5752 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 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 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 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 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 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 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 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 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.

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 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.

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

The invention belongs to the technical field of aircraft engine compressor blade detection, and discloses an aircraft engine compressor blade erosion damage prediction method and system based on a PINN. The method comprises the following steps: constructing an erosion damage rate equation and a flow field-particulate matter coupling equation aiming at the erosion damage judgment of the blade of the gas compressor; performing dual-drive loss function design, constructing a physical information neural network (PINN) model, and performing adaptive training on the constructed physical information neural network (PINN) model; the invention relates to gas compressor blade erosion damage prediction and engineering application. According to the method, the limitation of the prior art is overcome, a more accurate and reliable damage prediction model is provided, and powerful support is provided for design, operation and maintenance and safety evaluation of the aero-engine. According to the innovative method, the physical law and the data driving technology can be combined, and the damage prediction precision and efficiency of the aero-engine compressor blade under the complex working condition are improved.

The invention relates to an optimization design method for a multi-fulcrum elastic ring supporting structure of an aero-engine rotor system, and belongs to the technical field of aero-engine vibration control and rotor dynamics simulation. Firstly, a finite element or lumped unit model is adopted to establish a coupling kinetic equation of a rotor and an elastic ring, and fluid-solid coupling factors such as inner and outer oil films of the elastic ring and orifice flow are considered; then, through critical rotating speed and vibration harmonic response analysis, sensitivity evaluation is carried out on rigidity and damping characteristics of different supporting positions, and target vibration reduction modes corresponding to all fulcrums are identified; then, coupling simulation and multi-objective optimization are carried out around the number of bosses of the elastic ring, the height of the bosses, the damping aperture, the thickness of a thin wall and other key structure parameters, and the optimal design meeting the vibration reduction requirement of a rotor system and the strength reliability requirement of the elastic ring is obtained through iteration; and finally, an optimization result is applied to structure manufacturing and assembling. The method can effectively reduce the vibration amplitude of the rotor when the rotor crosses the multi-order critical rotating speed.

The invention relates to the technical field of aero-engines, in particular to a sealing and cooling structure and method for adjusting the axial force of an aero-engine rotor, which comprises a turbine disc provided with a rear baffle, and further comprises a low-pressure turbine guide vane arranged opposite to the turbine disc, a low guide front inner supporting ring and a low guide rear inner supporting ring are arranged in the middle of the low-pressure turbine guide vane; labyrinth teeth correspondingly matched with the low-guide front inner supporting plate are formed on the rear baffle, radial labyrinth tooth gaps are formed, and when airflow in an air collection cavity enters a rotor cavity from a low-pressure pre-rotation nozzle, the airflow is embedded into an air supply channel from a front pre-rotation hole at the same time; one part of airflow entering the air supply channel enters the disc rear cavity, and the other part of airflow enters the rotor cavity. By adjusting the sealing labyrinth structure, it is ensured that the axial force of the rotor does not exceed the allowable maximum value in the large-state working process and is not subjected to light load in the small-state working process, meanwhile, sealing gas used behind a turbine disc is ensured, turbine hot gas is prevented from flowing into an inner disc cavity from a main channel, and it is ensured that an engine works reliably.

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 engine turbine shaft sequential optimization design method based on ensemble learning, and the method comprises the steps: carrying out the parametric modeling of an aero-engine turbine shaft through employing a finite element method, and determining a design variable; determining an optimized objective function and constraint conditions based on the turbine shaft structure; constructing a training point set by using the target function and the constraint condition, and establishing an ensemble learning agent model by using the training point set through an ensemble learning algorithm; executing a sequential optimization design process based on the ensemble learning agent model; searching a current optimal sequence design point by utilizing an intelligent algorithm and a sequential updating criterion, and judging whether the sequence design point meets a threshold value requirement or not; if the current optimal sequence design point meets the threshold requirement, outputting the current optimal sequence design point as a design result; otherwise, adding the sequence design points as new training points into the training point set to update the training point set, updating the ensemble learning agent model by using the updated training point set, and iteratively calculating the sequence design points.

The invention discloses an aero-engine gear damage analysis method and system based on image recognition, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a gear surface image of a to-be-detected aero-engine gear at multiple angles; preprocessing the gear surface image; sequentially performing gear region segmentation, damage region positioning and damage feature quantification processing on the preprocessed gear surface image to obtain gear damage features; and performing damage analysis on each damage area based on the gear damage characteristics to obtain a damage analysis result which at least comprises a damage type judgment result, a damage severity grading result and a residual life estimation result. The technical problems that the aero-engine gear damage detection efficiency is low, the omission ratio is high, tooth surface damage is difficult to detect, damage evolution cannot be predicted, the remaining life cannot be evaluated, and the maintenance requirement of the aero-engine gear cannot be met can be solved.

The invention provides a gas compressor blisk rupture rotation speed prediction method based on virtual-real fusion, belongs to the technical field of aero-engines, and particularly relates to an aero-engine gas compressor blisk rupture rotation speed prediction method by establishing a rupture rotation speed-oriented aero-engine gas compressor blisk digital twin model and performing double correction on the model and a failure criterion by using test real-time data. And in the simulation process, a parallel computing technology is used for acceleration, so that a high-precision fracture rotation speed prediction value which aims at a single individual of the aeroengine compressor blisk and accords with physical rules and actual test measurement can be quickly obtained.

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 discloses a complete machine variable-dimension simulation method based on a circumferential average flow model, belongs to the technical field of aero-engine numerical simulation and multi-scale modeling, and solves the problem that complete machine simulation efficiency and local flow detail precision are difficult to consider at the same time in the prior art. And traditional full-three-dimensional computing is too high in resource consumption and cannot quickly evaluate the performance of the whole machine. According to the method, a zero-dimensional complete machine and component two-dimensional model of the aero-engine is firstly established, dimensionality reduction is carried out by using a circumferential average N-S equation, the two-dimensional model is embedded into the zero-dimensional model, an equivalent replacement variable reconstruction equation set is solved, synchronous convergence of the complete machine and component models is realized, and simulation precision and efficiency are improved. According to the method, a full coupling mode is adopted, two-dimensional dimension reduction modeling is combined, minute-level convergence is achieved, the simulation error is not larger than 5%, the simulation precision is improved, the calculation efficiency is improved, rich S2 flow field information can be provided, and the research and development cost and period are reduced.