3906 results about "Aero engine" patented technology

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 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 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 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.

Disclosed in the present invention is an aircraft engine maintenance policy optimization method based on nonparametric reinforcement learning. Firstly, for sparse aircraft engine operation data, constructing an aircraft engine model by means of a Bayesian network and a Gaussian process; then, establishing a policy network and a value network, interacting with the aircraft engine model to form a state / action value group, storing same into a replay buffer, and performing random sampling for use in a training set; updating the policy network and the value network, and updating the training set; and finally, performing aircraft engine maintenance optimization policy decision-making. Provided in the present invention is, for the first time, a Gaussian process-based nonparametric reinforcement learning method for an aircraft engine, which improves the degree of fit between overall training data and a model by means of dynamic data updating while integrating system uncertainty, thereby improving the sampling efficiency of an algorithm. Action selection is performed on the basis of prior maintenance experience data fused with uncertainty, improving the safety and sampling efficiency of a system and thereby solving the problem of predictive maintenance of an aircraft engine system.

The invention belongs to the field of uncertainty quantification and robustness design optimization of aero-engine air-cooled turbines, and particularly discloses a one-dimensional uncertainty design optimization method and system for a reverse cooling turbine based on a one-dimensional aerodynamic analysis method for the reverse cooling turbine. Forming an augmented space by the optimization variables and the uncertainty parameters, and generating a sample set; establishing a Kriging global agent model, and generating an uncertainty parameter sample set; an ASPC model is established, and one-dimensional uncertainty quantification of the reverse cooling turbine is completed; an NSGA-II multi-objective optimization algorithm is coupled, and one-dimensional robustness design optimization of the reverse cooling turbine is completed. The one-dimensional uncertainty design optimization method and system framework of the reverse cooling turbine are provided and established, aerodynamic performance analysis of the reverse cooling turbine can be completed through simple parameters, a geometric entity is not needed, and tasks such as one-dimensional uncertainty quantification and robustness design optimization of the reverse cooling turbine can be achieved.

The invention discloses an aero-engine residual life prediction method based on multi-modal deep learning, and relates to the field of aero-engine prediction and health management. The method comprises the following steps: acquiring and preprocessing multi-sensor time sequence data; constructing a degradation sensitive feature set through multi-scale analysis of a time domain, a frequency domain and a time-frequency domain; constructing a multi-modal deep learning model comprising an original data processing module and a multi-scale feature processing module, and introducing an attention mechanism and an uncertainty quantization module into the model; the model is subjected to lightweight processing to support embedded deployment. According to the method, multi-scale features and multi-modal deep learning are fused, the uncertainty quantification capability is achieved, high-precision and interpretable residual life prediction with uncertainty quantification is achieved, and reliable support is provided for engine maintenance decision making.

The invention relates to an implicit voxel Boolean subtraction optimization method in five-axis machining simulation. According to the method, an implicit tool geometric expression model based on a symbolic distance function is constructed, and a dynamic voxel modeling technology with a layered structure is combined, so that efficient interactive modeling of a material removal process between a tool and a workpiece is realized. The whole process of the method comprises four key stages of spatial positioning, vertex interference detection, projection correction and grid reconstruction, and can cope with geometric modeling challenges caused by continuous motion of a multi-degree-of-freedom and multi-attitude cutter. A dynamic block loading mechanism and a local state synchronization strategy are introduced, so that the real-time response capability of Boolean subtraction operation under complex working conditions is effectively improved, and meanwhile, the continuity and integrity of machining geometric boundaries are kept. The method has good expandability and robustness, obviously reduces space-time complexity of calculation on the basis of keeping geometric surface continuity and topological correctness, realizes millisecond-level response time in an experiment, and has a good application prospect. The method is widely applied to virtual machining simulation and intelligent process verification systems of complex free-form surface parts such as aero-engine blades and automobile structural parts.

The invention relates to the technical field of aero-engine fault prediction, in particular to an aero-engine residual life prediction method based on fuzzy modal regression SCN, and the method comprises the steps: obtaining multi-source monitoring data of an aero-engine under different working conditions; performing fuzzy clustering on the training sample set based on a fuzzy C-means algorithm; training the SCN structure according to the subspace training sample; optimizing the local prediction sub-model based on a modal regression model; according to a generalized maximum correlation entropy loss function, performing robustness optimization on the local prediction sub-model after modal regression in combination with a semi-quadratic optimization strategy and a sparse regular term; predicting a to-be-tested aero-engine sample according to the local aero-engine residual life prediction model; and fuzzy weighted fusion is carried out on the local residual life prediction value of the aero-engine. According to the method, the prediction information of each modal subspace can be integrated, the multi-modal degradation behavior is comprehensively represented, and the accuracy of prediction of the residual life of the aero-engine is remarkably improved.

The invention belongs to the field of aero-engines and turbines, and provides a quick prediction method, system and device for a two-dimensional viscous compressible flow field of a gas compressor and a medium, and the method comprises the steps: obtaining CFD simulation data of a two-dimensional blade profile of the gas compressor under multiple working conditions, and carrying out the preprocessing to obtain a standardized training data set; on the basis of the data set, constructing a hybrid neural network model which takes a blade profile geometric parameter and an operation condition parameter as input and takes a two-dimensional viscous compressible flow field as output; combining mean square error, gradient loss and physical consistency loss based on RANS equation residual error to construct a total loss function, and performing end-to-end training on the model; during reasoning, to-be-predicted parameters are input into the trained model according to the same preprocessing mode, and then a flow field prediction result can be rapidly output. According to the method, the problems of long consumed time, low precision and poor consistency of traditional CFD simulation can be solved, millisecond-level flow field prediction is achieved, high precision and high physical consistency are achieved, and the method is suitable for efficient optimization design of the blade profile of the gas compressor.

The invention belongs to the technical field of aero-engine maintenance, and discloses an aero-engine intelligent fault diagnosis and maintenance system based on a domain knowledge graph and a large language model. The system comprises four core modules, wherein a data processing module adopts an AemCasRel model to extract a fault entity and relation triple from maintenance data; the knowledge graph management module stores the triple into a Neo4j database, constructs a structured knowledge graph and provides a management interface; the knowledge visualization module realizes visual display and interactive retrieval of the atlas based on D3. Js; the intelligent question and answer module depends on LangChain and a stellar fire big model and combines a multi-hop path reasoning technology to generate an interpretable diagnosis report. According to the system, the whole process integration of fault knowledge from extraction and management to intelligent diagnosis is realized, the precision, efficiency and intelligent level of maintenance diagnosis are remarkably improved, and the system has a wide application prospect in the fields of aviation, aerospace, machinery and the like.

The invention provides an aero-engine remaining service life prediction method based on a space-time knowledge graph and SDCNN, and belongs to the field of aero-engine health management. According to the method, a space-time knowledge graph and SDCNN neural network architecture is constructed. According to the method, a spatio-temporal knowledge graph is innovatively constructed for an aero-engine, a BERT model is adopted to carry out data type conversion, and a multi-head graph attention network and a pooling graph attention network complete feature extraction and feature fusion to obtain fusion features; and finally, inputting the fusion features into a stacked expansion convolutional neural network to carry out regression learning on feature data, and then carrying out residual life prediction on the aero-engine. According to the method, modeling and prediction are carried out on complex spatial-temporal characteristic data, the remaining service life of the aero-engine can be effectively predicted under limited data, data support is provided for formulating an aero-engine maintenance strategy, and meanwhile a new thought is provided for predicting the remaining service life of other industrial equipment.

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 provides a multi-modal pulse fusion network prediction framework for aero-engine gas path performance parameters, and belongs to the technical field of aero-engine gas path performance parameter prediction and fault diagnosis. The method comprises the following steps: firstly, acquiring flight data of an aero-engine, and preprocessing data of each sensor; secondly, constructing a multi-modal pulse fusion neural network model comprising a time sequence data pulse processing module, an image data pulse processing module, a multi-modal feature fusion module and a prediction output module; and finally, training the multi-modal pulse fusion neural network model, and predicting gas path performance parameters by using the trained model. According to the method, the leakage integration distribution spiking neuron and the attention mechanism are introduced, the depth feature extraction and fusion of the time sequence data set and the image data set are realized, the complex nonlinear correlation between the data is effectively captured, the accuracy of the prediction result is remarkably improved, and the real-time prediction requirement in the operation process of the aero-engine can be met.

An aero-engine nut tightening machine arm device belongs to the technical field of aero-engine automatic assembly and comprises a rotary indexing assembly, a vertical lifting assembly, a central tightening assembly, a wrench overturning posture adjusting assembly and an auxiliary stability augmentation assembly. The rotary indexing assembly is connected with an aero-engine blade disc rotor through an external tool; the vertical lifting assembly is arranged above the rotary indexing assembly; the pivot tightening assembly is arranged in the middle of the vertical lifting assembly; the wrench overturning posture adjusting assembly is arranged between the vertical lifting assembly and the pivot tightening assembly. The auxiliary stability augmentation assembly is arranged at the bottom of the central tightening assembly. Compared with a traditional manual nut fastening process, the aircraft engine nut tightening machine arm device has the advantages that the labor intensity of workers can be greatly reduced, the execution of the nut fastening process is completed in an automatic mode, the consistency of the assembly quality process is effectively improved, the nut fastening torque precision is high, and the production efficiency is improved. And the production and assembly efficiency is further improved.

The invention relates to the technical field of fault diagnosis, in particular to a few-label self-supervised learning fault diagnosis method based on an interpretable neural network, and the method comprises the steps: collecting time domain data of a sensor of an aero-engine under different health conditions, carrying out the standardization processing, and dividing the time domain data into a pre-training set, a training set, a verification set and a test set; performing fast Fourier transform and data enhancement on the time domain data in the pre-training set to obtain frequency domain data and enhanced time domain data; constructing a pre-training framework, and performing pre-training to convergence based on the frequency domain data and the enhanced time domain data to obtain a pre-trained time encoder; constructing a fault diagnosis model based on the pre-trained time encoder, and performing iterative training to convergence by using the training set to obtain a trained fault diagnosis model; and performing fault diagnosis on the test set by using the trained fault diagnosis model, and performing visual interpretation on the diagnosis process of the fault diagnosis model by using a gradient weighting class activation mapping technology.

The invention provides an aero-engine complete machine full-three-dimensional performance simulation error transmission and control method, and relates to the technical field of aero-engine performance simulation, and the method comprises the following steps: decomposing an aero-engine into a plurality of core parts along an airflow direction, and building a pneumatic and thermal coupling simulation model between the parts; identifying and quantifying a simulation error source of each part, and defining a simulation error value of each part; constructing a whole machine error transfer network, wherein the whole machine error transfer network comprises a sensitivity weight and a coupling transfer coefficient; based on the sensitivity weight and the coupling transfer coefficient, calculating a whole machine accumulated error; in the whole machine full-three-dimensional simulation process, outlet airflow parameter simulation data of all the components are collected, the simulation data are compared with test data, and iteration correction is conducted on simulation models of all the components and whole machine accumulated errors according to comparison results. According to the method, the whole machine simulation precision is improved, the whole machine simulation error is finally reduced to be within 2.5%, and the engineering reliability of a simulation result is improved.

The invention relates to the technical field of aero-engine health management, in particular to a dual-channel feature interaction aero-engine remaining life prediction method and system, and the method comprises the steps: obtaining the time sequence monitoring data of an aero-engine sensor, and carrying out the normalization processing; inputting normalized aero-engine sensor time sequence monitoring data into the trained DCTT-LSTM model, and outputting an aero-engine residual life prediction value; the DCTT-LSTM model comprises a dual-channel feature extraction sub-model, a feature fusion layer and a residual life prediction sub-model, a first feature extraction channel is constructed based on a time convolutional network, a second feature extraction channel is constructed based on a converter encoder, and the residual life prediction sub-model is constructed based on a long short-term memory network. Through dual-channel feature extraction, interactive attention fusion and long and short-term memory network prediction mechanisms, the feature expression ability and anti-interference performance are significantly improved, and high-precision residual life prediction is realized.

The invention discloses an aero-engine combustion chamber ignition state prediction method and system based on deep learning. The method comprises the steps that a combustion chamber ignition parameter data set is collected; analyzing sample distribution to determine an augmentation amount, generating a new sample based on KNN interpolation, injecting adaptive Gaussian noise, and referring to physical relevance between a fuel-air ratio and a temperature-pressure ratio of a real sample during augmentation of a special working condition; an AttResVGG deep neural network is constructed, gradient disappearance is prevented by adopting residual connection, a multi-head self-attention mechanism is embedded to capture a long-range dependency relationship among parameters, and multi-scale features are integrated through a global feature fusion module; double classifier training is designed, a main classifier adopts category weighted cross entropy loss, an auxiliary classifier adopts label smooth loss, and model parameters are jointly optimized; and inputting working condition parameters for real-time prediction. According to the invention, parameter dependence of a traditional empirical model is broken through, and adaptive prediction of complex working conditions is realized; the calculation time consumption is reduced, and the real-time decision demand is met; and the device adapts to various combustion chamber structures, and re-modeling is not needed.

The invention relates to an aero-engine stator part crack critical dimension comprehensive determination method and system, and belongs to the technical field of aviation, the method comprises the following steps: obtaining an aero-engine typical hot end stator part geometric model and material parameters, and carrying out static strength analysis according to fluid-thermal-solid coupling load characteristics in actual working conditions; inserting an initial crack into a first principal stress plane of a dangerous part of the real component, carrying out crack propagation simulation to determine a crack propagation path, and calculating a critical crack size of a dangerous point; analyzing critical crack sizes under different failure modes, such as cooling failure and buckling failure; and integrating analysis results under the multi-failure mode, and determining a comprehensive critical dimension as a final judgment standard. According to the method, through combination of fracture failure and function failure analysis, accurate judgment of the critical dimension of the crack is realized, limitation of a traditional single fracture criterion is broken through, comprehensive function failure analysis is realized, the accuracy of a maintenance decision is remarkably improved, and a scientific basis is provided for condition-based maintenance decision.

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 application provides an aero-engine acceleration limiting method, and relates to the technical field of aero-engines, and comprises the following steps: receiving a flight control instruction, calculating a first fuel supply amount according to the flight control instruction, and executing single / dual-outer-envelop working mode conversion; detecting a position state of an actual mode selection valve and a rotating speed feedback value in real time; judging whether the current state of the engine meets the mode switching condition of single / dual-outer-envelop according to the position state of the mode selection valve; calculating a second fuel supply amount, a third fuel supply amount, and a fourth fuel supply amount according to the position state of the mode selection valve, combining the first fuel supply amount, a fifth fuel supply amount, taking the minimum value among the five fuel supply amounts, comparing the minimum value with a sixth fuel supply amount to take the maximum value, taking the minimum value with a seventh fuel supply amount, taking the maximum value of the comparison result with an eighth fuel supply amount as a control fuel supply amount output; and judging whether the engine is in an acceleration / deceleration state according to a rotating speed difference. The application solves the safety problem possibly caused by the acceleration / deceleration process of the dual-outer-envelop engine.

The invention provides an aero-engine test bed. The aero-engine test bed comprises a rack and an auxiliary support, the auxiliary support comprises a mounting platform, a mounting seat, a connecting piece, a supporting arm, a field force limiting mechanism and a controller; the mounting platform is arranged on the rack; the two mounting seats are oppositely arranged on the mounting platform; the connecting piece is arranged between the two mounting seats and is opposite to the mounting platform; the connecting piece is configured to move in the vertical direction; the supporting arm corresponds to the mounting seat; the two ends of each supporting arm are hinged to the corresponding mounting base and the corresponding connecting piece correspondingly. The supporting arm is provided with an installation joint used for being connected to an aero-engine. The field force limiting mechanism comprises a first component and a second component which are oppositely arranged; the first component is arranged on the mounting platform, and the second component is arranged on the connecting piece; and the controller is electrically connected with the field force limiting mechanism. Through cooperative work of the field force limiting mechanism and the controller, the supporting rigidity of the auxiliary support can be dynamically adjusted according to the real-time rotating speed of the aero-engine in the test run process.

The invention provides an aero-engine test whole-process business management method and system, and belongs to the technical field of whole-process business management, and the method comprises the steps: extracting information in a test item information document and a tester information document, and determining structured data; on the basis of the structured data, forming an initial test plan, performing test personnel allocation optimization on the initial test plan, and determining a target test plan; generating a target test card on the basis of the target test plan, and continuously updating the target test card by adopting an editable block chain according to the data in the test process until the test is finished; and after the test is finished, the operation authority of the target test card is removed, and non-tampered and decentralized aero-engine test whole-process business data is formed. The information utilization efficiency of each link is effectively improved, the distribution rationality of testers is improved, and the safe storage of test data is ensured.

The invention relates to the technical field of aeroelastic mechanics of aero-engines, in particular to a method for identifying an aeroelastic instability mechanism of a damaged blade of a gas compressor rotor based on a spectral characteristic orthogonal mode decomposition technology. According to the method, through damaged blade parameterization modeling and aeroelasticity response solution modeling, an instability mechanism identification technology is developed based on a spectrum characteristic orthogonal mode decomposition technology, and mechanism identification of aeroelasticity instability of the damaged rotor blade of the gas compressor is achieved. Compared with a traditional method, the method has the advantages that the energy redistribution process of the damaged blade in the frequency domain dimension can be captured, and the characteristics of convergent vibration, limit cycle oscillation and divergent vibration under each typical aeroelastic working condition can be effectively identified. By analyzing flow field dominant structure characteristics and frequency characteristics, physical essence identification of the instability mechanism is realized, and the technical scheme of the aeroelastic instability mechanism of the damaged blade is further perfected.

The invention belongs to the technical field of aero-engine performance testing, particularly relates to a physical constraint-based aero-engine state prediction model construction method and system, and aims to solve the problems of high calculation complexity and poor data quality of an existing physical information model. The method comprises the following steps: acquiring historical operation data of the aero-engine and a physical constraint rule set of engineering simplification; predicting performance parameters by adopting a deep learning model; constructing a total loss function formed by weighting a data loss item and a physical loss item to train the model; wherein the physical loss item is generated based on the deviation degree of the predicted performance parameter and the engineering simplified physical constraint rule set, and is used for replacing the complex partial differential equation constraint. According to the method, the engineering simplified physical rule is introduced, so that the calculation overhead of model training is remarkably reduced, the model is effectively guided to learn the characteristics conforming to the physical rule, and the accuracy and generalization ability of the prediction model are remarkably improved under the condition of limited data.

The invention discloses high-performance anti-wear engine lubricating oil as well as a preparation method and application thereof. The high-performance anti-wear engine lubricating oil is prepared from the following raw materials: self-developed lubricating oil base oil, poly-alpha-olefin, zinc dialkyl dithiophosphate, calcium sulfonate, polyisobutylene succinimide, an antioxidant, polymethacrylate, an organic molybdenum complex, a de-foaming agent and a pour point depressant. The preparation method comprises the following steps: 1) synthesizing 9, 10-bis (2-tert-butyl-4-methylphenoxy) trimethylolethane stearate as base oil of the lubricating oil; and 2) mixing the base oil with other assistants to obtain the high-performance anti-wear engine lubricating oil. According to the preparation method, novel-like synthetic ester oil is firstly prepared, a phenol group and a large steric hindrance substituent group are introduced into a polymer, the anti-oxidation function is directly built in base oil molecules, meanwhile, super-strong heat resistance is achieved, and finally the high-performance anti-wear engine lubricating oil is obtained and can be applied to high-end application scenes such as aero-engines or gas turbines.

The invention discloses a rare earth modified cast high-temperature titanium alloy and a preparation method and application thereof, and belongs to the technical field of high-temperature titanium alloys. The alloy comprises the following components in percentage by mass: 5.50% to 6.50% of Al, 3.50% to 5.00% of Sn, 3.50% to 5.00% of Zr, 0.20% to 0.80% of Mo, 0.10% to 0.30% of Si, 0.25% to 0.55% of Nb, 0.20% to 0.40% of Ta, 0.40% to 0.60% of W, 0.25% to 0.35% of Nd and the balance of Ti and impurities. By adding narrow-interval rare earth Nd and utilizing the synergistic effect of structure purification and dispersion strengthening, the effective Al equivalent of an alpha phase is reduced, so that precipitation and growth of a Ti3Al brittle ordered phase in the high-temperature long-time service process are remarkably inhibited. The preparation method comprises a vacuum melting process and an innovative five-stage hot isostatic pressing process, and the process realizes densification, homogenization and structure stabilization integrated treatment through programmed temperature and pressure regulation and control. The obtained alloy has excellent high-temperature strength, plasticity and long-time structure stability at the temperature of 650 DEG C or above, and is suitable for high-temperature structural parts of aero-engines and gas turbines.

The invention relates to the technical field of aero-engine rotor vibration tests, in particular to an aero-engine rotor unsteady-state thermally induced vibration test system which comprises a rotor experiment table, an air inlet and exhaust system, a lubricating oil system, a protection system and a heating system. The rotor experiment table comprises a first supporting system, a second supporting system, a rotor outer protection casing, a gas compressor disc, a gas compressor sealing disc, a turbine disc, a turbine front sealing disc, a front shaft diameter, an inter-disc drum, a rear shaft diameter, a first squirrel-cage elastic support, a ball bearing, a second squirrel-cage elastic support and a rolling rod bearing. The first supporting system is composed of a first squirrel-cage type elastic support and a ball bearing, meanwhile, an air inlet is formed in the first supporting system, and the ball bearing in the first supporting system is connected with the front shaft diameter in an interference fit mode. Rotor unsteady-state characteristics considering heating conditions are finally created, and vibration response and durability of a wheel disc structure under the unsteady-state thermal load condition are studied in combination with test conditions such as temperature vibration and the like.