62 results about "Diffusion equation" patented technology

The invention relates to the technical field of data processing, and discloses a water quality and water quantity accurate simulation method and system based on an SWAT model and a storage medium. The method comprises the following steps: analyzing rainfall and evapotranspiration data through a two-parameter trend test, and generating standard hydrological data by adopting a triple configuration algorithm; dividing sub-basins by combining topographic information, and calculating a hydrological response unit; performing water balance operation and water conservation evaluation; pollutant diffusion is analyzed, and a diffusion equation is solved; running state monitoring is implemented, and an early warning system is designed; and comparing and correcting measured data, and constructing a hierarchical monitoring mechanism. According to the invention, the whole-process precise management from hydrological data acquisition, water quality evaluation to early warning monitoring is realized, and the accuracy and reliability of water quality and water quantity monitoring are remarkably improved.

The invention discloses an algae biomass prediction method based on a bidirectional coupling hydrodynamic model and a physical information neural network, and relates to the field of algae biomass prediction.The algae biomass prediction method comprises the steps that firstly, water regimen data, water quality data and meteorological data are obtained and preprocessed, and then a hydrodynamic model based on a shallow water equation set is constructed; the model is coupled with a convection-diffusion equation and a wind stress-turbulence model so as to simulate spatial distribution of nutritive salt concentration, water temperature distribution and a vertical and horizontal mixing process driven by wind power; operating the model based on a time sequence of meteorological and water regimen data, and outputting multi-dimensional physical field data; thirdly, performing spatial interpolation on the water quality data to enable the water quality data to be in space-time alignment with grid nodes of the hydrodynamic model; a target loss function is constructed by taking a residual error in the shallow water equation set as a regular term, and a physical information neural network is trained to finally obtain a target prediction model; and the accuracy of algae biomass prediction and the robustness of the target prediction model are obviously improved.

The invention discloses a fuzzy logic self-adaptive adjusting system of box-type temperature control equipment, and relates to the field of fuzzy logic self-adaptive adjustment, and the fuzzy logic self-adaptive adjusting system comprises a feature extraction module, a mapping module, an activation module, a control module and an adjusting module. The method comprises the steps of obtaining a temperature field, energy consumption and airflow velocity, establishing a thermodynamic vortex field model and performing feature extraction analysis, obtaining a curvature phase energy third-order feature tensor, performing high-order singular value decomposition, constructing a five-dimensional manifold space, and obtaining a final geometric invariant curvature membership degree and a learnable parameter tensor integral solution through a reaction diffusion equation and minimum projection. The method comprises the following steps: constructing a high-dimensional rule network, obtaining a rotation invariant rule activation tensor through quantum entropy screening and nonlinear activation, constructing a fractional order model, obtaining a final control signal through potential energy well resonance and coherence optimization analysis, constructing a parameter update vector through a key coefficient, and adjusting the final control signal in real time.

The invention provides a monitoring data linkage method between code-free applications, automatic identification and mapping of data structures between the applications are realized by constructing a metadata description model and a field similarity function, and data change detection is performed by adopting an improved locality sensitive hash algorithm. Through a distributed transaction control mechanism, data consistency in cross-application data linkage is ensured, and a multi-stage bloom filter and a distributed transaction feature evaluation model are designed. Furthermore, data synchronization is realized by utilizing a synchronous vector field, and data synchronization batch processing and concurrency adjustment are optimized in combination with a Gaussian divergence theorem. Besides, the invention provides abnormal recovery path optimization based on the minimum action quantity, the reliability of data linkage is ensured, and a dynamic data security protection mechanism is constructed through a wave function, a diffusion equation and nonlinear mapping.

The invention belongs to the technical field of nuclear reactor physical numerical calculation, and particularly relates to a method for improving precision and convergence of solving a diffusion equation based on a PINN, and the method comprises the following steps: S1, building a neural network algorithm model combining a physical information neural network PINN and source iteration; s2, aiming at a multi-region smooth transition neutron diffusion problem with small cross section difference between materials, optimizing the neural network algorithm model by adopting an optimization strategy of a training level; and S3, aiming at the multi-region neutron diffusion problem of large neutron flux gradient at the boundary due to large cross section difference between materials, improving the neural network algorithm model by adopting a domain-divided neural network architecture. According to the method, the problem that the calculation cost is exponentially increased in a high-dimensional or complex geometric scene due to the fact that a traditional finite difference method and a finite element method depend on grid division is avoided.

The invention discloses a boiler energy efficiency prediction method and system based on sampling flue gas component analysis. The method comprises the following steps: acquiring heat flux density data of a plurality of radial positions, and inverting a temperature distribution profile through a radiation heat flux prediction model; constructing a multi-component concentration layering graph based on the diffusion equation and the component transport equation; constructing a sampling path optimization model by using temperature gradient and concentration fluctuation intensity information, driving a dynamic telescopic sampling probe to arrange a sampling position, and collecting a flue gas sample; the analysis model determines original concentration data, thermal-mass offset correction is executed in combination with temperature distribution, and a concentration correction function is constructed to obtain a corrected concentration value; and fusing boiler operation condition data, and inputting into the energy efficiency prediction model to output a heat efficiency index. According to the method, temperature field inversion driven by heat flux density is constructed, a multi-component concentration layering graph is generated in combination with a diffusion and transport model, and the spatial representativeness of flue gas sampling, the physical authenticity of component data and the accuracy of energy efficiency prediction are remarkably improved.

The invention provides a linear equation set of a diffusion equation diagram neural network model for water quality prediction, an F-GNN method and a system, and the F-GNN method comprises the steps: S1, collecting the monitoring historical data of each station, and constructing a space-time diagram; s2, on the basis of the space-time diagram, calculating a diffusion equation calculation formula by adopting a finite difference method, constructing an overdetermined linear equation set according to the diffusion equation calculation formula, and estimating parameters of the diffusion equation of each river reach by applying a least square method; and S3, if the parameters of the diffusion equation of each river reach are positive numbers, performing F-GNN prediction to obtain feature prediction of each station. According to the method, the defect that a water quality prediction model lacks space-time joint modeling is overcome, the problem that a traditional data driving model is prone to overfitting under the small sample condition is solved, diffusion equation constraints are introduced, the defect is effectively overcome, meanwhile, physical constraints are introduced, prediction precision and reliability are improved, and the method is suitable for large-scale popularization and application. The method is suitable for various environment monitoring scenes.

The application discloses a kind of gas-liquid two-phase flow numerical simulation method considering coalbed gas desorption lag, comprising the following steps: using well logging, indoor experiment means to obtain productivity calculation parameter;Desorption equation of matrix system is defined in combination with desorption lag effect, diffusion equation;Gas-water two-phase percolation equation of fracture system is defined, and desorption equation of matrix system, diffusion equation is combined, and double medium gas-water two-phase coupling flow equation set is formed;The porosity and permeability change of matrix system and fracture system in production process is calculated respectively, and the physical property change equation set of double medium is deduced;Using the numerical solver of COMSOL, the coupling equation set is solved using iterative method, and the gas production and water production are calculated.The application is simple to operate, can more scientific and reasonable assess the influence of coalbed gas desorption lag effect on gas well productivity, while greatly reducing the cost and time of gas well numerical simulation calculation, and also has important inspiration for other unconventional gas reservoir development affected by desorption lag.

The invention discloses a gas-liquid two-phase flow numerical simulation method considering coal bed gas desorption lag. The method comprises the following steps: acquiring productivity calculation parameters by using logging and indoor experiment means; defining a desorption equation and a diffusion equation of the matrix system in combination with a desorption hysteresis effect; defining a gas-water two-phase seepage equation of the fracture system, and forming a dual-medium gas-water two-phase coupling flow equation set in combination with a desorption equation and a diffusion equation of the matrix system; the porosity and permeability changes of a matrix system and a fracture system in the production process are calculated respectively, and a physical property change equation set of the double media is deduced; and solving the coupling equation set by using a numerical solver of COMSOL and adopting an iteration method, and calculating to obtain gas production and water production. The method is easy and convenient to operate, the influence of the coal bed gas desorption lag effect on the gas well productivity can be evaluated more scientifically and reasonably, meanwhile, the gas well numerical simulation calculation cost and time are greatly reduced, and important inspiration is also provided for development of other unconventional gas reservoirs influenced by desorption lag.

The invention relates to the field of operation and maintenance management of Internet of Things equipment, in particular to a periodic maintenance task automatic scheduling system and method, and the method comprises the steps: firstly collecting vibration, current and environment signals, and constructing a three-dimensional digital twin map; obtaining a health index and a remaining available life in a preset window through time sequence alignment convolution and an ordinary differential equation recursive network, generating a risk coefficient through index mapping, and driving a reaction-diffusion equation to form a continuous degradation field; the scheduling optimization unit obtains an initial solution through symplectic gradient Hamiltonian-Monte Carlo sampling, and maintenance scheduling data meeting resource constraints are generated through bose sampling, topological bose evolution and dual gaming; the mixed reality terminal displays a risk hotspot and an operation instruction on site, retest data is recorded after maintenance is completed, and reinforcement learning updates a risk assessment and scheduling model on line according to an execution result to form a closed loop; according to the method, accurate quantification of risks, global optimization of scheduling and adaptive feedback of execution are realized, and the fault rate and the maintenance cost can be remarkably reduced.

The invention discloses an osmotic pressure analysis method and device fusing model optimization and physical inversion and a medium, and the method comprises the steps: calculating correlation coefficients of a specified reservoir water level and osmotic pressure under different time lags based on cross-correlation function analysis, and recognizing a differential nonlinear feature corresponding to the maximum correlation coefficient; aiming at the differentiated nonlinear characteristics of different dam types, constructing a plurality of corresponding diagnosis prediction models in parallel, and establishing a model selection decision rule to select an optimal diagnosis model; according to delay parameters obtained through cross-correlation analysis, a permeability coefficient is reversely deduced through a pore medium heat transfer diffusion theoretical formula; according to the regression slope and the theoretical value attenuation factor of the optimal model, a permeability coefficient is reversely deduced in combination with a correction diffusion equation, credibility evaluation and weighted fusion are performed on the permeability coefficient, and a comprehensive permeability coefficient is calculated; and diagnosing the soil type and the permeability characteristic grade. According to the method, the propagation delay characteristic of reservoir water level change in the pore medium can be accurately quantified, and the inversion precision of the permeability coefficient and the engineering applicability are improved.

The invention provides a drilling fluid invasion prediction method and system based on an invasion depth operation model, and the method comprises the steps: building a dynamic invasion numerical simulation model of drilling fluid based on two-phase seepage, mineralization degree ion convection and diffusion equations in combination with a mud cake dynamic growth equation and an auxiliary equation; analyzing an intrusion depth operation model meeting a standard condition in combination with a set stratum characteristic coefficient; respectively analyzing the influence of each influence parameter on the invasion depth at different time to determine primary and secondary control factors; after establishing a judgment matrix, carrying out normalization processing on the feature vectors to obtain a weight coefficient; and determining an intrusion depth prediction model comprehensively considering the pressure difference, the intrusion time, the stratum physical property and the fluid characteristic by combining the intrusion depth relationship under different pressure differences, so as to realize drilling fluid intrusion prediction for the required drilling well. According to the scheme, the problems that in the prior art, factors considered in simulation operation are not comprehensive, and accuracy is insufficient can be solved, accurate implementation intrusion data are obtained, and accurate resistivity correction and reservoir damage evaluation can be achieved.

The application provides a geochemical exploration anomaly ore-bearing property evaluation method and device, obtains geochemical exploration information of a target anomaly region to be evaluated in ore-bearing property, obtains reconstruction concentration information of a chemical element at each sampling point according to the geochemical exploration information and a trained physical information neural network model, wherein the trained physical information neural network model comprises a physical simulation layer embedded with a fractional order reaction-diffusion equation; and then the target anomaly region is evaluated in ore-bearing property according to original concentration information and the reconstruction concentration information of the chemical element at each sampling point. The fractional order reaction-diffusion equation describing the ore-forming geochemical process is learned and simulated through the physical information neural network model, so that the reconstruction concentration information is more in line with the ore-forming regularity, and field verification or drilling is performed when the ore-bearing property evaluation grade of the target anomaly region is high, thereby reducing the exploration cost.

The application discloses an image generation method and device based on a heat kernel theory on a Riemannian manifold, wherein a geometric encoder and a geometric decoder are trained by limiting a Riemannian metric, representation of an image in a low-dimensional manifold is learned, target data distribution is mapped to a special distribution on a low-dimensional manifold through a diffusion equation, and a neural network is used to learn an inverse diffusion process, so that the learning precision and the learning efficiency are improved. When performing an image generation task, sampling is performed from the special distribution on the low-dimensional manifold, a target data distribution on the low-dimensional manifold is estimated through a heat kernel generation model on the Riemannian manifold, and the image distribution in a high-dimensional Euclidean space is restored through the geometric decoder.

The present invention provides a linear equation system, F‑GNN method and system for a diffusion equation graph neural network model for water quality prediction. The F‑GNN method includes: step S1, collecting historical data monitored at each site and constructing a spatiotemporal graph; step S2, based on the spatiotemporal graph, using the finite difference method to calculate the diffusion equation formula, constructing an overdetermined linear equation system based on the diffusion equation formula, and then using the least squares method to estimate the parameters of the diffusion equation for each river section; step S3, if the parameters of the diffusion equation for each river section are all positive numbers, then performing F‑GNN prediction to obtain a characteristic prediction for each site. The present invention solves the problem of lack of spatiotemporal joint modeling in water quality prediction models. Traditional data-driven models are prone to overfitting problems in small sample conditions. The present invention effectively compensates for this deficiency by introducing diffusion equation constraints. At the same time, the introduction of physical constraints improves prediction accuracy and reliability, making it suitable for a variety of environmental monitoring scenarios.

The application belongs to but is not limited to the technical field of electronic core industry, and discloses a polycrystalline NCM electrode particle cracking and pulverization prediction method and system, which comprises the following steps: constructing an anisotropic polycrystalline fracture phase field model, including a grain boundary phase field equation, a solid mechanics equation, a Li + diffusion equation and a fracture phase field equation; constructing a two-dimensional geometry of a polycrystalline NCM electrode particle, setting required parameters of physical fields, carrying out grid division and setting initial conditions and boundary conditions; using a finite element simulation software to perform numerical solution, and obtaining cracking and pulverization distribution of the polycrystalline NCM electrode particle. + The application considers bidirectional coupling of Li diffusion and mechanical stress, cracking and pulverization and mechanical stress, and uses grain boundary phase field calculation to correct the critical energy release rate, so that the application can accurately reflect the mechanism of cracking and pulverization of the polycrystalline NCM electrode particle, and improve the accuracy of cracking and pulverization prediction.

The present invention discloses a high-performance simulation method for simulating the total dose effect of a MOSFET device, and provides the electrical information inside and outside the MOSFET device under the total dose effect. The method first divides the geometric model of the MOSFET device by a free hexahedral mesh; then, the control volume finite element method (CVFEM) is used to discretize the drift-diffusion equations, wherein the flux term of the drift-diffusion equations is interpolated by the hexahedral vector basis function, the finite difference method and the Scharfetter-Gummel (SG) method; finally, the Newton iteration method is used to solve the discrete drift-diffusion equations to obtain the potential and carrier concentration field information inside the MOSFET device, and the current-voltage curve of the MOSFET device is obtained by post-processing. The simulation method of the present invention has the advantages of high confidence, high numerical accuracy, high numerical stability and scalability. It can be used for efficient and high-precision numerical simulation of multi-physical effects such as the total dose of MOSFET devices, thereby improving the performance, robustness and reliability of integrated circuits.

This invention discloses a method for calculating gas-gas diffusion concentration distribution considering the influence of gravity, relating to the field of oil and gas field development. This method introduces a gravity drift velocity term generated by density difference into the traditional diffusion equation, realizing a unified characterization of molecular diffusion and gravity differentiation. It constructs a dynamic mass transfer model applicable to multi-temperature, multi-pressure, and multi-component conditions, which is used to calculate the concentration distribution and corresponding dynamic diffusion coefficient of injected gas and formation gas during the vertical diffusion process. This provides a more consistent calculation basis for predicting the migration front and distribution pattern of injected gas, optimizing gas injection parameters, and assessing gas channeling risk. It has applicable value for improving the recovery rate and the operational safety of gas storage facilities.

The invention relates to a fractional order partial grey model short-time traffic flow prediction method based on a two-dimensional advection diffusion equation, and belongs to the technical field of intelligent traffic. The method comprises the following steps: constructing original traffic flow data into traffic flow data in a matrix sequence form; processing the original matrix sequence, and calculating an accumulation generation sequence and a mean value sequence and a partial derivative sequence corresponding to the accumulation generation sequence; establishing a fractional order partial grey short-time traffic flow prediction model AD-FPGM (2, 1) based on a two-dimensional advection diffusion equation; constructing matrixes B and Y to estimate model system parameters, and searching a fractional order accumulation order r and a parameter lambda in a time response formula by adopting a particle swarm algorithm; calculating a simulation value and a reduction value of the AD-FPGM (2, 1) model; and carrying out error test according to each evaluation index of the comparison model, if the error test is passed, predicting a future trend by using an AD-FPGM (2, 1) model, otherwise, reconstructing traffic flow data to carry out model optimization. The prediction result can provide reliable information for a traffic management system, and the research method of traffic flow prediction is enriched.

The invention discloses a fractional order diffusion equation efficient solving method based on POD dimension reduction and RBF, and the method comprises the steps: carrying out the discrete solving of a time-space fractional order diffusion equation through combining an L1 format and a Grunwald-Letnikov difference format, and obtaining snapshot data for training; secondly, extracting a main mode of the system by applying a POD technology, constructing a radial basis function interpolation neural network model, and reducing a solution dimension; and training a radial basis function neural network model, learning a nonlinear mapping relationship between a parameter space and a POD coefficient space, and realizing rapid prediction of the POD coefficient under a target parameter, thereby reconstructing a high-dimensional numerical solution field. According to the method, dimension reduction modeling and neural network prediction technologies are effectively fused, the calculation amount can be greatly reduced, the solving precision and efficiency are improved, and the method is suitable for multi-parameter and high-dimension fractional order diffusion problem modeling and simulation calculation and has good engineering application prospects.

The invention relates to the field of roadway surrounding rock strengthening control, in particular to a long-term water immersion roadway surrounding rock strengthening control method and system based on fracture feature classification, and the system comprises a multi-dimensional sensor network module, a graph nerve-random differential prediction module, an optimal control module and a chaotic hyper-parameter self-evolution module. A Bayesian digital twin base is established for the multi-dimensional sensor network, and multiple parameters are acquired after a sensor is self-checked; the graph neural random differential prediction module is used for voxelizing a coal bunker, calculating a concentration deterministic trend by using a Fick diffusion equation, and generating a probability trajectory by stacking random differential terms; an optimal control module constructs a Wasserstein fuzzy set to solve a worst case expectation, converts a hard limit into a barrier function constraint, and solves convex quadratic programming to output an optimal control sequence in combination with a Lyapunov constraint; the chaotic hyper-parameter self-evolution module performs quantum coding on hyper-parameters, and the parameters are optimized by taking a safety and energy consumption Nash equilibrium value as fitness and by means of Chebyshev mapping. According to the method, accurate gas prediction, robust control and parameter self-optimization are realized, the over-limit risk is reduced, and the energy consumption is reduced.

The application discloses a method for obtaining C / C composite ablation morphology based on physical information neural network, and relates to the field of aerospace, comprising the following steps: taking the coordinate data of a C / C composite three-dimensional space model under a Cartesian coordinate system, an ablation time, a dimensionless parameter Sh and a dimensionless parameter A as inputs of a pre-trained multilayer perceptron network, outputting oxygen molar concentration and solid volume fraction through the pre-trained multilayer perceptron network; solving a gas phase diffusion equation and a solid phase evolution equation through the oxygen molar concentration and the solid volume fraction output by the multilayer perceptron network to obtain the C / C composite ablation morphology. The method can efficiently and accurately solve the gas-solid coupling equation, overcome the limitations of traditional numerical methods, accurately obtain the ablation morphology of the C / C composite under the gas-solid coupling effect at a low calculation cost, and provide strong theoretical support and technical support for the application of the C / C composite in the field of aerospace and the like.

The application provides a kind of fast solver of radiation diffusion equation based on meshless discrete, it is related to the technical field of numerical simulation and calculation of radiation diffusion problem.It includes: a computing node configuration module for determining the solution threshold of radiation diffusion, and constructing a set of configuration points, a set of center points and a set of evaluation points within the solution region; a compactly supported kernel function definition module for configuring a radial basis function with a fixed support radius for each center point as a compactly supported kernel function; a solver execution module for rigidly decomposing the solution problem to obtain rigid sub-problem partial differential equations and non-rigid sub-problem partial differential equations; discretizing the rigid and non-rigid sub-problem partial differential equations using implicit and explicit formats respectively, solving the two partial differential equations using the radial basis function collocation method to obtain the coefficient vector of the radial basis function; and calculating the numerical solution of the evaluation point set using the coefficient vector.The scheme can achieve fast solution of the radiation diffusion partial differential equation using a meshless method.

The application relates to the field of building information model application technology and engineering project management technology, and discloses a building engineering progress intelligent prediction system based on BIM, which firstly analyzes BIM and progress data, constructs a semantic constraint topological graph containing logical dependence and spatial proximity relationship, then discretizes the building space, constructs an anisotropic diffusion tensor based on component semantics, realizes physical tensorization of geometric constraints, on the basis of which, the system performs multi-physical field coupling calculation, solves the distribution of material flow resistance coefficient field and risk field by using a potential field method and a diffusion equation respectively, finally, the environmental resistance factor in the process operation space is calculated to correct the process prediction duration, and iterative control is performed based on a field-graph closed loop feedback mechanism. The application can quantize the spatial congestion effect and risk non-uniform conduction under a complex building environment, solves the problem of logical plan and physical constraint separation, and significantly improves the accuracy of construction progress prediction.

The invention discloses a blasting process simulation method and system based on bidirectional fluid-solid coupling, and relates to the technical field of rock blasting. The method comprises the following steps: firstly, modeling a solid computational domain by adopting a discrete element method, and modeling a fluid computational domain by adopting a finite volume method; and then applying a dynamic load to simulate an initial crack in a shock wave stage, mapping crack information to a fluid computational domain grid, dynamically updating local permeability, solving a compressible gas diffusion equation, applying a pressure field to the surface of a solid computational domain particle through an interpolation method, and driving the crack to continuously expand. A fluid-solid two-way feedback mechanism covering detonation gas and rock is established, through closed-loop coupling of crack-permeability-gas pressure-new crack, the driving effect of gas at the tip of the crack is accurately captured, the experimental crack form can be accurately reproduced, and the experiment precision is improved. And the influence of the initial air pressure on the crushing range, the crack number and the damage mode can be quantitatively analyzed, and the method is suitable for blasting optimization design under the condition of high-gas-yield explosives.

The application discloses a coupling algorithm for studying the influence of microplastics on fish based on an unstructured grid, and aims to deeply understand the influence of microplastics on the ecology of fish in the ocean. The method combines a Lagrangian advection model and a diffusion equation to simulate the transmission and diffusion process of microplastic particles, calculates the spatial position of each particle and tracks the trajectory of each particle by introducing real ocean current data. The GMRES method is used to solve the large-scale sparse linear system generated in the simulation process to ensure the efficiency and stability of the calculation. The Crank-Nicolson method is also used for time stepping to improve the numerical stability. The Lagrangian advection and diffusion processes are coupled by staggered time steps to truly reflect the spatial distribution of microplastics. Considering the specific behavior of the fish population, the density change rate is determined by the inherent motion, diffusion and microplastic concentration gradient, so as to evaluate the potential influence of microplastics on the marine fish ecology.

The invention discloses a pit throwing site selection and mud throwing strength planning method and system in sea reclamation engineering. The method comprises the steps that firstly, a hydrodynamic mathematical model and suspended load sediment transportation are constructed through a two-dimensional shallow water equation and a convection diffusion equation; secondly, on the basis of the established mathematical model, a model capable of being applied to an actual project is formed through a parameterization means by combining the actual situation of the project, and finally the loss rate calculation under different pit throwing positions and mud throwing strengths is carried out through the general model. According to the scheme, multi-dimensional information such as construction time, mud blowing ship construction parameters, hydraulic reclamation construction data and soil texture detection reports is comprehensively considered, and an advanced numerical simulation technology and a flow field analysis method are introduced, so that the silt loss rate under different working conditions is accurately predicted; meanwhile, according to the scheme, direct planning of pit throwing site selection and mud throwing strength is concerned, prediction and control of the wastage rate are indirectly optimized by introducing numerical simulation and flow field analysis, and therefore systematicness and comprehensiveness of sea reclamation project planning are enhanced.

The invention relates to a method and device for evaluating an electrolytic bath pole plate structure, a storage medium and electronic equipment, and the method comprises the steps: constructing a multi-physics field model based on an alkaline electrolytic bath, and carrying out finite element grid division to obtain a multi-physics field finite element model; when the multi-physics field finite element model is used for calculating the volume distribution of the bubbles in the small chamber, the bubbles are equivalent to particles, homogeneous flow is adopted, and the radial speed in the running speed is ignored; on the basis of enabling bubbles to be equivalent to particles and adopting homogeneous flow, setting a single-phase flow model equation for describing electrolyte flow by using single-phase flow in each finite element grid in a multi-physics field finite element model, and describing a convection-diffusion equation for describing bubble movement by using convection-diffusion; and iteratively operating the multi-physics field finite element model step by step, obtaining a multi-physics field distribution result of the to-be-evaluated electrolytic bath pole plate, and evaluating the structure of the to-be-evaluated electrolytic bath pole plate based on the multi-physics field distribution result. The structural design evaluation efficiency can be improved.

This invention relates to the field of electronic digital data processing technology and discloses a method for generating structural parameters of a static mixer. The method includes: selecting the inclination angle of the crossbars and the aspect ratio of the mixer as structural parameters to be optimized, with the optimization objectives being minimizing the relative standard deviation of the concentrations of each component at the outlet section and minimizing the pressure drop; constructing a three-dimensional geometric model of the static mixer based on the structural parameters and setting the inlet and outlet positions; solving the flow field based on the Navier-Stokes equations and the continuity equation, and solving the concentration field of each component based on the convection-diffusion equation; obtaining the concentration distribution and pressure drop data of each component at the mixer outlet section under different structural parameters through multiphysics simulation, and calculating the relative standard deviation of each component concentration; screening structural parameter combinations with relative standard deviations less than a threshold, and selecting the one with the smallest pressure drop as the optimal structural parameter. This method solves the problems of reliance on trial and error and long cycles, achieving the goals of high accuracy, high efficiency, and low cost.

The invention belongs to the technical field of oil reservoir development dynamic monitoring data interpretation and analysis, and particularly relates to a well testing analysis and interpretation method for a single-medium different-boundary oil reservoir inclined shaft. A well testing analysis and interpretation method for a single-medium different-boundary oil reservoir inclined shaft comprises the following steps that according to the characteristics of a single-medium oil reservoir, an instantaneous point source diffusion equation of the single-medium oil reservoir is listed in combination with different types of boundary conditions; according to a mirror image reflection rule, carrying out infinite multiple times of symmetry along top and bottom boundaries through a LordKelvin basic solution, and superposing sources to obtain a single-medium oil reservoir instantaneous point source function basic solution under different types of boundary conditions; and integrating along the track of the mirror image inclined shaft in combination with the track of the mirror image inclined shaft, and obtaining the bottom hole pressure response of the inclined shaft in the single medium oil reservoir under different types of boundary conditions through dimensionless. Compared with a conventional vertical shaft theory for explaining inclined shaft data, the method has the advantage that the adaptability and precision of analysis and explanation can be greatly improved.