2624 results about "Model simulation" patented technology

The invention relates to the technical field of twinborn model simulation, and discloses a twinborn model simulation method and system for hot working of large forgings. The method comprises the following steps: collecting and preprocessing process parameters, quality data and environment information in a multi-source manner, and obtaining hot working characteristic data; performing correlation analysis to construct a process knowledge graph; calculating the distribution of a temperature field, a stress field and an organization field by using a self-sensing multi-field coupling var value neural network; comparing and analyzing to obtain deviation data and correction parameters; adjusting a network parameter optimization prediction result; and executing multi-objective optimization calculation, and generating a whole-process technological parameter and a control instruction. According to the method, full-process multi-physics field coupling calculation from smelting, casting, forging and pressing to heat treatment can be achieved, model parameters are dynamically adjusted according to real-time production data, the optimal process scheme is generated, and therefore the manufacturing quality and efficiency of large forgings are improved.

The invention discloses an electromechanical system fault pre-diagnosis method and system based on digital twinning. The method comprises the following steps of obtaining multi-source data in an electromechanical system operation process; preprocessing the acquired multi-source data, wherein the preprocessing comprises data cleaning, normalization processing and feature extraction; and on the basis of the preprocessed multi-source data, an electromechanical system design drawing, a three-dimensional geometric model, material attributes and a kinetic equation are fused, and a digital twin model is constructed. According to the invention, through a digital twin model dynamic calibration and prediction algorithm, early abnormity of the equipment is identified in advance, the fault probability and the residual life are output, and non-planned shutdown is reduced; by constructing a cross-physical domain fault feature system and fusing model simulation and actual measurement data, the potential fault identification accuracy is improved, and the missed diagnosis rate is reduced; by calibrating parameters of the digital twin model in real time, the method adapts to nonlinear changes of equipment, ensures high-fidelity mapping of the model, and improves fault prediction precision.

The invention relates to the technical field of bridge structure health monitoring and early warning, and discloses a bridge support static displacement overrun early warning correction method based on a prediction model, and the method comprises the following steps: S1, simulating the dynamic response of a support and an overall structure under various working conditions through building a coupling finite element model of the bridge support and the overall bridge structure; s2, displacement, vibration and temperature data of the support are collected in real time through a multi-source sensor, and data fusion and noise reduction processing are carried out; s3, establishing a multi-level prediction model based on a physical model and a data driving model, and predicting a future displacement trend of the support; and S4, monitoring a prediction error in real time through an error feedback mechanism. Prediction errors are corrected in real time through recursive Kalman filtering and an error feedback mechanism, an early warning threshold value is dynamically adjusted through Bayesian updating, the precision and reliability of early warning are improved in combination with a double triggering mechanism, the method adapts to changes under complex working conditions, and false alarms and missing alarms are reduced.

The invention discloses a building intelligent operation and maintenance management system and method based on big data analysis, and relates to the field of building intelligent operation and maintenance, and the system comprises a data collection layer including high-precision multi-type sensors and adaptive collection and transmission, a data storage layer including distributed storage and high-redundancy cache, and a big data analysis layer including data cleaning and data storage. The operation and maintenance decision-making layer is used for generating a strategy according to analysis and performing dynamic adjustment; the user interaction layer is used for performing visual interface and convenient feedback; the intelligent early warning module is used for performing dynamic threshold and accurate early warning; the analog simulation optimization module is used for performing model simulation and VR / AR auxiliary decision making; and the operation and maintenance management level and the energy utilization efficiency are improved. The system integrates building data, monitors equipment in real time, accurately predicts faults and energy consumption, optimizes operation and maintenance decisions, improves the management efficiency and the energy utilization rate, reduces the cost, and improves the user experience.

The invention discloses a grading, zoning and differentiating risk early warning method for mountain torrent disasters in medium and small watershed. Firstly, a drainage basin risk area is determined through mountain torrent disaster data analysis; the drainage basin delay time is calculated, the section water level and the drainage basin submerging range are simulated through the simplified hydrodynamic model, and quantitative analysis is conducted on the model; according to the method, a high-precision grid database is formed by combining unstructured grid division and risk source identification, and then clustering grading is performed on multi-dimensional characteristics of grids through a Gaussian mixture model (GMM) to obtain risk levels of the grids in a drainage basin. Finally, a differential early warning threshold value and a response strategy are formulated according to a grid partition grading result, and dynamic updating and linkage issuing are achieved on a GIS platform in combination with actual measurement data. According to the method, limited real conditions of small and medium-sized basin data and precision requirements of model simulation are taken into consideration, fine setting of the early warning threshold value is realized on the basis of grading and partitioning, and the accuracy of mountain torrent risk identification is also remarkably improved.

The invention provides a numerical control machining model verification method and system combined with digital twinning, and the method comprises the steps: firstly obtaining a numerical control machining twinning data association set which is correspondingly formed by a physical machining process actual measurement data sequence and a virtual twinning model simulation data sequence according to a machining stage identifier; the actual measurement data sequence and the simulation data sequence both comprise processing action and material response feature sequences, and then carrying out feature space unification processing on the twin data association set to generate a verification feature corresponding set comprising actual measurement comprehensive features and simulation comprehensive features; performing deviation analysis on the verification feature corresponding set by using a pre-trained model deviation analysis model to obtain a feature matching and association influence relationship, and deducing and determining a specific deviation position of the virtual twin model in a processing action and material response modeling link based on an analysis result; and model optimization guidance information for defining the parameter adjustment direction and the structure correction content is generated, so that the precision and the reliability of the virtual twin model are effectively improved.

The invention discloses a lightweight concrete column beam durability evaluation method and device based on twinborn simulation, and relates to the technical field of twinborn simulation, and the method comprises the steps: collecting the degradation characteristics of a lightweight concrete column beam, and constructing a three-dimensional multi-scale structure model; executing coupling simulation to generate a twin simulation model; monitoring a column beam through a multi-source sensor, and determining a real-time performance degradation parameter; updating the twinborn simulation model by using the degradation parameters; and collecting future environment and load data, inputting the future environment and load data into the updated model, simulating a degradation evolution process, and generating a durability evaluation result. The technical problem that a prediction result and actual service performance have significant deviation due to the fact that a traditional durability evaluation method is difficult to dynamically couple a multi-physical field environment and a material microcosmic degradation mechanism is solved, and a dynamic evaluation system for virtual-real mapping is constructed through twinborn simulation. And the prediction precision of the degradation process of the lightweight concrete column beam and the dynamic deduction capability of the service life are improved.

The invention relates to a method and a device for optimizing a digital twin model of power equipment. The method comprises the following steps: acquiring operation monitoring data and a digital twinborn simulation result of target power equipment to perform difference analysis, and determining a deviation between a digital twinborn model of the target power equipment and an actual operation state of the equipment; a prediction error matrix updated in real time is obtained based on the deviation, parameters of the digital twin model are dynamically adjusted according to the prediction error matrix, and a model after parameter correction is obtained; simulating dynamic characteristics of the target power equipment under different fault conditions by adopting the model after parameter correction, and correcting according to the dynamic characteristics to obtain a fault scene adaptation model; and performing parameter distribution optimization and model uncertainty correction on the fault scene adaptation model through model iteration processing to obtain an optimized digital twin model of the target power equipment. By adopting the method, dynamic correction and optimization of the digital twinborn body of the power equipment under complex working conditions can be realized, and the precision and reliability of the digital twinborn model are effectively improved.

The invention relates to the technical field of manufacturing process control, and discloses a multi-dimensional process data co-simulation control method and system and a storage medium. The method comprises the following steps: firstly, collecting a multi-dimensional process parameter set of a plurality of process equipment in a manufacturing production line, carrying out cross-dimensional feature extraction, and generating a comprehensive feature matrix containing a time sequence feature, a spatial distribution feature and an energy consumption feature; according to feature relevance of different dimensions in the comprehensive feature matrix, a process parameter dynamic coupling model is constructed, co-simulation is carried out on interaction between process equipment, and a process state evolution sequence is output; extracting abnormal fluctuation characteristics in the sequence, and generating a process parameter adjustment instruction set; and finally, according to the adjustment instruction set and real-time process feedback data, dynamically correcting model simulation parameters, generating an optimized process control strategy, and executing the optimized process control strategy. According to the method, collaborative analysis and dynamic control of multi-dimensional process parameters are realized, and the accuracy and adaptability of process control are improved.

The invention provides a photovoltaic power station modeling simulation analysis system, and relates to the field of power station modeling simulation. Comprising the steps that a parameter acquisition module collects key data such as voltage, current, temperature and illumination intensity in real time through a sensor and detects the integrity of the data in combination with historical data; the modeling module adopts a segmented modeling mode according to the collected data and the physical topology of the power station, and the transient processes of different stages are respectively ensured, so that the simulation precision is ensured; the simulation module inputs different disturbance parameters based on the model, simulates load and power grid fluctuation through a disturbance generator, analyzes transient response of the system and corrects the model in real time; the multi-scene generation unit is used for automatically generating a simulation scene in an extreme environment by using historical weather and power grid data, and testing the stability of a power station; and the evaluation module generates an evaluation report by analyzing the simulation result, identifies equipment faults, proposes specific suggestions for optimizing operation, and ensures the performance and stability of the power station under complex operation conditions.

The invention discloses a digital twinborn-based irrigation area water distribution scheduling method and system, and relates to the technical field of water distribution scheduling, and the method comprises the steps: collecting data, and constructing a digital twinborn model; dynamically simulating water resources in an irrigation area by using a hydrological model simulation technology based on a digital twinborn model; performing irrigation demand prediction by using a machine learning algorithm according to a simulation result; optimizing a water distribution scheduling model according to a prediction result; performing real-time scheduling control according to the scheduling strategy; performing scheduling effect evaluation according to the scheduling control data; adjusting a scheduling strategy according to an evaluation result; and performing irrigation area water resource management and long-term optimization by using an irrigation area water distribution scheduling system based on digital twinning according to an adjustment result. According to the method, the limitation of static weight distribution of a traditional method is overcome, the accuracy and flexibility of water distribution scheduling are improved, the actual requirements of a complex irrigation area environment are met, the maintenance cost and the operation risk are reduced, and efficient and stable operation of an irrigation area water distribution system is guaranteed.

The invention discloses a tunnel grouting diffusion form prediction system and method. The system comprises a tunnel simulation device used for preparing a tunnel model with cracks to simulate a tunnel rock mass; the grouting simulation system is used for conducting grouting on the tunnel simulation device by adjusting grout diffusion influence factors, and the grout diffusion state under the influence of multiple factors is obtained. The grouting diffusion form quantification system is used for cutting the grouted tunnel simulation device to obtain a plurality of slices at depth positions, and performing grid division on the surface of the slice at each depth position to obtain a grout diffusion form quantification data set; and the grouting diffusion form prediction system is used for constructing a multi-modal space-time fusion depth prediction model and inputting the grout diffusion form quantitative data set into the multi-modal space-time fusion depth prediction model to predict the diffusion form of the future grout. According to the method, the prediction model is established, and the grout diffusion form quantitative data set is used as the space-time sequence data set, so that the space-time prediction of the grouting diffusion form is realized.

The invention relates to the technical field of building simulation, and discloses a BIM-based special-shaped hyperboloid building site model generation method and system, and the method comprises the steps: collecting the multi-source heterogeneous point cloud data of a special-shaped hyperboloid building site to construct a three-dimensional feature tensor model, and recognizing the key curvature mutation point of the tensor model to construct a space reference topology network; based on the digital skeleton model, geometric contour features are analyzed, hyperboloid fitting is carried out, and an initial BIM model is obtained; performing multi-physics field coupling simulation on the initial model to obtain a target BIM model; extracting curvature distribution and structural stress parameters of the target BIM model to obtain basic model parameters, and constructing a building performance prediction model in combination with historical performance reference data; and inquiring a bearing capacity standard and a deformation threshold value, and analyzing the optimal curved surface parameter combination and a prestress applying strategy to obtain optimized simulation parameters, so as to simulate the special-shaped hyperboloid building by utilizing target model simulation. According to the method, the simulation precision of the special-shaped hyperboloid building can be improved.

The invention discloses a vehicle surrounding structure fault prediction maintenance method and system based on machine learning, and relates to the technical field of vehicle intelligent monitoring and prediction maintenance. Through fusion of vibration, strain and environment corrosion data and a finite element simulation stress map, an environment-load collaborative damage effect is quantified through a physical degradation model, and a multi-modal feature vector is generated. Constructing a dynamic graph topology network, adjusting edge weights in real time, and synchronously capturing spatio-temporal mechanics characteristics by using spatio-temporal graph convolution; and further identifying key risk nodes through a graph attention mechanism, simulating a damage propagation path in combination with a corrosion attenuation coefficient and a graph diffusion model, and generating a probabilistic fault thermodynamic diagram. The fault prediction precision of the vehicle surrounding structure is improved, an adversarial generative network is constructed to optimize the sample weight, and a quantile maintenance list is output in combination with a physical constraint loss function. Fault prediction and maintenance optimization of the vehicle surrounding structure can be effectively carried out, the service life of the structure is prolonged, and the maintenance cost is reduced.

The invention belongs to the technical field of greenhouse gas emission monitoring, and discloses an observation and simulation integrated city CO2 emission plume monitoring method and system. The method comprises the following steps: collecting and preprocessing multi-source data; building an atmospheric CO2 column concentration (XCO2) prediction model based on machine learning and multi-source satellite observation; performing artificial CO2 concentration long-term simulation by using an atmospheric transmission model; artificial CO2 concentration data per hour is simulated; and verifying an artificial CO2 emission estimation model based on optimal estimation and a result. The system is high in automation degree, can automatically recognize the emission plume on the basis of multi-source data fusion and model simulation, reduces the demand of manual intervention, improves the monitoring efficiency, and is suitable for monitoring and management of large-scale urban emission sources.

The invention relates to a quick river flood forecasting method based on a physical information neural network, and belongs to the field of river flood forecasting. The method comprises the following steps: on the basis of a traditional physical information neural network (PINN), introducing a boundary condition parameter as an input variable, and enabling the PINN to learn a flood wave propagation rule under different boundary conditions. Furthermore, on this basis, a physical information neural network flood fast forecasting framework (RFF-PINN) integrated with a hydrodynamic method is provided, a numerical solution based on grid discretization is reconstructed into a continuous function in a time-space domain through a piecewise polynomial interpolation method, residual error loss between network output and a hydrodynamic model simulation value is constructed, and therefore, a flood fast forecasting result is obtained. The network parameters are optimized in cooperation with the PDE loss, and the problem that the network parameter optimization effect is reduced due to the fact that the PDE loss of the complex flow state area is difficult to converge is solved. The method has the beneficial effect that the water depth change process of each section of the river channel under any boundary condition can be accurately and quickly predicted.

The invention provides a method and system for predicting the uplift bearing capacity of a mud mixed pile. According to the method, modeling is carried out by integrating geomechanics and monitoring data. The method comprises the following steps: firstly, acquiring geometric parameters of a multi-layer heterogeneous soil layer interface, constructing a database through a mechanical test, and improving the reliability by combining gradient recognition and cooperative verification of pile deformation and load data; dividing longitudinal segmentation units, adjusting anti-slip parameters to generate a space mapping table, establishing a debonding dynamic model containing an interlayer retardation effect, simulating a debonding expansion process, and outputting load evolution data. And judging the maximum load of the critical state based on data convergence analysis, and taking the maximum load as a predicted value of the uplift bearing capacity. According to the technical scheme, through dynamic parameter mapping and debonding process simulation, accurate prediction of the uplift bearing capacity of the pile foundation in a complex soil layer is achieved, and the defect that a traditional model is insufficient in characterization of interlayer heterogeneity and dynamic debonding behaviors is overcome.

The invention provides a macroscopic finite element simulation analysis method and device for a sealing element considering plastic deformation, and belongs to the field of sealing element design. The method comprises the following steps: acquiring an engineering stress-strain curve of a plastic material in a compression state; converting the engineering stress-strain curve into a real stress-strain curve; determining a yield limit, and taking a strain point corresponding to the yield limit as a demarcation point of elasticity and plastic deformation; an elastic section of a real stress-strain curve is extracted, the elastic modulus is calculated, and the Poisson's ratio is obtained through axial and transverse strain measurement in a uniaxial compression test; a true stress-strain curve plastic section is extracted, and a multi-linear isotropic hardening model is established; constructing a complete elastic-plastic constitutive model covering the elastic deformation area and the plastic deformation area; the stress distribution, the strain response and the contact performance of the plastic sealing element in a compression deformation state are simulated. According to the method and the device provided by the invention, the problem that the result does not conform to the reality due to the fact that only elastic modulus and Poisson's ratio are used in existing sealing element simulation can be solved.

The invention relates to a task and communication combined path planning method for a multi-base unmanned system. The method comprises the following steps: mapping task distribution into a target node layer, and constructing a communication link into a transmission link layer to form a task and communication double graph; establishing a dynamic coupling edge between the graphs to represent the influence of a task execution behavior on the communication quality; an interactive influence propagation model is introduced in the time dimension, and the feedback effect of communication link fluctuation on the task progress is simulated; providing time-space dynamic cost evaluation input for path planning; defining a task feasible domain and a communication strong connectivity domain in a time-space domain based on the double-layer graph output by the technology; constructing a joint feasibility domain (JFE), only allowing path planning to be expanded in the domain, and processing a splitting problem of communication repair after the path planning; performing multi-target compression on the path, and balancing task path time consumption, node connection stability and path redundancy consumption indexes; a graph homotopy simplification mechanism is introduced, and redundant edge paths in the JFE are compressed.

The invention relates to the technical field of bridge bearing capacity evaluation, and discloses an existing continuous beam bridge bearing capacity comprehensive evaluation method which comprises the steps that multi-source data are collected to construct a three-dimensional damage voxel field, and an initial finite element model is generated; simulating a heavy load identification control section, and designing an equivalent static load test scheme according to an equivalent internal force principle; executing a field static load test, collecting elastic response data and constructing an actual measurement response vector; calculating errors and iteratively adjusting parameters in a voxel field constraint range to finish model correction; and generating an operation space-time security envelope based on the correction model, and comparing the state in real time to output a control instruction. A three-dimensional damage voxel field is constructed, voxels are used as a normalized data carrier, discrete apparent and internal damage data such as three-dimensional laser scanning point cloud, ultrasonic velocity and rebound strength are mapped into a continuously distributed space field, and the space variability of the rigidity of an existing bridge material can be represented in a refined mode. And the accuracy of describing the physical current situation of the structure by the initial finite element model is improved.

Example solutions for real-world evidence generation using artificial intelligence models and performing trial simulations include: training a large language model (LLM) to receive medical documents that include medical text associated with a patient output predicted values for medical attributes of the patient based on the medical text; performing attribute extraction from structured medical documents, including extracting values for a first plurality of attributes associated with the plurality of patients; performing attribute extraction from a plurality of unstructured medical documents of the plurality of patients using the LLM, including extracting predicted values for a second plurality of attributes associated with the plurality of patients; and performing a survival model simulation that computes estimations of hazard ratio (HR) between cases and controls using real-world data of the plurality of patients extracted in the first attribute extraction and second attribute extraction.

The invention discloses a porous structure generation method and a streamer simulation method thereof, and relates to the technical field of porous structure generation and simulation, and the porous structure generation method comprises the following steps: defining and determining phase parameters of a porous structure; selecting nodes with a preset proportion from all nodes in the set area, setting the nodes as pore seed nodes, and simulating the growth process of pores by using a result of numerical iteration solution processing based on initial values of phase parameters of the set nodes; and constructing a porous structure model. The porous structure streamer simulation method comprises the following steps: removing pores which are not communicated with the surface of a porous structure to obtain a porous solid medium model; setting parameters of gas, and performing transient solution on the discrete electron diffusion model of streamer development; and according to the transient development process of the streamer, analyzing and obtaining the influence of the pore structure on the development characteristics of the streamer. By applying the method provided by the invention, the mesh generation density in porous structure model simulation is reduced, and the efficiency of streamer simulation is improved.

The invention relates to the technical field of integrated circuit manufacturing, in particular to a precise electroplating control system and method for an electroplated layer of a double-layer circuit board, and the method comprises the steps: building a multi-physical field coupling simulation model, and simulating the internal current density and ion concentration distribution of a via hole; constructing a mapping relation between the current density and the side wall electroplating thickness based on a simulation result, and predicting the electroplating thickness; through thickness prediction feedback, a time sequence adjustable pulse current control strategy is designed, the pulse amplitude, the duty ratio and the on-off period are dynamically adjusted, and current distribution optimization is guided; the state of the electroplating process is sensed in combination with real-time monitoring and analysis of multi-source parameters of electrode potential, conductivity and fluid velocity; and based on the prediction model and the real-time parameters, adjusting the pulse waveform and the time sequence through closed-loop compensation. According to the method, the uniformity and controllability of the thickness of the electroplated layer on the side wall of the via hole can be remarkably improved.

The invention provides an aero-engine wide-range component performance self-adaptive correction method and device, and the method comprises the steps: building an aero-engine component-level nonlinear model according to the aero-thermal characteristics of components of an aero-engine; a component characteristic self-adaptive correction factor is introduced to adjust the model performance, matching of a model simulation result and a target measurement parameter is taken as an optimization target, and component performance matching is converted into a self-adaptive optimization problem; establishing a curved surface fitting database, and constructing an adaptive correction factor curved surface fitting model through a moving least square method; the characteristics of the model component are adjusted through the self-adaptive correction factor curved surface, so that the output result of the aero-engine component-level simulation model is kept consistent with the measurement parameters under the large-range working condition, and the large-range correction of the component characteristic diagram is realized. According to the method, the simulation output of the corrected model can be kept consistent with the target engine within a large working condition range, and the deviation degree of the actual characteristics can be accurately represented through the self-adaptive correction result.

The present application relates to the field of nuclear reactor simulation. Disclosed are a fast simulation method and system for multi-physical field coupling and a storage medium. The method comprises: at intervals of a first preset interval time, collecting state information of each physical field of a nuclear reactor in a transient process, so as to obtain physical field snapshots of a plurality of physical fields, the transient process being simulated by a reactor simulation model corresponding to the nuclear reactor; stitching the physical field snapshots of the plurality of physical fields, so as to obtain a fully-concatenated physical field; constructing a matrix on the basis of the fully-concatenated physical field; performing order reduction processing on the matrix, so as to construct a timing model corresponding to the fully-concatenated physical field; and decomposing the timing model corresponding to the fully-concatenated physical field, so as to obtain timing models corresponding to the plurality of physical fields. The fast simulation method for multi-physical field coupling of the present application can reduce the inconsistency between different physical fields while increasing the computation speed.

The invention discloses a blade repairing method and system based on a laser cladding simulation model, and relates to the technical field of blade repairing, the laser cladding simulation model is established, high-speed laser cladding process parameters are set as model variables to be input into the laser cladding simulation model, and the laser cladding process under different parameter combinations is simulated. The method comprises the following steps: preparing a cladding layer, acquiring a simulation result of the cladding layer, preparing cladding samples under different technological parameter combinations, performing characteristic analysis, recording related analysis data, and establishing a performance prediction model of a corresponding mapping relationship between the technological parameter combinations and blade coating performance based on the related analysis data so as to predict the coating performance of the blade under different technological parameter combinations. Combining specific service environment conditions of the blade, setting performance constraint conditions, optimizing a process parameter combination, determining an optimal preparation process parameter combination, performing laser cladding repair on the actually damaged blade, monitoring the laser cladding process in real time, determining whether to intervene or not, and performing quality detection after the repair is completed.

The invention discloses a multi-mode intelligent inspection system and device for an oil field gathering and transportation station yard, and belongs to the technical field of oil field gathering and transportation station yard inspection. The multi-mode intelligent inspection system comprises a data acquisition module, an abnormity judgment module, a station yard model simulation analysis module and a decision analysis and execution module; comprising a vibration camera sensor, a temperature camera sensor, a pressure camera sensor, a flow camera sensor, a liquid level camera sensor, a gas camera sensor, an ultraviolet flame camera sensor, an infrared thermal imaging camera sensor, a visible light camera sensor and a depth camera sensor and is used for collecting process equipment, environment states and personnel operation behavior data of a current oil field gathering and transportation station and recording the data collected by the sensors in a near-field edge server and a cloud server; the time difference of data acquisition is eliminated through a space-time alignment algorithm, and a time sequence health vector in a unified format is generated. According to the multi-mode intelligent inspection system and device for the oil field gathering and transportation station yard, multi-mode inspection can be carried out on the oil field gathering and transportation station yard based on the type of the oil field gathering and transportation station yard, and typical anomalies are recognized and processed.

The invention relates to the technical field of nuclear power internal disaster analysis, and discloses a nuclear power plant internal flooding experiment verification method, device, equipment and medium, and the method comprises the steps: carrying out the modeling analysis of a target plant prototype, and determining a plurality of model parameter values of an experiment scaling model; constructing an experiment scaling model according to the plurality of model parameter values, and determining experiment parameters of an internal water logging experiment; performing an internal water logging experiment on the experimental scale model by using the experimental parameters to obtain an internal water logging experiment result of the experimental scale model; and obtaining an internal water logging simulation result of the numerical model, comparing the internal water logging simulation result with an internal water logging experiment result, and verifying the accuracy of the internal water logging simulation result according to a comparison result. According to the method, the experiment scale model of the nuclear power plant prototype is constructed, the flooding spreading conditions under different working conditions are simulated and compared with the flooding simulation result, the accuracy of flooding simulation calculation is verified, and more accurate guidance is provided for follow-up nuclear power project internal flooding analysis.

The invention discloses a gas pipeline and subway safety distance optimization method based on numerical simulation, and relates to the technical field of subway tunnel safety, and the method comprises the steps: simulating gas pipeline explosion data through a KFX Exsim gas explosion model; establishing a fluid-solid coupling model, a soil dynamic model and a thermodynamic coupling model, and coupling the three models to obtain a multi-physics coupling model; based on a multi-physics field coupling model, performing decomposition and region division on a numerical simulation task in combination with a parallel computing framework, and simulating the influence of gas pipe explosion on the subway; based on a numerical simulation result, evaluating the safety of the tunnel under the action of the explosion load; based on a safety evaluation result, optimizing a safety distance between the gas pipeline and the tunnel; according to the method, the fluid-solid coupling model, the soil dynamic model and the thermodynamic coupling model are established and coupled to form a multi-physical field coupling model comprehensively covering the explosion environment, and accurate simulation of complex behaviors of the subway tunnel in the explosion environment is achieved.

The invention provides a five-axis series-parallel numerical control machine tool machining process virtual monitoring simulation method and system based on digital twinning, and the method comprises the steps: building an information interaction mechanism between a physical machine tool and a digital twinning model, enabling a server side to collect sensor data, and transmitting the real-time data to a client side through a communication protocol; the client processes the data and drives the digital twin model. And aiming at potential errors of twin system forecast, tool setting error compensation can be carried out in real time, so that the machining precision is improved. Meanwhile, the system supports an offline simulation function, combines an inverse kinematics algorithm and an NC code, fuses a material removal model, simulates a machining process, evaluates machining performance and errors, and provides an optimization basis for machining strategies of workpieces with different geometrical shapes and materials. According to the method, bidirectional interaction between the physical entity and the virtual model is achieved, the real-time monitoring and visualization capability of the machining process is enhanced, a reliable simulation evaluation and optimization means is provided for machining of complex parts, and the method has wide industrial application prospects.