2611 results about "Finite element simulation" patented technology

The invention relates to the technical field of water photovoltaic flexible support pile foundation construction data processing, in particular to a process for a water photovoltaic flexible support pile foundation, which comprises the following steps: fusing meteorological, hydrological and pile deformation multi-source data through a dynamic weight distribution mechanism, and generating a space-time associated dynamic load spectrum by adopting wavelet packet transformation and entropy feature extraction; a collaborative analysis architecture of a finite element simulation and machine learning agent model is constructed, an anchoring depth mapping relation and a real-time design parameter adjustment instruction are output in parallel, and macroscopic fluid parameters and microscopic material degradation characteristics are associated through a data cascade interaction correction mechanism; full-life-cycle data version management is achieved through the block chain technology, and finite element model re-calibration and protection strategy dynamic updating are triggered. The dynamic load response analysis precision and long-term stability of the pile foundation are improved, and the deformation coordination requirement of the flexible support in the complex water area environment is met.

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 relates to the technical field of wind power generation, in particular to a long flexible blade monitoring method and system based on multi-source data fusion, which adopts a multi-source data fusion technology, collects optical fiber strain, vibration, environment and image data by arranging sensor nodes on a fan blade, and generates comprehensive feature data through preprocessing; and extracting potential feature vectors by a multi-modal auto-encoder, and mapping the potential feature vectors into predicted stress data by using a physical information neural network in combination with offline finite element simulation data and physical constraint conditions. Then, a control decision is generated through the multi-modal deep fusion network and hierarchical reinforcement learning, the blade state is adjusted in real time, and the model is optimized through a closed-loop feedback mechanism; according to the invention, precise monitoring and active regulation and control of special working conditions such as blade pollution and icing 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 a valve performance simulation and optimization method and system based on mixed deep learning, and the method comprises the steps: S1, carrying out the finite element simulation analysis of a valve, and solving through the thermosetting coupling of a multi-physics field, stress, deformation and temperature distribution data of the valve under different working conditions and maximum thermal stress and maximum thermal deformation displacement of key parts of the valve are obtained; s2, after data processing, taking stress, deformation and temperature distribution data of the valve under different working conditions as input parameters, taking maximum thermal stress and maximum thermal deformation displacement as output parameters, training a neural network model, and establishing a thermal-structural performance prediction model; s3, on the basis of a prediction result, a reference point-based non-dominated genetic algorithm is adopted, a reference point generation and self-adaptive agent model is combined, and multi-objective optimization is carried out to output an optimal parameter combination so as to adjust valve design; according to the invention, deep learning and multi-objective optimization are carried out on the finite element simulation result by using the neural network, and the valve performance prediction precision and optimization efficiency are improved.

The invention discloses a numerical control machining path control system based on artificial intelligence, particularly relates to the field of numerical control machining, is used for solving the problems of continuity and stability of a curvature mutation area in cutter path planning, and accurately captures global and local geometric characteristics of a curved surface through a curvature constraint feature matrix and a curvature adaptive convolutional network. A machining track meeting the differential geometric continuity is generated, and the problem of path breakage of a curvature sudden change area is effectively avoided; meanwhile, energy scale criteria and curvature manifold constraints are introduced, the machining track is dynamically repaired, geometric repair and physical stability are balanced, cutting force sudden change and stress concentration are reduced, and therefore the machining reliability is enhanced; in addition, through finite element simulation of the digital twin platform and synchronous updating of servo system parameters, dynamic closed-loop matching of a control instruction and a machining state is achieved, and the control efficiency and the surface quality are further optimized. And therefore, high-precision, high-stability and high-efficiency collaborative optimization is realized in complex curved surface processing.

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 relates to the technical field of computer science, in particular to a stamping prediction method based on a multi-machine learning model and an automatic compensation device.The stamping prediction method comprises the steps that firstly, a sheet microstructure and material characteristics are obtained through a transmission electron microscope and an atomic probe tomography technology, and then finite element simulation sampling is conducted; a data set is expanded by using an adaptive algorithm, multiple models are constructed and trained to obtain a final springback prediction model, an automatic compensation device is embedded to realize automatic compensation, and the automatic compensation device covers visual parameter input, embedded springback prediction, springback compensation calculation and an automatic control module. Parameters can be visually input, springback can be accurately predicted, a compensation value can be calculated, and the process can be adjusted. The method aims at solving the problems that in the high-strength plate stamping process, due to springback, the size and shape of a part deviate from the design requirement, and a traditional prediction and compensation technology is insufficient in precision and efficiency.

The invention relates to the field of durability evaluation and life prediction of reinforced concrete structures, in particular to a reinforced concrete life prediction system and method based on material degradation simulation, and the system comprises an erosion leading edge tracking module which employs a three-dimensional convolutional neural network fused with topology invariance to extract chloride ion distribution image features, generating steel bar corrosion front three-dimensional coordinates and depth information; the transfer learning parameter inversion module is used for inputting the steel bar corrosion front three-dimensional coordinates into a finite element simulation platform, executing parameter inversion calculation and generating a corrosion rate evolution curve; the corrosion rate prediction module is used for generating a corrosion rate prediction curve by using a long-short-term memory network; and the online correction module is used for collecting temperature and humidity information in the concrete structure, calculating the influence weight of environmental factors on the corrosion rate, correcting a corrosion rate prediction curve, updating long-term and short-term memory network parameters, identifying and tracking the steel bar corrosion leading edge in a high-precision manner, and accurately capturing a complex three-dimensional corrosion form.

The invention discloses an intelligent monitoring management system and method for a wire harness production line, and relates to the technical field of wire harness manufacturing, and the method comprises the steps: extracting data of crimping force and mold temperature from historical wire harness production data, coding the data into a real number coding gene chain, generating an initial gene pool in combination with material attributes, and constructing a digital twin model; the method comprises the steps of simulating stress field distribution and a heat conduction effect in a wire harness crimping process through finite element simulation, obtaining a twinning predicted value, pre-verifying process parameters in an initial gene bank based on a digital twinning model, screening process parameter combinations with deformation errors smaller than an error threshold value, and issuing the process parameter combinations to a crimping machine through a production line control center. According to the method, data of real-time wire harness deformation quantity is obtained, the data of the real-time wire harness deformation quantity is compared with a twinborn predicted value frame by frame, a deformation error rate is generated, and an abnormal traceability report is generated based on the deformation error rate, pre-verification is performed by constructing an initial gene pool and combining a digital twinborn model, so that optimization of process parameters is more accurate and effective.

The invention relates to the technical field of track structure health monitoring, in particular to a track crack recognition and fatigue life prediction method and system based on deep learning and finite elements, and the method comprises the steps: collecting and preprocessing a track surface crack image, and obtaining crack image data; a deep neural network model is trained, and a corresponding track crack recognition algorithm based on deep learning is determined; identifying and extracting positions and geometric dimensions of the cracks; based on the geometrical characteristics and the material attributes of the track, establishing a corresponding track finite element model, and inputting the position and the geometric size of the crack into the track finite element model to generate and obtain a track model containing crack damage; based on wheel-rail rolling contact fatigue finite element simulation, simulating the mechanical response of a track structure, determining the fatigue life of the track in combination with the stress-strain distribution of the track, and predicting the development trends of cracks under different operation conditions; automatic and efficient identification of track cracks is realized, and the accuracy of fatigue life evaluation is improved.

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 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 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 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 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 belongs to the technical field of finite element simulation optimization, and provides a hot upsetting process multi-target collaborative optimization method and system based on finite element simulation, and the method comprises the steps: firstly constructing a hot upsetting process multi-field coupling finite element model, and then deploying a sensor network in hot upsetting equipment to collect actual process data. Actual data and simulation data are matched through a space-time registration algorithm, then a parameter optimization model is constructed based on a dual-channel depth deterministic strategy gradient algorithm, and a technological parameter solution set is obtained through calculation; and finally, a multi-objective optimization function is defined according to a preset optimization index, and optimal hot upsetting process parameters are screened out in combination with an entropy weight method and a reference point-based non-dominated genetic algorithm. According to the method, high-precision simulation, real-time monitoring and multi-target collaborative optimization of the hot upsetting process are achieved, and the production efficiency and the product quality of the hot upsetting process are remarkably improved.

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.

A finite element simulation technology-based atmospheric-corrosion prediction method for an air-conditioner heat exchanger is provided. In the method, a material corrosion prediction model for the material of a heat conduction pipe of an air-conditioner heat exchanger to be tested and the material of heat dissipation fins of said air-conditioner heat exchanger is debugged by means of step S1 and step S2; an assembly corrosion prediction model is then optimized by means of step S3 and step S4; and when a multi-physics field in an air-conditioner serving environment is comprehensively simulated and coupled by using a working condition environment field, the atmospheric corrosion of said air-conditioner heat exchanger after operating in an outdoor serving environment to be tested under a working condition for any preset duration is finally predicted by means of step S5 and step S6, such that an atmospheric-corrosion prediction result is obtained.

The invention relates to the technical field of synchronous reluctance motor structure optimization, in particular to a permanent magnet auxiliary synchronous reluctance motor structure parameter multi-objective optimization method. The method comprises the steps that motor structure optimization parameters are selected, and the change range of the parameters is determined; carrying out comprehensive sensitivity analysis according to the selected optimization target; performing hierarchical processing on the optimization variables according to the comprehensive sensitivity index, dividing the optimization variables into a strong sensitive layer and a weak sensitive layer, and taking parameters in the strong sensitive layer as to-be-optimized parameters of the next algorithm; comparing the prediction precision of the back propagation neural network, the radial basis, the extreme learning machine, the support vector machine and the kernel extreme learning machine, and selecting an agent model with an optimal prediction effect; combining the established high-precision agent model with a fast non-dominated sorting genetic algorithm, and searching an optimal combination of to-be-optimized parameters; and finally, carrying out single parameterization scanning determination on parameters in the weak sensitive layer by utilizing finite element simulation so as to obtain optimal structural parameters of the permanent magnet auxiliary synchronous reluctance motor.

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 bird-related fault identification and early warning method based on a transmission tower gap model, and relates to the technical field of power system safety monitoring. The method comprises the following steps: firstly, constructing a three-dimensional transmission tower gap model containing a local insulated armor, and obtaining an electric field distortion feature library under various working conditions through finite element simulation; further simulating interference behaviors of the bird body, the bird nest and the excrement in the safety gap, calculating the discharge probability, and synthesizing and labeling a bird image for training a risk decision model. And in combination with a target detection algorithm integrating an attention mechanism and an SPPCSPC module, space coordinates of bird targets are extracted in real time and mapped to a high-risk area, and a discharge risk score and a breakdown trend index are output by using an LSTM network. And when the risk index exceeds a dynamically adjusted early warning threshold, generating an early warning signal and a fault coordinate. According to the invention, the precision and foresight of bird-related fault identification are improved, and the method has good engineering adaptability.

The invention provides a three-dimensional laser point cloud detection method and system for a tunnel water-endowed area lining wide and thick belt, and the method comprises the steps: detecting the tunnel water-endowed area lining wide and thick belt through a rolling ball method, scanning the tunnel point cloud through three-dimensional laser, and collecting the laser point cloud data of the tunnel water-endowed area lining wide and thick belt; establishing a high-fidelity tunnel twinborn model; simulating tunnel deformation conditions through finite elements, constructing a finite element simulation tunnel deformation analysis model, and outputting a tunnel deformation true value of the tunnel twinning model; building a tunnel simulation scanning virtual simulation platform according to the tunnel twinborn model and the laser scanning characteristic data; performing simulated three-dimensional laser scanning on the tunnel twin model in a virtual environment to obtain large sample detection data; training a deformation detection neural network model in an auxiliary manner according to the large sample detection data; performing tunnel point cloud registration through a neural network model; a tunnel section point cloud is obtained by fitting the central axis of the tunnel, and tunnel deformation analysis is carried out; and the tunnel deformation analysis processing accuracy is obviously 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 low-temperature minimal quantity lubrication cooling method in aluminum alloy precision machining, and relates to the field of aluminum alloy precision machining. Machining parameters such as the cutting speed and the feeding amount are determined according to the aluminum alloy material, the machining requirement and the tool performance, and finite element simulation optimization is carried out; high-purity vegetable oil-based lubricating oil is selected, a nano anti-wear agent and an antioxidant are added, and cooling liquid is prepared by mixing compressed air through vortex tube refrigeration; spraying to a processing area through a special multi-channel nozzle, and dynamically adjusting the flow; multiple sensors are used for monitoring the temperature, the tool abrasion and the surface quality, and adjustment is conducted when threshold values are exceeded; and the cooling liquid is recycled after being subjected to centrifugation and membrane filtration treatment, and the metal filing is classified, recycled and reused. The cooling and lubricating effects are good, cutting heat and tool abrasion are reduced, the machining precision is improved, and the tool service life is prolonged; environment-friendly lubricating oil is recycled, so that pollution and cost are reduced; real-time monitoring and adjustment guarantee stable machining quality, machining efficiency is improved, and high-end manufacturing requirements are met.

The invention provides an intelligent heat dissipation optimization method for an AI chip based on core particle heterogeneous integration and embedded with a bionic fractal micro-channel, and relates to the technical field of AI chip heat dissipation. Comprising the following steps: designing an orthogonal test according to fluid characteristic parameters and micro-channel manifold structure parameters; determining an orthogonal test table corresponding to the junction temperature of the chip according to an orthogonal test and finite element simulation with set boundary conditions; determining the priority of parameter combinations in the orthogonal experiment table according to the orthogonal experiment table by using range analysis and variance analysis; according to the orthogonal experiment table of which the priority is determined, fitting a function of the junction temperature of the chip by adopting a machine learning method, and optimizing by utilizing a multi-objective variable method to determine an optimal heat dissipation parameter; and dynamically adjusting the heat source layout based on the optimal heat dissipation parameter according to a core particle heterogeneous integration technology to obtain a core particle heterogeneous integration AI chip with optimal heat dissipation. According to the invention, the problems that the cooling requirement of the traditional micro-channel design structure in the vertical direction is unbalanced, and overheat points exist in the chip layer are solved.

The invention discloses a high-precision and high-reliability harmonic reducer tooth profile simulation machining design method, and belongs to the field of precision transmission. A composite cycloid flexible gear tooth profile is designed by establishing a harmonic reducer geometrical relationship model, deducing a flexible gear deformation equation, an accurate rotation angle relationship and a meshing theory based on an envelope method, and a conjugate region and a conjugate tooth profile are calculated by utilizing MATLAB. Actual deformation and theoretical deformation errors of the flexible gear are analyzed through finite element simulation, flexible gear profile parameters are optimized, an actual neutral layer curve is generated, and then a rigid gear profile is enveloped and calculated. The tooth profile design adopts a non-zero inclination angle composite cycloid, so that the meshing area is expanded by more than 30%, and the maximum stress of the flexible gear is reduced by 22.75% in combination with a linear modification method and a finite element modification method. In addition, through a multi-physical field simulation verification system (including static deformation, dynamic contact and fatigue life prediction) and a neural network optimization tooth shape parameter, the transmission performance is remarkably improved. According to the method, the torsional rigidity can be improved by 66%, and the transmission error is controlled within a high-precision range.

The invention discloses a carbon fiber cloth reinforced damaged concrete structure detection and analysis method, and relates to the technical field of concrete structure health monitoring, and the method comprises the steps: extracting a high-risk point set from a three-dimensional damage distribution map, carrying out the weighted K-means clustering to construct a Voronoi subspace, calculating the failure probability, and dividing an emergency repair block set; generating a processing queue by using the repairing blocks according to priorities, planning a spiral path of the plasma spray gun by adopting a TSP algorithm, and generating a surface roughness distribution diagram in combination with laser confocal and white light interference detection; according to the roughness partition matching epoxy resin-carbon fiber dynamic matching scheme, a pressure thermodynamic diagram is generated through finite element simulation and electrode time-sharing electrification. Through fusion of the damage evolution graph and the digital twin model, the plasma etching parameters and the conductivity of the carbon fiber cloth are dynamically adjusted, so that the tensile strength of the repaired area is improved, and full-life-cycle health monitoring is realized.

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.