3113 results about "Turbine blade" patented technology

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 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 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 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 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 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 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 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.

The invention discloses a single crystal turbine blade crystal orientation defect detection system based on adaptive optics, particularly relates to the field of nondestructive testing, and is used for solving the problem that thermal damage and signal consistency are difficult to consider in acoustic imaging of complex structural parts. Through partition energy threshold setting, dynamic pulse planning, photoacoustic processing and unified coordinate cross-subarea data synthesis, local thermal damage caused by excessive concentration of energy is effectively avoided in the material detection process, and the requirement for collecting high-bandwidth signals can be met in a large range. The signal-to-noise ratio and the detection efficiency are improved by regulating and controlling pulse energy and a scanning path, the amplitude comparability and the image coherence among regions are ensured through signal normalization correction and subregion image set splicing, high-resolution and comprehensive-coverage acoustic mapping is output, the crystal orientation defects of the single crystal turbine blade are positioned and evaluated, the detection precision is improved, the measurement period is shortened, and the method is suitable for large-scale popularization and application. And the method has an applicable value for aero-engine parts requiring rapid and high-precision nondestructive detection.

The invention relates to the technical field of wind power blade material testing, and discloses a testing device and method for a salt spray corrosion test of a wind power blade composite material layer. According to the method, wind power blade composite material coating preparation parameters are obtained, and coating basic information is provided for subsequent performance evaluation; based on preset salt spray test environment control parameters, dynamic salt spray environment simulation conditions are generated so as to better fit the actual outdoor environment; then, a multi-sensor coupling monitoring network is constructed, real-time data acquisition is conducted on the corrosion process of the composite material coating, and comprehensive tracking of the corrosion process is achieved; processing real-time data by utilizing a corrosion characteristic analysis algorithm, generating a multi-dimensional corrosion characteristic index, and quantitatively representing the corrosion condition of the coating; and finally, evaluating the anti-corrosion stability of the new material coating in combination with the coating characteristic parameters and the multi-dimensional corrosion characteristic indexes. According to the method, the corrosion resistance of the coating in the actual environment can be more accurately reflected, and support is provided for research, development and application of the composite coating of the wind power blade.

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

The invention discloses a wind turbine blade semi-coupling aeroelastic modeling method considering nonlinear deformation, and belongs to the technical field of fan simulation calculation. In combination with calculation parameters used by three-party software, establishing a blade structured load model and a blade correction aerodynamic model, calculating an aerodynamic load and a structural load, and superposing: superposing the aerodynamic load, the gravity load and the centrifugal force load of the blade as an external load of a nonlinear structure control equation of the blade, and taking the external load as a calculation parameter; according to the method, the blade nonlinear structure model and the semi-coupling simulation process of the three-party wind turbine simulation software are constructed, and under the same simulation environment and load conditions, the blade nonlinear structure model and the three-party wind turbine simulation software can be subjected to semi-coupling simulation, so that the blade nonlinear structure model and the three-party wind turbine simulation software can be subjected to semi-coupling simulation calculation. The higher-precision non-linear motion response characteristics of the super-long flexible wind turbine blade are calculated, and a good effect can be achieved in the aspects of initial structure design and response evaluation of the large wind turbine blade.

The invention discloses a double-wall blade internal particle deposition characteristic rapid prediction method based on a reduced-order model. The method comprises the steps of model establishment, grid division, numerical simulation, data set establishment and establishment of a rapid prediction model combining POD and MLP. A deep learning method is combined with an internal channel of a cooling structure, high-dimensional data are converted into low-dimensional modal coefficients representing particle deposition characteristics based on POD decomposition for three parameters of film hole diameter, air blowing ratio and temperature ratio, an MLP deep neural network method is used for fitting the modal coefficients and related parameters, a rapid prediction model is established, and a rapid prediction result is obtained. And rapid analysis and evaluation of the particle deposition rule in the blade are realized. According to the method, the particle deposition condition in the double-wall turbine blade can be effectively predicted, and certain data support and guidance are provided for design and timely cleaning of a cooling channel in the blade.

The invention belongs to the technical field of aero-engine high-temperature part test, and particularly relates to a thermal mechanical composite fatigue test device in a turbine blade rotation environment, which comprises a high-temperature gas system, a stator system, a rotor system, a support system, a test system, an auxiliary system and a turbine rotor blade test piece, the high-temperature fuel gas system is used for providing a fuel gas environment for a turbine rotor blade test piece and controlling the high-low temperature circulation state of fuel gas, a combustion chamber is arranged at the front end of the high-temperature fuel gas system and provided with a double-oil-way nozzle, a fuel gas flowing system is arranged at the rear end of the high-temperature fuel gas system, and a blade grid structure is designed for the fuel gas flowing system according to the geometrical shape of a turbine rotor blade. And a spray cooling section is arranged at the rear part of the test section. The device is composed of six core systems including a high-temperature fuel gas system, a stator system, a rotor system, a supporting system, a testing system and an auxiliary system, all the systems work cooperatively, and integrated simulation of the high-temperature fuel gas environment, rotating centrifugal loads and heat-force synchronous circulation is achieved.

The invention provides a wind turbine generator blade pneumatic unbalance monitoring method and system based on deep learning, and belongs to the field of deep learning. Airflow motion state data generated by a distributed sensing array in the operation process of a wind turbine generator blade are collected; performing aerodynamic feature extraction and unbalance degree quantization processing on the airflow motion state data to obtain a blade aerodynamic feature vector set; correlation learning is carried out on the blade aerodynamic feature vector set and a preset blade historical aerodynamic performance evolution trajectory, and an aerodynamic imbalance development prediction vector is generated; based on the pneumatic unbalance development prediction vector, performing unbalance trend dynamic analysis to obtain a key influence factor and an influence degree sequence of pneumatic unbalance of the blade; and outputting an evaluation result of the pneumatic imbalance of the blade and a corresponding predictive maintenance strategy according to the key influence factor and the influence degree sequence. The adaptability of the maintenance strategy and the pneumatic unbalance state of the blade can be improved, and monitoring and predictive maintenance of the pneumatic unbalance of the blade of the wind turbine generator system are supported.

A mold arrangement for producing a preform element of a wind turbine blade includes a mold carrier and a plate- or tray-like mold element arranged at the mold carrier with a form defining mold surface adapted to receive preform building material, a first heating device arranged below the mold element for heating the preform building material from below the mold element, and a second heating device for heating the preform building material from above.

The invention relates to the technical field of fan blades, in particular to an automatic fan blade coating spraying method and system based on an unmanned aerial vehicle. Comprising the steps that a plurality of blade subareas are set according to the surface structure of the fan blade; according to a preset wear simulation model, an expected wear value of each blade sub-region is generated, and whether a primary spraying strategy is generated or not is judged according to the expected wear values; acquiring a spraying monitoring packet according to a preset feedback time node, and judging whether to correct the primary spraying strategy according to the spraying monitoring packet; the wear degree of each area of the fan blade is remotely predicted according to the wear simulation model in combination with environmental monitoring parameters, each wear point of the protective coating is accurately positioned in combination with image data acquired by the unmanned aerial vehicle, and accurate control of the flight position of the unmanned aerial vehicle is realized by acquiring feature points of each blade sub-area. And meanwhile, the flight speed and the coating flow of the unmanned aerial vehicle are dynamically corrected in time by periodically collecting the thickness of a spraying layer in the spraying process, and the spraying execution efficiency is improved.

The invention discloses a wind turbine blade fault diagnosis method based on aerodynamic noise and image recognition, and relates to the technical field of wind turbine blade fault diagnosis, and the method comprises the steps: building a plurality of typical fault models of a wind turbine blade based on a numerical simulation platform, and extracting simulation acoustic image data; the position of the aerodynamic noise source is positioned through an acoustic camera, and corresponding blade image information is collected by an unmanned aerial vehicle; and carrying out feature training on the image sample and the acoustic image data by using a deep neural network and executing fault matching judgment. According to the method, a fault acoustic image database under multiple working conditions is constructed through numerical simulation, outfield data acquisition is performed in combination with an acoustic camera and an unmanned aerial vehicle, and joint feature training and fault matching of acoustic frequency spectrums and images are realized by using a deep neural network. The method can break through the limitations of the prior art in the aspects of real-time performance, accuracy and intelligence, and achieves the efficient and reliable diagnosis of the fault of the wind turbine blade.

The invention relates to the technical field of wind power operation and maintenance, and discloses a wind power blade crack diagnosis method and system based on knowledge graph large model enhancement, and the method comprises the steps: S1, edge end image collection and crack semantic recognition: an edge end carries out the crack detection and semantic feature extraction of a collected wind power blade image sequence through an ECL-Net lightweight network, generating structured semantic information and transmitting the structured semantic information to a cloud; s2, cloud knowledge graph retrieval and semantic reasoning: the cloud performs semantic retrieval and causal reasoning on the structured semantic information through a KFR-Net knowledge graph reasoning network based on a pre-constructed wind power blade operation and maintenance knowledge graph, and outputs a knowledge retrieval result; and S3, multi-modal information is fused through an MMR-Cogntive multi-modal memory retrieval-cognitive generation network, and a structured maintenance report including crack detection summarization, cause analysis, maintenance measures and risk assessment is generated. The method has the advantage that intelligent closed-loop management of wind power blade cracks from detection to maintenance suggestions is realized.

The utility model provides an adhesion agent emulsification reaction kettle which comprises a kettle body, an inclined motor, an inclined stirring component, a stirring driving motor, a stirring shaft, a plurality of stirring blades and turbine blades, the kettle body is provided with a closed inner cavity, a feeding port is formed in the top of the kettle body, a discharging port is formed in the bottom of the kettle body, and the inclined motor is arranged at the bottom of the kettle body. The inclined stirring component is fixedly connected with the power output end of the inclined motor and located at the bottom of the closed inner cavity, the stirring driving motor is arranged at the top of the kettle body, the power output end of the stirring driving motor is fixedly connected with a stirring shaft, and the stirring shaft is located in the closed inner cavity; the multiple stirring blades are fixed to the surface of the stirring shaft at intervals in the axis direction of the stirring shaft, the turbine blades are fixed to the bottom end of the stirring shaft, and a gap is formed between the turbine blades and the cavity bottom of the closed inner cavity. According to the utility model, materials at the bottom of the kettle generate stronger radial flow to form local strong turbulent flow, so that the mixing and circulation of the materials in the kettle are enhanced.

The invention discloses a micro-scale turbine blade boundary layer heat-flow double-field synchronous test method and system. The test method comprises the steps that temperature field initial data and velocity field initial data are acquired; based on the initial data of the temperature field and the initial data of the velocity field, the distribution state of the microparticles in the boundary layer is diagnosed, and according to the diagnosis result, the optimal distribution of the microparticles in the boundary layer is obtained by feeding back and controlling the operation parameters of the reverse cyclone separation system; based on the optimal distribution, an improved PIV algorithm is adopted to calculate a boundary layer velocity field, and the improved PIV algorithm is obtained by introducing multistage grid adaptive encryption, physical constraint conditions and a transition area recognition mechanism into the PIV algorithm; a boundary layer temperature field reconstruction model is established, depth phase coupling is carried out on a temperature field reconstruction result and a boundary layer velocity field, synchronous analysis of heat-flow coupling behaviors in a turbine blade boundary layer is achieved, and finally a boundary layer coupling cloud picture reflecting heat-flow interaction characteristics is obtained.

The invention discloses a heavy-duty gas turbine blade creep fatigue life design method considering an anisotropic effect, and relates to the technical field of gas turbine key component creep fatigue life evaluation.The method comprises the steps that orientation factor parameters on characteristic orientation are determined based on tensile data on [001] crystal orientation; constructing a modified stress relaxation formula; constructing an anisotropic failure strain energy density equation based on the pure creep performance data and the orientation factor parameters, and determining an upper platform of failure inelastic strain energy density on [001] crystal orientation; further determining an upper platform of the failure inelastic strain energy density on the feature orientation; constructing a creep damage prediction model based on a modified stress relaxation formula and an anisotropic failure strain energy density equation; and adopting the creep damage prediction model to obtain the creep fatigue life of the anisotropic turbine blade material sample. The method meets the actual requirements of service life evaluation of the high-temperature part of the heavy-duty gas turbine.

The invention provides a turbine blade with a cooling structure for a gas turbine, and relates to the technical field of gas turbine blade cooling, the turbine blade comprises an inner layer wall and an outer layer wall, the inner layer wall is a hollow pipe body extending out from the normal direction of a turbine rotating shaft, and the hollow part is used as a cold air flow channel and receives cooling air flow. The outer layer wall is arranged on the outer side of the inner layer wall in a sleeving mode at intervals, an annular runner is defined by the outer layer wall and the inner layer wall, a plurality of impact holes are formed in the inner layer wall, a plurality of air film holes are formed in the outer layer wall, and cooling air flow in the cold air flow channel enters the annular runner through the impact holes to impact and cool the outer layer wall and is attached to the outer layer wall after being jetted through the air film holes. And a protective layer is formed between the outer layer wall and high-temperature fuel gas to realize gas film cooling. The opposite surfaces of the inner layer wall and the outer layer wall are flow guide curved surfaces, the projection of the flow guide curved surfaces in the normal direction of the rotating shaft is a first curve, the projection of the flow guide curved surfaces in the tangential direction of the annular flow channel is a second curve, and the flow guide curved surfaces are modeled to restrain secondary contra-rotating vortexes generated when cooling airflow flows in the annular flow channel.

The disclosure relates to a blade transport vehicle, a monitoring system, a monitoring method and an electronic device, the blade transport vehicle comprising: a trailer having a length direction, a width direction and a height direction; the blades are fixed on the trailer through a mounting assembly; the vibration sensor is arranged on the trailer or the installation assembly, the vibration sensor is provided with at least one detection direction, and the at least one detection direction is parallel to at least one of the length direction, the width direction and the height direction of the trailer. According to the technical scheme, the possibility that the blades are damaged can be reduced, and the installation quality of the wind power blades is guaranteed.

The invention discloses a fan blade monitoring system and method based on multi-modal data fusion analysis, and belongs to the technical field of wind power generation. The method comprises the steps that multi-modal characteristic data of a fan blade in operation are acquired; inputting the multi-modal feature data into a pre-constructed multi-modal feature fusion anomaly detection model, a damage factor quantitative calculation model and a health trend prediction model, and respectively performing comprehensive anomaly detection, comprehensive damage detection and health degree prediction on the operation state of the fan blade; based on calculation results of comprehensive anomaly detection, comprehensive damage detection and health degree prediction, comparing the calculation results with a corresponding normal state threshold range; and triggering early warning when any calculation result deviates from the normal state threshold range. According to the method, the multi-modal feature information in the operation of the fan blade is obtained, and the deep learning and machine learning algorithms are combined, so that the early fault under the complex operation condition is effectively identified, and the intelligent operation and maintenance requirements of a modern wind power plant are met.

The invention discloses a method for rapidly evaluating the service life of a turbine cooling blade based on flight parameters, and belongs to the technical field of aero-engines. The method comprises the following steps: acquiring a load spectrum, a material, a structure and working state parameters of the turbine blade; establishing a blade finite element model and performing fluid thermosetting coupling analysis to obtain temperature field and stress field distribution; determining a dangerous part of the blade based on the temperature field and stress field distribution; respectively establishing a creep durability model, a low-cycle fatigue model and a fretting fatigue life model for dangerous parts; and finally, updating the model based on load spectrum data monitored in real time, and calculating the residual life of the blade. According to the method, the intrinsic performance of the material is associated with the characteristic structure of the blade, and the efficient life prediction model is constructed based on limited test data, so that the life consumption of the turbine blade is quickly and accurately evaluated according to actual flight parameters.

The invention discloses a wind power blade structure health monitoring method based on multi-field decoupling optical fiber sensing, particularly relates to the technical field of wind power equipment state monitoring, and is used for solving the problem of health state misdiagnosis caused by coherent interference of environmental noise and damage signals. The method comprises the following steps: collecting vibration, strain and temperature signals and generating a background noise vector; calculating a frequency domain coherence function of the vibration signal and the noise vector to identify an interference danger frequency band; analyzing the polarization angle distribution of the vibration signal in the frequency band, and inverting the physical path topology of the noise transmitted into the blade; when the path passes through a predefined weak area, improving a damage weight coefficient of a strain signal of a corresponding frequency band and carrying out Hilbert phase shift reconstruction to generate an anti-interference strain feature vector; calculating the energy dissipation rate of the vector in a dangerous frequency band, and judging an energy leakage event caused by damage by combining a viscoelastic constitutive threshold value of the composite material; the damage type and the spatial position are judged according to the time domain correlation between the energy leakage event and the temperature signal, and the hidden damage detection reliability is improved.

The invention belongs to the field of ground heavy duty gas turbines, and discloses an overall design method of a combustion-spraying combination, which comprises the following steps: obtaining overall thermodynamic parameters based on performance requirements of the combustion-spraying combination; on the basis of the total thermodynamic parameters and the inlet and outlet radiuses, a meridian flow channel design is obtained through a flow channel optimization method; based on the meridian flow channel design, adopting a parametric modeling method to obtain a flow surface model; based on the flow surface model, a three-dimensional accumulation method is adopted to obtain an initial configuration of the turbine blade; and based on combustion design, cooling design and pneumatic design performance, checking and optimizing the initial configuration of the turbine blade to obtain an optimized blade configuration and a combustion and spraying combined body pneumatic design scheme.

The invention provides a wind power blade fatigue damage early warning method and system based on modal energy excursion, and relates to the technical field of wind driven generators. According to the method, a multi-dimensional health baseline library is established under the healthy state of a wind power blade, and a current total strain signal and temperature distribution are synchronously collected in the operation process of a fan; performing temperature-strain decoupling on the total strain by using a healthy baseline library, and accurately extracting a real mechanical strain signal caused by the mechanical load; on the basis, a working condition modal recognition method is adopted to steadily obtain a current inherent frequency and a strain modal shape from real mechanical strain; calculating a current modal energy proportion and comparing the current modal energy proportion with the health baseline to generate an offset index reflecting a modal energy localization degree; and finally, in combination with the amplitude, the duration and the dynamic evolution trend of the offset index, and in combination with a fatigue accumulated damage assessment result, performing graded early warning judgment, and realizing early perception, accurate positioning and risk graded output of the fatigue damage of the blade.

The invention relates to a turbine blade cooling optimization method and device based on non-Newtonian fluid. Comprising the following steps: step 1, carrying out grid division and simulation processing on a three-dimensional geometric model of the micro-channel of the turbine blade containing a shear groove to obtain a simulation grid model; 2, on the basis of the simulation grid model, determining a non-Newtonian fluid constitutive model suitable for the simulation grid model, and associating the non-Newtonian fluid constitutive model to the simulation grid model to obtain a fluid characteristic grid model; step 3, calculating index data based on the fluid characteristic grid model and the target equation; 4, adjusting the structure parameters of the shearing grooves in the simulation grid model, repeatedly executing the step 1 to the step 3 according to the adjusted structure parameters of the shearing grooves, and recording index data corresponding to the structure parameters of each group of shearing grooves to obtain a candidate data set; and 5, selecting the structure parameter of the shear groove with the comprehensive optimal resistance performance and heat transfer performance from the candidate data set according to the index data, and taking the structure parameter as an optimized target shear groove parameter.