2084 results about "Finite element simulation" patented technology

The invention discloses a metal surface quality detection method and system, and relates to the technical field of metal surface quality detection. The method is used for solving the problems of low microdefect detection precision, weak technological parameter relevance and closed-loop control deficiency of the high-reflection surface. The metal surface is irradiated through multi-angle coherent light field serialization, the phase offset of interference fringes is analyzed to generate three-dimensional shape data, and reflection noise interference is restrained. Defect depth gradient is extracted based on dynamic segmentation of process parameter constraint, deposition temperature and pressure deviation are quantified through deconvolution calculation, and process deviation feature distribution is constructed. Finite element simulation is utilized to generate a process-morphology mapping atlas library, cross-domain invariance features are extracted through depth constraint manifold alignment and comparative learning, and a causal correlation model of defect types and process parameters is established. And dynamically adjusting process parameters according to the weight gradient, and reflowing data to update the manifold rule. And high-precision three-dimensional defect detection, process deviation traceability and adaptive parameter optimization are realized.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention discloses an intelligent fault diagnosis method fusing state monitoring and a multi-modal large model, and the method specifically comprises the steps: synchronously collecting time sequence data and a space image through a heterogeneous sensor group and monitoring equipment disposed in power grid equipment, and forming original data; based on the original data, a physical constraint feature vector is generated in combination with an equipment thermodynamic equation and a material deformation rule; performing health index prediction through the lightweight LSTM network based on the physical constraint feature vector; when detecting that the health indexes continuously decrease, clustering an HI time sequence curve by adopting a Gaussian mixture model, judging a degradation stage according to a clustering center distance, and obtaining a stage recognition result; and based on finite element simulation parameters, introducing a reinforcement learning model, optimizing the simulation parameters by taking maintenance cost minimization as a target, and outputting a predictive maintenance work order. According to the invention, intelligent fault diagnosis and accurate maintenance of the power grid equipment are realized, the fault processing efficiency and accuracy are improved, and the power failure loss is reduced.

The invention provides a test risk digital twinning early warning method based on multi-domain cooperative monitoring, and belongs to the technical field of virtual-real fusion test and digital twinning, and the method comprises the steps: firstly, building a fine finite element simulation model which comprises a digital tool system, a digital sensor and a test piece and considers nonlinearity; secondly, performing nonlinear finite element simulation analysis, and constructing a multi-level mechanical response field inversion reduced-order model; thirdly, completing the construction of a complete sensor data set through a data filling algorithm, and carrying out the failure judgment of the first hierarchical structure based on the complete sensor data set; and finally, carrying out future loading level sensor data prediction and completing failure judgment of a second hierarchical structure. Carrying out full-field mechanical response inversion and online real-time correction; and performing response inversion of the region of interest to realize failure judgment of the third hierarchical structure. According to the invention, real-time dynamic monitoring and early warning of the structure test risk can be realized, the real-time performance, the robustness and the accuracy are high, and a powerful guarantee is provided for the safety and the reliability of the structure test.

The invention relates to the technical field of semiconductor device testing, in particular to a SiC MOSFET power cycle testing method which comprises the following steps: S1, building a composite environment testing platform, configuring dynamic testing parameters, automatically calculating a physical boundary and collecting sensor data in real time; s2, establishing a finite element simulation model based on the physical boundary and sensor data, and outputting optimized dynamic test parameters and a simulated stress distribution diagram; s3, synchronously applying composite stress according to the dynamic test parameters and the stress distribution diagram, and collecting multi-dimensional test data in real time; when the system is used, through dynamic boundary calculation and real-time data acquisition, the intelligent degree and reliability of the test are improved, the test period is shortened, the failure prediction accuracy is improved, the system is suitable for reliability evaluation of SiC MOSFET devices in the fields of new energy, aerospace and the like, a large number of physical tests are avoided through virtual simulation, and the reliability of the SiC MOSFET devices is improved. And reduction of device loss and resource waste is facilitated.

The invention belongs to the crossing field of road engineering technology, artificial intelligence and engineering mechanics, and particularly relates to a road structure design method based on a large model and reinforcement learning. A technical closed loop of'natural language input-parameter automatic mapping-specification standard value acquisition-finite element verification-reinforcement learning optimization 'is constructed: by constructing a load parameter mapping table and a material semantic encoder, the system can automatically convert natural language input into engineering parameters such as load, material, layer thickness and the like, and the problem of adaptability to non-standard working conditions is solved. And a deviation characteristic space is constructed by further combining a mechanical theory solution and finite element simulation, and the reinforcement learning agent is driven to quickly optimize under the condition of meeting theoretical constraints. Compared with a traditional trial calculation method and an existing intelligent optimization scheme, the method has the advantages that the number of design iterations and the material cost are greatly reduced, the safety and compliance of an output scheme are improved, and intelligent spanning from experience driving to theory guiding is achieved.

The invention discloses a digital twin system architecture of a vertical shaft and a structural performance monitoring method. The method comprises the following steps: constructing a five-dimensional digital twin system framework suitable for a vertical shaft based on a shaft operation mechanism and performance monitoring requirements; establishing a shaft digital twinborn model with dynamic characteristics; a finite element proxy model is constructed through a virtual-real mapping technology in combination with a grid dimensionality reduction finite element analysis method, and rapid generation of the digital twinborn body is realized. According to the system, a three-dimensional operation platform is constructed based on a Unity 3D virtual engine, and efficient mapping and bidirectional interaction between twin and finite element simulation data are realized by adopting a radial basis function (RBF) proxy model. And real-time acquisition and online prediction are carried out on structural performance parameters in the shaft operation process. And dynamically updating the twinborn model according to a prediction result, and constructing a high-precision and light-weight digital twinborn evolution model, thereby realizing real-time observation of the stress change of the shaft and intelligent monitoring of the structural performance. The method can be widely applied to the fields of shaft safety assessment, maintenance decision making, intelligent mine construction and the like.

The invention discloses a 3D printing path planning method based on electric arc additive anisotropy and a stress field, and the method comprises the steps: constructing a CAD three-dimensional model of a part, and obtaining the stress field of the part through finite element simulation; determining a slice plane; mapping the stress field to a slice plane to form a force flow line; according to the obtained force flow line, rotation transformation regeneration is carried out according to the anisotropy of the used electric arc additive, and a reference trajectory considering the anisotropy of the material is obtained; under the principle of alternate arc starting and extinguishing, the corresponding printing sequence in the layers and between the layers is further planned, and the reference trajectory lines are connected end to end to be planned into a continuous path; and generating a code file, and printing according to the printing sequence. The comprehensive mechanical property of a printed piece is improved.

The invention discloses a multi-fidelity data fusion and physical constraint-based cold heading process parameter staged optimization method, which comprises the following steps of: 1) performing calculation through Deform finite element simulation and an empirical formula, constructing a multi-fidelity initial data set, and improving data consistency through normalization and deviation calibration; 2) constructing a multi-fidelity physical information neural network (PINN) model, and establishing a mapping relation between process parameters and forming quality indexes by adopting a staged training strategy and an adaptive weight adjustment mechanism; and 3) verifying the generalization ability of the model by dividing a training set and a test set, ensuring that a prediction result accords with a volume conservation criterion and a material forming limit, and realizing optimization of cold heading process parameters. According to the method, through multi-fidelity data fusion and physical information embedding, on the basis of enhancing a physical mechanism and multi-data collaboration, the data acquisition cost is reduced, and the generalization of the model is improved; and through dynamic weight distribution and a staged training strategy, the prediction precision and reliability are improved.

The invention discloses a numerical simulation method for an avalanche impact protection structure based on bidirectional coupling, and relates to the technical field of mountain disaster protection, and the method comprises the steps: modeling and initializing an avalanche material source: employing a discrete element theory to simulate an avalanche process, and achieving the whole-process dynamic simulation and reproduction of an avalanche disaster from the starting to the movement to the impact protection structure; protection structure modeling and parameter setting: modeling an avalanche protection structure based on a universal finite element simulation platform; discrete element-finite element contact coupling setting: solving unit stress and strain data; numerical simulation control and solution; impact response extraction and result analysis: performing systematic analysis on the structure response and impact characteristics to evaluate the dynamic performance and safety margin of the structure under the action of avalanche impact; the method can be used for predicting the impact effect of the avalanche disaster on the downstream structure, and provides a theoretical basis and technical support for design and performance evaluation of a protection structure.

The invention discloses a method for simulating a mold temperature field and curing deformation through finite elements and a mold optimization design method based on the method. The finite element analogue simulation method comprises the following steps: firstly, modeling and simulating a mold grid; secondly, calculating equivalent thermal and mechanical parameters of the composite material by adopting a multi-scale homogenization method; then, a heat conduction model of the mold and the composite material blade is constructed and coupled with the curing dynamic model through temperature, and mold temperature distribution is simulated; and finally, establishing a mold thermal expansion and curing stress coupling model, and predicting thermal deformation and stress change of the mold. By identifying the weak area of the mold in the simulation process, the mold is optimally designed. By improving a mold temperature field distribution simulation method, optimizing a grid modeling process and introducing an accurate calculation method of equivalent material parameters, the precision of mold design and the controllability of a curing process are effectively improved, so that the problem of inaccurate mold temperature field distribution and curing deformation prediction is solved.

The invention relates to the technical field of mechanical performance testing of power transformer winding materials, in particular to a method for evaluating the mechanical strength of a transformer winding under a reclosing working condition, and the method comprises the steps: obtaining and preprocessing historical operation data, finite element simulation data and laboratory simulation test data; deploying a micro foil strain gauge, an MEMS acceleration sensor and a flexible piezoresistive sensor based on the preprocessed data, and synchronously collecting radial strain, axial acceleration and cushion block pressure data; after noise filtering, time alignment and feature extraction, multi-directional deformation cooperative influence is analyzed through methods of Pearson's correlation coefficients, partial least square regression and the like; and in combination with a historical damage sample training evaluation model, outputting a mechanical strength degradation level in real time. According to the method, the problems of synchronous acquisition and correlation analysis of multi-direction deformation data under reclosing impact are solved, and accurate evaluation of overall mechanical strength change caused by accumulated deformation is realized.

The invention belongs to the technical field of simulation analysis, and particularly relates to a medium-high voltage switch cabinet internal humidity inversion calculation method based on simulation analysis, which comprises the following steps: establishing a geometric model of a switch cabinet, importing the geometric model into finite element simulation software to obtain a simulation analysis model, adding each physical field and computational domain and boundary conditions thereof, and calculating the internal humidity of a medium-high voltage switch cabinet. All the physical fields are coupled, and mesh generation is carried out; performing numerical solution on the simulation analysis model by using a transient solver to obtain a temperature and humidity distribution condition in the switch cabinet; the simulation analysis model is optimized, a simulation data set is constructed, and a PINN is trained; based on the environment temperature and humidity and the operation current collected in the actual operation of the switch cabinet, the trained PINN is used for prediction, and the steady-state relative humidity of the current transformer is obtained. According to the invention, based on the limited boundary monitoring data, efficient and accurate reconstruction of the humidity field of the key area in the switch cabinet is realized, and a feasible path is provided for equipment state sensing and condensation risk early warning.

The invention provides an arch dam shape efficient intelligent optimization method and system based on an agent model. The method comprises the steps that an arch dam physical-numerical model is constructed, a double optimization target with structural safety and economical efficiency as the core is determined, design parameters are selected, a constraint function is set, and an evaluation index system is established; samples are generated through Latin hypercube sampling, a data set is constructed in combination with finite element calculation, and a multi-task learning architecture is adopted to train a high-precision agent model. And then, coupling the proxy model with a multi-objective optimization algorithm, quickly searching a Pareto optimal solution set, and screening out a comprehensive optimal figure by using a multi-attribute decision-making method. And finally, through a finite element simulation verification result, prediction precision and performance improvement are ensured. The method has the advantages of lightweight modeling, efficient prediction and accurate search, and provides an effective tool for intelligent optimization and rapid decision making of hydraulic structures such as arch dams and the like.

The invention discloses a multi-modal data driven commercial vehicle frame performance prediction method and system. The method comprises the following steps: uniformly coding design variables of a frame; constructing a multi-modal performance response data set based on finite element simulation and test results of mass production vehicle models, and realizing fusion of simulation and test data by adopting a maximum mean difference and related alignment algorithm; constructing a graph perception Transform multi-task prediction model fusing the structure topology and the physical position features, and predicting key performance indexes of the frame under a plurality of typical working conditions; through weighted multi-task loss function joint training, an uncertainty mechanism is introduced to dynamically adjust task weights; and after training is completed, deploying to an inference engine to realize second-level prediction and support increment fine adjustment updating. According to the method, repeated modeling and solving processes are avoided, the frame performance prediction efficiency and the adaptive capacity are remarkably improved, and the method is suitable for rapid evaluation of the frame performance of commercial vehicles of various structural configurations and material types.

The invention discloses an on-orbit spacecraft temperature prediction method and device based on a physical information neural network. The method comprises the following steps: S1, constructing an object model and an orbit heat source model according to a spacecraft to be predicted; s2, solving a space external heat flow of the orbit heat source model, and converting the space external heat flow into a volume heat flow of the object model; s3, constructing a physical information neural network; wherein a three-dimensional transient heat conduction equation is embedded in the physical information neural network; s4, designing a total loss function according to the volume heat flow and the physical information neural network; and S5, training the physical information neural network according to the total loss function to obtain a temperature prediction result of the spacecraft to be predicted. According to the method, the situation that model grids need to be divided in traditional finite element simulation is avoided, the calculation efficiency is improved, the calculation time is shortened, the temperature result can be predicted in real time, and the generalization ability and accuracy of the model are improved.

The invention discloses a micro-cantilever geometric structure parameter inversion system, method and device, and belongs to the technical field of micro-electro-mechanical systems. According to the system, a dynamic model containing geometry, material and electromechanical coupling nonlinearity is constructed, a PINNs network fused with physical constraints is designed, a staged training optimization strategy is adopted, and efficient inversion of micro-cantilever geometric parameters is achieved. The core of the method is that a nonlinear kinetic equation is used as a constraint to be embedded into a neural network for training, experimental data and physical residual errors are combined to construct a composite loss function, and the problems that a traditional method neglects a nonlinear effect, depends on finite element simulation and is low in inversion precision are solved. The average relative error of inversion of the system is lower than 1%, which is obviously superior to that of a traditional method, and the method can be widely applied to MEMS device design, online detection and closed-loop control scenes.

The invention discloses a continuous feeding manipulator control method which comprises the following steps: constructing a finite element simulation model aiming at each joint of a mechanical arm and a tail end execution structure, acquiring inherent frequency distribution data of the structure under different load states and posture working conditions, and generating a frequency sensitive area parameter set based on the data. The method has the advantages that the track segment exciting the resonance risk can be recognized before the path is executed, the inherent frequency of the structure is effectively avoided through frequency matching and path reconstruction, the dynamic adaptability is improved, and pose offset and structural fatigue caused by resonance are prevented. Meanwhile, the system has the self-adaptive closed-loop optimization capacity, disturbance injection testing and periodic response verification are combined, the structure state can be monitored in real time, path re-optimization can be triggered, the stability and precision of the mechanical arm in continuous high-frequency operation are ensured, and the overall reliability and intelligent level of a production line are improved.

The invention relates to the technical field of train bogies, and discloses a deep learning-based railway bogie structure optimization method and system, and the method comprises the steps: collecting a three-dimensional geometric model of a bogie bolster, and constructing a finite element simulation model; and applying a combined load to the swing bolster structure based on finite element simulation to obtain a stress map of the swing bolster structure. And mechanical indexes are extracted to judge whether structure optimization is performed or not. And when optimization is judged to be carried out, a deep learning model is trained based on historical structure samples. And inputting the to-be-optimized swing bolster structure parameters into the deep learning model to obtain parameter adjustment suggestions for optimization. And reconstructing the bolster structure model according to the parameter adjustment suggestion, carrying out finite element verification, and judging whether the optimized structure meets the target performance index requirement or not. According to the method, the design efficiency is improved, the development period is shortened, and efficient collaborative optimization of the railway bogie swing bolster structure among multi-target performance such as strength, rigidity, modality and fatigue life is achieved.

Disclosed in the present application are a dual-sided laser synchronous welding heat source model establishment method, a dual-sided laser synchronous welding heat source simulation method, a dual-sided laser synchronous welding system, and a medium. Firstly, combined heat sources each comprising a Gaussian cylindrical heat source attenuation model and a Gaussian surface heat source correction model are established, and a dual-sided laser synchronous welding heat source model is established by means of a rotating coordinate system; a three-dimensional geometric model and a three-dimensional mesh model of a T-joint are established; and the consistency between simulated molten metal morphology and an actual weld shape is used as a criterion for determining whether parameter settings of the dual-sided laser synchronous welding heat source model are rational, so as to obtain an optimal finite element simulation heat source model. By establishing two heat sources each obtained by combining a Gaussian cylindrical heat source attenuation model and a Gaussian surface heat source correction model, and symmetrically applying the two heat sources to two sides of a T-joint to act on the interior and surface of a weld, the present invention realizes the accurate simulation of a weld penetration zone in a T-shaped thin-plate weldment, and thus enables the prediction of residual stress and welding deformation during a design phase, thereby optimizing the welding process, improving the welding quality and efficiency, and reducing material waste and production costs.

The invention discloses a digital twinborn monitoring method for a self-evolution concrete-filled steel tube arch bridge, and the method comprises the steps: collecting multi-source monitoring data through deploying a multi-mode sensor at a key component of a bridge, and constructing an initial structure topological graph; and dynamic evolution and anomaly detection of the structure topology are realized by using a graph neural network and a Transform structure. Intelligent recognition and fault diagnosis of a structure state are realized by constructing a multi-physical field five-dimensional tensor and a health knowledge graph. And finally, generating and optimizing a structure alternative scheme by adopting a graph generative adversarial network and a genetic algorithm, verifying the performance of the scheme through finite element simulation, and realizing intelligence of bridge health monitoring and maintenance decision making. According to the method, real-time sensing, dynamic modeling and intelligent diagnosis of the structural state of the concrete-filled steel tube arch bridge are achieved by fusing multi-modal sensor data, multi-physical field coupling analysis, graph neural network modeling and an intelligent reasoning mechanism, the real-time performance and accuracy of monitoring are improved, and a scientific basis is provided for structural optimization design of the bridge.

The invention relates to the technical field of artificial intelligence, and provides a finite element simulation-oriented elastomer constitutive model parameter machine learning optimization method, which comprises the following steps of: obtaining stress data and strain data in an elastomer mechanics experiment through steps S1-S4, and respectively carrying out standardization processing on the stress data and the strain data to obtain a training data set; constructing a plurality of parallel physical information neural networks, wherein each physical information neural network is provided with a loss function; and inputting the stress data and the strain data of the to-be-analyzed material into the physical information neural networks, and performing parallel calculation by each physical information neural network to obtain material parameters. Based on the triaxial tensile strain energy stability and prediction precision index, an optimal constitutive model is screened out, and the optimal constitutive model and model parameters are packaged into a callable module. The invention discloses a system applying the method, and the method and the system adopt a multi-physical information neural network parallel prediction and intelligent screening architecture to realize efficient and automatic analysis of the elastomer constitutive model.

The invention discloses a lithium battery internal temperature prediction and reconstruction method based on multi-scale feature fusion, and relates to the field of lithium battery internal temperature prediction, and the method comprises the steps: constructing a lithium battery module electro-chemical-thermal coupling model, obtaining a temperature field data set through a finite element simulation experiment and a physical experiment, and obtaining a temperature field data set; inputting the temperature field data set into a network model based on multi-scale feature fusion to estimate the internal temperature of the lithium battery to obtain initial internal temperature space distribution of the lithium battery module, and reconstructing the initial internal temperature space distribution of the lithium battery module by adopting weighted space-time fusion to obtain final temperature space-time distribution; and constructing a state space model based on the final temperature spatial-temporal distribution, and deducing to obtain a thermal runaway remaining time cumulative distribution function so as to predict the thermal runaway remaining time, thereby realizing beforehand early warning. According to the method, the limitation that a physical model and a chemical model are complex in process is overcome, online monitoring is facilitated, early warning decision support is provided, and then safety and efficiency are improved.

The invention relates to an inertial sensor sensitive structure design method based on a generative adversarial network algorithm, and the method comprises the steps: defining geometric parameters and material parameters of an inertial sensor sensitive structure, and generating a three-dimensional geometric configuration of the inertial sensor sensitive structure; performing finite element simulation on the three-dimensional geometric configuration to obtain a simulation result containing performance indexes; adjusting the geometric parameters and the material parameters for a plurality of times, generating a large number of simulation results, forming a simulation database, and forming a sample data set formed by structure parameter combination and target performance pairing according to the simulation database; a generative adversarial network is adopted, the sample data set is input into the generative adversarial network for training, a reverse structure generation model is obtained, and the reverse structure generation model is adopted for prediction; through an innovative framework of simulation data driving, neural network modeling and closed-loop optimization, a systematic solution is provided for sensor development which is high in performance, low in cost and rapid in iteration.

The invention discloses a Bayesian algorithm-based swivel bridge spherical hinge structure optimization design method, which is characterized in that a parameterized model of a swivel bridge spherical hinge structure is constructed, and a finite element simulation technology and a Bayesian optimization algorithm are combined, so that multi-target global optimization design is realized. The method specifically comprises the following steps: establishing a refined finite element model of the swivel bridge spherical hinge; defining input design variables (spherical radius, supporting radius, pin roll radius and the like) and output optimization targets (maximum contact stress, horizontal and vertical friction moment); adopting Latin hypercube sampling (LHS) to generate a plurality of groups of initial parameter combinations; dynamically selecting a high-value parameter combination through a Bayesian optimization framework to carry out finite element simulation; training a Gaussian process agent model and carrying out iterative optimization; and quantizing the parameter sensitivity and outputting a Pareto optimal solution set. According to the method, the simulation frequency can be remarkably reduced, the design efficiency is effectively improved, and the problem that traditional experience design is prone to falling into local optimum is solved.

The invention provides a high-strength aluminum alloy selective laser melting forming thermal stress prediction method based on deep learning. The method comprises the steps of 1, finite element model establishment and simulation, wherein a heat transfer model and a thermal coupling model are established; an SLM process is used as a simulation object, a Gaussian model is adopted to define a laser heat source, and thermal stress distribution under different process parameters and different sample sizes is simulated; wherein the process parameters comprise laser power, scanning speed and hatch spacing; step 2, data processing and deep learning model training; comprising the steps of data preprocessing, neural network model architecture determination, adversarial network part generation and model configuration and training. 3, evaluating and optimizing the model; and 4, thermal stress prediction and process optimization. By learning a complex mode in finite element simulation data through a deep learning model, efficient and accurate thermal stress prediction is achieved, and the problems that traditional finite element analysis is complex in calculation and large in resource consumption are solved.

The invention discloses a method and device for dynamically adjusting the clamping force of a thin-wall part, equipment and a storage medium, and relates to the field of data simulation. The method comprises the following steps: determining the clamping form and size of a pneumatic clamp tool according to the size of a thin-wall part, simulating a clamping stress process through finite element simulation, and establishing a mapping model between clamping force and deformation; initial machining process parameters are set according to the size and precision requirements of the thin-wall part, and the maximum allowable clamping force under different material allowances is obtained based on the mapping model; stability qualitative analysis is conducted on the clamping stability, and milling parameters are optimized according to the analysis result; and the milling process and the milling parameters are obtained in real time, and the clamping force in the milling process is dynamically adjusted at fixed time by controlling the pneumatic clamp tool through the PLC. Dynamic adjustment of the clamping force is achieved through the established mapping model of the clamping force and deformation, and the problem of deformation caused by rigidity change of the thin-wall part in the machining process is solved by combining layered milling optimization and PLC timing control.

The invention provides a special-shaped air film hole thermal mechanical fatigue simulation part design method, which comprises the following steps of: 1, analyzing the strength and the service life of a turbine blade, and identifying a key part of the service life of a special-shaped air film hole; 2, determining that the thermal mechanical fatigue simulation piece is of a hollow structure; 3, parameters of a clamping part of the hollow simulation piece are stipulated; 4, determining the mechanical stress of the key part through three-dimensional finite element simulation of the turbine blade; 5, verifying the included angle between the principal stress direction and the growth direction of the key part of the simulation part; step 6, measuring stress by using a strain gauge under a normal-temperature initial mechanical load, and verifying whether the load is compliant or not; 7, performing three-dimensional finite element simulation analysis on the temperature field of the test piece and calculating thermal stress; 8, controlling the thermal stress distribution of the section of the simulation piece by controlling the temperature distribution; step 9, when cooling gas is introduced into the inner cavity of the special-shaped gas film hole simulation piece, carrying out a thermal mechanical fatigue test by adopting an in-phase mechanical-temperature load; and step 10, optimizing the structure of the special-shaped gas film hole and analyzing the strength.

The invention relates to a building structure health monitoring method and system based on machine learning, and the method specifically comprises the following steps: firstly, building a target building three-dimensional numerical model through finite element simulation, generating a simulation signal, injecting Gaussian white noise, and adjusting model parameters to form a data set with health category labels; performing data enhancement by combining adaptive wavelet denoising with dynamic normalization, and extracting and enhancing high-resolution time-frequency features through adaptive window short-time Fourier transform and adaptive frequency band enhancement; then, a neural network model fusing structure physical prior guidance and multi-scale space-time interaction is constructed, a feature matrix is modulated, fused and coded to obtain a refined feature vector, and damage state probability distribution output is achieved; and training is carried out by using a feature consistency and prediction smoothness regularization term constraint model, and finally, the trained model is deployed, so that building structure health state evaluation and safety early warning are realized, the monitoring accuracy and reliability are improved, and effective technical support is provided for building safety guarantee.

The invention discloses a movable formwork construction monitoring method and system based on finite element simulation, and relates to the field of bridge construction monitoring and structure health monitoring. The problems that in traditional movable formwork construction monitoring, a model is fixed, early warning lags behind, and it is difficult to dynamically reflect the real state of a structure are solved. According to the method, a digital structure module containing adjustable parameters is established, and working condition simulation is carried out to identify a theoretical high-risk area and optimize sensor layout; performing pre-simulation before each process to output a predicted value, synchronously acquiring actual measurement data during construction, and calculating a standardized deviation sequence; dynamic safety evaluation and early warning are carried out based on time domain feature extraction and multi-index fusion; reverse calibration and online updating are carried out on model parameters through Bayesian variational inference by utilizing historical deviation data, so that closed loop of simulation, monitoring, early warning and model optimization is realized, and the safety monitoring precision and real-time performance in the construction process of the movable formwork are remarkably improved.