Method and system for identifying dynamic flow pattern of solid-liquid two-phase flow

Abstract

The invention discloses a method and a system for identifying a dynamic flow pattern of a solid-liquid two-phase flow, and relates to the technical field of solid-liquid two-phase flow detection. Through a dynamic mode decomposition method, dimension reduction processing is carried out on a snapshot matrix formed by flow pattern images at different moments, and the defects that the efficiency is low and real-time prediction cannot be carried out due to the fact that a traditional computational fluid mechanics method is purely used for iteration solving are overcome. Meanwhile, no auxiliary sensor is needed, and compared with a current complex identification system, the flow pattern of the solid-liquid two-phase flow in the dynamic change process can be rapidly and accurately identified only by combining a high-speed flow camera. The problems of poor real-time performance, high economic cost, complex operation system, narrow application range and the like generally existing in the current identification method are solved. The method has important engineering value for guaranteeing the flowing safety of solid-liquid two-phase flow related to industrial systems such as deep-sea hydrate exploitation and conveying, circulating fluidized bed reactors and dynamic ice storage ice slurry storage.

Description

Technical Field

[0001] This invention relates to the field of solid-liquid two-phase flow detection technology, and specifically to a method and system for identifying dynamic flow patterns in solid-liquid two-phase flows. Background Technology

[0002] Solid-liquid two-phase flow is widely used in various industrial production scenarios, such as the extraction and transportation of deep-sea hydrates, the flow of binary media in circulating fluidized beds, sediment settling during river dredging, and ice slurry storage in dynamic ice storage systems. In solid-liquid two-phase flow systems, due to the significant differences in the physical and chemical properties of the solid and liquid phases, and the fact that the liquid phase is usually the flow carrier, the flow pattern is constantly changing during the flow process.

[0003] When the flow pattern changes, key parameters such as velocity distribution, solid concentration distribution, and pressure drop distribution also change synchronously. Therefore, accurately identifying the dynamic flow pattern of solid-liquid two-phase flow is of significant engineering value for optimizing actual flow parameters in industrial production, reducing system energy consumption, and ensuring the safe and stable operation of industrial systems.

[0004] Currently, numerous literature reports exist on methods for identifying solid-liquid two-phase flow patterns. One common method involves using a high-speed camera to capture images of the solid-liquid two-phase flow within a visualized pipe section to determine the flow pattern. This method is simple to operate and provides intuitive results, but it relies on subjective human judgment, which may lead to biased identification. To achieve quantitative testing, another method involves inserting a fluid velocity meter probe and a local concentration sampling probe into the pipe. By measuring the velocity and concentration distribution along the pipe cross-section, the flow pattern is determined based on the asymmetric changes in the flow and concentration fields. While this method can achieve quantitative testing of flow pattern distribution, the probe insertion into the pipe disturbs the fluid, affecting testing accuracy.

[0005] In recent years, undisturbed testing techniques have been applied in solid-liquid two-phase flow pattern testing. Common techniques include Doppler ultrasonic velocimetry, electrical resistance tomography, and nuclear magnetic resonance imaging. These techniques indirectly obtain the flow pattern distribution by measuring parameters such as sound waves and electrical resistance. Because the test probe is placed on the outer wall of the pipe, the problem of probe-induced flow disturbance is effectively overcome. However, such test probes and equipment are expensive, and the test system structure is complex, resulting in shortcomings in terms of economy and ease of operation.

[0006] Furthermore, most existing solid-liquid two-phase flow pattern identification methods are designed for steady-state flow processes, which are difficult to adapt to the identification needs of dynamic flow patterns and cannot meet the requirements of real-time and accurate identification of dynamic flow patterns in industrial production. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for identifying the dynamic flow pattern of solid-liquid two-phase flow, which can quickly and accurately identify the flow pattern of solid-liquid two-phase flow during dynamic changes without relying on any sensors.

[0008] According to a first aspect of the present disclosure, a method for identifying dynamic flow patterns in a solid-liquid two-phase flow is provided, comprising:

[0009] The dynamic flow pattern images of solid-liquid two-phase flow at different times are obtained, the feature data of each time image are extracted, a snapshot matrix X is constructed, and the snapshot matrix X is split into matrix X1 and matrix X2.

[0010] Perform singular value decomposition on the matrix X1 to obtain unitary matrix U, unitary matrix V and diagonal matrix Σ;

[0011] Based on the unitary matrix U, unitary matrix V, diagonal matrix Σ, and matrix X2, construct the similarity matrix of system matrix A.

[0012] For the similarity matrix Eigenvalue decomposition is performed to obtain the eigenvector matrix W and the eigenvalue diagonal matrix Λ;

[0013] Based on the eigenvalue diagonal matrix Λ and the time interval Δt between adjacent times, the growth rate g of each mode is obtained. j and frequency ω j ;

[0014] Based on the matrix X2, unitary matrix V, unitary matrix U, diagonal matrix Σ, and eigenvector matrix W, the mode Φ and mode amplitude α are obtained;

[0015] The modes are sorted according to the modal amplitude α, and the first m modes are selected to reconstruct the flow pattern. The dynamic flow pattern of the solid-liquid two-phase flow is identified through the reconstruction results.

[0016] According to a second aspect of the present disclosure, a system for identifying dynamic flow patterns in a solid-liquid two-phase flow is provided, comprising:

[0017] The flow pattern image acquisition and matrix construction module acquires dynamic flow pattern images of solid-liquid two-phase flow at different times, extracts feature data of the images at each time, constructs a snapshot matrix X, and splits the snapshot matrix X into matrix X1 and matrix X2.

[0018] The singular value decomposition processing module performs singular value decomposition on the matrix X1 to obtain unitary matrix U, unitary matrix V and diagonal matrix Σ.

[0019] The similarity matrix construction module constructs a similarity matrix of system matrix A based on the unitary matrix U, unitary matrix V, diagonal matrix Σ, and matrix X2.

[0020] The eigenvalue decomposition and extraction module performs the following steps on the similarity matrix: Eigenvalue decomposition is performed to obtain the eigenvector matrix W and the eigenvalue diagonal matrix Λ;

[0021] The modal parameter calculation module obtains the growth rate g of each mode based on the eigenvalue diagonal matrix Λ and the time interval Δt between adjacent times. j and frequency ω j ;

[0022] The mode and amplitude solving module obtains the mode Φ and mode amplitude α based on the matrix X2, unitary matrix V, unitary matrix U, diagonal matrix Σ, eigenvector matrix W, and eigenvalue diagonal matrix Λ.

[0023] The flow pattern reconstruction and identification module sorts the modes according to the modal amplitude α, selects the first m modes to reconstruct the flow pattern, and identifies the dynamic flow pattern of the solid-liquid two-phase flow through the reconstruction results.

[0024] According to a third aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the memory, wherein the processor executes the program to implement the method for identifying dynamic flow patterns of solid-liquid two-phase flow.

[0025] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method for identifying dynamic flow patterns of a solid-liquid two-phase flow.

[0026] Compared with existing technologies, the technical solution adopted in this invention has the following advantages: This invention only requires flow pattern images captured by a high-speed camera as snapshot data, and extracts the modal features of flow pattern transformation through dynamic mode decomposition. Without the need for auxiliary sensors, it can continuously and rapidly identify the flow patterns corresponding to the dynamic flow within the pipeline during system operation.

[0027] This invention overcomes the shortcomings of traditional numerical methods in predicting physical fields, such as poor real-time performance and low efficiency. It also compensates for the common deficiencies of existing identification methods, such as high economic costs, complex system operation, and narrow applicability. It has significant engineering value for ensuring the safety of solid-liquid two-phase flow in industrial systems such as deep-sea hydrate mining and transportation, circulating fluidized bed reactors, and dynamic ice storage. Attached Figure Description

[0028] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.

[0029] Figure 1 This is a flow pattern characteristic diagram of solid-liquid two-phase flow;

[0030] Figure 2 This is a flowchart of a method for identifying dynamic flow patterns in solid-liquid two-phase flows.

[0031] Figure 3 A comparison of the actual flow pattern (left) and the identified flow pattern (right) of dynamic solid-liquid two-phase flow in a straight pipe;

[0032] Figure 4 A comparison of the actual flow pattern (left) and the identified flow pattern (right) of the solid-liquid two-phase flow in a converging tube.

[0033] Figure 5 A comparison of the actual flow pattern (left) and the identified flow pattern (right) of the solid-liquid two-phase flow in a diffuser. Detailed Implementation

[0034] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0035] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0036] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0037] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0038] Example 1:

[0039] In solid-liquid two-phase flow, due to the density difference between solid particles and the liquid carrier, solid particles tend to aggregate towards the bottom or top of the pipe, forming a gradient distribution of particles along the pipe cross-section. Taking solid-liquid two-phase flow composed of suspended particles as an example... Figure 1 The flow pattern distribution characteristics under different flow velocities are shown: when the flow velocity is high, solid particles are uniformly dispersed in the liquid carrier fluid, corresponding to a homogeneous flow pattern; as the flow velocity decreases, a certain degree of phase separation occurs between the solid particles and the liquid carrier fluid, and the particle distribution in the upper half of the pipe cross-section becomes more concentrated, at which point the flow pattern changes to a heterogeneous flow; if the flow velocity decreases further, the aggregation of solid particles at the bottom of the pipe intensifies, forming a moving or stationary particle bed, corresponding to moving bed and fixed bed flow patterns, respectively. Therefore, when the flow velocity changes continuously or abruptly, the dynamic flow pattern of the solid-liquid two-phase flow will correspondingly transform between homogeneous flow, heterogeneous flow, moving bed, and fixed bed. To this end, this embodiment provides a method for identifying the dynamic flow pattern of a solid-liquid two-phase flow, including the following steps:

[0040] S1. Obtain dynamic flow pattern images of solid-liquid two-phase flow at different times, extract feature data of images at each time, construct snapshot matrix X, and split the snapshot matrix X into matrix X1 and matrix X2;

[0041] Specifically, a high-speed camera is used to capture the dynamic flow pattern of the solid-liquid two-phase flow within the visualized pipe section, and the color values ​​of each pixel in the flow pattern image at different times are read. The flow pattern image color value data corresponding to time 1 to time N are arranged according to the shooting time sequence, and the data of each time moment is stored in a column vector x, thereby constructing a snapshot matrix X as shown in equation (1); the snapshot matrix is ​​further split to obtain matrices X1 and X2, as shown in equations (2) and (3) respectively:

[0042] X = {x1, x2, x3, ..., x} i ,…x N}(1)

[0043] X1 = {x1, x2, x3…x} N-1}(2)

[0044] X2={x2,x3,x4…x N}(3)

[0045] Where, x i Let X1 represent the color value data vector of the flow pattern pixel corresponding to time i; the matrices X1 and X2 satisfy the following relationship, as shown in equation (4):

[0046] X2 = AX1

[0047] Where A represents the system matrix. Dynamic mode decomposition predicts the manifold data at the next time step using the system matrix A and the manifold data at the previous time step, as shown in equation (5):

[0048] x i =Ax i+1 (5)

[0049] S2. Perform singular value decomposition on the matrix X1 to obtain unitary matrix U, unitary matrix V and diagonal matrix Σ;

[0050] Specifically, to avoid solving the high-dimensional system matrix A, a similarity transformation matrix solution method is introduced to perform singular value decomposition on matrix X1, as shown in equation (6):

[0051] X1=UΣV H (6)

[0052] Where U and V are unitary matrices, i.e., satisfying UU H =I,VV H =I, where I represents the identity matrix, Σ represents the diagonal matrix, and the elements on the diagonal are the singular values ​​of matrix X1.

[0053] S3. Based on the unitary matrix U, unitary matrix V, diagonal matrix Σ, and matrix X2, construct the similarity matrix of system matrix A. As in equation (7):

[0054]

[0055] S4. For the aforementioned similarity matrix Eigenvalue decomposition yields the eigenvector matrix W and the eigenvalue diagonal matrix Λ, as shown in equation (8):

[0056]

[0057] Where W is the feature vector w j As a matrix composed of column vectors, Λ is a diagonal matrix with diagonal elements of . The eigenvalues ​​are shown in equation (9):

[0058] Λ=diag(λ1,λ2...λ N-1 )

[0059] Where λ is the similarity matrix The eigenvalues ​​(i.e., the eigenvalues ​​corresponding to the modes);

[0060] S5. Based on the eigenvalue diagonal matrix Λ and the time interval Δt between adjacent times, obtain the growth rate g of each mode. j and frequency ω j As shown in equations (10) and (11):

[0061] g j =Re{ln(λ j )} / Δt (10)

[0062] ω j =Im{ln(λ j )} / Δt / 2π (11)

[0063] Where Δt is the time interval between adjacent flow patterns;

[0064] S6. Based on the matrix X2, unitary matrix V, unitary matrix U, diagonal matrix Σ, and eigenvector matrix W, the mode Φ and mode amplitude α are obtained, as shown in equations (12) and (13):

[0065] Φ=X2VΣ -1 W(12)

[0066] α = W -1 U H x1(13)

[0067] Where Φ represents the mode and α represents the mode amplitude.

[0068] S7. Sort the modes according to the modal amplitude α, select the first m modes to reconstruct the flow pattern, and identify the dynamic flow pattern of the solid-liquid two-phase flow through the reconstruction results, as shown in Equation (14).

[0069]

[0070] Where, x i This represents the reconstructed manifold at time i, i.e., the manifold pixel color value data vector.

[0071] In summary, based on the principle of dynamic mode decomposition, the following is the execution flow of a method for identifying dynamic flow patterns in solid-liquid two-phase flows: Figure 2 As shown.

[0072] To verify the feasibility of the above method, Figure 3 , Figure 4 and Figure 5 The results of identifying flow patterns in straight pipes, converging pipes, and diverging pipes using the method proposed in this invention are presented respectively. Comparison of the identification results with experimental results shows that the maximum relative error for identification in straight pipes is 11%, in converging pipes it is 10%, and in diverging pipes it is 7%. The identification results show good consistency with the measured results, all reflecting the dynamic evolution characteristics of the flow pattern. Therefore, this invention can quickly identify the flow pattern changes of solid-liquid two-phase flow in different pipe fittings. Furthermore, it eliminates the need to deploy data sensors on the pipeline. This solves the problems of improving and refining the real-time performance, economy, convenience, and applicability of methods for identifying dynamic changes in solid-liquid two-phase flow patterns.

[0073] Example 2:

[0074] This embodiment provides a system for identifying dynamic flow patterns in solid-liquid two-phase flow, including:

[0075] The flow pattern image acquisition and matrix construction module acquires dynamic flow pattern images of solid-liquid two-phase flow at different times, extracts feature data of the images at each time, constructs a snapshot matrix X, and splits the snapshot matrix X into matrix X1 and matrix X2.

[0076] The singular value decomposition processing module performs singular value decomposition on the matrix X1 to obtain unitary matrix U, unitary matrix V and diagonal matrix Σ.

[0077] The similarity matrix construction module constructs a similarity matrix of system matrix A based on the unitary matrix U, unitary matrix V, diagonal matrix Σ, and matrix X2.

[0078] The eigenvalue decomposition and extraction module performs the following steps on the similarity matrix: Eigenvalue decomposition is performed to obtain the eigenvector matrix W and the eigenvalue diagonal matrix Λ;

[0079] The modal parameter calculation module obtains the growth rate g of each mode based on the eigenvalue diagonal matrix Λ and the time interval Δt between adjacent times. j and frequency ωj ;

[0080] The mode and amplitude solving module obtains the mode Φ and mode amplitude α based on the matrix X2, unitary matrix V, unitary matrix U, diagonal matrix Σ, eigenvector matrix W, and eigenvalue diagonal matrix Λ.

[0081] The flow pattern reconstruction and identification module sorts the modes according to the modal amplitude α, selects the first m modes to reconstruct the flow pattern, and identifies the dynamic flow pattern of the solid-liquid two-phase flow through the reconstruction results.

[0082] Example 3:

[0083] An electronic device includes a memory, a processor, and a computer program stored in the memory and running thereon, wherein the processor, when executing the program, implements the aforementioned method for identifying dynamic flow patterns in a solid-liquid two-phase flow, comprising:

[0084] The dynamic flow pattern images of solid-liquid two-phase flow at different times are obtained, the feature data of each time image are extracted, a snapshot matrix X is constructed, and the snapshot matrix X is split into matrix X1 and matrix X2.

[0085] Perform singular value decomposition on the matrix X1 to obtain unitary matrix U, unitary matrix V and diagonal matrix Σ;

[0086] Based on the unitary matrix U, unitary matrix V, diagonal matrix Σ, and matrix X2, construct the similarity matrix of system matrix A.

[0087] For the similarity matrix Eigenvalue decomposition is performed to obtain the eigenvector matrix W and the eigenvalue diagonal matrix Λ;

[0088] Based on the eigenvalue diagonal matrix Λ and the time interval Δt between adjacent times, the growth rate g of each mode is obtained. j and frequency ω j ;

[0089] Based on the matrix X2, unitary matrix V, unitary matrix U, diagonal matrix Σ, and eigenvector matrix W, the mode Φ and mode amplitude α are obtained;

[0090] The modes are sorted according to the modal amplitude α, and the first m modes are selected to reconstruct the flow pattern. The dynamic flow pattern of the solid-liquid two-phase flow is identified through the reconstruction results.

[0091] Example 4:

[0092] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for identifying dynamic flow patterns in a solid-liquid two-phase flow, comprising:

[0093] The dynamic flow pattern images of solid-liquid two-phase flow at different times are obtained, the feature data of each time image are extracted, a snapshot matrix X is constructed, and the snapshot matrix X is split into matrix X1 and matrix X2.

[0094] Perform singular value decomposition on the matrix X1 to obtain unitary matrix U, unitary matrix V and diagonal matrix Σ;

[0095] Based on the unitary matrix U, unitary matrix V, diagonal matrix Σ, and matrix X2, construct the similarity matrix of system matrix A.

[0096] For the similarity matrix Eigenvalue decomposition is performed to obtain the eigenvector matrix W and the eigenvalue diagonal matrix Λ;

[0097] Based on the eigenvalue diagonal matrix Λ and the time interval Δt between adjacent times, the growth rate g of each mode is obtained. j and frequency ω j ;

[0098] Based on the matrix X2, unitary matrix V, unitary matrix U, diagonal matrix Σ, and eigenvector matrix W, the mode Φ and mode amplitude α are obtained;

[0099] The modes are sorted according to the modal amplitude α, and the first m modes are selected to reconstruct the flow pattern. The dynamic flow pattern of the solid-liquid two-phase flow is identified through the reconstruction results.

[0100] Those skilled in the art will understand that the modules or steps described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, which can then be stored in a storage device for execution by a computer device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. This disclosure is not limited to any particular combination of hardware and software.

[0101] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0102] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A method for identifying dynamic flow patterns in solid-liquid two-phase flow, characterized in that, include: The dynamic flow pattern images of solid-liquid two-phase flow at different times are obtained, the feature data of each time image are extracted, a snapshot matrix X is constructed, and the snapshot matrix X is split into matrix X1 and matrix X2. Perform singular value decomposition on the matrix X1 to obtain unitary matrix U, unitary matrix V and diagonal matrix Σ; Based on the unitary matrix U, unitary matrix V, diagonal matrix Σ, and matrix X2, construct the similarity matrix of system matrix A. For the similarity matrix Eigenvalue decomposition is performed to obtain the eigenvector matrix W and the eigenvalue diagonal matrix Λ; Based on the eigenvalue diagonal matrix Λ and the time interval Δt between adjacent times, the growth rate g of each mode is obtained. j and frequency ω j ; Based on the matrix X2, unitary matrix V, unitary matrix U, diagonal matrix Σ, and eigenvector matrix W, the mode Φ and mode amplitude α are obtained; The modes are sorted according to the modal amplitude α, and the first m modes are selected to reconstruct the flow pattern. The dynamic flow pattern of the solid-liquid two-phase flow is identified through the reconstruction results.

2. The method for identifying dynamic flow patterns in solid-liquid two-phase flow according to claim 1, characterized in that, The snapshot matrix X, matrix X1, and matrix X2 are respectively represented as follows: X={x1,x2,x3,…,x i ,…x N } X1={x1,x2,x3…x N-1 } X2={x2,x3,x4…x N } Where, x i This represents the color value data vector of the manifold pixel at time i; matrices X1 and X2 satisfy the following relationship: X2 = AX1 Where A represents the system matrix.

3. The method for identifying dynamic flow patterns in solid-liquid two-phase flow according to claim 1, characterized in that, The singular value decomposition method for matrix X1 is as follows: X1=UΣV H Where U and V are unitary matrices, i.e., satisfying UU H =I,VV H =I, where I represents the identity matrix, Σ represents the diagonal matrix, and the elements on the diagonal are the singular values ​​of matrix X1.

4. The method for identifying dynamic flow patterns in solid-liquid two-phase flow according to claim 1, characterized in that, The similarity matrix for: For similar matrices Perform eigenvalue decomposition: Where W is the feature vector w j As a matrix composed of column vectors, Λ is a diagonal matrix with diagonal elements of . Eigenvalues: Λ=diag(λ1,λ2…λ N-1 ) Where λ is the similarity matrix eigenvalues.

5. The method for identifying dynamic flow patterns in solid-liquid two-phase flow according to claim 4, characterized in that, Growth rate g of each mode j and frequency ω j for: g j =Re{ln(λ j )} / Δt oh j =Im{ln(λ j )} / Δt / 2π。 6. The method for identifying dynamic flow patterns in solid-liquid two-phase flow according to claim 1, characterized in that, The mode Φ and mode amplitude α are: Φ=X2VΣ -1 W α=W -1 U H x1.

7. The method for identifying dynamic flow patterns in solid-liquid two-phase flow according to claim 6, characterized in that, The reconstruction method for the first m modalities is as follows: Where, x i This represents the reconstructed manifold at time i, i.e., the manifold pixel color value data vector.

8. A system for identifying dynamic flow patterns in solid-liquid two-phase flow, characterized in that, include: The flow pattern image acquisition and matrix construction module acquires dynamic flow pattern images of solid-liquid two-phase flow at different times, extracts feature data of the images at each time, constructs a snapshot matrix X, and splits the snapshot matrix X into matrix X1 and matrix X2. The singular value decomposition processing module performs singular value decomposition on the matrix X1 to obtain unitary matrix U, unitary matrix V and diagonal matrix Σ. The similarity matrix construction module constructs a similarity matrix of system matrix A based on the unitary matrix U, unitary matrix V, diagonal matrix Σ, and matrix X2. The eigenvalue decomposition and extraction module performs the following steps on the similarity matrix: Eigenvalue decomposition is performed to obtain the eigenvector matrix W and the eigenvalue diagonal matrix Λ; The modal parameter calculation module obtains the growth rate g of each mode based on the eigenvalue diagonal matrix Λ and the time interval Δt between adjacent times. j and frequency ω j ; The mode and amplitude solving module obtains the mode Φ and mode amplitude α based on the matrix X2, unitary matrix V, unitary matrix U, diagonal matrix Σ, eigenvector matrix W, and eigenvalue diagonal matrix Λ. The flow pattern reconstruction and identification module sorts the modes according to the modal amplitude α, selects the first m modes to reconstruct the flow pattern, and identifies the dynamic flow pattern of the solid-liquid two-phase flow through the reconstruction results.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running thereon, characterized in that, When the processor executes the program, it implements the method for identifying dynamic flow patterns of solid-liquid two-phase flow as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements a method for identifying dynamic flow patterns of solid-liquid two-phase flow as described in any one of claims 1-7.

Images