71 results about "Macroscopic model" patented technology

The invention relates to a method for establishing and optimizing a multi-scale prediction model for constitutive parameters of a fiber reinforced composite material. Comprising the following steps: constructing a fiber-matrix microscopic RVE model, applying a boundary condition and a load, determining a failure criterion, and calculating equivalent mechanical parameters of a fiber bundle; a fiber bundle-matrix mesoscopic RVE model is generated, fiber bundle equivalent mechanical parameters serve as material attributes of fiber bundles in the mesoscopic RVE, a viscoelastic model is adopted for a matrix, and fiber reinforced material unit cell equivalent mechanical parameters are calculated; constructing a macroscopic fiber reinforced composite material finite element model, endowing a macroscopic model with unit cell equivalent mechanical parameters, defining an orthotropic material card, simulating an explosion impact process by adopting an explicit dynamics algorithm, outputting structural displacement, a damage area and stress distribution, and comparing calculation and test data. And correcting the microscopic RVE model and the mesoscopic RVE model through reverse optimization iteration to obtain a final macroscopic prediction model. The explosion-proof performance of the fiber composite structure is remarkably improved.

The invention discloses a mechanical property prediction method of a composite material based on an improved sparrow algorithm-random forest, and belongs to the technical field of composite material property prediction, and the method comprises the following steps: carrying out finite element multi-scale simulation on a carbon fiber composite material to obtain an elastic constant matrix; connecting the elastic constant matrix material to a macroscopic model for macroscopic simulation to obtain sample data; a random forest RF model is constructed in Matlab software; an improved sparrow search algorithm is introduced into a random forest RF model to construct a CFSSA-SF prediction model; training a CFSSA-SF prediction model by using the training set data; and inputting test set data into the CFSSA-SF prediction model to obtain a prediction result. According to the method, the finite element method is adopted to carry out multi-scale modeling, the improved sparrow algorithm is combined with the random forest to construct the fusion CFSSA-RF prediction model, and the mechanical properties of the carbon fiber composite material under different parameters are effectively predicted.

The invention discloses an airborne infrared-visible double light combined building outer wall hollowing identification and positioning method. The method comprises the following steps: designing flight parameters of an unmanned aerial vehicle; designing a calibration checkerboard; shooting an infrared thermal image and a visible light image of the checkerboard calibration target, recording the images as dual-light images for calibration, and calibrating and calculating internal and external parameters and a distortion coefficient of a camera; collecting images of all external facades of the target building; carrying out distortion removal processing on the dual-light image to form a dual-light image database for detection; the registration between the dual-light images synchronously shot by the unmanned aerial vehicle is realized; realizing registration between the building facade view and the visible light image; determining a correct hollowing area identification result, and marking the infrared thermal image corresponding to the correct hollowing area identification result as a hollowing infrared thermal image; calculating a pixel coordinate range of the hollowing area; and accurate positioning of the hollowing area is realized. According to the method, the visible light image serves as a medium, registration of the infrared thermal image and the macroscopic 3D model is achieved, the problem that calibration and registration are difficult due to the fact that the imaging quality of the infrared thermal image is low is solved, and high readability and user friendliness of an output result are guaranteed.

The invention discloses a load data determination method and device, equipment and a storage medium, and the method comprises the steps: obtaining initial parameter data of a steel skeleton light plate, and carrying out the data processing of the initial parameter data, and obtaining target parameter data; according to the target parameter data, establishing a macroscopic model and a microscopic model for the steel skeleton light plate through a finite element network; and performing coupling iteration processing on the macroscopic model and the microscopic model, and when a coupling ending condition is met, obtaining target load data based on the macroscopic model and the microscopic model after coupling. According to the technical scheme, safe, reliable and accurate model construction analysis and numerical simulation are realized.

The invention relates to multi-scale modeling and mechanical and thermodynamic property simulation of a particle reinforced biological composite material, and belongs to the crossing field of biology, material science and computer simulation. The method specifically comprises the following steps: constructing a polyether-ether-ketone (PEEK) high-molecular polymer and a graphene reinforced composite material model thereof through a cross-linking algorithm under a microscopic scale, and simulating and calculating the mechanical and thermodynamic properties of the model by adopting molecular dynamics; in the microscopic scale, constructing a representative volume element model of the microstructure artificial skeleton, inputting property parameters calculated by molecular dynamics into a microscopic model, and calculating microscopic mechanical and thermodynamic properties of the model by adopting a substance point method; and inputting the property parameters obtained under the microscale into the macroscopic model, and calculating the overall macromechanical and thermodynamic behavior response of the material by adopting a finite element method. According to the method, mechanical and thermodynamic responses of the biological composite material are finally simulated through transmission of property parameters of different scales, and theoretical support is provided for research and formula design of the particle reinforced biological composite material.

The invention discloses a physical information enhanced space-time diagram network trusted computing method for traffic flow anomaly prediction, and the method comprises the steps: building an LWR model physical constraint through an embedded traffic flow LWR equation, correcting critical density and free flow speed parameters through a Kruithof curve, and improving the flow density of an LWR model; carrying out road discretization and establishing a macroscopic CTM model; carrying out dynamic weight fusion on a prediction result of the macroscopic CTM model and a correction factor generated by the microscopic IDM model, and calculating a fusion result; constructing a space-time diagram convolution network model and a loss function, constructing a space diagram convolution module and a time sequence convolution module, and extracting a space feature matrix and a time sequence feature matrix; fusing and updating the spatio-temporal characteristics by using a gating circulation unit; performing dynamic anomaly detection and hierarchical response; a TEE is deployed in a traffic network sensing device, and evidence storage is carried out through a block chain. According to the invention, the traffic flow change is predicted in real time, the abnormal fluctuation in the traffic flow is identified, and the traffic flow prediction accuracy is improved.

When a sliding main bearing of a wind driven generator is under a heavy-load working condition, a lubricating oil film in a starting stage is not enough to form a complete fluid lubricating film, namely, a friction surface forms a boundary lubrication state, and friction in the stage seriously affects the service life of the bearing. In this way, a macroscopic model and a microscopic model are coupled, and a multi-scale model is verified through experiments. The macroscopic model is established based on an actual structure of a wind power main shaft sliding bearing, and is equivalent to a three-layer structure of a rigid bearing base, an elastic surface supporting layer and a main shaft; a three-layer microcosmic molecular model representing'main shaft-lubricating oil-elastic supporting layer 'is established by the microcosmic model facing the boundary lubrication state of a friction pair. Wherein the thickness of the supporting layer is changed by the macroscopic model, the components of the supporting layer material are changed by the microscopic model, the friction coefficient of the tribological interface under different supporting layer thicknesses and material components is analyzed through the macro-micro cross-scale analysis model, and the optimal thickness and the optimal material component proportion suitable for the service condition of the wind power main shaft sliding bearing are obtained.

The invention provides an aircraft tire shoulder heat generation optimization method and system based on interlayer pixel-level modeling, and relates to the technical field of aircraft tires, and the method comprises the steps: outputting an interlayer pixel-level model; extracting basic parameters of a tire shoulder area; tire shoulder heat generation simulation is executed, and a tire shoulder area temperature distribution cloud picture is output; positioning the highest temperature extreme value and the coordinate position of the high-temperature accumulation area; taking minimization of the maximum temperature extreme value as a heat generation temperature optimization target, taking a condition that the area of a high-temperature accumulation area is smaller than 15% of the total area of a tire shoulder area as a heat generation range constraint condition, and setting a parameter adjustment vector array of basic parameters of the tire shoulder area; executing particle swarm iterative optimization, and outputting tire shoulder optimization correlation parameters; and outputting the thermodynamic optimization parameters of the tire shoulder. According to the method, the technical problem that the durability and the critical speed of the tire are finally affected due to the fact that a macroscopic model is mostly adopted for calculation in an aviation tire shoulder heat generation optimization method in the prior art, and a detailed physical process between different hierarchical structures is ignored is solved.

The invention relates to the field of composite material prediction based on a specific model, and discloses a mechanical property simulation method for simulating defects of a carbon fiber composite material based on Monte Carlo-finite element, which comprises the following steps: constructing a carbon fiber composite material RVE model in Monte Carlo software, setting an irradiation physical reaction process, and carrying out neutron irradiation analogue simulation. The method comprises the following steps: obtaining internal void defect information of a material, constructing a finite element RVE model containing void defects in finite element software, obtaining an elastic constant matrix of the whole RVE model, constructing a macroscopic tensile finite element model, carrying out material parameter setting and simulation boundary condition setting on the macroscopic model, and carrying out macroscopic tensile simulation to obtain simulation mechanical data. According to the method, the problems of high cost and long time of an irradiation experiment and a mechanical experiment are solved, a technical basis is provided for reliability evaluation research of the carbon fiber composite material in a nuclear radiation scene, and a large amount of time and economic cost are saved.

A ceramic matrix composite micro-hole drilling quality and material removal mechanism analysis method comprises the following steps: determining a damage model and performance parameters of carbon fibers, a damage model and performance parameters of a SiC matrix, and a damage model and material attributes of an interface layer according to ceramic matrix composite micro-holes with the diameter phi of 0.5-2mm; a drill bit model and a ceramic matrix composite macroscopic model are established in simulation software, macroscopic finite element simulation is carried out, and a macroscopic finite element simulation result is obtained; a drill bit model and a ceramic matrix composite microscopic model are established in simulation software, microscopic finite element simulation is carried out, and a microscopic finite element simulation result is obtained. Carrying out a drilling test to obtain a test result; and comparing the test result, the macroscopic finite element simulation result and the microscopic finite element simulation result, and verifying the effectiveness of the macroscopic finite element simulation result and the microscopic finite element simulation result.

The invention discloses a cluster-CPU-GPU heterogeneous parallel method for solving response of a large-scale anisotropic material composition structure, and belongs to the technical field of high-performance calculation simulation. The method comprises the following steps: firstly, establishing a macroscopic structure and a microstructure, dividing a finite element mesh for a macroscopic model, dividing a calculation region by using a region decomposition algorithm, and realizing macroscopic model unit stiffness matrix and displacement calculation through OpenMP acceleration; secondly, reconstructing the model at the Gaussian point of the macroscopic model, partitioning by means of the MPI technology, calculating the displacement of the microscopic model by using multiple threads in each process, and accelerating the calculation of a stiffness matrix of a microscopic model unit by using OpenMP to realize cluster MPI parallel calculation; during post-processing, visualization is realized by utilizing GPU multi-core advantage calculation macroscopic model stress and strain, and cross-scale heterogeneous parallel simulation is achieved. The method is combined with related algorithms, can efficiently utilize resources to solve the analog simulation problem, and is wide in application prospect.

The present invention relates to the technical field of geomagnetic activities, and in particular to an auroral substorm simulation method and macroscopic model based on neutral atom imaging measurement. The method includes the following steps: establishing a neutral atom imaging simulation equation for dynamic evolution of ion flux distribution of a ring current guided by ion pitch angle diffusion; and using the neutral atom imaging simulation equation, forward modeling neutral atom simulation images in an ecliptic plane corresponding to different ion pitch angle distribution functions during a geomagnetically quiet period and during an auroral substorm period to reproduce ring current ion flux distribution patterns in a growth phase stage and a recovery phase stage of an auroral substorm process. The present invention can emulate and reproduce ring current ion flux distribution patterns in a growth phase stage and a recovery phase stage of an auroral substorm process, which lays a foundation for further research.

The invention discloses a biphase titanium alloy hot working deformation prediction method based on finite element simulation, and belongs to the technical field of titanium alloy hot working, a Voronoi mosaic algorithm is utilized to generate a volume element RVE containing an alpha phase and a beta phase in Digimate, and grid division is carried out based on Abaqus. An HCP crystal plastic model is adopted as an alpha-phase ontology model, and a BCC crystal plastic model or a J2 plastic model is adopted as a beta-phase ontology model. A cohesion unit is embedded in an alpha-phase interface and a beta-phase interface, and a temperature-dependent critical fracture energy degradation function is defined; the thermal activation energy is dynamically corrected, and the dislocation motion threshold value is dynamically adjusted. And finally, mapping the RVE response to the macroscopic model, and outputting simulation results of a stress-strain field, damage distribution and microstructure evolution. According to the method, the anisotropic deformation prediction precision of the double-phase titanium alloy is remarkably improved, the interface cracking prediction accuracy is improved by 30% or above, and the engineering adaptability is high.

The invention discloses a mine earthquake risk main control factor analysis method based on rock stratum macro-microscopic numerical simulation. The method comprises the following steps: firstly, collecting a rock sample and testing to obtain microscopic rock mechanical parameters; secondly, constructing a standard sample model based on discrete element numerical analysis software, and performing heterogeneity parameter assignment on the standard sample model by utilizing a Weibull function in combination with fractal dimensions and mesoscopic rock mechanical parameters, so as to realize accurate matching of rock mass mesoscopic-macroscopic mechanical properties; the mesoscopic rock mechanical parameters are converted into macroscopic rock mechanical parameters for discrete element analysis according to the conversion relation between the rock quality indexes and the rock strength, and high-fidelity reconstruction of a discrete element macroscopic model is achieved; and subsequently performing an orthogonal test and range analysis to quantify the macroscopic heterogeneous main control factors, thereby realizing priority ranking of each macroscopic heterogeneous main control factor as a key disaster-causing factor. Simulation can be carried out under the condition of considering the rock stratum heterogeneity, the result fits the actual rock stratum condition, and data support is provided for subsequent mine earthquake risk prevention and control treatment on the rock stratum.

The invention discloses a variable-density bone porous prosthesis design method based on bone density distribution, and relates to the technical field of biomedical engineering.The variable-density bone porous prosthesis design method comprises the following steps of S01, parametric modeling of unit cells, S02, pretreatment of thighbone CT data, S03, mechanical testing and mechanical property grading of porous materials, S04, establishment of a porous thighbone prosthesis model, and S05, design of a variable-density bone porous prosthesis model. According to the variable-density bone porous prosthesis design method based on bone mineral density distribution, the real bone mineral density form is simulated based on CT data of bones, parametric design is adopted in the method, quantitative research is facilitated, and micro design and then filling to a macroscopic model are utilized, that is, design is started from a minimum unit cell structure unit, and then design is started from a second unit cell structure unit to a third unit cell structure unit to a fourth unit cell structure unit to a fifth unit cell structure unit; on the basis of the bone density distribution condition of a three-dimensional CT value, the designed unit cell structures with different porosities are filled into three-dimensional bone CT data, the bone prosthesis designed through the method can be matched with the elastic modulus of surrounding bones, and a good bionic microenvironment can be provided for growth of bone tissue.

The invention relates to a preparation method of a near-infrared luminous AgPd super-atomic cluster and application of the near-infrared luminous AgPd super-atomic cluster in 3D printing, and belongs to the crossing field of coordination chemistry and nano materials. The chemical formula of the AgPd super-atomic cluster is C216H150Ag14F30P10PdS6 (abbreviated as Ag14Pd), the AgPd super-atomic cluster belongs to a hexagonal crystal system, and the space group is a chiral space group R3 () c. The cluster has strong near-infrared luminescence at room temperature, wherein the optimal emission wavelength in a solid state is 780 nm; the optimal emission wavelength in a solution state is 768nm; the maximum light-emitting quantum yield in a solid state reaches up to 15%. In addition, the super-atomic cluster also shows excellent thermal stability. The cluster has high optical activity and solubility at room temperature, can be used as ink for 3D printing to print macroscopic models of various shapes and sizes, and shows that the near-infrared luminous cluster material has potential application prospects in photoelectric devices.

The present invention discloses a method for controlling a variable speed limit for mixed traffic in a near-signal area based on a macro model, comprising the following steps: dividing the road sections in the near-signal area by function and length; periodically feeding back the traffic flow information of each road section to vehicles within the control area; constructing a variable speed limit model for mixed traffic in the near-signal area based on the macro model; determining the objective function and constraints according to the control objective, and designing a method for controlling a variable speed limit for mixed traffic based on the macro model; and controlling the vehicles in each controlled road section within a set time interval to execute a speed adjustment process according to the calculated optimal speed. The present invention provides a variable speed limit control method for effectively controlling mixed vehicle groups in the near-signal area under new mixed traffic conditions, providing a new approach to alleviating the adverse effects of periodic regulation of traffic lights and lane changes of vehicles in the near-signal area.

The invention provides a geological unit attribute characterization method and system based on multi-scale cascade fusion, and belongs to the technical field of geotechnical engineering. The method comprises the steps that seismic wave data and sound wave data of a target area are acquired and preprocessed; performing parameter consistency conversion on the seismic wave data and the sound wave data by using the scale conversion coefficient; dividing the target area into grids of different scales based on the geological attribute change rate; on the basis of the divided grids, the converted seismic wave data and the converted sound wave data are input into a cascade fusion model, the converted seismic wave data serve as the basis of a macroscopic model, the sound wave data serve as local fine constraints, and attribute fusion of the seismic wave data and the sound wave data is achieved through a multi-scale coupling algorithm; and carrying out attribute prediction by using an attribute prediction algorithm to obtain a geological unit attribute representation graph. The synergistic effect of macroscopic and microscopic scales is effectively realized, and the problems of insufficient seismic wave and drilling sound wave data fusion and insufficient precision in the prior art are solved.

The application relates to the technical field of energy storage safety, and discloses a multi-scale multi-physical-field energy storage emergency space twin modeling method, which comprises the following steps: presetting macro low-dimensional and micro high-dimensional models in an edge computing device, measuring residual computing power, setting a concurrent threshold, calculating space-time gradient characteristics by using a macro model based on annular buffer data to mark a candidate region, calculating a thermodynamic urgency index, sorting the candidate region, and screening out an activated region, calling historical data to drive a micro model to perform super-real-time operation, completing internal state initialization, establishing a heterogeneous model physical boundary bidirectional coupling by using flux impedance matching, outputting a fault evolution prediction result, and generating a fixed-point emergency control instruction. By means of thermodynamic urgency scheduling and boundary coupling technology, the application solves the high-fidelity simulation problem under the condition of limited computing power on the edge side, and improves the prediction accuracy and response speed of the multi-scale multi-physical-field energy storage emergency space twin modeling.

The invention provides an asphalt pavement damage multi-scale model establishment method considering dynamic-static-water combined action and application. The multi-scale model establishment method comprises the following steps: determining dynamic-static-water combined action comprehensive damage factors through test analysis of water damage characteristics of asphalt mortar and asphalt mixtures; the method comprises the following steps of: establishing a mesomechanics model which comprises a water damage viscoelasticity constitutive model of the asphalt mortar, a still water damage evolution process model and a cohesion model for describing cohesion and cohesion damage; and establishing a macromechanical model, wherein the macromechanical model comprises a water damage viscoelastic constitutive model, a still water damage evolution process model and a dynamic water damage evolution process model of the asphalt mixture. Respectively establishing an asphalt pavement RVE model and a macroscopic model by adopting finite elements, writing a viscoelastic constitutive model into a UMAT subprogram, and carrying out macroscopic and microscopic water damage model numerical calculation under the combined action of dynamic water and static water; and connecting macro and micro numerical models through a homogenization method to realize water damage prediction from micro characteristics to macro performance.

The application provides a geological unit attribute characterization method and system based on multi-scale cascade fusion, and belongs to the technical field of geotechnical engineering. The method comprises the following steps: acquiring seismic wave data and acoustic wave data of a target region and performing pretreatment; performing parameter consistency conversion on the seismic wave data and the acoustic wave data by using a scale conversion coefficient; dividing the target region into grids of different scales based on a geological attribute change rate; inputting the converted seismic wave data and acoustic wave data into a cascade fusion model based on the divided grids; taking the converted seismic wave data as the basis of a macro model and taking the acoustic wave data as local fine constraints; and realizing attribute fusion of the seismic wave data and the acoustic wave data by using a multi-scale coupling algorithm; and performing attribute prediction by using an attribute prediction algorithm to obtain a geological unit attribute characterization map. The method effectively realizes the synergistic effect of macro and micro scales and solves the problems of insufficient fusion and insufficient precision of seismic wave and borehole acoustic wave data in the prior art.

The application discloses a load data determination method and device, equipment and a storage medium, comprising: obtaining initial parameter data of a steel skeleton light plate, and performing data processing on the initial parameter data to obtain target parameter data; according to the target parameter data, a macro model and a micro model for the steel skeleton light plate are established through a finite element network; the macro model and the micro model are coupled and iteratively processed, and when a coupling end condition is met, target load data is obtained based on the coupled macro model and micro model. The above technical solution realizes safe, reliable and accurate model construction analysis and numerical simulation.

The invention belongs to the field of material science and engineering, and discloses a full-automatic modeling method and system for a macro-micro multi-scale model based on a real microstructure, which adopts a fine EBSD data processing technology, not only can accurately obtain the orientation, grain boundary, size and other information of grains, but also can accurately obtain the size of the grains by effectively filtering the extremely small grains, thereby improving the modeling efficiency of the macro-micro multi-scale model based on the real microstructure. The overall quality of the data is improved; according to the method, through accurate data input, it is ensured that the microstructure can be fully reflected in the macroscopic model; according to the method, the EBSD data and the ABAQUS finite element simulation are effectively integrated, and the close association between the macro scale and the micro scale is successfully established. On this basis, the change of the microstructure can be tracked in real time and transmitted to the macroscopic stress-strain response model, and it is ensured that stress distribution and plastic behaviors of the material under different load conditions are described more accurately.

The invention discloses a ceramic matrix composite tenon feature structure uniaxial tensile failure simulation analysis method, and belongs to the field of CMC structure tensile mechanical property and failure mechanism analysis. The method specifically comprises the steps that firstly, a fiber-matrix longitudinal stretching mesoscopic model corresponding to a CMC system is established in ABAQUS, the random failure strength of fibers and matrixes is introduced into a VUSDFLD subprogram, and the bilinear characteristic material constitutive of the CMC in the fiber direction is fitted; secondly, establishing a macroscopic model of a CMC tenon feature structure, and adding a glue layer to simulate an internal interface of the tenon; compiling a VUMAT subprogram to dynamically update a reduction stiffness matrix of the macroscopic model based on the constitutive of the bilinear characteristic material and a self-defined damage state variable; and the strength of a continuous triangular area, where pore defects are easily formed, of the inclined surface of the tenon insertion layer group is weakened. And finally, different failure modes are obtained by changing performance parameters of the tenon interface, and a failure mechanism is analyzed. According to the method, the material constitutive of the microscopic model is transmitted to the macroscopic model, and the structural damage starting and evolutionary process is simulated.

The invention relates to the technical field of intelligent traffic, and particularly discloses a traffic flow prediction system based on trip chain dynamic evolution. The system comprises a trip chain real-time sensing and reconstruction module, a trip decision behavior dynamic deduction module, a group trip demand evolution calculation module and a macroscopic traffic flow dynamic prediction module which are connected in sequence. Through real-time sensing and reconstruction of an individual trip chain, individual behaviors are dynamically deduced, group demands are simulated and aggregated based on an intelligent agent, and a macroscopic model is driven to carry out prediction. The system constructs a complete technical closed loop from microscopic individual behavior real-time perception to macroscopic road network flow dynamic prediction. According to the system, through cooperation of the travel decision behavior dynamic deduction module and the group travel demand evolution calculation module, fine simulation of a group travel behavior emergence effect under an emergency situation is realized.

The invention relates to the technical field of geotechnical engineering, in particular to an analysis method and device for the plugging effect of a grouting material, and the method comprises the steps: collecting a whole grouting process image of a cement-based material in a micron-sized crack or pipeline; carrying out error elimination and binarization processing on the image; extracting edge features from the processed image to segment a plurality of particles which have adhesion and are not agglomerated, and determining the particles; determining particles of which the areas are greater than a preset agglomerated particle threshold value, and determining and marking the agglomerated particles to generate an image quantitative index of the plugging process; and generating an analysis result of the plugging effect of the grouting material according to the evolution curve corresponding to the image quantification index in the plugging process. Therefore, the problems that the micro change process of the particles is difficult to record in real time and the spatial distribution and change rule of the particles cannot be comprehensively revealed due to the fact that related technologies are mainly based on macroscopic model tests or analysis of final-state slurry consolidated bodies, and consequently grouting material plugging effect prediction and construction reliability are affected are solved.

The application discloses a kind of coupling molecular dynamics first principle's gas cooled reactor cladding surface particle adsorption calculation method, steps are as follows: (1) based on computational fluid dynamics and particle discrete phase model establishes numerical simulation framework, including cladding, break, flow channel modeling, grid drawing, boundary condition setting;(2) according to the first principle of molecular dynamics, the binding surface energy of fission product molecules released into the gas gap and the cladding material molecular base is calculated;(3) establish the particle deposition model including particle's own attribute, effective modulus, relaxation time and surface energy;(4) the binding surface energy is implanted into the particle deposition model, and the particle deposition model is used as the wall boundary, and the gas-solid two-way coupling calculation of particle deposition is carried out.The method solves the problem that traditional macroscopic model depends on empirical parameters, realizes high-precision simulation based on physical mechanism, reveals the mechanism of micro-interface properties on macroscopic deposition behavior, and predicts complex deposition morphology.

The invention discloses a super-large-scale high-performance three-dimensional rock-fill dam multi-scale numerical simulation method, and belongs to the technical field of rock-fill dam numerical calculation, and the method comprises the steps: building a stress-strain coupling relation between a macroscopic model and a mesoscopic model, and obtaining a three-dimensional rock-fill dam multi-scale model; after a load is applied to the macroscopic model, carrying out multi-scale coupling operation on the multi-scale model based on a stress-strain coupling relationship, and in the process of carrying out the multi-scale coupling operation on the multi-scale model, decoupling the time step length of the macroscopic model and the time step length of each RVE model in the mesoscopic model, the time step length of the macroscopic model is greater than that of each RVE model in the mesoscopic model; and updating the macroscopic model according to an operation result of the multi-scale coupling operation to realize three-dimensional rock-fill dam multi-scale numerical simulation. According to the method, the time spent on multi-scale numerical simulation of the rock-fill dam can be remarkably shortened.

This invention belongs to the field of metal smelting technology and relates to a method for predicting alloy element segregation in large-scale titanium alloy VAR ingots. The method includes: acquiring VAR furnace structure data; establishing a macroscopic FE model based on the VAR furnace structure data to calculate the ingot temperature field distribution; acquiring microscopic CA grids and state parameters; establishing a microscopic CA model using the CA grids and state parameters to calculate the microscopic solute concentration distribution; mapping the ingot temperature field distribution to the microscopic CA model for FE-CA cross-scale coupling to generate an FE-CA cross-scale coupled model for simulating the VAR process under different process parameter combinations, and generating an optimal process parameter combination with the objective that the maximum segregation degree of the target element should not be less than a preset threshold. This invention simulates the solidification process under different process conditions to predict the occurrence and development of compositional segregation in large-scale VAR titanium alloy ingots, thereby reducing segregation.

The invention discloses a novel buffer layer material evaluation method for solving the problem of buffer layer ablation, and belongs to the field of electrical engineering. The method comprises the following steps: acquiring a two-dimensional fault slice of a buffer layer material by adopting CT scanning, and reconstructing a microscopic model reflecting a real pore and fiber structure through a volume data method through AI artifact removal, filtering and ternary processing; discretizing the model into a finite element mesh, optimizing the finite element mesh, and embedding the finite element mesh into a cable macroscopic model to construct a macro-micro coupling three-dimensional finite element model; accelerating simulation by adopting local grid refinement, parallel calculation, model order reduction or a reasonable selection solution method, and calculating electric field and current distribution in an electricity-heat-force multi-physics field coupling environment; and by taking the polyester non-woven fiber water-blocking tape as a reference, comparing the ablation resistance of the novel buffer material, and judging whether the ablation resistance of the novel material reaches the standard or not. According to the method, quantitative evaluation from a real microstructure to system performance is realized, and the method has the advantages of high precision, good efficiency and high engineering applicability.