Blade fluid-solid coupling method based on DMD reduced-order model

Abstract

The invention belongs to the field of unmanned aerial vehicle aerodynamics, and particularly relates to a blade fluid-solid coupling method based on a DMD reduced-order model, and the method comprises the following steps: obtaining key parameter variables of blade deformation, and setting a variation range corresponding to each key parameter variable according to constraint conditions formed by the configuration of the key parameter variables; establishing a sampling space based on key parameter variables by adopting a Latin super-cubic method; through fluid simulation software, a CFD simulation method is adopted to obtain flow field aerodynamic force data of a flow field on the sampling space; establishing a flow field price reduction model in a sampling space by adopting a DMD dynamic mode decomposition method through simulation software according to the flow field aerodynamic data; according to the flow field price reduction model, a velocity field and a pressure field of the flow field are obtained, the initial aerodynamic force of the paddle is obtained through integral calculation, and the initial aerodynamic force of the paddle is guided into the CSD model; and on the basis of the flow field reduced-order model, considering the condition of elastic deformation of the blade, solving the aerodynamic force of the blade again in a loose coupling manner in combination with the blade CSD model until convergence, and extracting the aerodynamic force of the blade.

Description

Technical Field

[0001] The invention belongs to the field of unmanned aerial vehicle aerodynamics, and in particular is a blade fluid-solid coupling method based on a DMD reduced-order model. Background Art

[0002] Traditional fixed-wing aircraft have the characteristics of high flight speed, large mission load, and wide adaptability, but they have high site requirements and need to build a runway. Therefore, they are not suitable for mission deployment in relatively complex environments. Rotary-wing aircraft (helicopters, multi-rotor aircraft) are more suitable for small-scale and high-maneuverability use requirements.

[0003] A tiltrotor is a new concept rotorcraft between a helicopter and a fixed-wing aircraft. It combines the advantages of rotorcraft and propeller aircraft. It not only has a much higher forward flight speed than conventional rotorcraft, but also has vertical take-off and landing and hovering capabilities that propeller aircraft do not have. It can meet the needs of a variety of flight missions, greatly expands the flight envelope of rotorcraft and fixed-wing aircraft, and has a very wide range of uses.

[0004] At present, relatively mature tiltrotor aircraft include XV-15 tiltrotor aircraft, BA-609 tiltrotor aircraft, V-22 tiltrotor aircraft, V-280 tiltrotor aircraft, etc. Among them, the V-280 tiltrotor aircraft was jointly developed by Bell Helicopter and Lockheed Martin, and is currently the latest technology verification aircraft. Its principle is mainly to realize the conversion of the helicopter mode and fixed-wing mode of the tiltrotor aircraft by tilting the rotor nacelles at both ends of the wing. In the helicopter mode, the two nacelles are perpendicular to the ground, and the rotor rotation plane is parallel to the ground. When the helicopter mode is flying, the attitude is kept stable by two pairs of counter-rotating rotor systems. After climbing a certain height from the ground in the helicopter mode, the nacelle tilts forward and turns into a fixed-wing mode of high-speed level flight. In the above way, the V-280 takes into account the characteristics of vertical take-off and landing, hovering in the air and high-speed forward flight.

[0005] However, in the design process of the tiltrotor, the aerodynamic calculation of the rotor is crucial, which is directly related to the helicopter mode and fixed-wing performance of the tiltrotor. In the actual design process, elastic deformation occurs during the rotation of the blades, which affects the calculation accuracy of the rotor. In addition, the calculation efficiency of the rotor CFD aerodynamic force is low, which seriously affects the calculation efficiency of the blades. Therefore, it is necessary to design an aerodynamic calculation method that can improve the calculation accuracy of its aerodynamic force and improve the calculation efficiency. Summary of the invention

[0006] In response to the above problems, the purpose of the present invention is to provide a method for predicting rotor aerodynamics taking into account blade deformation. Through a certain amount of simulation calculations, an aerodynamic reduction model is established to estimate the blade aerodynamics under different parameters. With the assistance of the CSD model, a blade fluid-solid coupling model is established to predict the rotor aerodynamics and improve the speed and accuracy of the rotor aerodynamic calculations.

[0007] The technical solution adopted by the present invention to achieve the above-mentioned purpose is: a blade fluid-solid coupling method based on a DMD reduced-order model, comprising the following steps:

[0008] 1) Obtain key parameter variables of blade deformation, and set the variation range corresponding to each key parameter variable according to the constraint conditions formed by the configuration of the key parameter variables;

[0009] 2) According to the key parameter variables of the blade and the range of variation of the key parameter variables, the Latin hypercube method is used to establish a sampling space based on the key parameter variables;

[0010] 3) Using CFD simulation method through fluid simulation software, obtain the flow field aerodynamic data of the flow field in the sampling space;

[0011] 4) Using simulation software and DMD dynamic mode decomposition method according to the flow field aerodynamic data, a flow field price reduction model in the sampling space is established;

[0012] 5) According to the flow field reduction model, the velocity field and pressure field of the flow field are obtained, the initial aerodynamic force of the blade is obtained by integral calculation, and the initial aerodynamic force of the blade is introduced into the CSD model;

[0013] 6) Based on the reduced-order model of the flow field, taking into account the elastic deformation of the blade, the blade CSD model is combined in a loosely coupled manner to re-solve the blade aerodynamic force until convergence and extract the blade aerodynamic force.

[0014] In step 1), the key parameter variables include: rotor inflow velocity, rotor collective pitch, and blade twist angle;

[0015] The constraints of the key parameter variables are: the range that can be set according to the mechanical structure configuration of the tiltrotor aircraft and the actual working conditions, that is, the parameter variable interval is formed.

[0016] The step 2) comprises the following steps:

[0017] 2-1) Taking the variation range corresponding to each dimension of the key parameter variable as the division target, the variation range corresponding to each dimension of the key parameter variable is divided into m non-overlapping intervals, so that each interval has the same probability;

[0018] 2-2) Randomly select a point in each interval of each dimension of the key parameter variable;

[0019] 2-3) Map all randomly selected points to normally distributed sample points through the inverse function of the standard normal distribution;

[0020] 2-4) Randomly extract the sample points mapped in step 2-3) from each dimension of the key parameter variables and form a sample vector; arrange the elements of each column of the sample vector from small to large to form an X-dimensional sample space composed of n key parameter variables, namely the sampling space.

[0021] The step 4) comprises the following steps:

[0022] 4-1) Define the input and output matrix, that is, define the parameter variables and key flow field information, extract the key flow field information, and form the matrix X;

[0023] 4-2) Perform singular value decomposition on the matrix to extract the main features of the data;

[0024] X=UΣV *

[0025] Among them, U is a left singular matrix, Σ is a singular value diagonal matrix, V * Conjugate transpose of a right singular matrix.

[0026] 4-3) Construct a linear mapping matrix and calculate a low-dimensional approximate linear mapping matrix;

[0027] 4-4) Perform eigendecomposition on the mapping matrix, that is:

[0028]

[0029] Where W is the eigenvector matrix and Λ is a diagonal matrix whose diagonal elements are eigenvalues.

[0030] 4-5) Reconstruct the flow field, then reconstruct the feature vector is the original data matrix, that is, the basis eigenvector of the reduced-order model, V r is the first r-order truncated matrix of V, Σ r The first r-order truncated matrix;

[0031] The flow field price reduction model on the sampling space is constructed according to the basis eigenvectors.

[0032] The step 4-3) is specifically:

[0033] a. Define the data matrix:

[0034] The original data matrix is ​​divided into X′=(x1,x2,x3,…,x m-1 ) removes the last data snapshot, and X″=(x2,x3,x4,…,x m) Remove the first data snapshot to build the relationship between the data at adjacent moments;

[0035] b. Solve the linear mapping matrix:

[0036] According to the assumption of local linear approximation, there exists a linear mapping matrix A such that X″≈AX′;

[0037] Using the relationship between the results of singular value decomposition and the data matrix, we can get in, represents pseudo-inverse operation;

[0038] c. Calculate the low-dimensional approximate linear mapping matrix:

[0039] Compute the truncated low-dimensional approximation of the linear mapping matrix Right now:

[0040]

[0041] Among them, U r is the first r order of the matrix of U, that is, the truncated left singular vector matrix, It means U r The conjugate transposed matrix of .

[0042] In step 6), the blade aerodynamic force is re-solved by combining the blade CSD model in a loosely coupled manner, specifically:

[0043] 6-1) The Hamilton variation principle is used to establish the rotor blade dynamics equation. The mathematical expression is as follows:

[0044]

[0045] Among them, U is the strain energy of the blade, T is the kinetic energy of the blade, and W is the work done by the external force of the blade;

[0046] 6-2) Taking into account the elastic deformation of the blade, a beam unit model was established based on the Timoshenko beam theory to improve the calculation accuracy of the rotor's aerodynamic force;

[0047] 6-3) The finite element method is used to solve the dynamics of the structure, discretize the blade structure, and obtain the rotor aerodynamic force;

[0048] 6-4) After each rotation cycle, the blade reduced-order model is coupled with the blade discrete model to exchange data and complete the aerodynamic calculation considering the blade deformation.

[0049] The beam unit model is established based on the Timoshenko beam, specifically:

[0050] A beam element model is established for the Timoshenko beam, with 20 degrees of freedom;

[0051] The middle node of each unit has 4 degrees of freedom, and the two end nodes have 8 degrees of freedom, including displacement (q1, q2) and rotation (q1′, q2′) in the swing and shimmy directions, torsional displacement φ and tensile displacement q3, as well as interface shear degrees of freedom in two directions.

[0052] The degree of freedom of the rotation angle (q1′, q2′) adopts two-node Hermite interpolation, and the other degrees of freedom adopt three-node Lagrange interpolation to ensure the displacement at the nodes at both ends of the beam and the continuity at the rotation angle.

[0053] The finite element method is used to solve the dynamics of the structure and discretize the blade structure. The structural dynamics equation is as follows:

[0054]

[0055] Among them, M is the mass matrix, C is the damping matrix, K is the stiffness matrix, q is the acceleration, velocity, and displacement vector of the finite element node, respectively, and Q is the external load vector, that is, the aerodynamic load is the rotor aerodynamic force to be solved.

[0056] The present invention has the following beneficial effects and advantages:

[0057] 1. The aerodynamic force prediction method of the present invention adopts the DMD reduced-order model, retains the accuracy of the CFD calculation method, and improves the efficiency of aerodynamic force prediction under parameter changes;

[0058] 2. The present invention adopts the Latin hypercube method to minimize the sampling space, reduce the amount of calculation in the model building process, and improve the calculation efficiency of model generation.

[0059] 3. The Latin hypercube sampling in the present invention has the characteristic of uniform stratification, and can obtain more uniformly distributed sample values ​​with less sampling, which is conducive to the implementation of the dynamic mode decomposition method.

[0060] 4. The dynamic modal decomposition method in the present invention can extract a reduced-order model containing only key features from a high-order complex flow, which greatly reduces the amount of calculation generated in the design process and can greatly improve the calculation efficiency under parameter changes.

[0061] 5. The energy share method in the present invention can effectively screen out eigenvalues ​​and eigenvectors that meet calculation requirements or usage requirements through quota screening of energy information for subsequent calculations.

[0062] 6. The fluid-solid coupling calculation method of the present invention improves the aerodynamic calculation accuracy of the rotor through CFD / CSD coupling calculation and Timoshenko beam unit model. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a diagram of the tilt-rotor mechanism of this embodiment;

[0064] Figure 2 This is a schematic diagram of the Latin hypercube sampling method;

[0065] Figure 3 The DMD dynamic mode decomposition method flow chart of the present invention;

[0066] Figure 4 Schematic diagram of establishing the blade CSD model in the present invention. DETAILED DESCRIPTION

[0067] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0068] like Figure 1 As shown, it is a configuration diagram of a tiltrotor machine whose rotor needs to be calculated in this embodiment. This embodiment can also be replaced by an XV-15 tiltrotor, a BA-609 tiltrotor, a V-22 tiltrotor, a V-280 tiltrotor, etc.;

[0069] The specific method flow of the present invention is as follows Figure 3 As shown in the figure, it is a DMD dynamic modal decomposition flow chart of the present invention. The reduced-order model method of the present invention first needs to determine the key parameter variables and the range of variation; then use the Latin hypercube method to establish a sampling space for the sample; in the above sample space, perform CFD numerical simulation to calculate and solve the flow field in the sampling space; establish a flow field reduced-order model through the DMD dynamic modal decomposition method; according to the flow field reduced-order model, obtain the velocity field and pressure field of the flow field, and obtain the aerodynamic force on the blade surface through integral calculation; on the basis of the reduced-order model, combine the blade CSD model in a loosely coupled manner, re-solve the blade aerodynamic force until convergence, and finally output the rotor aerodynamic force.

[0070] The present invention provides a blade fluid-solid coupling method based on a DMD reduced-order model, which specifically comprises the following steps:

[0071] 1) Obtaining the key parameter variables of the rotor, and setting the variation range corresponding to each key parameter variable according to the constraint conditions formed by the configuration of the key parameter variables;

[0072] Among them, the key parameter variables include: rotor inflow, rotor collective pitch, and blade twist angle. The variation range is the range within which the key parameter variables can be set according to the mechanical structure configuration and actual working conditions of the tiltrotor aircraft.

[0073] like Figure 2 FIG. 1 is a schematic diagram of the Latin hypercube sampling method of the present invention;

[0074] 2) According to the key parameter variables of the tiltrotor aircraft and the variation range of the key parameter variables, the Latin hypercube method is used to establish a sampling space based on the key parameter variables; specifically, the following steps are included:

[0075] 2-1) Taking the variation range corresponding to each dimension of the key parameter variable as the division target, the variation range corresponding to each dimension of the key parameter variable is divided into m non-overlapping intervals, so that each interval has the same probability;

[0076] 2-2) Randomly select a point in each interval of each dimension of the key parameter variable;

[0077] 2-3) Map all randomly selected points to normally distributed sample points through the inverse function of the standard normal distribution;

[0078] 2-4) Then randomly extract the sample points mapped in step 2-3) from each dimension of the key parameter variable and form a sample vector; arrange the elements of each column of the sample vector from small to large, forming an X-dimensional sample space composed of n key parameter variables, that is, the sampling space, that is, Figure 3 shown.

[0079] 3) Using CFD simulation method through fluid simulation software, obtain the flow field aerodynamic data of the flow field in the sampling space;

[0080] 4) Using simulation software and DMD dynamic mode decomposition method according to the flow field aerodynamic data, a flow field price reduction model in the sampling space is established;

[0081] The principle of the reduced-order model method of the present invention, in which the dynamic mode decomposition (DMD) is to project a high-dimensional vector into a low-order vector space through a set of optimal orthogonal bases, retain the main features, and can basically reconstruct the corresponding original high-order vector. The steps are as follows:

[0082] 4-1) Define the input and output matrix, that is, define the parameter variables and key flow field information, extract the key flow field information, and form the matrix X;

[0083] 4-2) Perform singular value decomposition on the matrix to extract the main features of the data;

[0084] X=UΣV *

[0085] Among them, U is a left singular matrix, Σ is a singular value diagonal matrix, V * Conjugate transpose of a right singular matrix;

[0086] 4-3) Construct a linear mapping matrix and calculate a low-dimensional approximate linear mapping matrix;

[0087] a. Define the data matrix:

[0088] The original data matrix is ​​divided into X′=(x1,x2,x3,…,x m-1 ) removes the last data snapshot, and X″=(x2,x3,x4,…,x m ) Remove the first data snapshot to build the relationship between the data at adjacent moments;

[0089] b. Solve the linear mapping matrix:

[0090] According to the assumption of local linear approximation, there exists a linear mapping matrix A such that X″≈AX′;

[0091] Using the relationship between the results of singular value decomposition and the data matrix, we can get in, represents pseudo-inverse operation;

[0092] c. Calculate the low-dimensional approximate linear mapping matrix:

[0093] Compute the truncated low-dimensional approximation of the linear mapping matrix Right now:

[0094]

[0095] Among them, U r is the first r order of the matrix of U, that is, the truncated left singular vector matrix, It means U r The conjugate transposed matrix of .

[0096] 4-3) Construct a linear mapping matrix and calculate a low-dimensional approximate linear mapping matrix;

[0097] 4-4) Perform eigendecomposition on the mapping matrix, that is:

[0098]

[0099] Where W is the eigenvector matrix and Λ is a diagonal matrix whose diagonal elements are eigenvalues.

[0100] 4-5) Reconstruct the flow field, then reconstruct the feature vector X″ is the original data matrix, which is the basis eigenvector of the reduced-order model, V r is the first r-order truncated matrix of V, Σ r The first r-order truncated matrix;

[0101] The flow field price reduction model on the sampling space is constructed according to the basis eigenvectors.

[0102] 5) According to the flow field reduction model, the velocity field and pressure field of the flow field are obtained, and the aerodynamic force on the blade surface is obtained by integral calculation.

[0103] like Figure 4 As shown, it is a schematic diagram of establishing the blade CSD model in the present invention, wherein step 6) based on the reduced-order model, the blade CSD model is combined in a loosely coupled manner to re-solve the blade aerodynamic force until convergence;

[0104] The blade aerodynamic force is re-solved by combining the blade CSD model in a loosely coupled manner, specifically:

[0105] 6-1) The Hamilton variation principle is used to establish the rotor blade dynamics equation. The mathematical expression is as follows:

[0106]

[0107] Among them, U is the strain energy of the blade, T is the kinetic energy of the blade, and W is the work done by the external force of the blade;

[0108] 6-2) Taking into account the elastic deformation of the blade, a beam unit model was established based on the Timoshenko beam theory to improve the calculation accuracy of the rotor's aerodynamic force;

[0109] The beam element model is established based on the Timoshenko beam, specifically:

[0110] A beam element model is established for the Timoshenko beam, with 20 degrees of freedom;

[0111] The middle node of each unit has 4 degrees of freedom, and the two end nodes have 8 degrees of freedom, including displacement (q1, q2) and rotation (q1′, q2′) in the swing and shimmy directions, torsional displacement φ and tensile displacement q3, as well as interface shear degrees of freedom in two directions.

[0112] The degrees of freedom of the rotation angle (q1′, q2′) use two-node Hermite interpolation, and the other degrees of freedom use three-node Lagrange interpolation to ensure the continuity of the displacement at the nodes at both ends of the beam and at the rotation angle, that is, Figure 4 As shown;

[0113] 6-3) The finite element method is used to solve the dynamics of the structure, discretize the blade structure, and obtain the rotor aerodynamic force. The structural dynamics equation is as follows:

[0114]

[0115] Among them, M is the mass matrix, C is the damping matrix, K is the stiffness matrix, q is the acceleration, velocity, and displacement vector of the finite element node, and Q is the external load vector, i.e., the aerodynamic load.

[0116] 6-4) After each rotation cycle, the blade reduced-order model is coupled with the blade discrete model to exchange data and complete the aerodynamic calculation considering the blade deformation;

[0117] The reduced-order model method of the present invention uses the eigenvalues ​​and eigenvectors established by the dynamic mode decomposition method to complete the construction of the flow field reduced-order model. Then, the flow field reduced-order model can be used to quickly predict the rotor aerodynamic force under different parameters. After combining the CSD to establish a fluid-solid coupling model, the prediction accuracy of the rotor aerodynamic force can be improved.

[0118] The above description is only an embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, expansion, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A blade fluid-structure coupling method based on DMD reduced-order model, characterized in that: The following steps are involved: 1) Obtain key parameter variables of blade deformation, and set the variation range corresponding to each key parameter variable according to the constraint conditions formed by the configuration of the key parameter variables; 2) According to the key parameter variables of the blade and the range of variation of the key parameter variables, the Latin hypercube method is used to establish a sampling space based on the key parameter variables; 3) Using CFD simulation method through fluid simulation software, obtain the flow field aerodynamic data of the flow field in the sampling space; 4) Using simulation software and DMD dynamic mode decomposition method according to the flow field aerodynamic data, a flow field price reduction model in the sampling space is established; 5) According to the flow field reduction model, the velocity field and pressure field of the flow field are obtained, the initial aerodynamic force of the blade is obtained by integral calculation, and the initial aerodynamic force of the blade is introduced into the CSD model; 6) Based on the reduced-order model of the flow field, taking into account the elastic deformation of the blade, the blade CSD model is combined in a loosely coupled manner to re-solve the blade aerodynamic force until convergence and extract the blade aerodynamic force.

2. The blade fluid-structure coupling method based on the DMD reduced-order model according to claim 1, characterized in that: In step 1), the key parameter variables include: rotor inflow velocity, rotor collective pitch, and blade twist angle; The constraints of the key parameter variables are: the range that can be set according to the mechanical structure configuration of the tiltrotor aircraft and the actual working conditions, that is, the parameter variable interval is formed.

3. The blade fluid-structure coupling method based on the DMD reduced-order model according to claim 1, characterized in that: The step 2) comprises the following steps: 2-1) Taking the variation range corresponding to each dimension of the key parameter variable as the division target, the variation range corresponding to each dimension of the key parameter variable is divided into m non-overlapping intervals, so that each interval has the same probability; 2-2) Randomly select a point in each interval of each dimension of the key parameter variable; 2-3) Map all randomly selected points to normally distributed sample points through the inverse function of the standard normal distribution; 2-4) Randomly extract the sample points mapped in step 2-3) from each dimension of the key parameter variables and form a sample vector; arrange the elements of each column of the sample vector from small to large to form an X-dimensional sample space composed of n key parameter variables, namely the sampling space.

4. The blade fluid-structure coupling method based on the DMD reduced-order model according to claim 1, characterized in that: The step 4) comprises the following steps: 4-1) Define the input and output matrix, that is, define the parameter variables and key flow field information, extract the key flow field information, and form the matrix X; 4-2) Perform singular value decomposition on the matrix to extract the main features of the data; X=UΣV * Among them, U is a left singular matrix, Σ is a singular value diagonal matrix, V * Conjugate transpose of a right singular matrix; 4-3) Construct a linear mapping matrix and calculate a low-dimensional approximate linear mapping matrix; 4-4) Perform eigendecomposition on the mapping matrix, that is: Where W is the eigenvector matrix, Λ is a diagonal matrix whose diagonal elements are eigenvalues; 4-5) Reconstruct the flow field, then reconstruct the feature vector X″ is the original data matrix, which is the basis eigenvector of the reduced-order model, V r is the first r-order truncated matrix of V, Σ r The first r-order truncated matrix; The flow field price reduction model on the sampling space is constructed according to the basis eigenvectors.

5. The blade fluid-structure coupling method based on the DMD reduced-order model according to claim 4 is characterized in that: The step 4-3) is specifically: a. Define the data matrix: The original data matrix is ​​divided into X′=(x1,x2,x3,…,x m-1 ) removes the last data snapshot, and X″=(x2,x3,x4,…,x m ) Remove the first data snapshot to build the relationship between the data at adjacent moments; b. Solve the linear mapping matrix: According to the assumption of local linear approximation, there exists a linear mapping matrix A such that X″≈AX′; Using the relationship between the results of singular value decomposition and the data matrix, we can get in, represents pseudo-inverse operation; c. Calculate the low-dimensional approximate linear mapping matrix: Compute the truncated low-dimensional approximation of the linear mapping matrix Right now: Among them, U r is the first r order of the matrix of U, that is, the truncated left singular vector matrix, It means U r The conjugate transposed matrix of .

6. The blade fluid-structure coupling method based on DMD reduced-order model according to claim 1, characterized in that: In step 6), the blade aerodynamic force is re-solved by combining the blade CSD model in a loosely coupled manner, specifically: 6-1) The Hamilton variation principle is used to establish the rotor blade dynamics equation. The mathematical expression is as follows: Among them, U is the strain energy of the blade, T is the kinetic energy of the blade, and W is the work done by the external force of the blade; 6-2) Taking into account the elastic deformation of the blade, a beam unit model was established based on the Timoshenko beam theory to improve the calculation accuracy of the rotor's aerodynamic force; 6-3) The finite element method is used to solve the dynamics of the structure, discretize the blade structure, and obtain the rotor aerodynamic force; 6-4) After each rotation cycle, the blade reduced-order model is coupled with the blade discrete model to exchange data and complete the aerodynamic calculation considering the blade deformation.

7. The blade fluid-structure coupling method based on the DMD reduced-order model according to claim 6, characterized in that: The beam unit model is established based on the Timoshenko beam, specifically: A beam element model is established for the Timoshenko beam, with 20 degrees of freedom; The middle node of each unit has 4 degrees of freedom, and the two end nodes have 8 degrees of freedom, including displacement (q1, q2) and rotation (q1′, q2′) in the swing and shimmy directions, torsional displacement φ and tensile displacement q3, as well as interface shear degrees of freedom in two directions.

8. The blade fluid-structure coupling method based on the DMD reduced-order model according to claim 7, characterized in that: The degree of freedom of the rotation angle (q1′, q2′) adopts two-node Hermite interpolation, and the other degrees of freedom adopt three-node Lagrange interpolation to ensure the displacement at the nodes at both ends of the beam and the continuity at the rotation angle.

9. The blade fluid-structure coupling method based on DMD reduced-order model according to claim 6, characterized in that: The finite element method is used to solve the dynamics of the structure and discretize the blade structure. The structural dynamics equation is as follows: Among them, M is the mass matrix, C is the damping matrix, K is the stiffness matrix, q is the acceleration, velocity, and displacement vector of the finite element node, respectively, and Q is the external load vector, that is, the aerodynamic load is the rotor aerodynamic force to be solved.

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