5388 results about "Turbine blade" patented technology

The invention relates to the technical field of fan blade fatigue damage prediction. The invention provides a fan blade fatigue damage prediction method and system. The method comprises the following steps: constructing a coupling finite element model based on blade anisotropy parameters; blade surface three-dimensional strain field data, blade vibration acceleration signals, environment temperature and humidity and wind speed and direction data are obtained in real time, and a multi-dimensional monitoring data set is constructed; based on the multi-dimensional monitoring data set, nonlinear coupling features of all the load components are extracted, a multi-dimensional feature tensor is obtained, and a reference stress field matched with the current working condition is generated; inputting the multi-dimensional feature tensor and the reference stress field into a bidirectional long-short-term memory network, and establishing a data-physics combined driven damage evolution model; and positioning a damage area based on a damage probability distribution diagram output by the damage evolution model. The problems that in the prior art, prediction errors are obvious, sensitivity to early damage is insufficient, the false alarm rate is high, and accurate positioning of the damage position and quantitative prediction of the residual life are difficult to achieve are solved.

The invention discloses a wind turbine generator blade crack defect detection method based on a feature recombination network, and the method comprises the steps: obtaining a to-be-detected wind turbine generator blade image, inputting the to-be-detected wind turbine generator blade image to a pre-trained feature recombination network model, and obtaining a crack defect recognition result of the to-be-detected wind turbine generator blade image. According to the method, multi-scale feature extraction is carried out on the wind turbine generator blade image through the small target feature extraction capability of the backbone network layer by using an optimized and constructed feature recombination network model, and then more crack detail information in the multi-scale features is reserved by means of feature recombination and feature expansion of the neck network layer. The context information of the small target is fully acquired for feature fusion, and then the crack defect identification result of the wind turbine generator blade image is acquired through the detection head, so that the crack defect of the small target is detected more accurately.

The invention relates to the technical field of wind turbine generators, particularly provides a blade load evaluation method, electronic equipment and a storage medium, and aims to solve the problem of load evaluation after elastic deformation of blades of a wind turbine generator. Therefore, the blade load evaluation method provided by the invention comprises the following steps: acquiring a pre-constructed aeroelastic model and a blade tip loss coefficient database; based on the aeroelastic model and the blade tip loss coefficient database, iteratively calculating and determining the final deformation quantity of the blade; and determining at least one of an aerodynamic load, a centrifugal force load, a rotation load and a gravity load of the blade based on the final deformation quantity of the blade. According to the method, the final deformation quantity of the blade is determined through the pre-constructed aeroelastic model and the blade tip loss coefficient database, the deformation state of the blade can be accurately predicted, the aerodynamic load, the centrifugal force load, the rotation load and the gravity load are calculated based on the final deformation quantity of the blade, and high-precision data support is provided for design, operation and maintenance of the blade.

The invention provides an intelligent inspection method and system for fan blades based on an unmanned aerial vehicle. The method comprises the steps that a visible light camera, an infrared thermal imager and a laser radar are integrated on the unmanned aerial vehicle; generating a flight route covering the whole surface based on the fan blade three-dimensional model; when the unmanned aerial vehicle is controlled to fly along the flight route, visible light images, infrared temperature data and laser point clouds are synchronously collected; the visible light image, the infrared temperature data and the laser point cloud are input into a defect analysis model, the defect type and grade are identified through conjoint analysis, a temperature abnormal area is positioned, and geometric deformation is quantified; a health assessment report is generated, the health assessment report comprises a defect positioning map and a graded maintenance decision matched with defect grades, multi-source data are collected through integration of a visible light camera, an infrared thermal imager and a laser radar, and then fusion analysis and recognition are carried out, so that compared with a manual inspection mode, the intelligent level of fan blade inspection is improved, and the inspection efficiency is improved. And the inspection efficiency of the fan blade is also improved.

The invention relates to the technical field of fatigue evaluation, and discloses an offshore wind turbine blade frequency domain fatigue evaluation method and system based on an MIMO nonlinear dynamic system, and the method comprises the steps: collecting the load data of an offshore wind turbine blade; preprocessing the load data; an MIMO nonlinear dynamic system is established; establishing a dynamic mapping relation between the preprocessed load data and the frequency domain response by using an MIMO nonlinear dynamic system; decomposing the time domain dynamic response data by using a frequency domain decomposition method, and quantifying the influence weight of each frequency band load on the blade stress based on a frequency domain energy distribution matrix to obtain a frequency domain energy distribution function of the total stress; and calculating the fatigue damage of the blade through a frequency domain fitting optimization method based on the frequency domain energy distribution function of the total stress. The method solves the problems that fatigue damage evaluation confidence is insufficient under the multi-load coupling effect and actual engineering requirements are difficult to meet in the prior art, and has the characteristic that the coupling effect of the wind load and the wave load can be accurately processed.

The invention relates to the technical field of aero-engines, and discloses an automatic design method for aero-engine turbine blades, which analyzes design requirements through a language large model, combines a retrieval enhancement technology and a turbine blade expert knowledge base, performs interdisciplinary coupling verification by utilizing a step-by-step reasoning template, and generates and optimizes part design schemes and parameters. And then calling parametric modeling and an AI simulation agent model to carry out multi-physics field prediction, comparing a result with a requirement, if the result meets the requirement, outputting design, otherwise, automatically iterating and adjusting parameters until the parameters reach the standard or reach the maximum number of iterations. According to the method, end-to-end intelligent closed-loop design from design requirements to design results can be realized, manual intervention is reduced, a design scheme meeting performance requirements can be quickly generated, the design efficiency can be improved, the design cost can be reduced, and the design quality can be improved.

The invention relates to the technical field of new energy, in particular to a fan blade composite deicing system and method.The fan blade composite deicing system comprises a sensing layer, a decision-making layer, an execution layer and a feedback layer, and the decision-making layer comprises a graph neural network ice type recognition module, a CFD-icing coupling simulation module, a health management module and an energy consumption scheduling algorithm unit; the execution layer comprises a control unit and an execution unit, the control unit comprises a dynamic partition heating controller and a self-adaptive vibration controller, and the execution unit comprises an electric heating system and a vibration deicing system; compared with the prior art that a single deicing mode is usually adopted, and the adaptability to different ice type physical characteristics is poor, the scheme adopts a differential execution strategy, and partition electric heating control is carried out on different chord length areas of the blade based on the ice type recognition result; and high-frequency piezoelectric standing wave vibration is adopted for glaze ice, low-frequency eccentric wheel vibration is adopted for glaze ice, and the beneficial effects that the deicing efficiency is remarkably improved, invalid energy consumption is reduced, and damage to blades is reduced are achieved.

The invention discloses a tenon morphology contour detection method and a contour tolerance, tooth pitch and cross-rod distance evaluation method in the technical field of aero-engine turbine blade detection, and the method achieves the spatial scanning of the tenon morphology contour through a four-axis coordinate device and a confocal spectrum sensor, collects data points, and can achieve the non-contact detection of the tenon morphology contour. The actual contour of the appearance contour of the tenon is obtained, the detection efficiency is higher, meanwhile, the surface of the tenon cannot be damaged, high-precision and high-efficiency detection of the appearance contour of the tenon is achieved, and the defects that current tenon measurement adopts manual operation, precision is not high, and digitization cannot be achieved are overcome. The detection requirements of large size curvature, high precision and complex evaluation parameters of the blade tenon of the aero-engine can be met at the same time.

The invention relates to the technical field of fan defect detection, in particular to a fan blade inspection method based on an unmanned aerial vehicle, which comprises the following steps: acquiring static basic data of a fan; constructing a blade reference model based on the static basic data of the fan, and collecting dynamic vibration response data according to a preset short-time dynamic vibration excitation rule; identifying a blade high-risk area according to the dynamic vibration response data, and generating a layered surrounding route in combination with a blade reference model; the unmanned aerial vehicle is controlled to collect blade multi-angle images according to the layered surrounding route, the blade multi-angle images are processed to recognize surface defects, and visual confidence is generated; and mapping the dynamic vibration response data to a blade reference model by using a space-time alignment model, extracting power spectral density characteristics of corresponding positions, calculating a damage index by fusing visual confidence, and outputting an inspection report. The objective of the invention is to solve the technical problem that blade internal damage is difficult to identify in a static inspection mode.

The invention discloses a fan blade diagnosis method and system based on voiceprint perception, and relates to the technical field of intelligent monitoring of wind power equipment, and the method comprises the steps: carrying out frequency domain compensation processing, stripping environment wind noise components, and generating a pure voiceprint signal based on a wind flow characteristic parameter set; based on the pure voiceprint signal, identifying a sensitive acoustic frequency band of the fan blade, exciting a sound wave phase synergistic effect in the sensitive acoustic frequency band, and generating an enhanced voiceprint signal; quantifying the internal damage depth of the fan blade material according to the enhanced voiceprint signal, and calculating a sound energy flow disorder degree index; according to the sound energy flow disorder degree index, the blade health state of the fan is judged through a multi-dimensional acoustic characteristic state space mapping mechanism, and a three-dimensional health assessment report is generated; through a physical level wind noise stripping technology, a transfer function matrix is generated based on vortex interference modeling and sound wave-airflow phase offset correction, frequency domain energy reweighted decoupling is realized, and environmental wind noise is accurately stripped in a strong turbulence environment.

The invention relates to a turbine blade global temperature field reconstruction method based on a multi-modal physical constraint network, and belongs to the technical field of aero-engine thermal management. Aiming at the problems of insufficient global heat conduction path modeling, lack of physical rules and blank region differentiation perception, carrying out normalization and position coding on sparse temperature measurement points to form a Transform encoder input sequence; extracting features through an encoder and compressing the features into global feature vectors through self-attention pooling; decoding the image into a temperature field image by adopting a U-Net generator fused with jump connection; a multi-scale discriminator is constructed, a semantic weighting mechanism is introduced, and differentiated weights are distributed to cooling holes, tenons and pressure surface areas; a physical constraint loss and local energy conservation loss are constructed based on a heat conduction equation, and a weighted training model is combined with confrontation loss and temperature error loss. The method is used for providing high-precision temperature distribution data support for turbine blade structure optimization design, cooling system efficiency evaluation and thermal fatigue prevention.

The invention provides a turbine blade design method which comprises the following steps: S1, dividing a turbine blade profile into four sections at equal intervals along the radial direction according to the size of a turbine, and calculating section parameters of a middle-section turbine blade profile; s2, connecting the four sections by adopting a third-order Bezier curve, defining a blade front edge angle theta as an included angle between the Bezier curve and a blade front edge radial inlet angle alpha, and generating a three-dimensional blade model by changing the inclination angle theta of each section; s3, performing optimization to obtain an optimized theta parameter; and S4, according to the optimized theta parameter, fitting each section blade profile by using a third-order Bezier curve to generate a turbine stator and rotor three-dimensional model. According to the method, the machine learning method is applied to design of the turbine blade, the three-order Bezier curve, the CFD numerical simulation method and the particle swarm optimization are utilized, the blade parameters are given, the geometric parameters can be quickly and accurately predicted, the calculated amount is reduced, the cost is reduced, the design efficiency is improved, and the quality of the designed blade is guaranteed.

The invention relates to the technical field of fan blade deicing, and discloses an intelligent temperature control type fan blade electric heating deicing control system and method, and the system comprises a meteorological sensing module which is used for collecting the environment temperature, humidity, wind speed and rainfall type; the blade state monitoring module is used for acquiring blade surface temperature distribution and ice layer thickness data; the dynamic decision-making module is used for generating a partition heating instruction based on the data of the weather sensing module and the blade state monitoring module; the temperature control execution module is used for converting the instruction into a gradient signal; the heating array module is used for executing electric heating deicing operation in the blade surface subareas; and the energy coordination module is used for dynamically constraining the total power of the system. The meteorological sensing module and the blade state monitoring module are integrated through the system, real-time collection and fusion of multi-source data of blade surrounding environment parameters and the thickness of an ice layer are achieved through the design, comprehensiveness and accuracy of icing monitoring are improved, a reliable basis is provided for dynamic decision making, and therefore the response capacity and early warning precision of the system to icing risks are improved.

The invention provides a wind power blade surface multi-scale defect detection method based on an SASED-YOLO algorithm. The wind power blade surface multi-scale defect detection method comprises the steps of performing preprocessing and image enhancement processing on a wind power blade defect image; cascading the feature extraction network with M first modules, performing down-sampling on the processed wind power blade defect image, and performing feature extraction based on a down-sampling result to obtain a feature map of the wind power blade defect image; calibrating the channel weight of the feature map based on a second module; contextual information from different receptive fields is fused based on a third module; the attention weights of the multi-scale targets in different feature maps are distributed based on the dependency relationship between attention mechanism attention space and channels and the dependency relationship; and training the network model according to the training set and the verification set based on a weight distribution result to obtain a defect detection model, and performing defect detection on the wind power blade surface detection image based on the defect detection model.

The invention provides a wind power blade aeroelastic coupling modeling method considering a pre-bending shape, which comprises the following steps of: establishing a finite element kinetic equation of a geometric precise beam structure of a pre-bending blade and solving to obtain the section speed of the blade; calculating the inflow velocity according to the section velocity of the blade; calculating a lift coefficient after dynamic stall of the blade; establishing an unsteady aerodynamic load calculation equation according to the inflow velocity and the lift coefficient after dynamic stall of the blade; and constructing a pre-bent blade aeroelastic coupling model according to the unsteady aerodynamic load calculation equation. The method can solve the technical problems that a modeling method in the prior art cannot accurately describe nonlinear deformation of the blade, the continuity of a blade model is poor, the number of calculation units is too large, the calculation efficiency is low, and the modeling method is not suitable for the long flexible blade.

The invention relates to the technical field of aero-engine combustion chamber outlet temperature field monitoring, and discloses a multi-source data fusion method for an aero-engine combustion chamber outlet temperature field, and the method comprises the steps: unifying a multi-source data space-time reference through a standardized space grid and a precise time synchronization protocol; mapping the data to the same space-time grid by adopting a self-adaptive interpolation algorithm, and embedding a temperature gradient continuity constraint; the confidence coefficient weight is dynamically calculated based on a sensor drift error model and CFD residual feedback, and working condition self-adaptive weighted fusion is realized by using a fuzzy logic rule base; and the interpolation parameters and the weight distribution threshold values are optimized in a distributed mode through a federated learning framework, and fuel injection parameters are adjusted in combination with combustion chamber closed-loop control. The temperature field reconstruction precision and reliability are remarkably improved, the service life of turbine blades can be prolonged, the combustion efficiency can be optimized, and the method is suitable for real-time monitoring and regulation of the combustion state of an aero-engine.

The invention discloses a composite material wind power blade damage detection method, and aims to solve the problems of difficulty in quantitative analysis and low distinguishing precision of various damage types in composite material wind power blade damage detection in the prior art. Comprising the following steps that acoustic emission signals in the operation process of the wind power blade are obtained in real time and preprocessed, and the acoustic emission signals comprise parameter data and waveform data; performing unsupervised clustering on the preprocessed parameter data to obtain a preliminary clustering result; training the preprocessed waveform data by adopting an improved multi-branch convolutional neural network, and outputting an accurate classification result; the unsupervised clustering result is verified by using the supervised learning classification result, and the accuracy of the clustering result is evaluated; and fusing the quantitative index accumulated energy, the damage type and the frequency, and constructing a comprehensive damage evaluation model to determine the damage degree. According to the method, the unsupervised clustering method and the supervised learning method are combined, and accurate identification and quantification of the damage are realized.

The invention discloses a waste wind power blade recycling process. The waste wind power blade recycling process comprises the following steps that S1, a waste wind power blade is cut into blocks; wherein manufacturing materials of the waste wind power blade comprise carbon fibers and epoxy resin; s2, the block is put into a swelling solution to be subjected to swelling treatment; wherein the swelling liquid comprises a swelling agent and a solvent, the swelling agent is an inorganic metal chloride, the solvent is composed of acetone and water, in the solvent, the mass percentage of acetone is 10-40%, the mass percentage of water is 60-90%, and in the swelling liquid, the molar concentration of the swelling agent is 0.004-0.1 M; s3, cracking the block subjected to swelling treatment to obtain a cracking product containing the inorganic filler and the carbon fiber material; and S4, recycling the carbon fiber material in the cracking product. The recycling process has the advantages of being efficient, environmentally friendly, low in cost and the like, and is suitable for large-scale resource recycling of the wind power blades.

The invention provides an optimization method and device for a blade casting process, equipment and a storage medium, and the method comprises the steps that process parameters of at least one process of blade casting are obtained; generating input sequence data based on the process parameters; inputting the input sequence data into a pre-trained process quality prediction model to obtain a predicted casting qualification rate and a forging defect occurrence probability output by the process quality prediction model; wherein the process quality prediction model is a transformation neural network model, and the process quality prediction model has learned in advance to obtain process parameters based on a blade casting process, and has the capability of predicting the qualification rate of castings and the occurrence probability of forging defects; and optimizing the process parameters based on the predicted qualified rate of the casting and the occurrence probability of the forging defects. According to the technical scheme, the forging qualification rate of the blade can be increased, and the development iteration frequency and period of the turbine blade casting process are reduced.

The invention provides a multi-angle cutting laser pipe cutting machine, and belongs to the technical field of laser cutting equipment, the multi-angle cutting laser pipe cutting machine comprises a machining module, a feeding module is arranged at one end of the machining module, a discharging module is arranged at the other end of the machining module, and the multi-angle cutting laser pipe cutting machine further comprises a cutting assembly connected with the machining module and used for cutting pipes; the device has the advantages that flexible adjustment of the position of the focus lens is achieved through cooperation of air cylinder driving and telescopic spring buffering, focus requirements of pipes with different thicknesses are met, flexible connection of a cooling channel is achieved through the telescopic pipe, continuous and stable heat dissipation of the focus lens is guaranteed, and the service life of the lens is prolonged; the electromagnetically-driven rotating cylinder drives the turbine blades to rotate to form negative-pressure airflow, an air curtain is formed at the cutting end, metal residues and dust are prevented from being diffused to the focus lens and the lens cone, a physical isolation barrier is formed through the protection sleeve, impurities are prevented from being attached to equipment parts or blocking a pipeline, long-term stable operation of equipment is guaranteed, and diffusion of the equipment to the working environment is reduced.

The invention relates to the technical field of torque converters, in particular to a hydrodynamic transmission power chain optimization method and system.The method comprises the steps that a hydrodynamic torque converter parameterized geometric model is built, and a pump impeller blade outflow angle, a guide wheel runner input / output angle, turbine blade geometric parameters and the relative position of a pump impeller and a turbine are set as design variables; based on CFD three-dimensional flow field simulation, a k-epsilon model is adopted for rapid analysis, an SST model is adopted for transient simulation, and simulation data under different variable combinations are obtained; training a BP neural network by using the simulation data, inputting each design variable, and constructing a multi-target optimization model with maximization of efficiency and minimization of noise as targets; a design variable combination is selected in the Pareto optimal solution set, a model is reconstructed, and an optimized hydraulic torque converter is obtained; according to the method, the problems that the design optimization efficiency of the hydraulic torque converter is low, the parameter adjusting period is long, and good compromise between the transmission efficiency and noise suppression is difficult to obtain can be solved.

The invention discloses a pyrolysis recovery system for cooperative treatment of waste wind power blades by a power station boiler, and belongs to the technical field of solid waste recovery. The system comprises a power station boiler, a pyrolyzing furnace, a mixed flue, a nitrogen air flue and a controller. Oxygen is accurately controlled by mixing flue gas at different temperature levels and mixing nitrogen, and a safe and stable heat source is provided for the pyrolyzing furnace; pyrolysis gas returns to a boiler hearth through an outlet flue to be burnt out, and recyclable fibers and residues are conveyed out through a chain grate stoker; and the controller is linked with each sensor to execute oxygen control, temperature regulation, fiber quality protection and safety interlocking operation. The problems that in pyrolysis of the waste wind power blade, heat source adaptability is poor, oxygen concentration is out of control, fiber quality is poor, the risk is high, and pyrolysis gas is difficult to dispose are solved, and gradient utilization of energy, intrinsically safe operation, high-quality and high-purity recovery of fibers and collaborative treatment of pollutants are achieved.

The invention relates to a multi-dimensional high-resolution turbine blade heat-flow coupling field synchronous measurement method and system, and the method comprises the steps: obtaining a target spot image of known space distribution in a measurement space, and building a three-dimensional space mapping function; carrying out particle matching on the velocity field measurement original image based on a three-dimensional space mapping function, and reconstructing a three-dimensional particle trajectory by adopting a self-adaptive grid algorithm combined with machine learning to obtain a high-resolution three-dimensional velocity vector field; acquiring a temperature field measurement original image, performing three-dimensional space registration according to a three-dimensional space mapping function, further calculating voxel-level temperature distribution, and inverting a three-dimensional temperature field; and carrying out coupling analysis on the three-dimensional velocity vector field and the three-dimensional temperature field, and outputting a three-dimensional heat-flow coupling cloud picture. According to the invention, multi-dimensional and high-precision synchronous measurement of the temperature field and the velocity field in the complex flow field of the turbine blade is realized, the spatial resolution and dynamic response capabilities are remarkably improved, and a reliable experimental basis is provided for analysis and optimization of the cooling performance of the blade.

The invention provides a large-size fan blade double-heat-transfer-mode microwave pyrolysis system and method.In the system, ceramic chips inlaid with SiC wave-absorbing agents are arranged on the surface of a conveying belt of a material conveying unit, and a tunnel type microwave pyrolysis reactor utilizes microwaves to conduct microwave pyrolysis on the large-size fan blade under the assistance of the ceramic chips inlaid with the SiC wave-absorbing agents; the pyrolysis unit is used for pyrolyzing fan blade materials conveyed into the material conveying unit by the material conveying unit; the pyrolysis gas treatment unit is used for purification-fractional condensation and collection of organic components; and the glass fiber treatment unit realizes carbon residue separation and low-loss recovery of glass fibers through ultrasonic infiltration. The problems that waste fan blades are difficult to recycle, performance degradation of recycled glass fibers is serious, and recycling benefits are low are solved, the requirement for continuously preparing high-strength glass fiber products and co-producing liquid oil through large-size retired fan blades can be met, and microwaves can be driven through wind power generation; and a gas device is additionally arranged to supply partial energy to the system, so that the resource recovery rate and the energy utilization rate are greatly improved.

The invention belongs to the technical field of wind power generation equipment state monitoring and intelligent fault diagnosis, and discloses a wind turbine generator blade acoustic fault detection method based on transfer learning, and the method comprises the steps: building a standardized sample through acoustic signal simulation and multi-dimensional data enhancement, and extracting weak fault features through an STFT-Mel frequency spectrum; a dual-scale time-frequency attention module is introduced into the lightweight MobileNetV3, so that the focusing and recognition capability on early crack features is improved; by combining transfer learning with MMD domain difference and entropy minimization regularization, efficient alignment of a simulation domain and actually-measured wind field data is achieved, and the generalization performance of the model under the small sample condition is enhanced; a transverse difference spectrogram and longitudinal historical baseline self-evolution double-flow feature fusion mechanism is adopted, common-mode noise is suppressed, and single-blade positioning, progressive degradation early warning and synchronous aging recognition are achieved. The method is small in parameter quantity, low in calculation overhead and suitable for high-precision and low-cost intelligent detection of early damage of the wind turbine generator blades.

The invention discloses a simulation part design method based on stress gradient fitting and a blade root characteristic simulation part. The simulation part design method comprises the following steps that S1, a dangerous area of a blade root part is determined based on simulation analysis of a turbine blade under a real working condition, and characteristic parameters are extracted; s2, designing a simulation piece examination section configuration based on a low-cycle fatigue simulation piece design criterion and the extracted characteristic parameters; s3, extracting the maximum stress gradient of the maximum first principal stress point of the blade root part of the turbine blade in a preset range in a preset direction, and carrying out normalization processing to obtain a stress gradient range of the blade root part of the real blade; s4, extracting the stress gradient of the simulation piece examination section, performing same normalization processing, comparing stress gradient fitting conditions, and performing fitting on the stress gradient by modifying and adjusting non-characteristic parameters of the simulation piece examination section and simulation piece configuration to control a stress gradient error; s5, a clamping section and a transition section are designed, and the strength of the clamping section and the strength of the transition section meet the requirement; and S6, the overall configuration design of the blade root feature simulation part is completed.

The invention provides an aerodynamic geometry co-evolution fan blade intelligent generation system, and relates to the field of fan blade design, and the system comprises an aerodynamic geometry co-evolution module which constructs a blade geometric model in a unified high-dimensional space, generates a coupling performance field in real time, responds to disturbance, updates the performance field, and feeds back index changes; establishing a performance target balance path by adjusting disturbance; a manufacturing constraint processing module identifies a non-adjustable area to construct a tension avoidance ring, limits the approach to a path and redirects the path; the precipitation feedback module monitors and recognizes lag signal storage, remaps the lag signal to a structural area in a static and stable state and applies a correction coefficient to intervene in subsequent evolution; the mutual exclusion coordination center module establishes a conflict detection mechanism, judges conflicts based on a label atlas and executes a dislocation locking scheduling feedback chain; the potential state coordination module constructs a graph based on historical data to pre-judge a future conflict path, pre-locks a conflict mechanism and guides construction of an alternative path to form a non-concurrent dislocation execution model.

The invention relates to the technical field of life evaluation, in particular to a turbine blade life evaluation method which comprises the following steps: acquiring surface temperature gradient data of a turbine blade of a gas turbine, extracting the temperature change rate of each area of the blade, analyzing the influence of the temperature change rate on the local stress of the blade, and evaluating the life of the turbine blade. And calculating a thermal stress synthetic effect by combining the blade rotation inertia force and the aerodynamic load to obtain blade thermal stress distribution data. According to the method, the thermal stress synthesis effect is calculated by acquiring the temperature gradient data of the surface of the blade and combining the rotary inertia force and the aerodynamic load, the thermal stress distribution characteristics of the blade in different operation states can be accurately reflected, and the life attenuation rate is calculated by combining the current working condition, so that life prediction can be adjusted along with the operation states; the service life offset is calculated by analyzing the influence of the start-stop load of the gas turbine and comparing creep fatigue joint distribution, so that the influence of the running state of the gas turbine on the service life prediction can be dynamically updated in real time.

The invention belongs to the field of turbine tests, and discloses a flow field reconstruction method based on test measuring point position optimization and multi-source data fusion and a related device, and the method comprises the steps: building a CFD data set and a sparse measuring point test data set; carrying out POD decomposition on the CFD data set; according to the sparse measurement point test data set, a correction modal coefficient is obtained by using a Gappy POD algorithm; according to the POD mode and the correction mode coefficient, obtaining a Gappy POD reconstruction flow field parameter; taking the minimum error of the global low-fidelity flow field parameter and the Gappy POD reconstruction flow field parameter as a target to obtain an optimal measurement point position; based on the optimal measurement point position, high-fidelity discrete test data is obtained, and an optimized modal coefficient is obtained by using a Gappy POD algorithm; obtaining a flow field reconstruction result of the turbine blade according to the POD mode and the optimization mode coefficient; according to the method, fine reconstruction of the global flow field distribution of the turbine blade is realized.

The invention discloses a fan blade inspection system and method based on an unmanned aerial vehicle, and relates to the technical field of fan blade inspection, and the method comprises the following steps: a priority analysis module constructs a health scoring model through collecting the historical inspection data and working data of each fan blade of a wind power plant, generates a health priority queue, and sends the health priority queue to an unmanned aerial vehicle; the inspection target determination module selects inspection fans to form a target set according to the initial endurance distance and the health priority queue of the unmanned aerial vehicle, the inspection route planning module plans an inspection route based on the target set in combination with fan space distribution, and the blade defect detection module controls the unmanned aerial vehicle to inspect according to the planned route, collects image data and identifies defects. And the real-time target adjustment module analyzes the fans with defects, determines an associated detection field, adds the fans which are not inspected to a target set, and regenerates an inspection path, so that all inspection tasks are completed, and the flexibility of the inspection tasks and the resource utilization efficiency are improved.