rate of the boundary element, spatial smoothing processing is performed based on a Gaussian weighting function;

[0012] S3, after the smoothing, the ablation depth accumulation and deletion criterion is executed;

[0013] S4, cell deletion and mesh topology update.

[0014] Further, in step S1, the calculation and collection of the original ablation rate, specifically includes the sub-steps: for each cell located on the ablation boundary, calculate the average value of all integral point ablation rates on the cell, denoted as v raw,i ; wherein, i is the index number of the cell; and traverse all the cells on the ablation boundary, collect and store their original average ablation rates v raw,i and geometric center coordinates C i .

[0015] Further, in step S2, the spatial smoothing processing based on the Gaussian weighting function, specifically includes the sub-steps:

[0016] After obtaining the original ablation rate of all boundary cells, for each cell i , a smoothed ablation rate v smooth,i is recalculated based on the weighted average; wherein the smoothed rate of the cell i is the weighted average of the original rates of itself and the adjacent cells, and the weight depends on the distance between the adjacent cells and the current cell.

[0017] Further, the smoothed ablation rate v smooth,i is recalculated based on the weighted average, specifically includes the sub-steps:

[0018] For the cell i , its smoothed rate v smooth,i is calculated by the following formula:

[0019] (1);

[0020] Wherein:

[0021] N i is the neighborhood set of the cell i , including the cell i itself and all other ablation boundary cells R smooth within the preset smoothing radius j ;

[0022] vraw,j is the original average ablation rate of the cell j ;

[0023] w ij is the influence weight of the cell j on the cell i , which is calculated using the following Gaussian function:

[0024] (2);

[0025] wherein:

[0026] d ij is the Euclidean distance between the geometric center of the cell i and the cell j ;

[0027] β is an adjustable weight decay factor used to control the decay speed of the weight with distance;

[0028] When j=i , d ii = 0, the weight is 1, which ensures that the original rate of the cell itself has the highest proportion in the weighted average.

[0029] Further, in step S3, the ablation depth accumulation is accumulated with a deletion criterion using a criterion, specifically including the sub-steps of:

[0030] using the calculated smoothed ablation rate v smooth,i to update the cumulative ablation depth D elem,i of each cell:

[0031] (3);

[0032] D elem,i,new is the current ablation depth, D elem,i,old is the ablation depth of the previous time step; is the time step length;

[0033] Subsequently, the updated cumulative ablation depth is compared with the cell feature size L char,i ; if D elem,i ≥ L char,i , the cell i is marked for deletion; otherwise, the cell should be retained.

[0034] Further, in step S4, the unit deletes the mesh topology update, specifically including sub-steps:

[0035] After the marking is completed, the marked unit is deleted, the newly exposed surface due to the deletion is updated as a new ablation boundary, and island detection and removal are performed to ensure mesh integrity.

[0036] Further, the marked unit is deleted, specifically including:

[0037] The unit removal execution step: traversing the removal list, removing the specified unit and unnecessary nodes for the mesh data structure, updating the connection relationship, physically deleting these units and their associated nodes from the data structure of the calculation mesh, and not changing the coordinates of the remaining nodes.

[0038] Further, the newly exposed surface due to the deletion is updated as a new ablation boundary, specifically including:

[0039] The adjacent boundary update step: traversing each unit in the removal list, finding all adjacent units; if one adjacent unit is not in the removal list, identifying the shared interface between the two units; marking the shared interface as a new boundary and assigning it a preset boundary identifier.

[0040] Further, the island detection and removal are performed, specifically including:

[0041] The island unit detection and removal step: after performing unit removal, connectivity analysis is performed; starting from the predefined stable boundary, a graph search algorithm is used to traverse all active units that can be reached through unit adjacency relationships.

[0042] An ablation simulation and mesh processing system based on Gaussian weighted smoothing processing, comprising a computer device, the computer device comprising a processor and a memory, the memory storing a computer program, when the computer program is loaded by the processor, the method as claimed in any one of the above.

[0043] The beneficial effects of the present application include:

[0044] (1) The present application can improve the physical authenticity of the ablation surface: through Gaussian smoothing processing, the non-physical oscillation of the ablation rate is effectively eliminated, making the simulated ablation surface smoother and more natural, and more consistent with the real physical process.

[0045] (2) The present application can enhance the stability of numerical calculation: the smoothed ablation rate field provides a more "friendly" input for the solver, avoiding convergence difficulties caused by dramatic data jumps, significantly improving the stability and computational efficiency of the coupled simulation.

[0046] (3) The present invention can reduce grid dependence: Since neighborhood information is considered, the smoothing algorithm can, to a certain extent, bridge the local differences caused by different grid divisions, thereby reducing the sensitivity of the simulation results to grid quality and division method, and improving the repeatability and reliability of the results. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. 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.

[0048] Figure 1 This is a flowchart of the ablation simulation calculation based on Gaussian weighted smoothing in an embodiment of the present invention;

[0049] Figure 2 A diagram illustrating the Gaussian weighting function;

[0050] Figure 3a For the calculation results based on Gaussian weighted smoothing t =0s temperature distribution diagram;

[0051] Figure 3b For the calculation results based on Gaussian weighted smoothing t =15s temperature distribution diagram;

[0052] Figure 3c For the calculation results based on Gaussian weighted smoothing t =30s temperature distribution diagram;

[0053] Figure 3d For the calculation results based on Gaussian weighted smoothing t =50s temperature distribution diagram;

[0054] Figure 3e For the calculation results based on Gaussian weighted smoothing t =100s temperature distribution diagram;

[0055] Figure 3f For the calculation results based on Gaussian weighted smoothing t =120s temperature distribution diagram. Detailed Implementation

[0056] All features disclosed in all embodiments of this specification, or steps in all methods or processes implied in the disclosure, may be combined and / or extended or replaced in any way, except for mutually exclusive features and / or steps.

[0057] The specific implementation process of the present application is as follows:

[0058] The present application aims to solve the technical problems of spatial discontinuity of ablation rate caused by numerical calculation, existence of non-physical oscillation, and thus distortion of ablation morphology and instability of calculation in existing ablation simulation. In application scenarios, it specifically relates to a data processing method for improving the stability and physical reality of material thermo-chemical ablation simulation, especially suitable for scenarios such as high-speed aircraft thermal protection system analysis.

[0059] In the present application concept, a spatial smoothing method based on Gaussian weighting function is proposed to preprocess the ablation rate to obtain a smoother and more physically realistic ablation surface evolution process. Before determining whether to delete the unit, spatial smoothing processing of the ablation rate is introduced. Based on this, an ablation simulation and grid processing method based on Gaussian weighting smoothing processing is provided. In the preferred embodiment, the existing general finite element analysis software can be developed again in the form of user-defined modules for integrated implementation. For example, the ablation process of a two-dimensional carbon-based composite material head structure under high-temperature and high-pressure gas flow is simulated. It is assumed that the bottom of the structure is fixed and the top curved surface is the main ablation area. A two-dimensional geometric model of the structure is established and divided into finite element grids and boundary sets are defined, marking the "ablation boundary-top surface" that bears ablation, the "stable boundary-bottom surface" that is fixed, and the "side boundary" that is adiabatic. Then define the initial field: define the initial temperature field (e.g., uniform 300K) for the entire model. Perform the following steps:

[0060] Step S1: Calculation and collection of original ablation rate

[0061] For each unit located on the ablation boundary, first calculate its ablation rate. In this embodiment, the average value of the ablation rates of all integration points on the unit is calculated, denoted as v raw,i where i is the index number of the unit. The system will traverse all units on the ablation boundary and collect their original average ablation rates v raw,i and geometric center coordinates C i for storage.

[0062] Specifically, step S1 includes the following sub-steps:

[0063] S11, core physical field and material model configuration, including: heat conduction solver: configure a transient heat conduction solver to calculate the temperature field evolution inside the model; thermal physical material module: define the properties of the material, such as density, specific heat, thermal conductivity, etc. change with temperature; thermal environment definition module: through a constant module, define the gas recovery enthalpy outside the boundary layer, reference cold wall heat flow, pressure, gas component, etc. Environmental parameters; surface thermal chemical reaction module: this module is the core of ablation physics. It outputs the original ablation rate according to the current wall temperature and thermal environment parameters through complex iterative calculation; surface heat balance module: according to the output of the thermal chemical reaction module, calculate the net heat flow considering aerodynamic heating, radiation heat dissipation and ablation heat absorption, and take it as the boundary condition of the heat conduction solver at the "ablation boundary-top surface". The related environmental parameters are as follows: recovery enthalpy 13.9e6J / kg, cold wall heat flow 7.67e6W / m 2 , cold wall heat flow reference wall temperature 0K, total pressure 0.0592atm, oxygen partial pressure 0.0118atm, nitrogen partial pressure 0.0444atm, and mixed gas molar mass 0.02952kg / mol.

[0064] S12, calculation and collection of ablation rate: for each unit located on the ablation boundary, the ablation rate can be calculated according to the conventional method first. In this embodiment, the average value of the ablation rate of all integral points on the unit is calculated, denoted as v raw,i , where i is the index number of the unit. The system will traverse all the units on the ablation boundary, collect their original average ablation rates v raw,i and the geometric center coordinates C i and store them.

[0065] Step S2: spatial smoothing processing based on Gaussian weighting function

[0066] This is one of the core steps of the present application. After obtaining the original ablation rate of all boundary units, the system will recalculate a smoothed ablation rate i v smooth,i for each unit

[0067] The calculation is based on the idea of weighted average, that is, the smoothed rate of unit i is the weighted average of the original rates of itself and its adjacent units. The weight depends on the distance between the adjacent units and the current unit.

[0068] Specifically, for unit i , its smoothed rate v smooth,i is calculated by the following formula:​

[0069] (1) ;

[0070] wherein:

[0071] N i is a neighborhood set of the cell i , including the cell i itself and all other ablation boundary cells within a preset smoothing radius R smooth . j .

[0072] v raw,j is the original average ablation rate of the cell j in the neighborhood.

[0073] w ij is the influence weight of the cell j on the cell i , calculated using a Gaussian function:

[0074] (2) ;

[0075] wherein:

[0076] d ij is the Euclidean distance between the geometric center points of the cell i and the cell j .

[0077] β is an adjustable weight decay factor used to control the decay speed of the weight with distance.

[0078] When j=i , d ii = 0, the weight is 1, ensuring that the original rate of the cell itself has the highest proportion in the weighted average.

[0079] Specifically, step S2 includes the following sub-steps:

[0080] S21, determine the module type: this is a custom cell processing module that acts on the entire calculation domain.

[0081] S22, input parameters: ablation rate variable (coupled with the "ablation rate" field calculated in the above step), stable boundary name (specify "stable boundary-bottom surface" for island detection), spatial smoothing: (for example, can be set to true to activate the method steps of the invention), smoothing radius (tunable parameter, defines the range of neighborhood search, for example, set to 2.0, indicating a radius of 2 times the unit characteristic length), smoothing weight decay factor (tunable parameter, used to control the decay speed of the Gaussian weight function with distance).

[0082] S23, execute logic, specifically including data collection, smoothing and deletion processing, as follows:

[0083] S231, data collection stage (Execute): in the calculation process of each time step, the module traverses all the cells. If a cell has "ablation boundary-top surface", record the ID of the cell, the average value of the ablation rate, and the geometric center coordinates of the cell.

[0084] S232, smoothing and deletion stage (Finalize): at the end of the time step calculation, according to the collected data of all ablation cells, the smoothed ablation rate of each ablation cell is calculated v smooth , refer to formula (1).

[0085] Step S3: ablation depth accumulation and deletion cell marking with utilization criterion

[0086] Using the smoothed ablation rate calculated in the last step v smooth,i to update the cumulative ablation depth of each cell D elem,i :

[0087] (3);

[0088] Then, the updated cumulative ablation depth is compared with the unit characteristic size L char,i If D elem,i ≥ L char,i , then the cell i is marked for deletion; is the time step.

[0089] Specifically, step S3 includes the following sub-steps:

[0090] S31, ablation depth accumulation: using v smooth to update the cumulative ablation depth of each cell, refer to formula (3).

[0091] S32, deletion judgment: compare the cumulative ablation depth of the unit with its characteristic size, here the minimum unit side length is selected, and the condition is met to mark for deletion.

[0092] Step S4: unit deletion and mesh topology update

[0093] After the marking is completed, the system will batch delete all marked units, update the newly exposed surface due to deletion as a new ablation boundary, and perform island detection and removal to ensure mesh integrity. Specifically, for the unit deletion module, the unit deletion simulation material loss module, the main steps are introduced as follows:

[0094] (1) Adjacent boundary update step: traverse each unit in the "to-be-removed list" and find all adjacent units. If an adjacent unit is not in the "to-be-removed list", identify the shared interface (face or edge) between the two units. Mark the shared interface as a new boundary and assign it a preset boundary identifier (meaning that after material loss, the new material is subject to the action of the boundary). This new boundary identifier can be used to apply heat flow, pressure or other boundary conditions in subsequent calculations.

[0095] (2) Unit removal execution step: traverse the "to-be-removed list" and accurately remove the specified units and unnecessary nodes from the existing mesh data structure, update the connection relationship, physically delete these units and their associated nodes from the data structure of the calculation mesh, and do not change the coordinates of the remaining nodes. Different existing mesh reconstruction techniques regenerate the mesh, here the data structure of the mesh topology relationship is directly modified.

[0096] (3) Island unit detection and removal step: after executing unit removal, perform connectivity analysis. Starting from the pre-defined "stable boundary" (for example, the fixed constraint boundary of the model, which can be considered as the boundary that will not be ablated in practice), use a graph search algorithm (this embodiment uses breadth-first search BFS) to traverse all active units that can be reached through unit adjacency relationships.

[0097] Figure 1 Ablation simulation calculation flowchart based on Gaussian weighted smoothing processing. The figure should clearly show the complete steps from collecting the original ablation rate, to Gaussian smoothing calculation, to unit deletion and mesh update. The specific calculation process can refer to the specific embodiments of the above technical solutions. Figure 2 The technical idea of the Gaussian weighted smoothing algorithm is shown.

[0098] Blue block: represents the set of all units located on the ablation boundary at the current calculation time step. The figure shows an example of A , B , C , DFour ablation units. The black dot inside each unit represents its geometric center.

[0099] Gaussian circle: Each ablation unit has a virtual influence range centered at itself with a smooth radius R smooth , represented by the dashed circle in the figure. This circle defines the neighborhood range considered by the unit in the smoothing calculation.

[0100] Distance: The distance between the centers of two units is denoted by d AB , d CB , d CD . This is the basis for calculating the weight.

[0101] Calculation process of Gaussian weighted smoothing (take unit C as an example): When the invention calculates the smoothed ablation rate of unit C v smooth,C , the detailed process is as follows:

[0102] 1) Determine the neighborhood: Draw the Gaussian circle of unit C with R smooth as the radius. All ablation units whose center points fall within this circle (including C itself) belong to the neighborhood of C. As can be seen from the figure, the center points of units B and D fall within the Gaussian circle of C, while unit A is outside the range. Therefore, the neighborhood set of C is {B, C, D}.

[0103] 2) Calculate the weight: The system calculates the influence weight of each unit (B, C, D) in the neighborhood on C. The weight is determined by the Gaussian function, i.e. formula (2).

[0104] For unit C itself: the distance d CC = 0, and the weight w CC = exp(0) = 1. This is the maximum weight, meaning that the original ablation rate of the unit itself accounts for the largest proportion in the smoothing calculation.

[0105] For neighbor unit B: the distance is d CB . Because d CB < R smooth , its weight w CB is a value greater than 0 and less than 1. The closer the distance, the greater the weight.

[0106] For neighbor unit D: the distance is dCD Similarly, its weight w CD is also a value greater than 0 and less than 1. From the figure, d CD d CB Therefore, the weight w CD w CB That is, the influence of D on C is smaller than B.

[0107] For cell A: Since A is not within the Gaussian circle of C, its weight on C is 0, and it does not participate in the smoothing calculation of C.

[0108] 3) Calculate the weighted average: Finally, the smoothed ablation rate of cell C is the weighted average of the original ablation rates of all cells in its neighborhood:

[0109] (4);

[0110] Where, v raw is the original ablation rate of each cell without smoothing.

[0111] The significance and advantages of the Gaussian weight function:

[0112] 1) Physical proximity effect simulation: In real physical processes, the ablation state of a point is affected by the state of its immediate vicinity, and the farther away, the smaller the influence. The "near large and far small" characteristics of the Gaussian function perfectly simulate the continuous decay effect of local influence.

[0113] 2) Ensure smooth transition: The Gaussian function is a smooth function of infinite order derivative. Using it as a weight function can ensure that the smoothed ablation rate field calculated is also smooth in space, making the simulated ablation surface topography more realistic.

[0114] 3) Preserve the dominant position of its own information: Since the weight of itself is always the maximum value 1, this algorithm ensures that the final rate of a cell is mainly determined by its own original rate while smoothing, avoiding the loss of physical information caused by excessive smoothing.

[0115] 4) Parameter adjustable, strong adaptability: By adjusting the smoothing half and weight decay factor, the strength and range of smoothing can be flexibly controlled. For example, for areas that need to capture small-scale features, the radius can be reduced; for areas that need to suppress oscillation, the radius or decay factor can be increased.

[0116] ​​In summary, the introduction of the distance-based Gaussian weight function is the key to the present application to achieve spatial smoothing of the ablation rate field, improve the physical authenticity and numerical stability of the simulation results.

[0117] Figures 3a~3f For specific calculation results, the calculation results at different times are given respectively. The analysis is as follows:

[0118] Ablation recession morphology analysis: From the series of cloud images (t=0s to t=120s), the whole ablation process shows the following key features:

[0119] Initial stage (t=0s~t=30s): heat is transmitted from the un-ablated semicircular top boundary, and the material is rapidly heated. The high temperature zone (red, about 1800-1900K) penetrates from the surface to the interior, forming an obvious temperature gradient. In this stage, ablation begins, and the top cells are gradually deleted, and the initial smooth semicircular profile begins to become irregular.

[0120] Development stage (t=30s~t=100s): ablation continues, and the head region is significantly "flattened". Since the heat flux is strongest at the stagnation point (center of the top), and gradually weakens on both sides, the ablation recession amount in the center is the largest, and that on both sides is smaller, which conforms to the general ablation law of the head of a high-speed vehicle. The temperature field also develops with the recession, and the high temperature zone is always concentrated near the newly formed ablation surface.

[0121] Late stage (t=100s~t=120s): ablation recession further develops, and even some depressions appear in the center of the head region, which is because the present application uses the minimum cell size as the criterion for whether a cell is deleted or not, which is related to the shape of the grid cell. L char Although there are sawteeth, these sawteeth do not develop into sharp and isolated "needles" or "valleys" indefinitely. At t=100s and t=120s, it can be seen that after some cells are deleted, their neighbors form steps, but the "envelope" of the whole ablation front is still relatively smooth, which is the effect of the Gaussian smoothing method.

[0122] In summary, from the overall morphology evolution, the simulation successfully reproduces the typical ablation process of a blunt body in a high-enthalpy gas flow: that is, gradually ablates from the initial shape, the center region recedes the fastest, and finally forms a relatively flat or slightly concave head.

[0123] The units described in the embodiments of the present application can be implemented by software or by hardware, and the described units can also be arranged in a processor. In some cases, the names of these units do not constitute a limitation on the units themselves.

[0124] According to an aspect of the embodiments of the present application, there is provided a computer program product or computer program, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method provided in various optional implementation manners described above.

[0125] As another aspect, the embodiments of the present application also provide a computer readable medium, which can be included in the electronic device described in the above embodiments, or can exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which, when executed by the electronic device, enable the electronic device to implement the method described in the above embodiments.

Claims

1. A Gaussian-weighted smoothing process-based ablation simulation and mesh processing method, characterized in that, The method comprises the following steps: S1, calculation and collection of original ablation rate; S2, after obtaining the original ablation rate of the boundary unit, spatial smoothing processing is performed based on a Gaussian weighting function; specifically including the following sub-steps: after obtaining the original ablation rate of all boundary units, for each unit i , a smoothed ablation rate is recalculated based on weighted average v smooth,i ; Wherein, the smoothing rate of the unit i is a weighted average of the original rate of itself and the surrounding adjacent units, and the weight depends on the distance between the adjacent units and the current unit; the smoothing ablation rate is recalculated based on the weighted average v smooth,i , specifically comprising the following sub-steps: For the unit i its smoothing rate v smooth,i by the following formula: （1）； Wherein: N i is a cell i a neighborhood set of cells, including the cell i itself and all other ablation boundary cells within a pre-set smoothing radius R smooth j ;​ v raw,j is a unit within the neighborhood j the original average ablation rate; w ij is a unit j The influence weight of the unit i is calculated using the following Gaussian function: （2）； Wherein: d ij is a unit i and a unit j the Euclidean distance between the geometric center points of the units β is an adjustable weight decay factor for controlling the decay speed of the weight with distance; When j = i Time, d ii = 0, the weight is 1, which ensures that the original rate of the unit itself occupies the highest proportion in the weighted average. S3, after completing the smoothing process, perform ablation depth accumulation and use criteria to mark the unit for deletion; specifically including the following sub-steps: using the calculated smoothed ablation rate v smooth,i to update the cumulative ablation depth for each cell D elem,i : （3）； D elem,i,new is the current ablation depth, D elem,i,old is the previous time step's ablation depth; is the time step size; Subsequently, the updated cumulative ablation depth is compared to a cell feature size L char,i If D elem,i ≥L char,i The cell i is marked for deletion; otherwise, the cell should be retained. S4, unit deletion and mesh topology update. 2.The Gaussian-weighted smoothing-based ablation simulation and mesh processing method of claim 1, wherein, In step S1, the calculation and collection of the original ablation rate specifically includes the sub-step of: for each cell located on the ablation boundary, calculating the average value of the ablation rates of all integral points on the cell, denoted as v raw,i ; wherein i is an index number of the unit; and traversing all the cells on the ablation boundary, their original average ablation rate v raw,i and geometric center coordinates C i gathered and stored. 3.The Gaussian-weighted smoothing-based ablation simulation and mesh processing method of claim 1, wherein, In step S4, the unit deletion and mesh topology update, specifically including the following sub-steps: After the marking is completed, the marked units are deleted, the newly exposed surfaces due to the deletion are updated as new ablation boundaries, and island detection and removal are performed to ensure mesh integrity.

4. The Gaussian-weighted smoothing process-based ablation simulation and mesh processing method of claim 3, wherein, The marked units are deleted, specifically including: The unit removal execution step: traversing the list to be removed, removing the specified units and unnecessary nodes for the mesh data structure, updating the connection relationship, physically deleting these units and their associated nodes from the data structure of the calculation mesh, and not changing the coordinates of the remaining nodes.

5. The Gaussian-weighted smoothing process-based ablation simulation and mesh processing method of claim 3, wherein, The newly exposed surfaces due to the deletion are updated as new ablation boundaries, specifically including: The adjacent boundary update step: traversing each unit in the list to be removed, finding all adjacent units; if one adjacent unit is not in the list to be removed, identifying the shared interface between the two units; mark the shared interface as a new boundary and assign it a preset boundary identifier.

6. The Gaussian-weighted smoothing process-based ablation simulation and mesh processing method of claim 3, wherein, The execution of island detection and removal, specifically including: Island unit detection and removal step: after performing unit removal, perform connectivity analysis; starting from the pre-defined stable boundary, use a graph search algorithm to traverse all active units that can be reached through unit adjacency relationships.

7. A Gaussian-weighted smoothing process-based ablation simulation and mesh processing system, characterized by, The computer device comprises a processor and a memory, and the memory stores a computer program which, when loaded by the processor, executes the method of any one of claims 1-6.

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