Finite element method-based aero-engine fan rotor keyless phase virtual dynamic balance method and system

Abstract

The invention relates to the technical field of vibration active control and artificial intelligence, and discloses an aero-engine fan rotor keyless phase virtual dynamic balance method and system based on a finite element method, and the system comprises a modeling and simulation module which is configured to obtain the first-order critical speed and vibration mode of an initial rotor dynamics simulation model; the model calibration module is configured to calibrate the initial rotor dynamics simulation model according to the first actual amplitude to obtain a calibrated rotor dynamics simulation model; the model updating module is configured to update the calibration rotor dynamics simulation model to obtain an updated rotor dynamics simulation model; and the trial weight and counterweight module is configured to substitute the second actual amplitude into particle swarm optimization, calculate the actual total unbalance amount of the rotor system and the trial weight angle difference, and finally add a counterweight and select a direction until the vibration is reduced to realize dynamic balance. According to the invention, the position of the unbalance can be accurately determined only through amplitude analysis.

Description

Technical Field

[0001] The present invention relates to the field of active vibration control and artificial intelligence technology, and in particular to a finite element method-based keyless phase virtual dynamic balancing method and system for an aircraft engine fan rotor. Background Art

[0002] Under the harsh conditions of high speed, high temperature, and high load, the rotor system of an aircraft engine may experience deformation, wear, and changes in concentricity. These changes can cause the rotor's center of mass to deviate from the center of rotation, causing vibration and, in turn, compromising the stability of the entire engine. Imbalance is the primary cause of engine vibration, with vibration failures caused by mass imbalance being particularly common and potentially harmful. In actual dynamic balancing operations, the influence coefficient method is commonly used. However, this method requires at least one balancing weight application, and the weight size often relies on empirical judgment, resulting in high balancing costs. Furthermore, frequent engine startups and shutdowns significantly increase production costs. Therefore, while meeting balancing requirements, reducing the number of startups and shutdowns to improve dynamic balancing efficiency is crucial. Currently, the influence coefficient method, modal balancing method, coordinate rotation optimization, and its optimization methods are relatively mature dynamic balancing techniques. The influence coefficient method and modal balancing method require multiple test runs to collect data to identify imbalances, and are considered steady-state balancing methods. The coordinate rotation optimization method, however, requires a test run before each balancing adjustment to determine the next adjustment method and direction. While this method is simple and practical, applicable to both steady-state and unsteady-state conditions, its trial adjustment process is somewhat blind and can lead to misadjustments. Furthermore, each optimization attempt starts from scratch, failing to effectively utilize previous useful data, resulting in low balancing efficiency. Virtual dynamic balancing technology, as an emerging approach, significantly improves the cost-effectiveness of balancing by adding virtual excitations to simulation models to analyze unbalanced responses.

[0003] In actual on-site dynamic balancing operations, it is often necessary to measure the rotor vibration phase to determine the counterweight position. This requires the installation of a key phase sensor to ensure smooth on-site rotor balancing. However, due to the unique structure of aircraft engines, the conditions for outputting phase information are sometimes not available, which poses a significant challenge to actual dynamic balancing.

[0004] Therefore, it is necessary to design a keyless phase virtual dynamic balancing method and system for aircraft engine fan rotor based on finite element method to solve the problems existing in current technology. Summary of the Invention

[0005] In view of this, the present invention proposes a keyless phase virtual dynamic balancing method and system for aircraft engine fan rotors based on the finite element method, aiming to solve the problem in current technology that due to the particularity of the aircraft engine structure, the conditions for outputting phase information are sometimes not met, which brings great challenges to actual dynamic balancing.

[0006] In one aspect, the present invention proposes a keyless phase virtual dynamic balancing system for an aircraft engine fan rotor based on the finite element method, comprising:

[0007] a modeling and simulation module configured to establish an initial rotor dynamics simulation model, perform modal analysis on the initial rotor dynamics simulation model using a finite element method, and obtain a first-order critical speed and vibration mode of the initial rotor dynamics simulation model;

[0008] a model calibration module configured to measure a critical speed of an actual rotor, compare the critical speed with the first-order critical speed, and determine whether to calibrate the initial rotordynamics simulation model based on the comparison result; if so, measure a first actual amplitude of the actual rotor, and calibrate the initial rotordynamics simulation model based on the first actual amplitude to obtain a calibrated rotordynamics simulation model;

[0009] a model updating module configured to calculate the bearing stiffness and the original unbalance by particle swarm optimization using the measured first actual amplitude, and update the calibrated rotor dynamics simulation model to obtain an updated rotor dynamics simulation model;

[0010] The test weight and counterweight module is configured to add a test weight to the actual rotor according to the particle optimization result and measure a second actual amplitude; substitute the second actual amplitude into the particle swarm optimization, calculate the total imbalance of the actual rotor system and the test weight angle difference, and finally add a counterweight and select a direction until the vibration is reduced and dynamic balance is achieved.

[0011] Furthermore, the modeling and simulation module performs modal analysis on the initial rotor dynamics simulation model using the finite element method to obtain the first-order critical speed and vibration mode of the initial rotor dynamics simulation model, including:

[0012] The actual rotor system is converted into a discretized model with finite degrees of freedom using a finite element method;

[0013] Performing dynamic simulation on the discretized model through a finite element program and analyzing the steady-state unbalanced response;

[0014] A modal analysis is performed on the initial rotor dynamics simulation model to obtain a first-order critical speed and vibration mode of the initial rotor dynamics simulation model.

[0015] Furthermore, the model calibration module compares the critical speed with the first-order critical speed, and determines whether to calibrate the initial rotor dynamics simulation model according to the comparison result, including:

[0016] Calculating the difference between the critical speed and the first-order critical speed, and recording it as the critical speed deviation value;

[0017] Comparing the critical speed deviation value with a critical speed deviation threshold, and determining whether to calibrate the initial rotor dynamics simulation model according to the comparison result;

[0018] When the critical speed deviation value is within the critical speed deviation threshold, determining not to calibrate the initial rotor dynamics simulation model;

[0019] When the critical speed deviation value is outside the critical speed deviation threshold, it is determined to calibrate the initial rotor dynamics simulation model.

[0020] Furthermore, the model calibration module calibrates the initial rotor dynamics simulation model according to the first actual amplitude to obtain a calibrated rotor dynamics simulation model, including:

[0021] The first actual amplitude is the amplitude measured at three different measuring points of the actual rotor at the same speed or the amplitude measured at three different speeds at the same measuring point of the actual rotor;

[0022] According to the first actual amplitude, bearing stiffness parameters of the initial rotordynamics simulation model are adjusted to obtain a calibrated rotordynamics simulation model.

[0023] Furthermore, the model updating module calculates the bearing stiffness and the original unbalance by particle swarm optimization using the measured first actual amplitude, and updates the calibrated rotor dynamics simulation model. Obtaining the updated rotor dynamics simulation model includes:

[0024] Based on the calibration rotor dynamics simulation model, substituting the first actual amplitude into a particle swarm optimization algorithm for optimization;

[0025] defining a fitness function, taking the bearing stiffness and the original imbalance as optimization variables, and calculating a fitness value based on finite element simulation;

[0026] Initialize the particle swarm parameters and calculate the initial fitness value; wherein the particle swarm parameters include population size, number of iterations, speed, maximum and minimum position values;

[0027] Calculate individual extreme values and group extreme values, and continuously update the speed and position of the particles according to the optimization rules, and obtain the first particle swarm optimization result after multiple iterations; wherein the optimization rule is that each particle calculates the speed of the particle in the next generation according to the individual extreme value and group extreme value, and then calculates the position based on the speed;

[0028] According to the first particle swarm optimization result, the bearing stiffness and original unbalance parameters in the calibration rotor dynamics simulation model are updated to obtain an updated rotor dynamics simulation model.

[0029] Furthermore, the fitness function is:

[0030] Function[fitness]=Fitness(x,y,z);

[0031] Among them, Function is the definition function of Mat Lab, that is, defining a fitness function Fitness; x and y represent the bearing stiffness; z represents the original imbalance.

[0032] Furthermore, the model updating module initializes the particle swarm parameters and calculates the initial fitness value, including:

[0033] A population consisting of several individuals is randomly generated. The speed and position of each individual are three-dimensional vectors. The fitness function Fitness(x,y,z) is called with the individual position information as (x,y,z) and the output is the fitness value.

[0034] Furthermore, when the model updating module calculates individual extreme values and group extreme values, it includes:

[0035] Compare the fitness values to obtain the individual extreme value and group extreme value in the population;

[0036] The individual extreme value refers to the best fitness value experienced by an individual, and the group extreme value refers to the maximum fitness value in a certain generation of a population.

[0037] Furthermore, the test weight and counterweight module substitutes the second actual amplitude into the particle swarm optimization, calculates the total imbalance of the actual rotor system and the test weight angle difference, and finally adds counterweights and selects directions until the vibration is reduced and dynamic balance is achieved, including:

[0038] adding a test weight to the actual rotor, wherein the test weight is equal to the original unbalance amount calculated by particle swarm optimization, and measuring the second actual amplitude;

[0039] Substituting the second actual amplitude into the particle swarm optimization algorithm again to obtain a second particle swarm optimization result, and calculating the total unbalance amount and the trial weight angle difference of the actual rotor system;

[0040] Add counterweights based on the second particle swarm optimization results and select directions until the vibration is reduced and dynamic balance is achieved.

[0041] Compared to existing technologies, the present invention offers the advantage of eliminating the need for phase measurement. It is particularly suitable for rotor systems where key phase sensors cannot be installed, accurately determining the location of imbalance through amplitude analysis alone. It is particularly suitable for dynamic balancing of aircraft engine rotors and exhibits broad application potential.

[0042] This method significantly improves the accuracy of balancing results by combining a particle swarm optimization algorithm with virtual dynamic balancing technology. Virtual dynamic balancing utilizes a simulation model to simulate the actual rotor and applies virtual counterweights to obtain imbalance response analysis. This effectively replaces the tedious process of repeatedly starting the vehicle and adding actual counterweights, significantly improving dynamic balancing efficiency. Furthermore, the introduction of a particle swarm optimization algorithm, a machine learning algorithm, allows for more precise verification of the simulation model, ensuring high consistency with the actual rotor. This significantly improves accuracy compared to traditional virtual dynamic balancing techniques.

[0043] This technology calculates the system's initial imbalance from a single initial vibration measurement. When adding test weights, the system's initial imbalance is adjusted based on the calculated results, resulting in a 30% probability of achieving balance on the first test. Even if vibration reduction fails on the first test, there's a 50% chance that the second test will achieve complete reduction. Otherwise, the third test will guarantee reduction.

[0044] In another aspect, the present invention also proposes a keyless phase virtual dynamic balancing method for an aircraft engine fan rotor based on a finite element method, comprising the following steps:

[0045] S100: establishing an initial rotor dynamics simulation model, performing modal analysis on the initial rotor dynamics simulation model using a finite element method, and obtaining a first-order critical speed and vibration mode of the initial rotor dynamics simulation model;

[0046] S200: measuring a critical speed of an actual rotor, and comparing the critical speed with the first-order critical speed, and determining whether to calibrate the initial rotor dynamics simulation model based on the comparison result; if so, measuring a first actual amplitude of the actual rotor, and calibrating the initial rotor dynamics simulation model based on the first actual amplitude to obtain a calibrated rotor dynamics simulation model;

[0047] S300: Calculating bearing stiffness and original imbalance using particle swarm optimization using the measured first actual amplitude, and updating the calibrated rotor dynamics simulation model to obtain an updated rotor dynamics simulation model;

[0048] S400: Adding a test weight to the actual rotor according to the particle optimization result and measuring a second actual amplitude; substituting the second actual amplitude into the particle swarm optimization, calculating the total imbalance of the actual rotor system and the test weight angle difference, and finally adding a counterweight and selecting a direction until the vibration is reduced and dynamic balance is achieved.

[0049] It is understandable that the above-mentioned finite element method-based keyless phase virtual dynamic balancing method and system for aircraft engine fan rotor have the same beneficial effects and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0051] Figure 1 A structural block diagram of a keyless virtual dynamic balancing system for an aircraft engine fan rotor based on the finite element method provided in an embodiment of the present invention;

[0052] Figure 2 A flow chart of a keyless phase virtual dynamic balancing method for an aircraft engine fan rotor based on the finite element method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0054] See Figure 1 As shown, in some embodiments of the present application, this embodiment provides an aircraft engine fan rotor keyless phase virtual dynamic balancing system based on the finite element method, including:

[0055] a modeling and simulation module configured to establish an initial rotor dynamics simulation model, perform modal analysis on the initial rotor dynamics simulation model using a finite element method, and obtain a first-order critical speed and vibration mode of the initial rotor dynamics simulation model;

[0056] a model calibration module configured to measure a critical speed of an actual rotor, compare the critical speed with the first-order critical speed, and determine whether to calibrate the initial rotordynamics simulation model based on the comparison result; if so, measure a first actual amplitude of the actual rotor, and calibrate the initial rotordynamics simulation model based on the first actual amplitude to obtain a calibrated rotordynamics simulation model;

[0057] a model updating module configured to calculate the bearing stiffness and the original unbalance by particle swarm optimization using the measured first actual amplitude, and update the calibrated rotor dynamics simulation model to obtain an updated rotor dynamics simulation model;

[0058] The test weight and counterweight module is configured to add a test weight to the actual rotor according to the particle optimization result and measure a second actual amplitude; substitute the second actual amplitude into the particle swarm optimization, calculate the total imbalance of the actual rotor system and the test weight angle difference, and finally add a counterweight and select a direction until the vibration is reduced and dynamic balance is achieved.

[0059] It is understood that the finite element method-based keyless phase virtual dynamic balancing system for aircraft engine fan rotors provided in this embodiment achieves precise dynamic balancing without the need for phase measurement, significantly improving operational convenience and efficiency. In practical applications, the system first establishes an accurate initial rotor dynamics simulation model through the modeling and simulation module and performs modal analysis using the finite element method to obtain key parameters such as the first-order critical speed and mode shape. Subsequently, the model calibration module measures the critical speed of the actual rotor and compares it with the first-order critical speed of the simulation model to ensure the accuracy of the simulation model. If any deviation exists, the simulation model is calibrated based on the first actual amplitude of the actual rotor to obtain a calibrated rotor dynamics simulation model that is closer to reality. After model calibration is completed, the model update module further uses the first actual amplitude to calculate the bearing stiffness and original imbalance using an advanced particle swarm optimization algorithm, and then updates the calibrated simulation model to obtain an updated rotor dynamics simulation model. This process fully utilizes the global search capability and rapid convergence characteristics of the particle swarm optimization algorithm, effectively improving the accuracy and reliability of the simulation model. Finally, the test weight and counterweight module adds test weights to the actual rotor based on the particle swarm optimization results and measures a second actual amplitude. This second actual amplitude is then fed back into the particle swarm optimization algorithm to calculate the total imbalance of the actual rotor system and the test weight angle difference. Based on this information, counterweights are precisely added and oriented until vibration is reduced, achieving dynamic balancing. This process not only avoids the tedious process of repeatedly starting the machine and adding actual counterweights required in traditional dynamic balancing methods, but also significantly improves the efficiency and accuracy of dynamic balancing.

[0060] Specifically, the modeling and simulation module uses the finite element method to perform modal analysis on the initial rotor dynamics simulation model to obtain the first-order critical speed and vibration mode of the initial rotor dynamics simulation model, including:

[0061] The actual rotor system is converted into a discretized model with finite degrees of freedom using a finite element method;

[0062] Performing dynamic simulation on the discretized model through a finite element program and analyzing the steady-state unbalanced response;

[0063] A modal analysis is performed on the initial rotor dynamics simulation model to obtain a first-order critical speed and vibration mode of the initial rotor dynamics simulation model.

[0064] It is understandable that the two bearing stiffnesses are randomly set to 10 6 N / m, and the modal analysis of the initial rotor simulation model was performed, and the first-order critical speed of the model was obtained to be 3469r / min.

[0065] Specifically, the model calibration module compares the critical speed with the first-order critical speed, and determines whether to calibrate the initial rotor dynamics simulation model according to the comparison result, including:

[0066] Calculating the difference between the critical speed and the first-order critical speed, and recording it as the critical speed deviation value;

[0067] Comparing the critical speed deviation value with a critical speed deviation threshold, and determining whether to calibrate the initial rotor dynamics simulation model according to the comparison result;

[0068] When the critical speed deviation value is within the critical speed deviation threshold, determining not to calibrate the initial rotor dynamics simulation model;

[0069] When the critical speed deviation value is outside the critical speed deviation threshold, it is determined to calibrate the initial rotor dynamics simulation model.

[0070] In this embodiment, the critical speed deviation threshold is set based on empirical values and industry standards, and is used to determine whether the deviation between the actual critical speed of the rotor and the first-order critical speed of the simulation model is within an acceptable range.

[0071] It is understood that when the initial rotor dynamics simulation model is calibrated, the system adjusts the relevant parameters in the simulation model based on the actual rotor's first actual amplitude to ensure that the simulation results are closer to reality. This calibration process not only improves the accuracy of the simulation model but also provides a more reliable foundation for subsequent dynamic balancing operations.

[0072] Specifically, the model calibration module calibrates the initial rotor dynamics simulation model according to the first actual amplitude to obtain the calibrated rotor dynamics simulation model, including:

[0073] The first actual amplitude is the amplitude measured at three different measuring points of the actual rotor at the same speed or the amplitude measured at three different speeds at the same measuring point of the actual rotor;

[0074] According to the first actual amplitude, bearing stiffness parameters of the initial rotordynamics simulation model are adjusted to obtain a calibrated rotordynamics simulation model.

[0075] Understandably, when adjusting bearing stiffness parameters, the system comprehensively considers the actual rotor vibration characteristics and the simulation model's predictions, gradually approaching the optimal parameter combination through an iterative optimization algorithm. This process not only ensures that the calibrated simulation model accurately reflects the actual rotor's dynamic characteristics but also provides more precise guidance for subsequent dynamic balancing operations.

[0076] Specifically, the model updating module calculates the bearing stiffness and the original imbalance by particle swarm optimization using the measured first actual amplitude, and updates the calibrated rotor dynamics simulation model. Obtaining the updated rotor dynamics simulation model includes:

[0077] Based on the calibration rotor dynamics simulation model, substituting the first actual amplitude into a particle swarm optimization algorithm for optimization;

[0078] defining a fitness function, taking the bearing stiffness and the original imbalance as optimization variables, and calculating a fitness value based on finite element simulation;

[0079] Initialize the particle swarm parameters and calculate the initial fitness value; wherein the particle swarm parameters include population size, number of iterations, speed, maximum and minimum position values;

[0080] Calculate individual extreme values and group extreme values, and continuously update the speed and position of the particles according to the optimization rules, and obtain the first particle swarm optimization result after multiple iterations; wherein the optimization rule is that each particle calculates the speed of the particle in the next generation according to the individual extreme value and group extreme value, and then calculates the position based on the speed;

[0081] According to the first particle swarm optimization result, the bearing stiffness and original unbalance parameters in the calibration rotor dynamics simulation model are updated to obtain an updated rotor dynamics simulation model.

[0082] In this embodiment, the particle update speed and position formulas are as follows:

[0083]

[0084] Where w is the inertia weight (i nert i awe i ght), is the historical optimal solution searched by the i-th particle up to the t-th generation, is the optimal solution currently searched by the entire particle swarm, Vi(t) is the current position and flight speed of the i-th particle, c1 and c2 are non-negative constants called acceleration factors, r1 and r2 are random numbers between [0,1], and g is the index of the optimal particle position.

[0085] In this embodiment, a nonlinear decreasing dynamic weight is used to adjust the particle speed and position to balance the globality of the search and the convergence speed. The adjustment formula of the inertia weight w is:

[0086]

[0087] Among them, W max is the maximum weight set in the algorithm, W min is the minimum weight set in the algorithm, t max The maximum number of iterations to set.

[0088] In this embodiment, the three optimal particle positions are:

[0089] 1.054×10 6 N / m, 1.018×10 6 N / m, 6.226×10 -4 kg·m.

[0090] Comparing with the originally set values, it can be seen that the optimization errors of the two bearings are 5.4% and 1.8% respectively, and the optimization error of the original unbalance is 3.8%, which is relatively small and can be used.

[0091] It is understandable that

[0092] Specifically, the fitness function is:

[0093] Function[fitness]=Fitness(x,y,z);

[0094] Among them, Function is the definition function of Mat Lab, that is, defining a fitness function Fitness; x and y represent the bearing stiffness; z represents the original imbalance.

[0095] Specifically, the model update module initializes the particle swarm parameters and calculates the initial fitness value, including:

[0096] A population consisting of several individuals is randomly generated. The speed and position of each individual are three-dimensional vectors. The fitness function Fitness(x,y,z) is called with the individual position information as (x,y,z) and the output is the fitness value.

[0097] Specifically, when the model updating module calculates individual extreme values and group extreme values, it includes:

[0098] Compare the fitness values to obtain the individual extreme value and group extreme value in the population;

[0099] The individual extreme value refers to the best fitness value experienced by an individual, and the group extreme value refers to the maximum fitness value in a certain generation of a population.

[0100] It can be understood that during the particle swarm optimization process, the continuous updating of individual and group extreme values guides the particles toward the global optimal solution. The update of individual extreme values relies on a comparison of each particle's historical fitness, while the update of group extreme values is based on the optimal fitness of the entire swarm in the current iteration. Through continuous iteration and optimization, the particle swarm gradually converges to the optimal solution, accurately calculating the bearing stiffness and original imbalance. After obtaining the updated rotor dynamics simulation model, the test weight and counterweight module comes into play. This module first adds test weights to the actual rotor based on the particle swarm optimization results and measures the second actual vibration amplitude after the test weights are added. This amplitude value is then fed back into the particle swarm optimization algorithm to further accurately calculate the total imbalance and test weight angle difference of the actual rotor system. This process enables precise identification and location of the actual rotor imbalance, providing accurate guidance for subsequent counterweight addition. During the counterweight addition phase, the system accurately determines the counterweight mass and placement based on the calculated total imbalance and test weight angle difference. By gradually adding counterweights and measuring vibration, the system continuously adjusts the counterweight parameters until the vibration is reduced to a satisfactory level, thus achieving dynamic balancing. This process not only avoids the tedious process of repeatedly starting the engine and adding actual counterweights in traditional dynamic balancing methods, but also significantly improves the efficiency and accuracy of dynamic balancing, providing a strong guarantee for the stable operation of aircraft engine fan rotors.

[0101] Specifically, the test weight and counterweight module substitutes the second actual amplitude into the particle swarm optimization, calculates the total imbalance of the actual rotor system and the test weight angle difference, and finally adds counterweights and selects directions until the vibration is reduced and dynamic balance is achieved, including:

[0102] adding a test weight to the actual rotor, wherein the test weight is equal to the original unbalance amount calculated by particle swarm optimization, and measuring the second actual amplitude;

[0103] Substituting the second actual amplitude into the particle swarm optimization algorithm again to obtain a second particle swarm optimization result, and calculating the total unbalance amount and the trial weight angle difference of the actual rotor system;

[0104] Add counterweights based on the second particle swarm optimization results and select directions until the vibration is reduced and dynamic balance is achieved.

[0105] In this embodiment, particle swarm optimization iterations are performed. The dimension of the particle swarm is set to one dimension, and the calibration rotor dynamics simulation model is repeatedly updated to obtain an updated rotor dynamics simulation model. Through this process, we obtain the original unbalance amount and the angle difference between the test weight, which is 60°. Dynamic balancing is achieved by adding a virtual counterweight. Given that the initial excitation occurs in the 0° direction and the test weight is at 60°, the angle difference between the two calculated by the particle swarm algorithm is exactly 60°, which indicates that the calculated phase is correct. However, in actual operation, we are not sure whether the test weight is 60° in the clockwise or counterclockwise direction. Therefore, in the opposite direction of 0°, that is, in the 180° direction, a virtual counterweight of 6.226×10 obtained by the calculation step is added. -4 kg·m. Subsequently, we obtained a 7-node simulation response of 3.4 μm, which represents a 96.3% reduction in vibration compared to the original response. This demonstrates the feasibility of this simulation method.

[0106] As you can see, in the process of using the particle swarm optimization algorithm for test weights and counterweighting, we fully consider the complexity and dynamic characteristics of actual rotors. By precisely calculating the size and position of the test weights, as well as the direction and mass of counterweight addition, we achieve effective control and adjustment of rotor imbalance. This process relies not only on advanced algorithms and technical means, but also on the operator's extensive experience and expertise. In actual application, the operator will flexibly adjust and optimize the dynamic balancing operation based on the guidance and suggestions provided by the system and the actual situation to ensure the accuracy and effectiveness of the dynamic balancing operation. Furthermore, the system is highly scalable and flexible, capable of adapting to the dynamic balancing requirements of aircraft engine fan rotors of different models and specifications. By continuously optimizing and upgrading the system's functions and performance, we can further improve the efficiency and accuracy of dynamic balancing, providing more reliable support and guarantee for the stable operation and performance improvement of aircraft engines.

[0107] See Figure 2 As shown, in some embodiments of the present application, this embodiment provides a keyless phase virtual dynamic balancing method for an aircraft engine fan rotor based on a finite element method, comprising the following steps:

[0108] S100: establishing an initial rotor dynamics simulation model, performing modal analysis on the initial rotor dynamics simulation model using a finite element method, and obtaining a first-order critical speed and vibration mode of the initial rotor dynamics simulation model;

[0109] S200: measuring a critical speed of an actual rotor, and comparing the critical speed with the first-order critical speed, and determining whether to calibrate the initial rotor dynamics simulation model based on the comparison result; if so, measuring a first actual amplitude of the actual rotor, and calibrating the initial rotor dynamics simulation model based on the first actual amplitude to obtain a calibrated rotor dynamics simulation model;

[0110] S300: Calculating bearing stiffness and original imbalance using particle swarm optimization using the measured first actual amplitude, and updating the calibrated rotor dynamics simulation model to obtain an updated rotor dynamics simulation model;

[0111] S400: Adding a test weight to the actual rotor according to the particle optimization result and measuring a second actual amplitude; substituting the second actual amplitude into the particle swarm optimization, calculating the total imbalance of the actual rotor system and the test weight angle difference, and finally adding a counterweight and selecting a direction until the vibration is reduced and dynamic balance is achieved.

[0112] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware embodiments. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A keyless virtual dynamic balancing system for an aircraft engine fan rotor based on the finite element method, characterized in that: include: a modeling and simulation module configured to establish an initial rotor dynamics simulation model, perform modal analysis on the initial rotor dynamics simulation model using a finite element method, and obtain a first-order critical speed and vibration mode of the initial rotor dynamics simulation model; a model calibration module configured to measure a critical speed of an actual rotor, compare the critical speed with the first-order critical speed, and determine whether to calibrate the initial rotordynamics simulation model based on the comparison result; if so, measure a first actual amplitude of the actual rotor, and calibrate the initial rotordynamics simulation model based on the first actual amplitude to obtain a calibrated rotordynamics simulation model; a model updating module configured to calculate the bearing stiffness and the original unbalance by particle swarm optimization using the measured first actual amplitude, and update the calibrated rotor dynamics simulation model to obtain an updated rotor dynamics simulation model; The test weight and counterweight module is configured to add a test weight to the actual rotor according to the particle optimization result and measure a second actual amplitude; substitute the second actual amplitude into the particle swarm optimization, calculate the total imbalance of the actual rotor system and the test weight angle difference, and finally add a counterweight and select a direction until the vibration is reduced and dynamic balance is achieved.

2. The keyless phase virtual dynamic balancing system for aircraft engine fan rotor based on finite element method according to claim 1 is characterized in that: The modeling and simulation module performs modal analysis on the initial rotor dynamics simulation model using a finite element method to obtain a first-order critical speed and vibration mode of the initial rotor dynamics simulation model, including: The actual rotor system is converted into a discretized model with finite degrees of freedom using a finite element method; Performing dynamic simulation on the discretized model through a finite element program and analyzing the steady-state unbalanced response; A modal analysis is performed on the initial rotor dynamics simulation model to obtain a first-order critical speed and vibration mode of the initial rotor dynamics simulation model.

3. The keyless phase virtual dynamic balancing system for aircraft engine fan rotor based on finite element method according to claim 2 is characterized in that: The model calibration module compares the critical speed with the first-order critical speed, and determines whether to calibrate the initial rotor dynamics simulation model according to the comparison result, including: Calculating the difference between the critical speed and the first-order critical speed, and recording it as the critical speed deviation value; Comparing the critical speed deviation value with a critical speed deviation threshold, and determining whether to calibrate the initial rotor dynamics simulation model according to the comparison result; When the critical speed deviation value is within the critical speed deviation threshold, determining not to calibrate the initial rotor dynamics simulation model; When the critical speed deviation value is outside the critical speed deviation threshold, it is determined to calibrate the initial rotor dynamics simulation model.

4. The keyless phase virtual dynamic balancing system for aircraft engine fan rotor based on finite element method according to claim 3 is characterized in that: The model calibration module calibrates the initial rotor dynamics simulation model according to the first actual amplitude to obtain a calibrated rotor dynamics simulation model, including: The first actual amplitude is the amplitude measured at three different measuring points of the actual rotor at the same speed or the amplitude measured at three different speeds at the same measuring point of the actual rotor; According to the first actual amplitude, bearing stiffness parameters of the initial rotordynamics simulation model are adjusted to obtain a calibrated rotordynamics simulation model.

5. The keyless phase virtual dynamic balancing system for aircraft engine fan rotor based on finite element method according to claim 4 is characterized in that: The model updating module calculates the bearing stiffness and the original imbalance by particle swarm optimization using the measured first actual amplitude, and updates the calibrated rotor dynamics simulation model. Obtaining the updated rotor dynamics simulation model includes: Based on the calibration rotor dynamics simulation model, substituting the first actual amplitude into a particle swarm optimization algorithm for optimization; defining a fitness function, taking the bearing stiffness and the original imbalance as optimization variables, and calculating a fitness value based on finite element simulation; Initialize the particle swarm parameters and calculate the initial fitness value; wherein the particle swarm parameters include population size, number of iterations, speed, maximum and minimum position values; Calculate individual extreme values and group extreme values, and continuously update the speed and position of the particles according to the optimization rules, and obtain the first particle swarm optimization result after multiple iterations; wherein the optimization rule is that each particle calculates the speed of the particle in the next generation according to the individual extreme value and group extreme value, and then calculates the position based on the speed; According to the first particle swarm optimization result, the bearing stiffness and original unbalance parameters in the calibration rotor dynamics simulation model are updated to obtain an updated rotor dynamics simulation model.

6. The keyless phase virtual dynamic balancing system for an aircraft engine fan rotor based on the finite element method according to claim 5 is characterized in that: The fitness function is: Function[fitness]=Fitness(x,y,z); Among them, Function is the definition function of MATLAB, that is, defining a fitness function Fitness; x and y represent the bearing stiffness; z represents the original imbalance.

7. The keyless phase virtual dynamic balancing system for an aircraft engine fan rotor based on the finite element method according to claim 6 is characterized in that: The model update module initializes the particle swarm parameters and calculates the initial fitness value, including: A population consisting of several individuals is randomly generated. The speed and position of each individual are three-dimensional vectors. The fitness function Fitness(x,y,z) is called with the individual position information as (x,y,z) and the output is the fitness value.

8. The keyless phase virtual dynamic balancing system for an aircraft engine fan rotor based on the finite element method according to claim 7 is characterized in that: When the model updating module calculates individual extreme values and group extreme values, it includes: Compare the fitness values to obtain the individual extreme value and group extreme value in the population; The individual extreme value refers to the best fitness value experienced by an individual, and the group extreme value refers to the maximum fitness value in a certain generation of a population.

9. The keyless phase virtual dynamic balancing system for an aircraft engine fan rotor based on the finite element method according to claim 8, characterized in that: The test weight and counterweight module substitutes the second actual amplitude into the particle swarm optimization, calculates the total imbalance of the actual rotor system and the test weight angle difference, and finally adds counterweights and selects directions until the vibration is reduced and dynamic balance is achieved, including: adding a test weight to the actual rotor, wherein the test weight is equal to the original unbalance amount calculated by particle swarm optimization, and measuring the second actual amplitude; Substituting the second actual amplitude into the particle swarm optimization algorithm again to obtain a second particle swarm optimization result, and calculating the total unbalance amount and the trial weight angle difference of the actual rotor system; Add counterweights based on the second particle swarm optimization results and select directions until the vibration is reduced and dynamic balance is achieved.

10. A method for virtual dynamic balancing of a fan rotor of an aircraft engine without a key phase based on the finite element method, applied to the virtual dynamic balancing system of a fan rotor of an aircraft engine without a key phase based on the finite element method as claimed in any one of claims 1 to 9, characterized in that: include: Establishing an initial rotor dynamics simulation model, performing modal analysis on the initial rotor dynamics simulation model using a finite element method, and obtaining a first-order critical speed and vibration mode of the initial rotor dynamics simulation model; measuring a critical speed of an actual rotor and comparing the critical speed with the first-order critical speed, and determining whether to calibrate the initial rotor dynamics simulation model based on the comparison result; if so, measuring a first actual amplitude of the actual rotor and calibrating the initial rotor dynamics simulation model based on the first actual amplitude to obtain a calibrated rotor dynamics simulation model; Calculating the bearing stiffness and the original unbalance by particle swarm optimization using the measured first actual amplitude, and updating the calibrated rotor dynamics simulation model to obtain an updated rotor dynamics simulation model; According to the particle swarm optimization results, a test weight is added to the actual rotor and a second actual amplitude is measured. The second actual amplitude is substituted into the particle swarm optimization to calculate the total imbalance of the actual rotor system and the test weight angle difference. Finally, a counterweight is added and the direction is selected until the vibration is reduced and dynamic balance is achieved.

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