6853 results about "Aero engine" patented technology

An aero-engine augmented reality virtual-real fusion method based on depth prior scene reconstruction comprises the following steps: firstly, collecting data of an aero-engine look-around RGB image and a depth image, calculating a camera frame pose by using an SFM algorithm and obtaining a sparse point cloud, and optimizing the camera pose by using an ICP algorithm and the point cloud converted from the depth image to obtain an aligned prior dense point cloud; gaussian points are initialized based on the priori dense point cloud and the sparse point cloud, and random generation of 3D Gaussian is limited by using the boundary of the dense point cloud as geometric constraint; through the three-dimensional reconstruction of the adjacent multi-view enhancement strategy, the spatial and visual consistency is improved, and an aero-engine model is reconstructed; then understanding the reconstructed aero-engine model based on semantic segmentation, identifying and marking core components, and finally performing virtual-real fusion visualization on the reconstructed aero-engine through augmented reality to guide training, assembling and maintenance operations. According to the invention, rapid high-quality three-dimensional reconstruction and augmented reality virtual-real fusion visualization of the aero-engine are realized.

The invention provides an aero-engine state monitoring method and device based on sound and vibration fusion and a computer readable storage medium, and relates to the technical field of aero-engine state monitoring. The method comprises the following steps: acquiring signals respectively acquired by a vibration sensor and a sound sensor; respectively intercepting a vibration signal low-frequency component and a sound signal high-frequency component based on the frequency response characteristic difference of the sensor; performing normalization processing on the intercepted signal to eliminate amplitude difference; constructing a transition region through an interpolation method, and splicing the continuous mixed frequency spectrum to obtain a sound-vibration fusion spectrum; and establishing a health benchmark based on the normal state fusion spectrum, and realizing abnormity monitoring and alarm through characteristic difference analysis. Advantages and characteristics of a low-frequency band of the vibration sensor and a high-frequency band of the sound sensor are fully utilized, frequency response limitation of a traditional single sensor is broken through, deep fusion of sound and vibration signals is achieved, the effective detection frequency range is remarkably widened, and accuracy and robustness of recognition of early faults such as aero-engine blade breakage are improved.

The invention relates to the technical field of simulation model optimization, and discloses an aero-engine model Bayesian optimization method for quantizing uncertainty, and the method comprises the steps: building a probability mapping relation from a component index to an output response through constructing a Bayesian neural network agent model based on a probability weight coefficient; and by taking the difference between the output response and the corresponding complete machine test data as a multi-objective loss function and taking the minimization of the multi-objective loss function as an optimization objective, optimizing the component indexes by adopting a Bayesian optimization method based on a Gaussian process to obtain an optimal component index combination. Not only is a nonlinear relationship between high-dimensional parameters and simulation-test deviation accurately modeled through a neural network, but also efficient search of a parameter space is realized through a Gaussian process. The technical problems that when a traditional optimization method is used for processing the high-dimensional, strong-nonlinearity and multi-parameter coupling complex optimization problem of the aero-engine, the calculation efficiency is low, local optimum is prone to occurring, and result uncertainty cannot be quantified are solved.

The invention discloses a thermal barrier coating multi-dimensional terahertz nondestructive testing method and system based on an adaptive optimization mechanism, and the method comprises the steps: constructing a matrix sample library, and preparing thermal barrier coating samples with different thicknesses, porosities and uniformity; obtaining terahertz detection data of three dimensions of geometric dimension stability, microstructure integrity and material structure uniformity of the thermal barrier coating; establishing a multi-parameter correction model based on surface roughness, and performing feature extraction, fusion analysis and error compensation on the detection data by adopting a multi-modal integration algorithm; an adaptive optimization model is established through a deep learning algorithm, and continuous iterative optimization of the detection system is realized in combination with a feedback correction mechanism; the method is applied to an engine blade thermal barrier coating, an actual blade coating is detected, detection data are collected for system optimization, system parameters and models are updated, and system applicability and robustness are improved. The method can be widely applied to the field of nondestructive testing of coating structures such as aero-engine blade thermal barrier coatings.

The invention belongs to the field of aero-engine bearings, and provides a method and system for predicting the remaining service life of an aero-engine bearing, and the method comprises the steps: carrying out the processing of an original vibration signal collected in the operation process of the engine bearing through a Pearson correlation analysis method, carrying out feature extraction to obtain a plurality of feature sequences of common bearing RUL prediction statistics; smooth processing and cumulative transformation are carried out on the statistical feature sequence, monotonicity and tendency values are calculated, screening is carried out, and a screened cumulative feature sequence is obtained; and inputting the screened accumulated feature sequence into an attention full convolutional network (AFCN) model based on physical information to predict a life ratio, and calculating a residual service life prediction value through the life ratio. According to the method, through the full convolutional network fusing the bearing physical degradation mechanism and the attention mechanism, the key feature capturing capability and prediction precision in the long-time-sequence degradation process are improved, and technical support is provided for safe operation and maintenance of an aero-engine.

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 physical information fused aero-engine residual life prediction method, and belongs to the field of neural network residual life prediction. The method comprises the following steps: acquiring sensor data of the whole process from engine simulation operation to fault, and performing standardization and windowing preprocessing; establishing a time-space dual-channel multi-layer perceptron model, extracting time dimension and feature dimension information of the signal, performing self-attention fusion with original data, completing mapping to a life prediction value, and calculating an error with a real life; establishing a physical information neural network model, learning a hidden partial differential equation among a predicted life value, time and a hidden state, and performing error calculation with an equation in an equilibrium state; and performing self-attention fusion with the life error to obtain comprehensive loss so as to continuously train the model until the error is minimum, and finishing final life prediction. Physical information is fused on the basis of the multi-layer perceptron, the complexity of the model is simplified, and the prediction precision of the residual life of the engine is improved.

The invention provides a DLH-IHBA-based adaptive cycle engine control law optimization method, and belongs to the technical field of aero-engine control, and the method comprises the steps: building an adaptive cycle engine model, and determining an optimization variable based on the adaptive cycle engine model; according to the control mode of each self-adaptive cycle engine, constraint conditions and an objective function based on optimization variables are determined, the control modes comprise a steady state control mode and a transition state control mode, and the steady state control mode comprises a minimum fuel consumption control mode, a maximum thrust control mode and a minimum turbine front temperature control mode; and based on the objective function and the constraint condition, optimizing the control rule of each control mode by adopting a multi-dimensional learning prey strategy fused with a badger optimization algorithm to obtain an optimal control variable, and adopting Tent mapping as initial particle swarm position mapping. According to the scheme, the convergence speed of engine control rule optimization is increased, and the optimization effect and the engine performance are improved.

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 discloses an engine residual service life prediction method and system based on space-time state selection, and belongs to the field of equipment residual service life prediction. According to the method, multiple technologies such as a time sequence-space double-branch coding and hierarchical fusion mechanism, a selective state space model, a cross-layer residual jump connection and parameter sharing mechanism and an efficient self-attention mechanism are integrated, and an STSSFormer network model is constructed to predict the residual service life of the aero-engine; the system mainly comprises a data selection and preprocessing module, an input sample construction module, a model training module and a residual service life prediction module. According to the method, the feature expression and fusion capability is comprehensively enhanced, and the method has remarkable advantages in the aspects of prediction accuracy of the remaining service life, model robustness and engineering practicability.

The invention discloses an aero-engine group health evaluation method based on multi-working-condition dynamic clustering, and belongs to the field of aero-engine health state evaluation. The method comprises the following steps: firstly, carrying out clustering analysis on set parameters in engine operation data, and carrying out merging processing on small-scale abnormal clusters to obtain a working condition category division result; secondly, constructing a health baseline data set, carrying out standardized preprocessing on sample data in a working condition category division result, and carrying out nonlinear dimensionality reduction to obtain a low-dimensional feature data set; thirdly, performing clustering analysis on the low-dimensional feature data set by adopting a Gaussian mixture model, and calculating an average mahalanobis distance between a sample of each clustering category and a health reference center to obtain multi-level health levels corresponding to different clustering categories; and finally, through fusing the membership soft probability and the sample individual mahalanobis distance, constructing a continuous health score and obtaining a health grade determination interval. According to the method, health state characteristics under different working conditions can be effectively identified, individual difference modeling and group transverse comparison evaluation are supported, and the accuracy is improved.

The invention discloses a gas compressor blade mean camber line design method, a gas compressor blade, a gas compressor and an aero-engine, and the method comprises the steps: S1, obtaining known design data, including an inlet blade angle theta in, an outlet blade angle theta out, a mean camber line starting point coordinate, a mean camber line end point abscissa, and turning angles delta theta 1, delta theta 2 and delta theta 4 of a first section arc, a second section arc and a fourth section arc, and abscissas of all tangency points; and S2, each section of arc is calculated through a geometric analysis method, a mean camber line is constructed after circle center coordinates, arc radiuses and tangent point coordinates of each section of arc are calculated, and the mean camber line is formed by sequentially and tangentially connecting four sections of arcs according to the sequence of convex, concave, convex and concave. The gas compressor blade mean camber line design method is good in adaptability and multiple in controllable point in the high Mach number state; therefore, the requirements of an advanced compressor are met; design parameters are greatly reduced, the optimizing period is greatly shortened, the adjusting efficiency is high, and the time is short.

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 discloses an intelligent aero-engine bearing fault migration diagnosis method based on multi-source data and an attention mechanism, and the method comprises the steps: automatically learning the importance difference of different modes in different fault states through an attention weight, and achieving the dynamic optimization combination of mode features; meanwhile, an attention mechanism is utilized to capture deep correlation characteristics among multi-modal data of the aero-engine bearing part, space-time coupling relations among heterogeneous signals such as vibration, temperature and current are excavated, and an attention weight is utilized to automatically suppress modal contribution polluted by noise, so that noise robustness is enhanced; according to the method, the weight difference under different modes and different fault states can be automatically learned, the deep correlation of the modes is captured, the noise pollution is inhibited, and the robustness of the model is enhanced.

The invention discloses an aero-engine residual service life prediction method, belongs to the field of aero-engine residual service life prediction, and aims to solve the problems of an existing aero-engine RUL prediction model. According to the method, prediction is carried out through a parallel space-time Transform model, and the parallel space-time Transform model comprises a time attention module TAM, a space attention module SAM, a space-time feature fusion device and a support vector machine MLP; the TAM and the SAM are arranged in parallel, and input samples are respectively input into the TAM and the SAM; obtaining a time sequence enhancement feature through TAM; obtaining spatial enhancement features through SAM; fusing the time sequence enhancement features and the spatial enhancement features by using a spatial-temporal feature fusion device; and the support vector machine MLP predicts the remaining service life of the aero-engine according to the fusion features output by the spatial-temporal feature fusion device.

The invention discloses an aero-engine system demand servitization collaborative management method, which comprises the steps of constructing a semantic mapping rule of an aero-engine demand document based on an OSLC specification, and converting an unstructured document into a standardized data model; standardized access of elements in the demand document is achieved through resource identification and a servitization interface, and a Web-based demand document interoperation mechanism is constructed; multidisciplinary collaborative design is carried out based on a demand document interoperation mechanism, and a demand document is updated in real time to form a complete design demand document; constructing an intelligent change analysis mechanism based on project tracking to realize multi-version comparison and change propagation path visualization of the design requirement document; constructing a fine-grained version evolution and conflict management mechanism based on the demand entry influence network, and executing demand version merging and conflict automatic detection in a parallel development scene; according to the method, aero-engine system demand document entry level collaborative editing and standardized service interface can be realized, and the development efficiency is remarkably improved.

The invention belongs to the technical field of material surface plating, and particularly relates to an aero-engine blade and a preparation method thereof. According to the preparation method, the component nano silicon carbide of the blade material is subjected to modification treatment, meanwhile, the carbon fiber composite material is introduced for common use, and the composite coating is formed on the surface through spraying treatment, so that the aero-engine blade is prepared, the cold and heat cycle resistance is effectively improved, and meanwhile, good erosion resistance is obtained.

The invention provides an aero-engine small sample fault diagnosis method for agency attention enhancement diffusion, and belongs to the technical field of aero-engine gas circuit fault diagnosis. According to the method, the proxy attention is added on the basis of the DDIM, so that the sample generation can be accelerated, the sample generation quality is obviously improved, and no extra training is needed. Inputting a small amount of pre-processed collected sensor signal data into the agent attention enhanced denoising diffusion implicit model for data enhancement to generate high-quality sample data so as to make up for the deficiency of an original data set and improve the performance of the model under the condition of scarce data; the expanded data set is used as a training set, the original data set is used as a test set for model training, the test set is used for testing the performance and accuracy of the model after training is completed, and a final aero-engine fault diagnosis model is established. According to the method, the problem of insufficient data in fault diagnosis is solved, and the accuracy and robustness of the model are improved.

The invention belongs to the technical field of intelligent fault diagnosis, and discloses an aero-engine bearing fault diagnosis method based on clustering enhancement domain generalization. Firstly, a time domain vibration signal is converted into frequency domain representation through fast Fourier transform, a two-stage convolutional neural network architecture comprising a domain alignment encoder and a classification encoder is constructed, and cross-working-condition feature alignment under adversarial training is realized by using a domain discriminator; introducing a multi-source domain maximum mean difference statistical alignment strategy and a clustering enhanced triple loss mechanism, and synchronously optimizing intra-class feature compactness and inter-class separability through pseudo label clustering center anchoring and dynamic interval constraint; and gradient inversion adversarial training and multi-modal feature fusion are combined. The method provided by the invention can effectively improve the accuracy and robustness of generalization judgment when the aero-engine bearing fault diagnosis lacks target domain data.

The invention discloses an aero-engine missing value filling method based on multi-scale time sequence and attribute feature extraction. Firstly, for data collected by each sensor of an aero-engine, a temporary replacement value of a missing value is generated by adopting linear interpolation; extracting a plurality of significant cycles of the aero-engine sequence data as a time scale, decomposing the sequence into trend terms and season terms according to the significant scale, and splicing all the trend terms and season terms in the time dimension; then, in-scale and inter-scale time sequence dependence is captured in a time dimension by using cross-scale graph convolution so as to carry out missing value filling in time sequence. In the attribute dimension, local neighborhood information is accumulated by adopting a cross-attribute graph volume, global attribute features are calculated by utilizing a relational network, node features are updated, and attribute association is supplemented on the basis of a time sequence filling result. And finally, aggregating the node features of a specific scale through a maximum pooling layer to represent the features, and calculating by adopting two linear layers and an activation function to obtain a final filling result.

The invention belongs to the technical field of aero-engine state monitoring and intelligent fault diagnosis, and discloses a multi-modal information fusion bearing fault diagnosis method based on self-supervised learning. The method comprises the following steps: firstly, through mask reconstruction self-supervision pre-training, extracting stable feature representation insensitive to mask disturbance from an unlabeled multi-modal signal, and dynamically updating each modal feature reference point by using an index moving average algorithm; in a downstream fault diagnosis task, a multi-modal joint decision model comprising a pre-training encoder, a single-modal classifier and a fusion classifier is constructed, and adaptive weighted fusion of multi-modal decision is realized through contribution degree calculation based on a cooperative game Shapley value in combination with a deviation degree of modal features and a reference point. According to the method, the dependence of the deep neural network on fault labeling data is effectively reduced, the accuracy and robustness of the diagnosis system in a multi-modal signal diagnosis scene are improved through a dynamic fusion mechanism, and the method is suitable for industrial scenes with limited sample label resources.

The utility model provides a special zero-point positioning clamp for an aero-engine moving blade, which is used for positioning and clamping the aero-engine moving blade and comprises a clamp body, a positioning piece, a deformation acting piece and a zero-point positioning assembly, a mortise with at least the top, the front and the back opened is arranged on the clamp body, a deformation groove is arranged on one side of the mortise, and the zero-point positioning assembly is arranged in the deformation groove. The deformation acting piece is connected with the deformation part and the clamp body in a matched mode, and the deformation acting piece drives the deformation part to deform so as to clamp and loosen the blade tenon. The zero point positioning assembly comprises a positioning piece and a blind rivet, and the positioning piece and the blind rivet are installed at the bottom of the clamp body in a positioning mode. According to the scheme, assembling is easy, using is convenient, stable clamping of the aero-engine moving blade and the quick-changing function and the zero-point positioning function applied to an automatic production line can be achieved, it is not needed to determine whether the position of the blade is correct or not every time of metering, manpower consumption is reduced, labor efficiency is improved, and the requirement of the automatic production line is met.

The invention belongs to the technical field of powder metallurgy, and relates to a preparation method and application of a high-performance isotropic GH4698 drum piece, prepared GH4698 high-temperature alloy powder with the particle size of 53-250 microns is loaded into a prefabricated sheath under the condition that the vacuum degree is smaller than or equal to 1 * 10 <-3 > Pa, after treatment is conducted 2-3 times through a multi-cycle hot isostatic pressing process system, furnace cooling is conducted, and the high-performance isotropic GH4698 drum piece is obtained. And machining is adopted to remove the sheath to obtain a GH4698 drum piece blank in a powder metallurgy state, and a solid solution and aging heat treatment process is adopted for the drum piece blank, so that the high-performance isotropic GH4698 drum piece can be obtained. According to the preparation method, the yield of the GH4698 drum part can be effectively increased, the GH4698 drum part with high performance and isotropic performance can be obtained by effectively improving the grain size and distribution of the part, the carbide distribution and the internal stress state of the part, the utilization rate of an alloy material is promoted to be increased, the stability of the production process is improved, and in addition, the yield of the GH4698 drum part is improved. All mechanical properties of the prepared GH4698 drum piece meet the performance requirements of a drum shaft for a novel aero-engine.

The invention discloses an equipment prediction maintenance framework based on probability residual life, and belongs to the technical field of fault prediction and health management (PHM). According to the framework, through combining a Bayesian neural network and a reinforcement learning technology, probability prediction and dynamic maintenance decision optimization of the residual life of equipment are realized. The method specifically comprises the following steps: acquiring sensor data in equipment operation, and preprocessing to generate a training data set; constructing a Bayesian neural network (BNN), utilizing variation reasoning to approximate posteriori distribution, and outputting probability distribution of residual life through Monte Carlo sampling; based on a probability prediction result, constructing a reinforcement learning environment model, and defining a state space containing residual life distribution, a spare part state and a maintenance action and a reward function; a Double DQN algorithm is adopted to optimize a maintenance strategy, and intelligent decision-making of the optimal maintenance time and the optimal ordering time of equipment is dynamically realized through an epsilon-greedy algorithm, so that the maintenance cost and the fault risk are minimized. According to the method, probability residual life distribution and reinforcement learning decision are innovatively combined, the problem that a traditional point estimation model ignores uncertainty is solved, an intelligent maintenance framework is constructed through a reinforcement learning method, and dynamic optimization of maintenance decision is achieved. The NASA aero-engine data set verification shows that compared with a traditional method, the optimization effects of prediction errors, uncertainty quantification, the maintenance cost rate and the like are remarkable, and the reliability and economical efficiency of equipment are effectively balanced.

The invention discloses an acceleration task test-run spectrum compilation method and system considering multiple modes and multiple dangerous points, and belongs to the technical field of aero-engine acceleration task test-run spectrums, and the method comprises the steps: obtaining the stress and temperature distribution conditions of parts under different loads and working conditions; selecting examination points of key characteristics in operation of a plurality of parts as potential dangerous points for analysis; fatigue and creep damage calculation and evaluation are carried out on each selected assessment point; statistical analysis is carried out on the rotating speed change rate distribution characteristics in the long-term life test run spectrum, and the switching rate from the slow state to the maximum state is determined; comparing different failure mode damage values of each assessment point; the acceleration task test-run spectrum compiling method and system considering the multiple modes and the multiple dangerous points are suitable for compiling the acceleration task test-run spectrums of most military or civil aero-engines, the damage state of parts in the actual service process can be accurately reflected, and powerful support is provided for optimization of the acceleration task test-run spectrums.

A thermal management system defines a thermal management system flowpath to provide a flow of a fluid to an electric machine and a power electronics assembly electrically connected to the electric machine. The thermal management system includes a first heat exchanger thermally connected to the thermal management system flowpath and to the electric machine, and a second heat exchanger thermally connected to the thermal management system flowpath downstream of the first heat exchanger. The second heat exchanger is thermally connected to the power electronics assembly.

The invention relates to the field of aero-engine bearing cavity structure system testing, and particularly provides an aero-engine bearing cavity inner lubricating oil flow state measuring system which comprises a test piece main body simulating an aero-engine bearing cavity, an oil gas supply and recovery system and a control system. The test piece main body comprises a rotor test piece and a stator test piece which are connected together, and the oil gas supply and recovery system comprises an oil supply system, a gas supply system, an oil return pool and an oil gas separation pump. The control system comprises a controller, and an oil supply throttle valve, an air supply throttle valve, an exhaust throttle valve, a test data acquisition assembly and a test piece main body driving device which are in signal connection with the controller. On the basis of the method, the lubricating oil-air flowing state in the bearing cavity can be monitored and evaluated in real time, and test support can be provided for follow-up bearing cavity optimization design.

The invention provides an aero-engine residual life prediction method and system based on deep learning, and relates to the technical field of engine life prediction. According to the method, a multi-scale convolution feature extraction module (SDMCN module) based on a graph attention mechanism and a GRU module (F-GRU module) fused with the feature attention mechanism are established. Through an SDMCN module, the spatial relevance of the features is fully extracted from the two aspects of the relevance between the feature maps formed under the convolution kernels of the same size and the relevance between the feature maps formed under the convolution kernels of different sizes. And meanwhile, the F-GRU module is used for dynamically adjusting the weights of the input characteristics under different time steps, so that differential updating of the sequence data is realized. By adopting the two parallel branches, extraction of spatial relevance and time sequence dependency relationship between features is realized, so that feature degradation information is captured more comprehensively, a more accurate life degradation model is established, and the accuracy of model prediction is improved.

The invention belongs to the field of aero-engines, relates to an engine main combustion chamber design technology, and provides a ceramic matrix composite material flame tube matching design method and system.The method comprises the steps that a flame tube wall temperature interval of a main working state point is obtained; determining the thermal-state radial dimensions of the feature points on the ceramic-based component and the metal component; converting the hot-state radial size into a cold-state radial size; carrying out two-dimensional cold-state flame tube profile design; and the cold-state radial size of the two-dimensional cold-state flame tube molded surface is converted into the hot-state size to obtain a two-dimensional hot-state flame tube molded surface, and a three-dimensional hot-state model is constructed and evaluated and iteratively optimized. According to the method, matching structure characteristic parameters of the ceramic-based component and the metal component are designed, and the molded surface of the flame tube made of the different materials is self-adaptively formed into smooth transition at main working state points, so that a backflow area is actively controlled, and the problems that the stability of a flow field of a main combustion chamber is influenced and pressure is lost due to different and relative displacement changes in the working process are solved.

The invention discloses a stator blade shape-following frequency modulation design method based on a bending vibration mode and application, belongs to the field of vibration design of stator blades of aero-engines and gas turbines, and is suitable for vibration frequency modulation design of non-cantilever type stator structure blades (such as fans and gas compressor stators) with inner rings and outer rings in the aero-engines or the gas turbines. According to the method, firstly, static frequency and dynamic frequency modal analysis is carried out through finite element modeling, and dangerous resonance points and corresponding vibration modes in a working rotating speed range are recognized by combining a Campbell diagram; based on a vibration mode amplitude normalization result, setting a vibration degree parameter and a scale factor, selecting a high response node and carrying out directed disturbance, and updating a structure model to improve a target vibration mode frequency; and through frequency amplification verification and iterative optimization, geometric reconstruction and frequency modulation confirmation are finally completed in combination with aerodynamic performance and processing requirements. The method can realize rapid and accurate frequency modulation of a specific bending vibration mode, avoids introduction of new resonance, and has the advantages of high efficiency, strong adaptability and the like.