54 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 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.

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 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 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 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.

The invention discloses a composite material aero-engine large-size blade multi-scale solidification analysis method, and relates to the field of aero-engine composite material part manufacturing, and the method comprises the steps: importing a pre-configured composite material aero-engine blade model into finite element analysis software, and carrying out mesh generation; giving parameters to the homogenized composite material, performing transient heat transfer analysis, and defining a temperature boundary condition, a third-class heat boundary condition and a heat generation rate of a boundary node to obtain a macroscopic model; performing blade curing degree field solving and thermal coupling analysis on the macroscopic model to obtain a temperature curve, a curing degree curve and residual stress of the macroscopic model, and embedding a representative volume unit in a macroscopic high-stress area; and constructing a representative volume element model and defining a fiber and resin material constitutive model. The method depends on finite element simulation and algorithm optimization, the number of physical experiments is greatly reduced, the research and development cost is reduced, and the development efficiency is improved.

This invention discloses a layered design method for snow surface overlay on ice-structured load-bearing structures, relating to the field of ice and snow engineering technology. The method includes the following steps: S1, determining the design constraints and performance indicators of the snow surface overlay based on target working conditions and environmental data; S2, obtaining the microstructural parameters of the snow material to be laid, including initial porosity, ice crystal size distribution, coordination number, and contact geometry. This invention establishes a cross-scale mechanical performance evolution model to accurately capture the microscopic mechanisms of ice crystal sintering, particle creep, and fatigue damage of snow material under long-term load and temperature, solving the problem of performance prediction deviations caused by the inability of existing macroscopic models to reflect microscopic evolution. By constructing a quantitative mapping relationship between microstructural parameters and macroscopic mechanical properties, combined with thermo-coupling load history simulation, accurate performance evolution data of the overlay can be obtained.

The present invention relates to the technical field of molding of components made from fiber fabric-reinforced resin matrix composite materials, and specifically relates to a simplified optimization method for an efficient molding process of a high-quality component made from a fiber fabric-reinforced resin matrix composite material. In the present invention, a finite element is used to perform a thermo-chemical analysis of a macroscopic model and a thermo-mechanical analysis of a microscopic model of a component, achieving simulation of the temperature gradient, curing degree, and residual stress of the component in a molding process; then, a genetic algorithm is used to simultaneously minimize the temperature gradient, molding time, and residual stress as optimization objectives, with a curing process curve as a parameter variable, to perform multi-objective optimization of molding process parameters, and obtain the efficient molding process of the high-quality component. The method of the present invention can reduce the temperature gradient and residual stress of the component in the molding process, shorten the molding time, and scientifically and effectively reduce the manufacturing cost while improving the quality of the component, and is more effective and simpler.

The present invention provides a macro- and micro-analysis method for solid propellant failure processes, comprising the following steps: 1) hierarchically dividing a particle-filled composite material from a macroscopic model to a microscopic model, and then superimposing the constitutive response from the microscopic model onto the macroscopic model, thereby achieving cross-scale constitutive modeling of the particle-filled composite material from the microscopic to the macroscopic scale; and 2) mapping the mesoscopic model structure to constitutive parameters, and from sub-model constitutive parameters to intermediate sub-model constitutive parameters, using an artificial neural network model. This method is well-conceived and can describe the influence of the local microstructure of the particle-filled composite material on its macroscopic mechanical properties, achieving a unified macroscopic and microscopic analysis, significantly improving the efficiency of macro- and micro-analysis of solid propellant failure processes.

An optimizing method for a molding process of a part made of a fiber-reinforced polymer composite is provided. A finite element method is adopted to perform thermochemical analysis of a macroscopic model of the part and thermomechanics analysis of a microscopic model of the part, so as to simulate temperature gradient, curing degree, and residual stress of the part during the molding process. A genetic algorithm is executed, with simultaneous minimization of temperature gradient, molding time, and residual stress as an optimization goal and a curing process curve as a parameter variable, to achieve the simultaneous multi-objective optimization of molding process parameters, so as to obtain an optimized molding process.

The invention provides an evaluation method for simulating gas storage capacity of hydrate based on molecular dynamics. The method comprises the following steps: 1, establishing a model to research microcosmic nanopores, 2, extending a porous structure model to a micron scale, and 3, establishing a unified evaluation model for gas storage capacity in a multi-scale pore environment. The invention aims to solve the limitation of the existing experimental method and macroscopic model in gas storage capacity evaluation. By constructing a molecular dynamics simulation system and combining different conditions, a porous structure model is extended to a micron scale, and quantitative analysis is performed on pore network complexity and migration and lattice stability of gas molecules in the hydrate, so that evaluation on the gas storage capacity of the hydrate is realized.

The present invention belongs to the technical field of material welding process, and specifically relates to a CET transformation simulation method in a lightweight magnesium alloy welding pool for automobiles based on phase field dynamics and its application. The simulation method comprises the following steps: (1) initializing the simulation conditions, initializing the model, and constructing the model according to the parameters of the welding pool solidification process; (2) constructing a macro model, assuming that the solidification area of ​​the welding pool is one-quarter of the ellipse, and establishing a macro heat transfer and growth model based on the assumption; (3) constructing a phase field model, establishing a phase field model that simulates the CET transformation; (4) calculating the phase field model, introducing the dendrite growth rate into the phase field model, introducing the nucleation model, solving, and calculating the macro-micro coupled phase field model. The present invention can dynamically reproduce the change process of columnar crystals and equiaxed crystals during welding, which helps to deepen the understanding of the evolution process of alloy welding microstructure and lays the foundation for the evolution of microstructure and optimization of welding process.

The application discloses a kind of rock-soil body permeability test methods, specifically related to rock-soil body material permeability detection technical field, for solving the problem that the sensitivity of traditional method is insufficient when testing high-performance concrete and bedrock interface due to low permeability and micro-pore structure of material, and the applicability deviation problem of macroscopic model;It is by constructing sealed test environment under keeping in situ stress state, applying multistage incremental fluid pressure step to stimulate interface micro-pore flow response, monitoring transient seepage signal and analyzing the nonlinear growth law of the decay rate required by initial signal amplitude to reach stability, combined with waveform form and the matching identification of preset typical seepage mode and form deviation quantification, finally based on multi-parameter fusion generates the equivalent permeability of interface to realize accurate and reliable permeability evaluation.

The application discloses a metal sealing structure leakage prediction method and system, first, a macroscopic service finite element model is constructed according to the structure and service parameters of the sealing structure, the service deformation behavior is simulated, the model is based on the rotation symmetry characteristic of the sealing structure, the axisymmetric shell model is adopted in the finite element software, the cross section is simplified, the grid is divided and the flange movement is set. Then, a microscopic crystal plastic model with a rough surface is constructed based on the surface roughness, the model construction and grid processing are carried out by considering the material microstructure and characteristics. The deformation amount of the contact position is extracted from the macroscopic model as the boundary condition of the microscopic model to simulate the microscopic deformation. Finally, the distance between the rough surface mean line and the flange surface distance is determined from the microscopic model as the gap height, and the internal and external pressure difference in the service of the sealing structure is combined to calculate the leakage amount of the sealing structure. In this way, the simulation result is more close to the actual situation by combining the macroscopic and microscopic models, and the leakage prediction precision and efficiency of the sealing structure are improved.