34 results about "Mesh adaptation" patented technology

The invention discloses an electromagnetic field simulation grid adaptive generation method based on a neural network. The method comprises the following steps: performing mesh generation on a semiconductor device simulation model to obtain original generation data, and constructing node features; an undirected graph is constructed, graph nodes of the undirected graph adopt node features of grid points, and connecting edges adopt Euclidean distances between the grid points and adjacent grid points; inputting the undirected graph into a double-branch neural network, and outputting a predicted node position; and correcting each node of the original subdivision data by using the predicted node position obtained by the double-branch neural network to form new subdivision data for electromagnetic field simulation. According to the method, physical field information can be fused to efficiently adjust the node density, and the geometric boundary and topological structure characteristics of the device can be strictly kept.

The invention discloses a micro-scale turbine blade boundary layer heat-flow double-field synchronous test method and system. The test method comprises the steps that temperature field initial data and velocity field initial data are acquired; based on the initial data of the temperature field and the initial data of the velocity field, the distribution state of the microparticles in the boundary layer is diagnosed, and according to the diagnosis result, the optimal distribution of the microparticles in the boundary layer is obtained by feeding back and controlling the operation parameters of the reverse cyclone separation system; based on the optimal distribution, an improved PIV algorithm is adopted to calculate a boundary layer velocity field, and the improved PIV algorithm is obtained by introducing multistage grid adaptive encryption, physical constraint conditions and a transition area recognition mechanism into the PIV algorithm; a boundary layer temperature field reconstruction model is established, depth phase coupling is carried out on a temperature field reconstruction result and a boundary layer velocity field, synchronous analysis of heat-flow coupling behaviors in a turbine blade boundary layer is achieved, and finally a boundary layer coupling cloud picture reflecting heat-flow interaction characteristics is obtained.

The embodiment of the invention discloses a space grid adaptive division method and device, equipment and a medium. According to the embodiment of the invention, the method comprises the steps: obtaining the multi-source heterogeneous three-dimensional data of a target region, carrying out the preprocessing of the multi-source heterogeneous three-dimensional data, and obtaining the vertex density of each vertex in the target region; according to the vertex coordinates of the vertexes, performing prediction based on a pre-trained environment factor continuous expression model to obtain multivariate environment factors corresponding to the vertexes; for any grid in the target area, determining whether the grid is a to-be-divided grid based on the vertex density of each vertex in the grid and the multivariate environment factor; and continuously dividing the grid to be divided based on the longest axis bisection strategy. Therefore, the vertex density and the environmental factors are integrated into the judgment mechanism of the grid to be divided, recursive division is performed through the judgment mechanism, so that a finer grid is obtained in a complex area or a high-risk environment, a thicker grid is maintained in a simple area or a low-risk environment, and non-uniform and differentiated adaptive division is realized.

This application discloses a spatial grid adaptive partitioning method, apparatus, device, and medium. The embodiment acquires multi-source heterogeneous 3D data of a target region and preprocesses the data to obtain the vertex density of each vertex within the target region. Based on the vertex coordinates, a pre-trained continuous environmental factor expression model predicts the multivariate environmental factors corresponding to each vertex. For any grid within the target region, based on the vertex density and multivariate environmental factors, it is determined whether the grid is to be partitioned. The grid to be partitioned is further partitioned based on the longest axis bisection strategy. Thus, by incorporating vertex density and environmental factors into the determination mechanism of the grid to be partitioned, and recursively partitioning through this mechanism, complex regions or high-risk environments obtain finer grids, while simple regions or low-risk environments maintain coarser grids, achieving non-uniform and differentiated adaptive partitioning.

The application discloses an intelligent dynamic grid self-adapting method and equipment for droplet pool simulation of additive manufacturing, and the method comprises the following steps: establishing a simulation model of additive manufacturing by using a pool simulation tool; extracting the simulation real-time gas phase fraction gradient, solid phase fraction gradient, solute concentration gradient and pool temperature gradient of each grid at each time step of simulation as the perceived physical parameters; adaptively calculating the encryption urgency index of each grid according to the perceived physical parameters; obtaining the historical encryption urgency index of each grid, calculating the second-order difference value, and predicting the change trend of the encryption urgency index of each grid at the future time; determining the encryption threshold and the coarsening threshold according to the encryption urgency index of all grids at the current time step, and combining the change trend of the encryption urgency index, the size relationship between the encryption urgency index and the threshold to determine whether the grid needs to be encrypted or coarsened; and iterating until the simulation is completed. The application has high precision, low cost and high automation degree.

This invention belongs to the field of numerical electromagnetic field solution, specifically a mesh adaptive method based on reconstructive posterior error estimation. The invention first performs finite element modeling of the target microwave component, introducing boundary conditions and excitations to establish a corresponding electromagnetic simulation model. Then, a tetrahedral mesh is used to divide the solution domain, and finite element electromagnetic simulation analysis is performed on the model to calculate the electric field at the centroid of the four faces of the tetrahedral mesh. The least squares principle is then used to refit the electric field on the tetrahedral mesh to reconstruct a first-order linear fitting expression. Next, the reconstructed solution at the nodes is calculated using the reconstructed fitting expression, and posterior error estimation is performed based on the electric field at the nodes. Finally, the mesh is refined according to the error on the tetrahedral mesh, and finite element electromagnetic simulation analysis is performed again until the finite element solution meets the accuracy requirements. This invention has the advantages of simple engineering implementation, low computational cost, and high universality.

The invention provides a numerical simulation method for fluid-structure interaction vibration of wind tunnel airflow and a tunnel wall structure, and belongs to the technical field of wind tunnels. Geometric parameters of a wind tunnel section are obtained, a multi-layer grid sequence is established, an implicit Runge-Kutta format and an explicit Newmak method are adopted to solve a flow field and a structure field respectively, and the wind tunnel airflow and tunnel wall structure fluid-structure interaction vibration is obtained. Mesh deformation is realized by using tightly-supported radial basis function interpolation, a volume conservation constraint is introduced, an interface projection matrix is constructed based on a virtual work equivalence principle to transmit interface data, a force balance and displacement coordination constraint is introduced, and coupling iteration convergence is accelerated through an inverse Broyden formula. A density gradient shock wave sensor is adopted to drive grid adaptive encryption and a mixed indicator function to control turbulence model switching, and the technical problems that wind tunnel section airflow and wall surface structure fluid-structure interaction vibration numerical simulation calculation is low in efficiency and insufficient in precision are solved.

The invention relates to the technical field of pipeline engineering, in particular to a pipeline real-time adjusting and optimizing system and method based on edge calculation and a dynamic evolution algorithm, five modules of distributed edge perception, position space prediction, real-time finite element analysis, multi-target optimization and layout adjustment are fused, firstly, an edge perception network collects multi-source data, and a unified tensor is generated; then, a position space prediction module predicts multi-time scale position space change based on tensor data by using a Shenchang differential equation; the real-time finite element analysis module receives prediction data, pipeline stress is calculated through a grid self-adaptive refinement technology, and hot spots are identified; the multi-objective optimization module adopts a dynamic variation differential evolution algorithm to generate a plurality of pipeline layout candidate schemes based on the hotspot data; the layout adjustment module performs multi-constraint conflict detection, adaptively adjusts candidate schemes and outputs a final pipeline layout; the system realizes intelligentization, precision and high efficiency of pipeline layout.

The application provides an improved sparse learning out-of-grid direction-of-arrival estimation method based on variational Bayesian. The method is characterized in that: the original data received by a hydrophone array is preprocessed, a real value transformation is used to convert a vectorized covariance matrix signal in a complex number field to a real number field, and the idea of variational sparse Bayesian learning and grid evolution is combined to make the grid evolve from an initial uniform grid to a non-uniform grid adaptively in an iteration process. The evolution process includes grid updating and grid fission. The evolved grid points are gradually close to the real source position through the alternately iterative grid updating process and grid fission process. Compared with the traditional compressed sensing method, the method has higher DOA estimation accuracy, reduces the operation complexity, optimizes the operation efficiency, improves the resolution capacity of the source, and has higher application value in actual engineering, especially in the case of few snapshots and low signal-to-noise ratio.

The present application relates to the technical field of computer aided design and real-time graphics computing, in particular to a grid adaptive subdivision method and real-time preview method for CAD modeling, comprising the following steps: sorting vertex coordinates of a model according to spatial positions, and constructing an adjacency relationship by using a half-edge structure for a grid data structure of the model; calculating a projection area of different grid subdivision regions in a view frustum as an importance weight ω, and distributing computing resources and arranging a subdivision order according to the importance weight ω. The present application quickly calculates the importance weight ω of the grid subdivision region, arranges computing resources for the subdivision of the subdivision region with a higher preview priority first, improves the dynamic adaptability of the grid subdivision, reasonably distributes the computing resources, and improves the computing efficiency.

The invention discloses a broadband scattering simulation method based on grid adaptive encryption, and belongs to the technical field of electromagnetic simulation. According to the method, independent self-adaptive grid encryption is carried out only at the lowest frequency point of the frequency band. For other frequency points in a frequency band, optimization grids are not independently generated any more, and optimization is guided by using error information at adjacent frequencies. And then, by adopting a Cauchy method, on the basis of frequency sampling points obtained by solving on the optimization grid, reconstructing response data between adjacent sampling points, thereby efficiently and accurately obtaining broadband electromagnetic response. According to the method disclosed by the invention, the grid at each frequency point can be efficiently optimized without carrying out independent adaptive optimization at each selected frequency point like a traditional adaptive encryption method, and meanwhile, the advantage that the adaptive encryption realizes higher precision with less unknown quantity is kept; the complex frequency band response can be efficiently and accurately reconstructed by using the Cauchy method.

This invention discloses a method, apparatus, and storage medium for establishing ACM weld point models. It automatically detects and deletes target weld points through scripts or program instructions, determines the size of the weld nugget refinement mesh based on the set weld nugget diameter, obtains modeling information and corresponding finite element models of non-coincident weld points, and automatically locates and adaptively updates the refinement area of ​​the connector based on the weld nugget refinement mesh and the modeling information of non-coincident weld points. It traverses all target weld points to be updated, completing the refinement of the weld point connector mesh and the batch automatic generation of new ACM weld points. This method ensures the accuracy of the ACM weld point model and improves modeling efficiency.

The application discloses a neural network-based electromagnetic field simulation grid self-adaptive generation method. The application performs grid partitioning on a semiconductor device simulation model, obtains original partitioning data, and constructs node features; a non-directed graph is constructed, the graph nodes of which adopt the node features of the grid points, and the connecting edges adopt the Euclidean distances between the grid points and adjacent grid points; the non-directed graph is input into a double-branch neural network, and a predicted node position is output; each node of the original partitioning data is corrected by using the predicted node position obtained by the double-branch neural network, and new partitioning data is formed to perform electromagnetic field simulation. The method can not only efficiently adjust the node density by fusing physical field information, but also strictly maintain the geometric boundary and topological structure characteristics of the device.

The application belongs to the technical field of three-dimensional electromagnetic radiation numerical solution, and relates to an electromagnetic radiation simulation analysis method based on local grid self-adaptive division. Firstly, the application carries out finite element modeling on a target antenna, introduces PML boundary and wave port excitation to establish a corresponding finite element calculation domain model, and adopts tetrahedral grid division to classify the calculation domain model; then finite element electromagnetic radiation simulation analysis is carried out to obtain finite element solution, and error estimation and encryption strategy are used to give the encryption size of the grid nodes in the non-PML region; finally, the grid is encrypted, finite element electromagnetic radiation simulation analysis is carried out again, and the process is repeated until the overall solution error is less than the set error limit, the final calculation grid is obtained, and finite element electromagnetic radiation simulation analysis is carried out. The application only encrypts the tetrahedral grid in the non-PML calculation domain, ensures the original characteristics of the tetrahedral grid in the PML calculation domain, and realizes the rapid simulation design of the antenna radiation characteristics while ensuring the calculation accuracy.

The application discloses a kind of multi-source data fusion's travel safety monitoring method and system, it is related to safety monitoring technical field, including, real-time acquisition multi-source heterogeneous data, and pre-processing is carried out, structured data stream is generated, constructs initial digital twin, based on the change of structured data stream, trigger adaptive grid reconstruction, obtain dynamic updated digital twin model, through dynamic updated digital twin model, using graph neural network constructs microcosmic, medium and macro three-level coupling risk transmission network, carries out cross spatio-temporal scale risk analysis, generates risk transmission analysis result, simulates N kinds of intervention schemes in virtual intervention sandbox based on risk transmission analysis result, obtains N kinds of simulation results, selects optimal intervention scheme by reinforcement learning algorithm, and sends to physical equipment and implements optimal intervention scheme;The application realizes the high-fidelity mapping of scenic spot physics and dynamic state by grid adaptive mechanism, supports real-time crowd distribution, optimizes computing resource allocation.

The invention relates to the technical field of motor electromagnetic field simulation, and discloses a permanent magnet synchronous motor electromagnetic field finite element rapid simulation calculation method. The method comprises the steps that a three-dimensional model is constructed, material attributes are loaded, and an initial grid is generated; after the electromagnetic field equation is solved, space gradient information of field distribution is extracted so as to accurately identify a magnetic field violent change area and a smooth area; dynamically adjusting the grid density according to the identification result, encrypting the key area, and keeping the non-key area sparse; and recalculating and evaluating the error based on the optimized grid, and performing loop iteration until the result meets the preset tolerance. According to the method, the gradient information is used for directly driving grid self-adaption, accurate distribution of computing resources is achieved, the problem that the efficiency is low due to the fact that a traditional method depends on experience or mathematical residual errors is solved, and the simulation speed is increased while the computing precision is guaranteed.

This invention discloses a finite element method, storage medium, and computer program product for the microstructural evolution of rock masses based on material heterogeneity mapping. The method includes data acquisition; parameter inversion; mesh construction; coordinate registration; weighted statistics; assigning equivalent parameters to elements; adaptive mesh refinement in high-gradient regions; model initialization and solution: initial field loading and boundary condition definition are completed, and elastoplastic or multi-field coupled calculations are performed. The advantage of this invention is that it improves the predictive ability of the finite element model for the non-uniform deformation and microstructural evolution of rock masses.

The invention relates to a metal casting process optimization method, and relates to the technical field of metal casting process optimization, 3D design software is used for constructing a casting three-dimensional model and a mold model, MAGMASOFT software is used for setting process parameters, the process parameters comprise temperature parameters, time parameters and cooling conditions, corresponding thermophysical parameters of a built-in material library of the MAGMASOFT software are called, and the temperature parameters, the time parameters and the cooling conditions of the casting three-dimensional model and the mold model of the casting three-dimensional model are calculated. MAGMASOFT software is operated to carry out multi-physics field coupling simulation, TAG grid adaptive subdivision is adopted, potential defect types and areas are identified, a temperature field cloud picture and solidification animation are generated, the defect types comprise shrinkage cavities, shrinkage porosity, air holes and hot cracks, technological parameters are adjusted, simulation is carried out again, a DOE experimental design method and genetic algorithm iteration are adopted to carry out technological parameter adjustment, and the temperature field cloud picture and solidification animation are obtained. And finally, the required process conditions are met. According to the technical scheme, the casting simulation precision is improved, defects in actual production are reduced, and the production efficiency is improved by optimizing process parameters, reducing the production cost and shortening the process development period.

The invention discloses an unstructured grid adaptive node splitting method for complex form three-dimensional modeling, which relates to the technical field of three-dimensional modeling and grid generation, and comprises the following steps: constructing an initial unstructured grid, and adopting a Delaunay triangulation generation unit to ensure that a covered modeling area has no overlapping gap; key features such as sharp corners and edges are extracted, geometric parameters are calculated, and feature priorities are divided; establishing a splitting judgment system based on a multi-dimensional index, and marking a to-be-split unit; determining split node coordinates, splitting according to unit types, and updating a topological relation; optimizing the grid quality through a smoothing algorithm, and deleting invalid units; and calculating the modeling precision and the grid quality, and if the convergence standard is not reached, repeating the process until the standard is reached. According to the method, complex morphological characteristics are accurately captured, a splitting strategy is adaptively adjusted, and node coordinates are highly fitted with boundaries; error accumulation is effectively controlled, numerical simulation and visualization requirements are met, and reliable support is provided for complex modeling in multiple fields.

The application discloses a sealing performance evaluation method and device of a sealing structure, and relates to the technical field of liquid rocket engine research, to solve the problem of poor sealing performance evaluation accuracy of the sealing structure in the prior art. The method comprises the following steps: obtaining static stress load data of a target sealing flange structure in a target liquid rocket engine; based on the static stress load data, determining wear parameters between a soft plating layer and a flange contact surface in the target sealing flange structure by using a soft plating layer friction and wear analysis model; the soft plating layer friction and wear analysis model is an analysis model obtained after at least grid ALE self-adaptation and setting of a grid node motion control mode are performed on a grid corresponding to the soft plating layer of the target sealing flange structure in a two-dimensional axisymmetric finite element model; and based on the wear parameters, evaluating the sealing performance of the target sealing flange structure; thereby the accuracy of evaluating the sealing performance of the sealing structure is improved.

This invention provides a method, apparatus, and device for generating anti-counterfeiting graphic mosaic backgrounds, relating to the field of image processing technology. The method includes: acquiring an original image and at least one graphic unit object; generating a set of directional guiding curves based on the original image; arranging the at least one graphic unit object to generate a graphic object group; aligning the original image, the directional guiding curve group, and the graphic object group to obtain an overlapping image; determining the corresponding point of each graphic unit object in the graphic object group on the vector curve in the directional guiding curve group within the overlapping image; rotating each graphic unit object according to the tangent direction vector of the corresponding point relative to the vector curve to obtain a rotated graphic unit object; and filling the rotated graphic unit object with color to obtain a target graphic mosaic background object. This invention combines directional curve guidance with adaptive grid rotation to generate a novel anti-counterfeiting mosaic background, improving both anti-counterfeiting performance and visual effect.

The embodiment of the application discloses a method for improving the efficiency and precision of three-dimensional high-precision flow field analysis, relates to the technical field of three-dimensional grid self-adaptive technology and deep learning technology for computational fluid dynamics. The method can efficiently generate high-quality three-dimensional self-adaptive grid with node density distribution consistent with flow field structure, thereby enhancing the accuracy and efficiency of flow field numerical calculation. The application comprises: a server receiving initial three-dimensional grid data and flow field condition parameters sent by a client; generating initial three-dimensional flow field data based on the flow field condition parameters, and generating corresponding three-dimensional self-adaptive grid by using the data; establishing a sample database according to the three-dimensional initial flow field data and the self-adaptive grid data; training a deep learning model by using the sample database; inputting to-be-predicted flow field condition parameters into the trained deep learning model, predicting the three-dimensional self-adaptive grid, generating high-quality three-dimensional grid consistent with the to-be-predicted flow field structure, and sending the high-quality three-dimensional grid to the client.

The application provides a kind of multi-level deep learning neural network adaptive global deformation visual measurement method and system, comprising: step S1: the optical axis of camera and lens is vertically aligned with sample surface, and the information of sample under different conditions is recorded;Step S2: based on multi-level deep learning neural network, the shape function is generated by global gridding in the region of interest in image, so as to realize global deformation measurement;Step S3: taking zero mean normalized minimum distance square standard as loss function, using Adam to optimize the parameters and node displacement of neural network, weight and deviation are the function of node position, and the grid is self-adapting by optimizing the parameters of neural network;Step S4: according to the optimization result, the global displacement field and strain field are derived, and the high-precision adaptive global deformation measurement is realized.The application can overcome the problem of incompatible displacement field in traditional method, so as to improve the precision of visual deformation measurement.

The application discloses a kind of unstructured grid adaptive mesoscale mixing parameterization method and system, the method includes: in the ocean model of unstructured grid, the sea surface horizontal density gradient defined on the edge is calculated, after the sea surface horizontal density gradient is corrected according to effective resolution and frontal scale, the sea surface horizontal buoyancy gradient is calculated again;Then, the sea surface horizontal Ekman buoyancy flux is calculated according to the sea surface horizontal buoyancy gradient and the sea surface wind stress;If the sea surface horizontal Ekman buoyancy flux is positive, then the mixing coefficient in the boundary layer is further calculated.The application can effectively introduce the influence of symmetric instability process on boundary layer mixing in global unstructured grid ocean model, and significantly improve the simulation effect of mixed layer depth under coarse resolution.

The invention discloses a sound source localization method and system based on a deep learning model, and the method comprises the steps: constructing a multi-sound-source random distribution scene, and collecting a multi-sound-source signal through a 16-channel microphone array; processing the multi-sound-source signal by using short-time Fourier transform to generate a time-frequency domain characteristic pattern; generating a sound field distribution cloud picture including environmental noise by using a beam imaging method; based on the actual coordinates and the sound pressure intensity of the multi-sound-source signal, generating a corresponding ideal sound source distribution cloud picture by using a meshless adaptive high-resolution imaging method, and taking the cloud picture as a target true value; constructing a positioning label based on the actual coordinates and the sound pressure intensity of the multi-sound-source signal; and inputting the sound field distribution cloud picture including the environmental noise into the trained improved deep learning model to obtain a predicted sound source position and sound pressure intensity, and completing positioning of the sound source. According to the method, the sound source localization precision and robustness in a complex environment can be remarkably improved, and the high-performance sound source localization requirement is met.

The invention relates to the technical field of finite element analysis, in particular to a finite element analysis model construction method for an intelligent in-place equipment prototype, which comprises the following steps: constructing a finite element model comprising the intelligent in-place equipment prototype, equipment and a positioning mechanism, applying a wind load, and sequentially dividing the whole positioning simulation process into a plurality of stages; for any stage, a grid adaptive iteration process is executed, and the process comprises the steps of calculating a positioning error component of a grid in each stage, adjusting the size of the grid in the stage to re-divide the grid, continuously iterating until the positioning error component of the grid in the stage meets the precision requirement, and calculating the positioning error component of the grid in the stage on the basis of the re-divided grid in the stage. Entering simulation of the next stage; and obtaining a final positioning error in the whole positioning simulation process, and evaluating the overall positioning performance of the intelligent in-place equipment prototype. The design reliability and the final in-place precision of the intelligent in-place equipment can be improved.

The application discloses a kind of three-dimensional electromagnetic potential forward modeling self-adapting finite element method, comprising the following steps: S1, using Coulomb specification to convert Maxwell equation group into the control equation of scalar potential and vector potential;S2, the gradient term of the control equation in introducing Lagrange multiplier, the solving region is discretized, and linear equation set of finite element is constructed;S3, the linear equation set of finite element is solved;S4, using the error generated by current density discontinuity as posteriori error estimator, dual weighted posteriori error estimator is added, and target grid needing refinement is determined;S5, using eight sub-refinement strategy to encrypt the target grid;S6, using the grid after encryption carries out finite element forward modeling calculation.The application can enhance the stability in the solving process of finite element system equation, and also can improve the calculation efficiency by reducing the calculation amount of additional system equation caused by grid self-adapting.

The application discloses a path-preserving curved-side P2 grid adaptive generation method, device and equipment and a medium, and belongs to the technical field of numerical simulation. The method comprises the following steps: constructing a Riemann metric field for controlling P2 interpolation error according to high-order derivatives of a physical field function; generating boundary points and internal points based on the metric field, and recording the generation path between each internal point and the generation source point; performing constrained Delaunay triangulation with the generation path as a constraint edge to generate an initial straight-side grid; and bending the straight side into a P2 curved side by solving a constrained optimization problem, wherein the optimization target is to minimize the geodesic line length of the curved side, and the constraint condition is to ensure that the unit shape is effective after bending. Through the technical closed loop of the physical driving metric field, the topological path keeping and the geometric effective curved side optimization, the application solves the problems of insufficient resolution in anisotropic regions and easy failure of curved side units, and improves the accuracy and stability of high-order numerical simulation.

The invention relates to the technical field of rock damage simulation, in particular to a rock crack propagation simulation method under the action of pulse fracturing, which comprises the following steps: introducing a phase field variable, and constructing a phase field method model; establishing a friction damage physical mechanism based on a rate state dependent friction law and a sliding law; introducing a friction damage physical mechanism into the phase field method model, establishing a coupling relationship between a friction state variable and the phase field method model, and constructing a phase field-friction damage coupling control equation set; constructing a rock loading model under the action of pulse fracturing; solving a phase field-friction damage coupling control equation set by adopting an adaptive alternating iteration algorithm introducing a dynamic local grid; a friction damage physical mechanism is introduced into a phase field method model, a rock crack network under the action of pulse fracturing can be simulated more truly and accurately, a dynamic local grid adaptive method is adopted, and waste of computing resources is reduced while rock crack propagation simulation precision is guaranteed.

The invention discloses a joint simulation prediction analysis method for the fatigue life of an axial flow fan. The method comprises three core steps of rotor system modeling, embedded wear iterative simulation and coupling life prediction. Firstly, a three-dimensional model containing a potential wear area is established, and pretreatment is completed; then, an Archard wear model is integrated in ABAQUS software through a UMESHMOTION subprogram, and dynamic wear iterative simulation is achieved through the ALE grid self-adaption technology; finally, stress-strain data obtained after abrasion are imported into Fesafe software, accumulated fatigue damage is calculated by combining a material S-N curve and a load spectrum, and life prediction is achieved. The method breaks through the limitation of a traditional method for analyzing fatigue and abrasion in a split mode, considers the coupling effect of the fatigue and the abrasion and the dynamic accumulation characteristic of the abrasion, improves the accuracy of life prediction through refined modeling and automatic data transmission, can provide a basis for rotor structure optimization, material selection and maintenance period formulation, and has a wide application prospect. The method is suitable for active reliability design in the axial flow fan design stage.