346 results about "Structural mechanics" patented technology

The invention provides a fan system multi-physics field coupling simulation and modeling method and device, and the method comprises the steps: building a coupling simulation frame of an electromagnetic field, a temperature field, a flow field and a structural mechanical field through multi-physics field collaborative modeling, employing a finite volume method FVM and multi-body dynamics MBS for collaborative solving, and precisely simulating the dynamic response of a fan under a complex working condition. And real-time simulation and verification: combining an S CADA system and a machine learning algorithm to realize online calibration and dynamic verification of a multi-physics field model, and supporting fan structure stability prediction under extreme conditions of typhoon, turbulence and the like. A multi-physics field coupling simulation software environment is developed, the functions of wake effect analysis, fan array layout optimization and the like are provided, the construction cost of an offshore wind field is reduced, and the power generation efficiency is improved.

The invention relates to the technical field of fluid machinery design, and discloses a fluid machinery through-flow component intelligent design simulation system, which comprises a multi-physics field modeling module, which is used for constructing fluid mechanics, structural mechanics and heat conduction models based on preset boundary conditions and physical attributes; the intelligent optimization decision module is used for constructing and defining a through-flow performance optimization objective function; the cooperative calculation module is used for constructing a comprehensive performance evaluation model based on the candidate design parameter set; the virtual experiment feedback module is used for reconstructing a corresponding through-flow component structure model according to the multi-target evaluation result; and the scheduling control module is used for controlling algorithm path selection and computing resource allocation in the optimization process. According to the method, a method of combining multi-physics field coupling modeling and a dynamic feedback mechanism is adopted, and design parameters are continuously adjusted through simulation data fed back in real time, so that the optimization process can be dynamically updated according to actual operation conditions, and the optimization convergence speed and the overall performance are improved.

The invention relates to the technical field of crack detection, in particular to a building crack intelligent detection and prediction method based on multi-modal data fusion, which comprises the following steps: constructing a multi-source heterogeneous data set including an image data set, a thermal imaging data set and a vibration data set; simulating complex environment interference through a GAN (Generative Adversarial Network), and dynamically enhancing image training data; designing a double-branch multi-mode fusion network, and extracting the edge and texture features of the crack; designing a multi-modal fusion branch, and carrying out feature alignment and weighted fusion on vision, thermal imaging and vibration data; and based on a long short-term memory network LSTM and a structural mechanics model, a crack trend prediction module is developed, and early warning is provided for building structure safety. According to the method, intelligent monitoring and early warning of building structure safety are realized by constructing a multi-source heterogeneous data set, enhancing image training data through a GAN, extracting features by using a double-branch multi-mode fusion network, predicting a crack trend based on an LSTM and a structural mechanical model, and finally integrating system deployment.

The invention discloses a steel-concrete hybrid girder bridge operation monitoring and static and dynamic analysis method based on multi-source data fusion, which comprises the steps of 1, structural response data acquisition, 2, multi-source data fusion processing, 3, building a plurality of operation state evaluation models based on parameters acquired by a sensor, and 4, carrying out static and dynamic analysis on the operation state evaluation models. 4, performing static analysis, dynamic analysis and static and dynamic collaborative analysis on the girder bridge based on the comprehensive feature set after multi-source data fusion, and 5, providing data support for the static and dynamic collaborative analysis by the operation state evaluation model through the comprehensive feature set, and meanwhile, evaluating the operation state of the girder bridge. Reversely supplementing the operation state evaluation model with the structural mechanical response characteristics excavated in the static analysis and dynamic analysis processes to form a data closed loop; the method has the advantages that through data sharing and feature interaction, evaluation result cross validation and multi-scale analysis linkage, cooperation of an operation state evaluation model and static and dynamic collaborative analysis is achieved, and a data closed-loop and multi-dimensional evaluation system is formed.

The invention relates to the technical field of flood discharge tunnel detection, and discloses a flood discharge tunnel automatic inspection and erosion defect intelligent detection method. The method comprises the following steps: acquiring structural state data in the flood discharge tunnel and performing standardization processing; dividing standard classification data according to preset hydrological working condition intervals, and extracting multi-modal sensing features of each interval to generate a working condition feature set; monitoring the switching frequency of the hydrological working condition interval, and activating a dynamic analysis strategy to identify an abnormal working condition interval when the switching frequency exceeds a preset threshold value; performing feature decoupling on the working condition feature set of the non-abnormal working condition interval according to the matching degree of the historical erosion stage feature and the current multi-mode sensing feature, and separating out an erosion feature subset; on the basis of coupling relevance of mechanical parameters and hydraulic parameters of the tunnel structure, parameter collaborative correction is carried out on the erosion feature subset; and finally, reversely tracing a potential defect source region along a defect evolution path by utilizing the corrected erosion feature subset, determining a repair priority and outputting a maintenance instruction.

The invention discloses a monitoring and simulation mutual feedback correction concrete dam structure parameter intelligent identification method, and belongs to the technical field of dam safety monitoring, and the method comprises the steps: S1, building a finite element model, and generating a parameter set X through LHS; s2, calculating a water pressure component to obtain a data set Y, and normalizing a sample S = [X, Y]; s3, dividing the training set and the test set according to a ratio of 4: 1, and training a CBLA model; s4, the HST is used for separating the water pressure component, and ICPKO is combined with CBLA for optimization; and S5, iteratively terminating the output of the optimal parameter, and performing finite element verification. According to the concrete dam structure parameter intelligent identification method based on monitoring and simulation mutual feedback correction, the problem that a traditional algorithm is prone to being caught in local optimum is solved, the efficiency and precision of gravity dam structure mechanical parameter feedback are improved, the optimal combination of deformation moduli of a dam body and all partitions of a dam foundation meeting the actual situation is successfully inverted, and the method is suitable for popularization and application. And a new technical means is provided for structural response analysis of the gravity dam in the operation period.

The present application relates to a BIM-based hanging basket design method, computer device and computer-readable storage medium, wherein the design method comprises following steps: a parameter acquisition step; a hanging basket modularization step: decomposing structures of hanging baskets into a plurality of design modules; a selection step; a calculation step: calculating parameters of the design modules using a structural mechanics calculation method and / or a finite element calculation method; preliminary BIM model building step: building models of the design modules and a preliminary BIM model of the hanging basket; an optimization iteration step: converting the preliminary BIM model of the hanging basket into a finite element model for calculation, and feeding calculation results back to the preliminary BIM model for structural adjustment, repeatedly performing iterative calculations to obtain optimized hanging basket parameter data; final BIM model acquisition step: the BIM model of the hanging basket adjusted according to the optimized hanging basket parameter data is a final BIM model of the hanging basket. The design method enables autonomous calculation and design of the hanging basket with good versatility.

The invention discloses a large transport vehicle bridge safety assessment method based on fuzzy comprehensive evaluation, which comprises the following steps: constructing a bridge bearing capacity reduction coefficient model, integrating four factors of material performance degradation, structural damage, environmental corrosion and load combination, and determining index weights by adopting an analytic hierarchy process; and a multi-stage evaluation system is established in combination with a fuzzy mathematics theory. A prestressed concrete continuous beam bridge in Jiangsu section of Jinghai highway is taken as an object, finite element simulation is used for analyzing the load effect of large vehicles under independent and mixed passing conditions, and verification shows that when the large vehicles pass at the mixing rate of 20%, the most unfavorable section safety coefficient of the bridge reaches 1.35, and the standard requirements are met. According to the method, fuzzy mathematics and structural mechanics analysis are creatively fused, a standardized process of parameter collection, model calculation and safety judgment is established, the efficiency is improved by 40% or above compared with a traditional static load test, the method is applied to transportation examination and approval of five bridges in Jiangsu province, a scientific basis is provided for selection of large transportation channels, and the risk of bridge damage is effectively prevented.

The invention relates to the technical field of crane stress analysis, in particular to a crane structure parameter stress analysis method and system. The method comprises the following steps: constructing an accurate three-dimensional structure simulation model by obtaining crane device data and operation logs, and carrying out crane operation analogue simulation based on the model; performing boom structure division on the simulation data, respectively obtaining simulation data of the truss boom type crane and the telescopic boom type crane, evaluating stress coupling increment of the truss boom type crane and telescopic boom type mechanical conduction response characteristics, and comprehensively detecting dynamic mechanical response of the crane; based on the dynamic response data, identifying the breakage risk of crane components, determining the chain type degradation condition of the components, and evaluating the operation stability gradient attenuation of the crane in combination with the three-dimensional structure model; carrying out structural mechanics optimization treatment according to the stability attenuation condition; through crane stress analysis, the crane can run more stably and efficiently.

The invention provides a bridge structure damage monitoring method and system based on multi-modal data fusion in the technical field of bridge structure damage monitoring. The method comprises the steps that S1, a server obtains a large amount of historical bridge monitoring data and emergency simulation data and inputs the historical bridge monitoring data and the emergency simulation data into a finite element model; s2, performing structural mechanical simulation on the finite element model to obtain bridge structure damage simulation data, and labeling historical bridge monitoring data through the bridge structure damage simulation data and the emergency simulation data to construct a data set; s3, training the bridge structure damage identification model through the data set; s4, the server collects real-time bridge monitoring data and inputs the real-time bridge monitoring data into the bridge structure damage recognition model, and a bridge structure damage recognition result is obtained; and S5, mapping the real-time bridge monitoring data and the bridge structure damage identification result to the BI model, and outputting a three-dimensional visualization report. The method has the advantages that the accuracy and the real-time performance of bridge structure damage monitoring are greatly improved.

The invention discloses a concrete arch dam global structural mechanical parameter zoning rapid inversion method, and relates to the technical field of dam operation safety monitoring and management, and the method comprises the steps: building a dam body and foundation three-dimensional finite element model through finite element software according to engineering design and monitoring data, and building a foundation three-dimensional finite element model according to damming material mechanical parameter information; partitioning the three-dimensional finite element model, analyzing the sensitivity of mechanical parameters of each region, determining sensitive mechanical parameters influencing the deformation of the concrete arch dam, and carrying out self-adaptive intelligent sampling on the sensitive mechanical parameters according to a sensitivity analysis result, and constructing a deep learning agent model reflecting a nonlinear relationship between the sensitive mechanical parameters of the dam and the deformation of each monitoring point, and carrying out deep learning inversion on the elastic modulus of the dam body and the deformation modulus of the bedrock. According to the invention, the method can efficiently and accurately invert and determine the structural mechanical parameters of the arch dam in the actual operation period, and provides a good basis for the safety analysis of the dam.

The invention discloses a multi-dimensional dynamic coupling water conservancy project toughness evaluation method. The method comprises the following steps: S1, data acquisition; s2, disaster chain dynamic simulation: training a GAN model by using the data collected in the step S1, the trained GAN model being used for generating virtual scenes of different disaster combinations, constructing a "structural mechanics-hydrodynamics-ecological model" coupling framework, simulating dynamic evolution after disasters occur, and generating multiple groups of disaster chain evolution processes; s3, calculating a comprehensive toughness index, and evaluating the engineering toughness of each group of disaster chain evolution process generated in the step S2; according to the method, a multi-dimensional dynamic coupling technology system is constructed, the precision and timeliness of toughness evaluation of the water conservancy project are improved, compared with a traditional static evaluation method, the technology realizes dynamic simulation of a disaster chain cascade process and quantitative analysis of multi-system interaction influence, and a more scientific solution is provided for safety management of the water conservancy project.

The invention discloses a system and method for health monitoring of a steel grid structure, and belongs to the technical field of structural mechanics testing, and the method comprises the steps: obtaining the position information of steel grid nodes, generating a node position distribution diagram through a self-positioning algorithm between wireless vibration sensors, recognizing a stress concentration region in the steel grid, and generating node key parameters. According to the node key parameters, the sampling frequency of the wireless vibration sensor is adjusted, and a dynamic sparse sampling instruction is generated; executing a dynamic sparse sampling instruction, and collecting steel net rack vibration data and current environment parameters; performing multi-physical field coupling analysis on the vibration data and the current environmental parameters to generate a comprehensive damage index; and when the comprehensive damage index exceeds a preset alarm threshold value, outputting an alarm signal. According to the method, the dynamic sparse sampling technology driven by the node key parameters and the multi-physics coupling analysis model are adopted, and low-energy-consumption and high-precision structural damage real-time monitoring and self-adaptive early warning can be achieved.

The invention relates to the technical field of building design and construction, and discloses a BIM (Building Information Modeling)-based building steel bar truss floor support plate design method which comprises the following steps: inputting design requirements of a building project, including a load requirement, a structure requirement and a construction requirement; an optimized mechanical model of the steel bar truss floor support plate is established according to the input design requirements, the model comprises stress, deformation and material performance of each rod piece, and stress analysis of the model is based on a balance equation of structural mechanics; and performing nonlinear finite element analysis, considering material nonlinearity and geometric nonlinearity effects, and calculating stress, deformation and displacement of the steel bar truss to obtain the stress state of the steel bar truss. Through BIM modeling, nonlinear finite element analysis, multi-objective optimization and robust optimization technologies, high consistency of design and construction of the steel bar truss floor support plate is realized, the design precision, the structural safety and the resource utilization efficiency are improved, and the stability and reliability of the design under uncertain conditions are enhanced.

The invention provides a construction tunnel surrounding rock stability evaluation method based on digital twinning and a related device. The method comprises the following steps: constructing a tunnel digital twinning model of a tunnel construction section; geological data and real-time progress position data of a target mileage segment in the construction process are obtained; mapping the geological data to a digital twinborn model, and updating soil body unit parameters; according to the real-time progress position data, synchronously updating activation states of a soil body unit and a supporting structure unit in the model by adopting a state change method; and extracting a surrounding rock stability index based on the updated digital twinborn model, evaluating the stability of the current construction tunnel surrounding rock in combination with a safety threshold to obtain a construction tunnel surrounding rock stability evaluation result, and constructing a tunnel construction process dynamic mapping-oriented digital twinborn model by utilizing the description capability of a finite element method on structural mechanical behaviors. Geological conditions and construction progress information which are obtained in real time on site are used as input, and the construction period structure safety risk of the drilling and blasting method tunnel is evaluated and predicted.

The invention discloses a deep learning-based road and bridge construction measurement data processing method and system, and the method comprises the steps: extracting spatial topological features in point cloud data through a GCN graph convolutional network, extracting texture and structural features in image data through improved ResNet-50, extracting time sequence features in sensor data based on an LSTM long-short-term memory network, and carrying out the deep learning-based road and bridge construction measurement data processing. Introducing a multi-head self-attention mechanism to dynamically calculate weights of different modal features; constructing a VAE variational auto-encoder based on the fusion feature vector, judging an abnormal point through a reconstruction error, and performing secondary verification on the abnormal point in combination with a bridge structure mechanical model; and adopting a GAN generative adversarial network to generate alternative data of the abnormal point detection result, and selecting an optimal correction scheme through Bayesian optimization. And the data processing efficiency and the abnormal point detection accuracy are improved.

The invention belongs to the technical field of make-up torque calculation, and discloses a casing direct connection screwed joint make-up torque prediction method, through an empirical formula formed by fitting of a structural mechanical model and test data, the casing direct connection screwed joint make-up torque can be calculated directly by measuring steel pipe basic structure parameters, repeated tests are not needed, and the prediction accuracy is high. And the working efficiency is greatly improved. The prediction process only depends on basic structure parameters capable of being rapidly measured and is not limited by working conditions. A fitting formula comes from verification of a large amount of test data and theoretical models, and precision and universality are both considered; and multi-parameter factor comprehensive modeling is adopted, errors caused by a single factor are effectively eliminated, the accuracy of a prediction result is high, and the stability and reliability of torque prediction are improved. By reasonably predicting the screwing-on torque, the connection strength of the steel pipe is effectively guaranteed, and underground potential safety hazards caused by improper screwing or excessive screwing are avoided, so that the integrity of a shaft structure and the safety of construction operation are guaranteed.

The invention relates to the technical field of vehicle intelligent management, and discloses a vehicle state monitoring method and system based on deep learning, and the method comprises the steps: analyzing the physical dependence relation among vibration abnormality detection, loosening positioning and fatigue evaluation monitoring tasks based on structural mechanics constraints, and carrying out the monitoring of the vibration abnormality detection, the loosening positioning and the fatigue evaluation; generating a task dependence directed graph; inputting the multi-modal monitoring data matrix into a shared encoder of a multi-task learning network to generate a unified representation vector; respectively inputting the unified representation vector into a vibration anomaly detection decoder, a loose positioning decoder and a fatigue evaluation decoder, and outputting an initial prediction result of each task; and establishing a conditional probability model between task outputs based on the constraint relationship defined by the task dependent directed graph, and generating a consistency diagnosis result meeting physical constraints through maximum posteriori estimation. The technical problems of one-sided diagnosis results, mutual contradiction and low calculation efficiency are solved.

The invention provides a method for calculating a non-center-guide multi-arch tunnel structure under blasting vibration velocity control, and belongs to the field of tunnel engineering design and construction, and the method comprises the following steps: 1, obtaining stratum physical mechanics and blasting excavation design parameters of a designed tunnel; 2, establishing a stratum-structure model of the non-middle-guide multi-arch tunnel, performing numerical simulation calculation of tunnel blasting load action, and obtaining a blasting excavation surrounding rock plastic zone and blasting vibration velocity distribution; 3, the relation between the surrounding rock peak blasting vibration velocity and surrounding rock rigidity reduction is established, and a surrounding rock rigidity reduction coefficient is obtained; 4, based on surrounding rock rigidity reduction, a load-structure model of the non-middle-guide multi-arch tunnel is established, and structural stress safety is analyzed; and step 5, based on a numerical calculation result and a safety discriminant of the non-center-guiding multi-arch tunnel lining, evaluating the mechanical safety reliability of the tunnel structure, and feeding back and optimizing the tunnel structure design according to the mechanical safety reliability of the tunnel structure, including structure enhancement or blasting vibration velocity control target adjustment.

The invention discloses a wind power generation tower drum steel-concrete composite structure design method based on genetic algorithm optimization. The method specifically comprises the steps that operation data and design parameters of different types of wind power generation tower drums under different working conditions are obtained to construct an original data set; secondly, determining a design target function and quantifying, and setting each target weight coefficient; a genetic algorithm population is initialized, and individuals are represented by design parameter codes; decoding individuals of the population to obtain an actual design scheme, performing structural mechanical analysis, and calculating mechanical property indexes; calculating an individual fitness value according to the target function and the weight coefficient; new individuals are generated through selection, crossover and mutation operation, whether termination conditions are met or not is judged, if yes, the design scheme corresponding to the individual with the highest fitness is output, and if not, iterative optimization continues. According to the method, the genetic algorithm is used for iterative optimization, the comprehensive performance of the design scheme of the steel-concrete composite structure of the wind power generation tower under multiple targets is effectively improved, and a better design result is obtained.

The invention discloses a U-shaped aqueduct prestress multi-beam cooperative tensioning control method and system. The method comprises the steps that S1, a quantitative derivation model is constructed, based on an aqueduct structural mechanical equilibrium equation, a prestress coupling effect item is introduced for calculation, a prestress coupling matrix is output, and an initial tensioning sequence is derived; the device is simple in structure, and an initial tensioning sequence is deduced by establishing a mechanical equilibrium equation and introducing a coupling effect item; meanwhile, stress / displacement deviation data collected by a monitoring system in real time are fed back to an edge calculation terminal to calculate a control strategy in real time, adjustment is executed by a tensioning device, and therefore a field data closed-loop optimization mechanism is constructed, the tensioning force and sequence can be dynamically corrected, the limitation of a static model is broken through, and the construction efficiency is improved. And a displacement constraint term is introduced as a punishment or control condition in the optimization process, so that deformation overrun of key parts of the structure is effectively prevented, and the precision, safety and intelligent level of prestress construction are remarkably improved.

The invention provides a method for determining the working load environment of an aero-engine rotor connection structure, belongs to the field of dynamic analysis and design of aero-engine rotors, and aims at a rotor structure system with a connection structure in an advanced aero-engine to determine the type of a load possibly borne by the connection structure. Based on a rotor finite element model and aerodynamic thermodynamic parameter input of an engine, aerodynamic load analysis, temperature distribution simulation, rotor dynamic characteristic simulation and the like are carried out respectively, and working load amplitudes of a connection structure under different working conditions are obtained. The energy differential method is innovatively adopted to obtain the bending moment at the connecting structure, and the accuracy of bending moment calculation is improved. According to the method, the working load environment of the rotor connection structure can be accurately obtained, accurate load boundary conditions are provided for mechanical property simulation of the rotor connection structure, so that the simulation analysis precision is improved, and meanwhile, reliable data support can be provided for the mechanical property of the connection structure and rotor dynamics optimization design.

The invention relates to the technical field of network public opinion monitoring, and discloses an emotion energy dynamic change-based public opinion inversion recognition method, which comprises the following steps of: constructing a weighted emotion potential energy field, and calculating effective emotion potential energy of nodes; and then calculating semantic field topological stress representing the viewpoint structure tearing degree and emotional potential energy field system entropy representing the energy distribution disorder degree, on this basis, constructing an entropy-stress two-phase state space and a dynamic hysteresis loop model, quantifying the social damping characteristics of the system, monitoring the system state in real time, and determining the state of the system. When the two-phase space yield judgment condition and the damping failure judgment condition are met at the same time, a public opinion inversion early warning signal and a risk probability index are output, the critical characteristics of public opinion system instability can be accurately recognized from the dimensions of structural mechanics and dynamics, and the false alarm rate of inversion early warning is effectively reduced.

The invention relates to the technical field of engineering monitoring, in particular to a bridge structure state monitoring method and system based on digital twinning, and the method comprises the steps: building a multi-source monitoring data matrix based on a data timestamp; establishing a geometric model, a material attribute model and a load model of a bridge structure based on the multi-source monitoring data matrix, and fusing to obtain a bridge digital twin initial model; and carrying out structural mechanical analysis based on the bridge digital twin initial model to obtain stress distribution data of each region of the bridge. Through real-time acquisition and analysis of multi-source monitoring data, the method can dynamically evaluate the structural state of the bridge and timely identify potential safety hazards, so that the safety of the bridge is improved; and through calibration of the initial model, the obtained digital twin precise model reflects the real state of the bridge, and a more accurate data basis is provided for subsequent structural analysis.

The invention provides a bridge health monitoring missing displacement reconstruction method based on a conditional diffusion model, which comprises the following steps: firstly, constructing a diffusion model taking multi-source physical sensing information as a guide condition, and inputting multi-source heterogeneous sensor information such as strain and acceleration as physical conditions; secondly, a residual module and a time coding mechanism are introduced in the reverse denoising process, and the time sequence feature and deep feature extraction capacity of the model is enhanced; and finally, gradually reconstructing a complete and physically reasonable displacement time history from missing displacement data containing noise by using the model, and realizing reliable reconstruction of bridge structure response. The method aims at stably and efficiently reconstructing a complete displacement time history which is accurate in numerical value and conforms to the structural mechanics principle from incomplete or completely missing displacement data, so that the integrity of SHM data is recovered, and a reliable data basis is provided for subsequent structural safety assessment and damage identification.

The invention belongs to the field of hydraulic engineering, and discloses a physical information network-based intelligent optimization method and system for a three-dimensional structure design of a long-distance water delivery tunnel lining, and the method comprises the steps: S1, building a three-dimensional geometric model of the long-distance water delivery tunnel lining; s2, establishing a structural mechanical analysis physical model considering surrounding rock stress and a construction excavation process; s3, establishing a multi-parameter physical information network model and performing training; and S4, the lining bearing capacity, the maximum deformation and the crack width serve as constraint boundaries, the engineering cost serves as an optimization target, a set of lining structure design parameters is obtained through optimization, and the lining structure design parameters comprise tunnel shape parameters, lining thickness and construction supporting parameters. The method is simple and practical, the lining bearing capacity, the maximum deformation and the crack width can be efficiently and accurately analyzed and calculated according to existing data, inversion optimization of lining structure design parameters is achieved, and the optimal design parameter set is obtained.

The invention provides a suspended dome structure stability detection method and system based on pre-stress analysis, and relates to the technical field of structural mechanics analysis, and the method comprises the steps: carrying out the key point analysis of a suspended dome structure, configuring a sensor group, and building a sensor network; establishing a structural state vector; performing reason backtracking according to a real-time prestress loss analysis result, and establishing a loss identifier; carrying out finite element model correction by utilizing the loss identifier and the structural state vector, carrying out buckling analysis and nonlinear time-history analysis, and establishing a stability critical value prediction result; and generating a stability detection result. According to the method and the device, the technical problem that the stability detection result of the suspended dome structure is affected due to the fact that the finite element model is generally based on idealized hypothesis and has deviation from the actual situation is solved, the suspended dome structure is monitored and analyzed in real time, the finite element model is dynamically corrected, the actual adaptability of the finite element model is improved, and the stability detection result of the suspended dome structure is improved. Therefore, the accuracy of stability detection results is improved.

The invention discloses a steel tower stress analysis method based on twinborn simulation, which relates to the technical field of structural mechanics simulation and comprises the following steps: constructing a high-precision simulation layer and a rapid calculation layer according to structural parameters and material parameters of a steel tower; inputting a preset multi-field environment condition and structure state combination into the high-precision simulation layer to generate a stress response result, extracting stress mode features, and generating a multi-field stress mode feature library; steel tower structure response data and environment data are collected and preprocessed to form observation vectors, the observation vectors are compared and matched with the multi-field stress mode feature library, and target stress mode features and corresponding simulation configuration parameters are generated; and inputting the target stress mode characteristics, the simulation configuration parameters and the observation vectors into a rapid calculation layer to carry out twinborn simulation calculation, and generating a stress distribution data set and a response index set. According to the method, the multi-field stress mode feature library is constructed, so that the problems of high computing resource consumption and incapability of effectively utilizing historical data are solved, and the computing efficiency is improved.

The invention discloses a method for setting and controlling a buckle cable adjustment threshold value in large-span arch bridge hoisting construction, which comprises the following steps of: acquiring construction parameters of a current buckle section, and acquiring a corresponding cable adjustment target value; constructing a structural mechanics finite element model of the target buckle cable based on the construction parameters, and introducing a buckle cable multi-harness nonlinear stress mechanism into the model to calculate upper and lower limit thresholds of cable force in the tensioning process; determining an elevation adjustment threshold according to the target axis, the target elevation, the accumulated deviation and the pre-deformation compensation amount in combination with a geometric linear control model; and generating an environmental influence correction coefficient by utilizing construction environmental parameters, and dynamically correcting the cable force and the elevation threshold value to realize real-time updating of the threshold value in the hoisting process. According to the method, the problems of nonlinearity of multiple wire harnesses of the large-span arch bridge buckle cable, high linear sensitivity of the arch rib, remarkable environmental influence and the like are solved, high-precision cooperation of cable force control and linear control is realized, and the safety, controllability and construction efficiency of hoisting construction are improved.

The invention belongs to the technical field of composite material structural mechanics and computational mechanics, and particularly relates to a composite material fan blade composite crack propagation simulation method, program and equipment and a storage medium. According to the method, key rotation and a failure judgment criterion based on a key deflection angle are introduced into the conventional state near-field dynamic model, so that the crack propagation behavior of the composite material under the shear or mixed mode load can be more accurately captured and simulated. According to the flat plate mapping method, the improved PD model suitable for the flat plate is successfully expanded and applied to the fan blade airfoil section, the problem of complex geometric modeling is solved, and the flat plate mapping method is suitable for complex geometric structures. According to the method, a near-field dynamics method is adopted, crack paths do not need to be preset or grid reconstruction does not need to be carried out, and complex expansion behaviors such as spontaneous bifurcation and merging of cracks can be naturally simulated; the PD model can be used for meticulously describing the mechanical behaviors and crack evolution of each layer and interlayer in the laminated plate, including intra-layer cracking and interlayer layering.