31 results about "Particle field" patented technology

The invention discloses a direct coal liquefaction pump thermal wear prediction method considering a high temperature effect, and belongs to the technical field of fluid machinery and reliability engineering.The method comprises the steps that the high temperature effect is considered, a liquid phase control equation and a solid particle dynamic model are subjected to coupling iteration, and a solid-liquid two-phase flow numerical model is obtained; the method comprises the following steps: performing full flow field modeling on a fluid domain formed by flow passage components of a pump for direct coal liquefaction, and performing grid division on the fluid domain; constructing a heat-flow-solid multi-field coupling calculation system, and solving the solid-liquid two-phase flow numerical model by adopting a large eddy simulation method based on temperature correction and a discrete element method coupling method to obtain information of flow fields and particle fields at all positions in the pump; operation condition parameters are obtained, the thermal wear rate is obtained through a thermal wear rate calculation formula on the basis of flow field and particle field information of all positions in the pump, and the predicted wear amount of the flow passage component is obtained; and the prediction precision and applicability of the wear model are improved.

This invention discloses a particle morphology recognition method based on YOLOv7 and interferometric imaging technology. The method uses an interferometric particle imaging system to obtain interferometric defocused images of a mixed particle field to create a dataset. The annotated interferometric defocused images are divided into training, validation, and test sets. The network is then improved by incorporating a collaborative attention module to enhance detection accuracy. Hyperparameters are adjusted, and training and testing are performed using the annotated dataset. After 200 epochs of training, the optimal weights are obtained, ultimately achieving mixed particle field morphology recognition. This method can achieve high-precision identification of particle morphology in mixed particle fields, providing technical support for acquiring cloud particle field information.

The invention discloses a particle surface coating behavior process simulation method and device, equipment and a medium, and the method comprises the steps: determining current airflow field information according to historical particle field information and historical airflow field information of a limited three-dimensional space at a previous moment; in the process of determining the current particle field information corresponding to the current cycle period according to the current airflow field information and the historical particle field information, simulating the surface adhesion behavior process of the reaction particles on the base material particles by determining at least one adhesion reaction particle corresponding to the base material particles; according to the method, deposition reaction particles are determined from at least one adhesion reaction particle based on the current adhesion duration of the at least one adhesion reaction particle, and the surface deposition behavior process of the reaction particles on substrate particles is simulated. And the accuracy and universality of particle deposition calculation are improved.

The application discloses a kind of unmanned aerial vehicle granular field uniformity detection device and detection method, belong to agricultural engineering and farmland detection technical field.The device includes detection device body, optical fiber sensing module, embedded PCB circuit board.The detection method is laid out and horizontally calibrated device, optical fiber sensing module collects the light signal variation of granular interruption detection beam, signal processing, effective counting obtains the number of particles per unit time, combined with single particle average mass conversion real-time spreading amount;Host computer collects multiple sampling point data to calculate the variation coefficient of spreading amount, compares preset threshold to determine uniformity grade, and accordingly guides unmanned aerial vehicle operation parameter adjustment.The application realizes real-time accurate detection of spreading uniformity, has strong environmental adaptability, is simple and easy to operate, effectively improves the intelligentization and high-efficiency level of field spreading monitoring, and is suitable for various agricultural unmanned aerial vehicle granular spreading operation scene.

The application discloses a kind of SPH steady flow processing method and related equipment, method includes the input entrance pressure data is converted into entrance speed data;Entrance speed data is based on setting virtual particle;And the data information of fluid particle is updated by the interaction between virtual particle and the fluid particle to be processed, and fluid particle data is judged based on setting parameter threshold, and the particle field information of output is obtained.The application embodiment can be converted into entrance speed data by the entrance pressure data, and virtual particle is set to simulate fluid particle, reduce the speed shock and data noise caused by particle simulation calculation based on entrance pressure, reduce the accumulation of entrance particle simultaneously, improve the stability of entrance boundary, so as to improve the convergence speed and processing efficiency of steady flow simulation.The application can be widely applied in computer simulation and data processing technical field.

The invention relates to a particle class manifold balance characterization method and device based on multi-view sparseness quantification. The method comprises the following steps: constructing and training a multi-view sparseness quantification network and a class manifold balance network; in the multi-view sparseness quantization stage, a three-dimensional digital holographic particle field obtained by combining angular spectrum method holographic reproduction with frequency domain high-pass filtering background field removal is mapped along a Z view angle, and coordinates of a target area containing existing particles are obtained through a main network and two auxiliary networks; in the accurate characterization stage, region data are cut out from the three-dimensional digital holographic particle field data according to the target region coordinates obtained in the multi-view sparseness quantification stage, feature extraction is carried out through a manifold-like balance network, and final particle three-dimensional morphological characterization and focusing center coordinates are obtained. Under the condition that holographic particle field imaging data quality is poor and real-time performance is required, detection and segmentation of the particles can be rapidly completed, space coordinates and a focusing layer are determined, and information such as the shape and the size of the particles is obtained.

The invention relates to an additive manufacturing laser-powder coupling process simulation method considering the real form of powder, and belongs to the technical field of additive manufacturing process simulation. Comprising the following steps of real form modeling, pneumatic behavior resolving, ray tracing space mapping and special-shaped coupling calculation. The core of the method is to construct a coupling framework for accurate transmission and calculation of non-spherical geometrical characteristics among a flow field, a particle field and a light field, and the problem of inaccurate calculation of powder flow characteristics, laser energy attenuation, powder energy absorption and the like caused by excessive simplification in existing directional energy deposition numerical simulation is solved.

The application discloses a kind of irregular particle shape reconstruction methods based on phase recovery, builds interference particle imaging system;In defocus image plane obtains the defocus speckle image I of irregular particle to be measured M ; The defocus speckle image I M It is sheared, and the speckle region image I located in the center position and the size of N×N after shearing is retained;Speckle region image I is input into phase recovery algorithm model and iterative calculation is carried out, and preliminary reconstruction result is obtained;Proportion factor factor SF is set, and the preliminary reconstruction result of the above iteration, i.e. new particle estimate p k+1 (x0,y0) is scaled with proportion factor SF, and final reconstruction particle shape is obtained.Compared with prior art, the application can directly obtain accurate shape information of irregular particle through particle interference defocus image, without additional optical path setting, and the device is simple, which provides basis for the measurement of complex particle field.

The invention discloses a particle field reconstruction model training method, a particle field reconstruction method, a device and a medium, and the method comprises the steps: pre-training a network model, employing a training data set to carry out the pre-training of the network model, and the training data set of the network model is a data sample of a particle light intensity distribution-light field image; the input of the network model is a light field image, and the output of the network model is a particle light intensity distribution predicted value; network model fine tuning: performing fine tuning on network model parameters obtained by pre-training; the fine tuning method comprises the following steps: carrying out light field imaging calculation on a particle light intensity distribution predicted value output by the network model to obtain a calculated light field image; and calculating a deviation between the original light field image and the calculated light field image by using a loss function, setting training times to enable the loss function to tend to a stable value, and converging the model to obtain a fine-tuned particle light intensity distribution neural network model. The model provided by the invention effectively improves the reconstruction quality of the three-dimensional particle field.

The present application relates to the technical field of aerospace target characteristic analysis, and particularly relates to a high-altitude multi-particle-size particle field radiation rapid analysis method, device, equipment and medium. The method comprises the following steps: S1, determining the initial multi-particle-size number density of the outlet section based on the engine outlet parameters and the particle size distribution model; S2, determining the total particle density of the external flow field based on the law of conservation of mass, then determining the multi-particle-size number density at each position, and calculating the temperature attenuation caused by the radiation heat dissipation of particles of different particle sizes respectively to obtain the temperature field; S3, analyzing and determining the infrared radiation characteristics of the high-altitude plume based on the obtained number density and temperature distribution. The present application introduces the particle size distribution model and differentiates the temperature evolution, improves the analysis accuracy, replaces the complex CFD simulation with the analytical / semi-analytical model based on the physical law, greatly improves the calculation efficiency, and is suitable for the rapid evaluation and scheme iteration of the infrared characteristics of the high-altitude plume in engineering.

This invention discloses a method for 3D reconstruction of coaxial digital holographic particle fields combining structural tensors and guided filtering. Specifically, the method involves: normalizing the coaxial digital hologram and performing multi-distance numerical reconstruction to obtain a 3D reconstruction intensity stack; constructing a focus response using structural tensors and generating spatially consistent fusion weights through guided filtering, fusing them to obtain an extended depth-of-field image and a particle soft depth map; constructing a candidate score map based on the extended depth-of-field image to achieve candidate localization, adhesion separation, and diffraction ring suppression; and finely focusing and segmenting the measured particle size within the depth neighborhood, ultimately outputting the particle's 3D coordinates and size parameters. This invention aims to utilize the effective characterization capability of structural tensors for focusing features and the edge-preserving properties of guided filtering to construct a fusion method for multi-depth particle field reconstruction results. This method enhances particle target features while suppressing artifacts and background interference, improving particle localization and measurement accuracy, and achieving higher-precision 3D reconstruction of coaxial digital holographic particle fields.

The present invention relates to a method for generating an analog spectrum of a tissue equivalent proportional counter and its application. By collecting the data measured by tissue equivalent proportional counters in the environments of the International Space Station, monoenergetic particles, γ fields, and neutron fields published at home and abroad, and processing the collected data to form a basic spectrum, an analog spectrum that satisfies the measurement distribution law of tissue equivalent proportional counters in the above different radiation fields is generated on the basis of the basic spectrum, and the quality factor Q, absorbed dose D, and dose equivalent H values are obtained from this spectrum, which can be applied to calibrating the hardware measurement system and signal processing module of the tissue equivalent proportional counter. The tissue equivalent proportional counter calibration method disclosed by the present invention has the advantages of high calibration efficiency, real-time calibration, and wide application range.

The invention discloses a particle position prediction method and system in a particle field based on a deep learning network, and the method comprises the steps: obtaining a particle field hologram, and marking the particle position; constructing a data set according to the particle field hologram of the known particle position; dividing the data set into a training set, a verification set and a test set; inputting the training set into the deep learning network, training the network, verifying the trained network by using the verification set, and storing the trained network; inputting the test set into the trained network to obtain the coordinate position and confidence of each particle; wherein the deep learning network is realized through improved YOLOV5s; according to the improved YOLOV5s, a multi-spectrum channel attention module is added to a backbone network of an original YOLOV5s network, and the multi-spectrum channel attention module is used for improving the capturing capability of the network on particle stripes.

The invention relates to the technical field of 3D printing, in particular to a CFD-DEM coupling-based wind field structure optimization method and system for a metal 3D printer, and the method comprises the steps: carrying out the three-dimensional modeling based on the characteristics of an SLM printer, and then carrying out the wind field numerical simulation of a three-dimensional model, and obtaining the boundary grid and flow field simulation data of the three-dimensional model; performing particle field simulation on the three-dimensional model to obtain particle field simulation data of the metal powder in the printing process; performing coupling simulation on the flow field simulation data and the particle field simulation data to obtain coupling simulation data of the metal powder under the influence of the flow field; and adjusting the three-dimensional model of the SLM printer based on the coupling simulation data, and returning to perform wind field numerical simulation on the three-dimensional model until the coupling simulation data reaches the target condition, thereby obtaining target coupling simulation data and a target three-dimensional model. Accurate, efficient and intelligent design of the wind field system is achieved, and finally the printing quality, the equipment reliability and the process economy are improved.

The present invention relates to a method for determining the safety factor of a slope applicable to smoothed particle hydrodynamics, belonging to the technical field of slope stability analysis. Based on the geometric parameters of the slope height and slope angle, the present invention establishes a numerical calculation model of the slope; determines the mechanical parameters of the slope rock and soil materials through mechanical tests, and constructs a strength attenuation strain softening model; uses the linked list search method to search and pair the domain particles of the slope numerical calculation model; solves the particle stress and strain according to the strength attenuation strain softening model; performs stress adjustment according to the particle stress and strain state; calculates the particle acceleration according to the adjusted particle stress, and updates the particle field variables; wherein the particle field variables include velocity, density and displacement; selects displacement characteristic points, and judges the slope stability according to the triple criterion. If the criterion is satisfied, the safety factor is output to evaluate the overall stability state of the slope; otherwise, it enters the automatic reduction program and continues to iteratively solve the slope stability until the criterion requirements are met.

This invention relates to a continuous three-dimensional particle field reconstruction method based on single-light-field imaging using EM deconvolution, belonging to the field of optical imaging technology. This method employs a light field camera to capture light field images of a discretized three-dimensional flow field, acquiring the light intensity information of scattered light emitted by particles on each pixel of a CCD detector. Based on the light intensity information, a weight matrix for each voxel is calculated, and the entire weight matrix is ​​loaded only once. The nth frame of the light field image is loaded, and the non-zero pixels in the nth frame are identified. The non-zero pixel sequence formed by the scattered light from tracer particles is extracted. An EM deconvolution algorithm is used to iteratively calculate the non-zero pixel sequence, updating the intensity of each voxel, thereby reconstructing the three-dimensional particle field of a single frame. The above steps are repeated to complete the reconstruction of the three-dimensional particle field from multiple frames of high temporal resolution light field images, resulting in higher reconstruction accuracy and consistency, and less time consumption.

The present application relates to a kind of methods for reconstructing the trajectory of particle field motion based on event camera, laser beam is passed through moving particle fluid domain, and the motion of particle field is recorded using event camera.Event stream data photographed by event camera is imported into event stream data reconstruction system, and the relationship between relevant event points is obtained and reconstructed using trained artificial intelligence algorithm.Local feature recognition algorithm is used to the original event stream data for local feature recognition, and the event set generated by the motion of the object is obtained in accordance with the motion characteristics of specific object.Thereby, the local area original event stream point set constituting the whole particle field event stream data and the trajectory line event set generated in accordance with the particle motion characteristics are obtained.The present application reconstructs the trajectory of particle field using autocorrelation or cross-correlation technology to obtain flow velocity field.In addition, particle field detection also has wide application in industrial detection, and the moving particle field can be obtained by detecting the trajectory of particle motion using the method shown in the present application.

The present invention discloses a post-processing method for reconstructing a particle field by tomographic particle image velocimetry, comprising the following steps: (1) obtaining a reconstructed particle field of a flow field to be measured; (2) performing a first deconvolution operation on the reconstructed particle field to obtain a first predicted particle field; (3) performing an anisotropic convolution kernel estimation based on the first predicted particle field and the reconstructed particle field; (4) performing a deconvolution operation on the reconstructed particle field using the anisotropic convolution kernel obtained by the latest estimation to obtain a second predicted particle field; (5) performing an anisotropic convolution kernel estimation based on the second predicted particle field and the reconstructed particle field; (6) repeating steps (4) and (5) until an iteration stop condition is met; (7) performing a deconvolution operation on the reconstructed particle field using the anisotropic convolution kernel estimated in step (6) to obtain a repaired particle field. Compared with the prior art, the present invention has the characteristics of effectively restoring the spherical shape of the original particles and significantly suppressing ghost particles in the reconstructed particle field.

This invention provides an underwater 3D reconstruction method based on adaptive constraints of dual-physics fields for spatial signals, comprising: acquiring multi-view RGB image data using underwater image acquisition equipment; establishing 3D Gaussian particles for coordinate points on an initial point cloud; and performing parameterization processing on the 3D Gaussian particles in Cartesian coordinate space to obtain parameter information such as their geometric center, 3D covariance matrix, normal vector, opacity, and color; establishing a variable-space Gaussian particle field to represent and reconstruct the scene based on the variable-space Gaussian particles at infinity in the far-viewing distance space; establishing a Gaussian frequency signal filtering field for near-viewing distance, performing frequency modeling and filtering constraints on Gaussian particles within the near-viewing distance to eliminate 3D Gaussian particle aliasing artifacts and achieve clear reconstruction of near-viewing distance targets in the underwater environment; and establishing an adaptive Gaussian particle opacity optimization model to eliminate redundant distribution of Gaussian particles in the near-viewing distance scattering region and compensate for sparse distribution of Gaussian particles at the far-viewing distance by observing the distance constraints of 3D Gaussian particles through variable-space parameters.

The invention provides a calculation method for particle motion effect and performance evaluation of a pneumatic conveying system, and belongs to the field of pneumatic conveying systems.The calculation method comprises the following steps that S1, a CFD data model of the pneumatic conveying system is established; s2, performing grid division on the CFD data model obtained in the step S1 to obtain a CFD grid model of the pneumatic conveying system; s3, performing flow field simulation on the CFD grid model of the pneumatic conveying system; s4, performing flow field simulation on the subsystems; s5, performing particle field simulation on the subsystems; s6, judging a particle motion effect of the subsystem, judging whether a set target is met or not according to a particle field statistical result, and if the set target is met, ending simulation; and if the set purpose is not met, the three-dimensional model of the pneumatic conveying system is adjusted, and the steps are repeated. The method has the beneficial effects that the development period of new parts is greatly shortened, the effect of the parts of the designed pneumatic conveying system is judged in the initial stage of design, and the cost is greatly reduced.

The invention discloses a deep learning-based turbid water body holographic turbulence field three-dimensional reconstruction method and system, and relates to the technical field of underwater optical imaging, and the method comprises the steps: irradiating a turbid water body through a holographic light path, and driving a camera to capture a transient holographic interferogram sequence; denoising by adopting a noise suppression model, and outputting an optimized hologram; positioning particles through a cascade convolutional neural network to generate an initial plane coordinate set; calculating the axial depth of the particles to obtain a transient space coordinate set; space-time diagram association is executed, and a three-dimensional particle field dynamic sequence is generated; and executing flow field inversion to generate a three-dimensional transient turbulence velocity field. The technical problem that a traditional image-based three-dimensional reconstruction method is difficult to overcome the defects of strong holographic image noise and particle signal distortion in a turbid water body scene is solved, interference caused by turbid water body scattering is effectively resisted, key data needed by turbulent flow field three-dimensional reconstruction are comprehensively and accurately obtained, and the reconstruction efficiency is improved. Therefore, the technical effect of three-dimensional reconstruction of the turbulent flow field of the turbid water body is accurately realized.

The invention discloses a solid-containing oil-gas-water conveying calculation method, and belongs to the field of petroleum engineering. The invention aims to solve the problem that a traditional calculation method is difficult to comprehensively consider respective physical characteristics of oil-gas-water three phases, complex interphase interface action and influence of particles on overall flow. The method comprises the following steps: step 1, determining large-scale analytic particles and small-scale semi-analytic particles in the multiphase flow according to a preset fluid domain grid size; 2, constructing a multiphase flow-particle coupling numerical model based on a particle classification result in the step 1, and distinguishing a control equation of a fluid phase and two types of particle phases; 3, configuring relevant parameters of fluid attributes and particle parameters based on the coupling numerical model; 4, on the basis of the relevant parameters, independent solution and inter-phase coupling effect calculation of the fluid field and the particle field are achieved through a two-way coupling algorithm, and calculation result data are obtained; and 5, outputting a simulation result based on the calculation result data.

This system identification device converts a lattice point field representing a geometric structure of a target object at one point in time into a particle field representing the geometric structure of the target object at that point in time, subjects the particle field to a physical simulation, calculates particle fields at a plurality of points in time after the one point in time, converts the particle fields at the one point in time and the plurality of points in time into a lattice point field, subjects the converted lattice point field to volume rendering, estimates measurement data that is a result of capturing images of the target object at each of the one point in time and the plurality of points in time, calculates a loss between the measurement data of the target object estimated at each of the one point in time and the plurality of points in time and measurement data obtained as a result of actually capturing images of the target object, and optimizes, on the basis of the loss, a field indicating the geometric structure of the target object.

The present invention relates to a convolution reconstruction method, system, device, and medium for tomographic particle image velocimetry. The convolution reconstruction method for tomographic particle image velocimetry comprises: S101 obtaining a particle image required for reconstruction and setting reconstruction parameters; S102 initializing the discretized spatial voxels; S103 re-dividing the original pixel grid of the particle image into levels to obtain a high-resolution pixel grid; S104 obtaining the grayscale of the high-resolution particle image; S105 calculating an approximate weight matrix of the spatial voxels based on the division levels; S106 projecting the sphere centers of all non-zero voxels onto a high-resolution pixel plane according to a mapping function, and assigning a value to the nearest pixel center to obtain a particle projection matrix; S107 using the approximate weight matrix as a convolution kernel and performing convolution with the particle projection matrix to generate a projection image; S108 calculating the ratio of each pixel corresponding to the projection image and the high-resolution image; and S109 updating the grayscale of the spatial voxels. The present invention improves the efficiency and quality of particle field reconstruction for tomographic particle image velocimetry.

The invention relates to the technical field of self-cleaning, and discloses a self-cleaning method based on airflow field reconstruction and particle track intervention. The method comprises the steps of collecting airflow and particle state parameters of a target space in real time, dynamically constructing and updating an airflow field model and a particle field model, predicting a target particle track based on the particle field model, calculating a risk coefficient of the target particle track relative to a sensitive area, and generating a regulation and control scheme containing parameters such as an airflow direction and a wind speed interval when the risk coefficient exceeds a preset threshold value. A guide airflow field is generated through the airflow regulation and control device and superposed with an original airflow field to form a reconstructed airflow field, and particles are guided to deviate from a sensitive area and converge to a collection area to complete capture. According to the method, accurate track intervention and efficient capturing of particles are achieved, sensitive areas are effectively protected, and self-cleaning pertinence and reliability are improved.

The invention provides a simulation method, device and equipment for soil-rock mixture slope sliding, in an initialization stage, a slope soil-rock mixture model is constructed, and in an execution stage, the following process is cyclically executed until a total time step is achieved: based on a soil body particle field model and a rock body particle field model constructed for the slope soil-rock mixture model, the slope soil-rock mixture model is constructed; soil body particle field data and rock body particle field data corresponding to all time steps are obtained through calculation, the soil body particle field model and the rock body particle field model each comprise a density field item, a speed field item, a displacement field item and a stress field item, and the soil body particle speed field item comprises a soil body particle contact acceleration item corresponding to the soil body particle contact resultant force. According to the method, a grid is not needed, the problem of grid distortion caused by large deformation and sliding is avoided, the discontinuous contact and sliding process of the earth-rock mixture can be simulated more accurately, the contact motion and large deformation of the earth-rock interface can be realized without coupling other numerical methods, the calculation efficiency is remarkably improved, and parameter calibration is simpler and more convenient.

The invention discloses a fuzzy cloud particle size accurate calculation method, which comprises a calibration module and a detection module, and is characterized in that the calibration module is used for placing standard plates with different diameters at different Z positions for imaging to obtain a series of standard plate dot images, and dividing the standard plate dot images into three categories according to the diameters of the standard plate dot images, respectively calculating the pixel diameter and ambiguity of each image in the three categories; obtaining three data sets respectively corresponding to the three types of images; fitting is carried out on the three data sets to obtain the corresponding relation among the particle ambiguity, the pixel diameter and the real diameter in each category; and the detection module is used for calculating the physical diameter of the particles by using the corresponding relation among the ambiguity, the pixel diameter and the real diameter of the particles in each category obtained by the calibration module. By adopting the embodiment to calculate the fuzzy cloud particle size, the calculation precision of the particle size can be improved, a guarantee is provided for subsequent parameter calculation, meanwhile, the sampling space is indirectly increased, and the actual particle field distribution is truly reflected.

The invention relates to the technical field of fluid measurement, and discloses a flow-controllable tracer particle generating device for PIV (particle image velocimetry) measurement, which comprises a mixing bin assembly, a tracer particle generating assembly, a flow control assembly, a concentration monitoring and feedback control assembly and an electrostatic synergistic assembly, the mixing bin assembly is composed of a first pneumatic quick connector, a second pneumatic quick connector, a mixing bin top plate, a mixing bin shell, a mixing bin bottom plate, a liquid discharging hole, a third pneumatic quick connector, a manual valve, a liquid collecting cup, an atomizer hole and an air outlet hole. The tracer particle generating assembly is composed of an atomizer body, a hose and an injection pump. The flow control and concentration monitoring feedback system is integrated, so that the gas flow and the particle concentration are synergistically and accurately regulated and controlled. The system can monitor the concentration of particles in the mixing bin in real time and intelligently adjust the speed of the injection pump, the stability of a particle field needed by PIV measurement is guaranteed from the source, and the problem of measurement errors caused by parameter fluctuation of a traditional device is effectively solved.

The application provides a simulation method, device and equipment for soil-rock mixed slope sliding. In an initialization stage, a soil-rock mixed body model of the slope is constructed. In an execution stage, the following process is cyclically executed until a total time step is reached: soil particle field data and rock particle field data corresponding to each time step are calculated based on a soil particle field model and a rock particle field model constructed for the soil-rock mixed body model of the slope. The soil particle field model and the rock particle field model each include a density field term, a velocity field term, a displacement field term and a stress field term. The soil particle velocity field term includes a soil particle contact acceleration term corresponding to a soil particle contact resultant force. Thus, the method does not require a grid, avoids grid distortion problems caused by large deformation and sliding, can more accurately simulate the non-continuous contact and sliding process of the soil-rock mixed body, and can realize the contact movement and large deformation of the soil-rock interface without coupling other numerical methods, significantly improves the calculation efficiency, and makes parameter calibration more convenient.

The application discloses a kind of particle multi-parameter in-situ measurement method based on double-pulse digital holography: two laser beams emitted by double-pulse light source are combined, filtered, expanded and collimated to form parallel light;Parallel light irradiates moving particle flow, and single-camera records single-frame double-pulse particle hologram;The hologram is reconstructed, and three-dimensional particle field information is obtained by inversion, to obtain the particle size and concentration information of particle;The three-dimensional position of particle is obtained, so as to calculate the actual position of particle, and the three-dimensional velocity of particle is obtained by using particle matching algorithm.The application also discloses a kind of particle multi-parameter in-situ measurement device based on double-pulse digital holography: double-pulse light source system, laser modulation system, measurement probe system, interference fringe recording system and particle multi-parameter processing system.The method and device realize multi-parameter, in-situ measurement of particle, are simple to operate, and have high test precision;Single-frame double-pulse mode greatly enriches the information of test speed, and has wide adaptability.