Intermittent Galerkin finite element and active noise reduction embedded fusion simulation system and method

Abstract

The invention discloses a discontinuous Galerkin finite element and active noise reduction embedded fusion simulation system and method, and belongs to the field of ANC active noise reduction simulation. Comprising a DG-FEM acoustic solving module which is used for carrying out acoustic simulation calculation and comprises grid topology construction, boundary condition construction and a DG-FEM acoustic solver; the ANC control module is embedded in a bottom numerical framework of the DG-FEM acoustic solver and is used for realizing active noise control; the closed-loop control coupling interface is used for realizing real-time data interaction between the DG-FEM acoustic solving module and the ANC control module; according to the method, an ANC core algorithm is deeply embedded into a bottom layer numerical framework of a DG-FEM acoustic solver, and native integrated simulation of the ANC and the DG-FEM is achieved. According to the method, the dependence on external software coupling is eliminated, and the need of a user to write and compile complex custom subprograms or scripts is avoided, so that the acoustic simulation modeling process involving active noise control is remarkably simplified, and the technical threshold is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the field of ANC active noise reduction simulation, and more specifically to a discontinuous Galerkin finite element and active noise reduction embedded fusion simulation system and method. Background Art

[0002] The finite element method (FEM), a core numerical simulation technology in computational acoustics, has undergone significant theoretical innovation and application expansion in recent decades. Among these, the discontinuous Galerkin FEM (DG-FEM) has emerged as a new generation of high-performance computational acoustics technology. Acoustic simulation based on the finite element method (FEM) effectively addresses physical phenomena such as sound wave propagation, reflection, diffraction, and absorption, and has been widely applied in industry. For example, in automotive interior acoustic design, FEM can accurately simulate the interaction of sound waves with complex structures such as seats and instrument panels, quantifying the resulting reflections (with phase shifts), diffraction (changes in diffraction paths), and energy attenuation caused by sound-absorbing materials.

[0003] At present, mainstream commercial multi-physics simulation software (such as COMSOL Multiphysics, Actran, etc.) has integrated mature acoustic simulation modules and plays an important role in many engineering fields such as aerospace, electronics and electrical appliances.

[0004] Active Noise Control (ANC) technology, which entered the practical stage in the 1980s, is based on the principle of destructive interference of sound waves. This technology uses a secondary sound source (such as a speaker) to generate an acoustic signal with equal amplitude and opposite phase to the target noise, effectively canceling the noise. Thanks to the rapid advancements in digital signal processor (DSP) performance, ANC technology has gained widespread application in fields such as automotive and aviation. It is particularly adept at processing low-frequency noise below 200 Hz (such as engine whine and tire noise). Due to its long wavelength, this type of noise is often difficult to effectively control with traditional passive sound-absorbing materials.

[0005] However, despite the maturity of ANC algorithms themselves, their integration into mainstream computer-aided engineering (CAE) acoustic simulation software remains insufficient. Current numerical simulations of ANC active noise cancellation (ANC) rely primarily on cumbersome external coupling or user-defined programming: COMSOL Multiphysics requires coupling with external software (such as MATLAB); ANSYS Actran requires users to write and compile complex user subroutines; and Siemens Simcenter™ 3D (Virtual.Lab) offers only limited scripting capabilities. These approaches require users to be proficient in both CAE software usage and secondary development, as well as the core principles and implementation of ANC algorithms, significantly increasing the technical threshold and implementation difficulty of simulation modeling. In practice, ANC experts are often unfamiliar with the complexities of CAE software, while professional CAE simulation engineers lack in-depth knowledge of ANC algorithms, resulting in inefficient technical collaboration. To address this, we propose an embedded fusion simulation system and method for discontinuous Galerkin finite element analysis and active noise cancellation (ANC). Summary of the Invention

[0006] The purpose of the present invention is to provide a discontinuous Galerkin finite element and active noise reduction embedded fusion simulation system and method to solve the problems raised in the above background technology: the existing computer-aided engineering (CAE) acoustic simulation software has defects such as cumbersome operation (relying on external software coupling or complex user-defined programming), high technical barriers (requiring users to be proficient in CAE software operation and secondary development as well as ANC algorithm principles), and insufficient integration when implementing active noise control (ANC) simulation, which leads to complex and inefficient processes in acoustic system design and performance prediction involving ANC.

[0007] To achieve the above object, the present invention provides the following technical solutions, including: The system and method for the embedded fusion of discontinuous Galerkin finite element and active noise reduction includes a DG-FEM acoustic solver module for performing acoustic simulation calculations, including mesh topology construction, boundary condition construction, and a DG-FEM acoustic solver. The mesh topology construction is used to establish connection relationships between nodes, units, faces, and edges based on the read mesh information, and to map parameter coordinates to physical coordinates and construct Jacobian matrices. The boundary condition construction is used to set basic acoustic boundaries and ANC boundaries. The ANC boundaries include specified speaker boundaries, reference microphone positions, and error microphone positions. The DG-FEM acoustic solver is used to perform discretization and time discretization based on the linear Euler acoustic control equation to obtain the solution vector of the acoustic equation. The ANC control module, embedded in the underlying numerical framework of the DG-FEM acoustic solver, is used to implement active noise control. It includes the LMS algorithm unit and the FxLMS algorithm unit. The LMS algorithm unit is used to train the secondary path and find a set of filter coefficients that minimizes the mean square error between the filter output and the desired signal. The FxLMS algorithm unit is used to introduce an estimation model for the secondary path, perform filter preprocessing on the reference signal, and generate an inverse sound wave. The closed-loop control coupling interface is used to implement real-time data exchange between the DG-FEM acoustic solution module and the ANC control module. The sound field solution obtained by the DG-FEM acoustic solution module is input into the ANC control module. At the same time, the reverse sound wave generated by the ANC control module is input into the DG-FEM acoustic solution module in the form of boundary conditions.

[0008] Preferably, the linear Euler acoustic governing equation is: The governing equations to be solved are as follows: ; is the acoustic conservation variable vector, is the flux vector, is the source term vector; Discretize and construct the mass matrix , stiffness matrix , flux etc., and its discrete form is: ; Among them, u is the matrix of variables to be solved, k is the number of discrete units, t For time.

[0009] Preferably, the time discretization adopts an explicit forward Euler format, and the time derivative is approximated as: ;in, u n+1 、 u n They are n +1 moment and n The solution variable matrix at time Δ t is the time step; Substituting the above formula into the discretized control equation, we can get: ; Solving the above equation can obtain the solution vector of the acoustic equation.

[0010] Preferably, in the LMS algorithm unit: The input signal vector is ; The filter coefficient vector is ; The filter output is ; The error signal is ,in, L is the filter order, d ( n ) is the expected signal.

[0011] Preferably, in the FxLMS algorithm: The reference microphone signal is ; Primary noise: (the original noise to be canceled); The adaptive filter weights are ; The secondary path transfer function is S ( z ) (acoustic path from filter output to error microphone); The error signal is ,in, is the actual effect of the anti-noise after passing through the secondary path, M is the adaptive filter order, d ( n ) is the primary noise.

[0012] The discontinuous Galerkin finite element and active noise reduction embedded fusion simulation method is applied to the above system and is characterized by comprising the following steps: Step S1, constructing a DG-FEM acoustic simulation model, establishing the connection relationship between nodes, units, faces, and edges based on the read mesh information through mesh topology construction, and performing mapping of parameter coordinates to physical coordinates and constructing the Jacobian matrix; Step S2, setting basic acoustic boundaries and ANC boundaries through boundary condition construction, where the ANC boundaries include designated loudspeaker boundaries, reference microphone positions, and error microphone positions; Step S3, using the DG-FEM acoustic solver to perform discretization and time discretization based on the linear Euler acoustic governing equation to obtain a solution vector of the acoustic equation; Step S4: The LMS algorithm unit in the ANC control module trains the secondary path to find a set of filter coefficients that minimizes the mean square error between the filter output and the desired signal. Step S5: The FxLMS algorithm unit in the ANC control module introduces an estimation model of the secondary path, performs filtering preprocessing on the reference signal, and generates a reverse sound wave; In step S6, the sound field solution obtained in step S3 is input to the ANC control module through the closed-loop control coupling interface, and the reverse sound wave generated in step S5 is input to the DG-FEM acoustic solution module in the form of boundary conditions to achieve real-time coupled simulation.

[0013] Preferably, in step S3, the linear Euler acoustic governing equation is: The governing equations to be solved are as follows: ; is the acoustic conservation variable vector, is the flux vector, is the source term vector; Discretize and construct the mass matrix , stiffness matrix , flux etc., and its discrete form is: ; Among them, u is the matrix of variables to be solved, k is the number of discrete units, t For time.

[0014] Preferably, in step S3, the explicit forward Euler format used for time discretization is: The time derivative is approximated by: ;in, u n+1 、 u n They are n +1 moment and n The solution variable matrix at time Δ t is the time step; Substituting the above formula into the discretized control equation, we can get: ; Solving the above equation can obtain the solution vector of the acoustic equation.

[0015] Preferably, in step S4, in the LMS algorithm unit: S4.1 Assume that the input signal vector is ; S4.2 Let the filter coefficient vector be ; S4.3 calculates the filter output as ; S4.4 calculates the error signal as ,in, L is the filter order, d ( n ) is the expected signal.

[0016] Preferably, in step S5, the FxLMS algorithm unit obtains the reference microphone signal, the primary noise, the adaptive filter weight, the secondary path transfer function and the error signal: The reference microphone signal is ; Primary noise: (the original noise to be canceled); The adaptive filter weights are ; The secondary path transfer function is S ( z ) (acoustic path from filter output to error microphone); The error signal is ,in, is the actual effect of the anti-noise after passing through the secondary path, M is the adaptive filter order, d ( n ) is the primary noise.

[0017] Compared with the prior art, the present invention has the following beneficial effects: (1) Breaking down technical barriers and achieving native ANC-CAE integration: By deeply embedding the ANC control law (FXLMS algorithm, secondary path identification) into the DG-FEM solver kernel, the need for cross-platform coupling (such as COMSOL+MATLAB) or user subroutine development (such as Actran URD) in traditional solutions is eliminated. Users no longer need to master CAE secondary development and ANC algorithm coding capabilities at the same time, improving technical collaboration efficiency.

[0018] (2) Breakthrough in complex scene modeling capabilities: Based on the native coupling framework of the DG-FEM sound field solver and the ANC module, users can efficiently build full-domain models of complex acoustic systems (such as vehicle cabins and aircraft engine nacelles), accurately simulate the reflection phase evolution, diffraction path distortion, and active interference cancellation effects during sound wave propagation, and provide a dynamic visualization decision-making basis for secondary sound source layout topology and structural acoustic optimization, significantly improving the noise reduction performance of the ANC system. Achieve industrial-grade high-fidelity sound field-ANC control joint simulation of complex geometries.

[0019] (3) Zero threshold for engineering deployment: Provides a parameterized ANC component library (secondary sound source / error sensor templates, predefined control strategies), allowing users to complete closed-loop simulation through GUI configuration, completely avoiding code writing. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is the technical roadmap of the DG-FEM+ANC overall system of the present invention; Figure 2Schematic diagram of ANC simulation of 2D pipe acoustics in Example 1; Figure 3 This is a time domain diagram of the sound wave at the speaker in Example 1; Figure 4 The sound pressure cloud graphs with and without ANC turned on in Example 1 are shown; Figure 5 Schematic diagram of 3D pipeline acoustic ANC simulation in Example 2; Figure 6 This is a time domain diagram of the sound wave at the speaker in Example 2; Figure 7 This is the sound pressure cloud map after ANC is turned on in Example 2; Figure 8 This is a schematic diagram of the ANC simulation of a range hood in Example 3; Figure 9 The sound pressure distribution cloud diagram of a certain cross section of the range hood at different times in Example 3; Figure 10 This is the sound pressure distribution cloud diagram of a certain cross section of the range hood at different times in Example 3. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0022] Example: See also Figure 1 , a discontinuous Galerkin finite element and active noise reduction embedded fusion simulation system and method, including: a DG-FEM acoustic solution module, used for acoustic simulation calculations, including mesh topology construction, boundary condition construction and a DG-FEM acoustic solver; wherein, the mesh topology construction is used to establish connection relationships between geometric entities such as nodes, units, faces, edges, etc. according to the read mesh information, and to map parameter coordinates to physical coordinates and construct the Jacobian matrix; the boundary condition construction is used to set the basic acoustic boundary and the ANC boundary, and the ANC boundary includes the specified loudspeaker boundary, the reference microphone position and the error microphone position; the DG-FEM acoustic solver is used to perform discretization processing and time discretization based on the linear Euler acoustic control equation to obtain the solution vector of the acoustic equation; The ANC control module, embedded in the underlying numerical framework of the DG-FEM acoustic solver, is used to implement active noise control. It includes the LMS algorithm unit and the FxLMS algorithm unit. The LMS algorithm unit is used to train the secondary path and find a set of filter coefficients that minimizes the mean square error between the filter output and the desired signal. The FxLMS algorithm unit is used to introduce an estimation model for the secondary path, perform filter preprocessing on the reference signal, and generate an inverse sound wave. The closed-loop control coupling interface is used to implement real-time data exchange between the DG-FEM acoustic solution module and the ANC control module. The sound field solution obtained by the DG-FEM acoustic solution module is input into the ANC control module. At the same time, the reverse sound wave generated by the ANC control module is input into the DG-FEM acoustic solution module in the form of boundary conditions.

[0023] The entire DG-FEM system mainly includes mesh processing, boundary conditions, matrix construction, time advancement, equation solving, etc.; the ANC system is mainly divided into secondary path training and FxLMS algorithm to generate reverse sound waves. ANC obtains DG-FEM data for training and controls sound wave generation. The generated signal is input into DG-FEM through boundary conditions, and then finite element simulation is performed to obtain the noise reduction process of the entire system.

[0024] This invention achieves native integrated simulation of ANC and DG-FEM by deeply embedding the core ANC algorithm into the underlying numerical framework of the DG-FEM acoustic solver. This approach eliminates reliance on external software coupling and the need for users to write and compile complex custom subroutines or scripts. This significantly simplifies the acoustic simulation modeling process involving active noise control, significantly lowering the technical threshold. This allows CAE engineers or acoustics experts, even those not proficient in low-level programming, to efficiently and conveniently design and predict the performance of complex acoustic systems incorporating active noise control strategies, effectively improving simulation efficiency and engineering application value.

[0025] Specifically, 1. DG-FEM acoustic solution module design: 1.1 Mesh topology construction and geometric information calculation 1) Based on the read mesh information, the connection relationship between the node, unit, face, and edge geometric entities is established to facilitate the solution of gradients, fluxes, etc.; 2) Parameter coordinate to physical coordinate mapping: The physical coordinates (x, y, z) of each node are known, and the mapping from the reference unit to the physical unit is established through the shape function Ni(ξ, η) (ξ, η are the local coordinates of the reference unit); 3) Jacobian matrix construction: Describes the transformation rate from local coordinates to physical coordinates, which is used for the conversion of integral and gradient operations.

[0026] 1.2 Construction of boundary conditions suitable for ANC 1) Basic acoustic boundaries: mainly including reflection boundaries, sound absorption boundaries, pressure boundaries, etc.; 2) ANC Boundary: Specifies the speaker boundary, whose input is calculated by the ANC algorithm; specifies the location of the reference microphone and error microphone based on coordinates to achieve data transmission; 1.3 DG-FEM acoustic solver.

[0027] 1) The governing equation of linear Euler acoustics is: The governing equations to be solved are as follows: ; is the acoustic conservation variable vector, is the flux vector, is the source term vector; Discretize and construct the mass matrix , stiffness matrix , flux etc., and its discrete form is: ; Among them, u is the matrix of variables to be solved, k is the number of discrete units, t For time.

[0028] 2) Time discretization uses the explicit forward Euler format, and the time derivative is approximated as: ;in, u n+1 、 u n They are n +1 moment and n The solution variable matrix at time Δ t is the time step; Substituting the above formula into the discretized control equation, we can get: ; Solving the above equation can obtain the solution vector of the acoustic equation.

[0029] 2. Low-level embedding of ANC control algorithm: 2.1 ANC system 1: LMS algorithm training secondary path; The goal of the LMS algorithm is to find a set of filter coefficients for time series data of length n so that the mean square value of the error between the filter output and the desired signal is minimized.

[0030] Specifically, in the LMS algorithm unit: The input signal vector is (filter order of length L); The filter coefficient vector is ; The filter output is ; The error signal is ,in, L is the filter order, d ( n ) is the expected signal.

[0031] 2.2 ANC System 2: FxLMS Algorithm The FxLMS (Filtered-x Least Mean Square) algorithm is a classic adaptive algorithm in active noise control (ANC), specifically designed to address the impact of secondary paths on noise cancellation. Its core approach is to incorporate a secondary path estimation model based on the LMS algorithm and perform "filter preprocessing" on the reference signal, effectively canceling primary noise.

[0032] The reference microphone signal is ; Primary noise: (the original noise to be canceled); The adaptive filter weights are ; The secondary path transfer function is S ( z ) (acoustic path from filter output to error microphone); The error signal is ,in, is the actual effect of the anti-noise after passing through the secondary path, M is the adaptive filter order, d ( n ) is the primary noise.

[0033] 3. Closed-loop control coupling interface: The sound field solution (sound pressure, etc.) obtained by DG-FEM is input into the ANC system. At the same time, the ANC system calculates the reverse sound wave y(n) of the speaker and inputs it into the DG-FEM solution system in the form of boundary conditions, thus achieving real-time coupling between the DG-FEM and ANC systems.

[0034] The discontinuous Galerkin finite element and active noise reduction embedded fusion simulation method is applied to the above system, including the following steps: Step S1, constructing a DG-FEM acoustic simulation model, establishing the connection relationship between nodes, units, faces, and edges based on the read mesh information through mesh topology construction, and performing mapping of parameter coordinates to physical coordinates and constructing the Jacobian matrix; Step S2, setting basic acoustic boundaries and ANC boundaries through boundary condition construction, where the ANC boundaries include designated loudspeaker boundaries, reference microphone positions, and error microphone positions; Step S3, using the DG-FEM acoustic solver to perform discretization and time discretization based on the linear Euler acoustic governing equation to obtain a solution vector of the acoustic equation; Step S4: The LMS algorithm unit in the ANC control module trains the secondary path to find a set of filter coefficients that minimizes the mean square error between the filter output and the desired signal. Step S5: The FxLMS algorithm unit in the ANC control module introduces an estimation model of the secondary path, performs filtering preprocessing on the reference signal, and generates a reverse sound wave; In step S6, the sound field solution obtained in step S3 is input to the ANC control module through the closed-loop control coupling interface, and the reverse sound wave generated in step S5 is input to the DG-FEM acoustic solution module in the form of boundary conditions to achieve real-time coupled simulation.

[0035] In this application, in step S3, the linear Euler acoustic governing equation is: The governing equations to be solved are as follows: ; is the acoustic conservation variable vector, is the flux vector, is the source term vector; Discretize and construct the mass matrix , stiffness matrix , flux etc., and its discrete form is: ; Among them, u is the matrix of variables to be solved, k is the number of discrete units, t For time.

[0036] In this application, in step S3, the explicit forward Euler format used for time discretization is: The time derivative is approximated by: ;in, u n+1 、 u n They are n +1 moment and n The solution variable matrix at time Δ t is the time step; Substituting the above formula into the discretized control equation, we can get: ; Solving the above equation can obtain the solution vector of the acoustic equation.

[0037] In this application, in step S4, in the LMS algorithm unit: S4.1 Assume that the input signal vector is ; S4.2 Let the filter coefficient vector be ; S4.3 calculates the filter output as ; S4.4 calculates the error signal as ,in, L is the filter order, d ( n ) is the expected signal.

[0038] In this application, in step S5, the FxLMS algorithm unit obtains the reference microphone signal, primary noise, adaptive filter weight, secondary path transfer function and error signal: The reference microphone signal is ; Primary noise: (the original noise to be canceled); The adaptive filter weights are ; The secondary path transfer function is S ( z ) (acoustic path from filter output to error microphone); The error signal is ,in, is the actual effect of the anti-noise after passing through the secondary path, M is the adaptive filter order, d ( n ) is the primary noise.

[0039] Example 1: 2D pipeline ANC simulation, see Figure 2-4 ; like Figure 1 The figure is a schematic diagram of the ANC simulation of 2D pipe acoustics. The pipe inlet is a plane wave with an amplitude of 1Pa and a frequency of 1000Hz. The system is simulated. Figure 2 The following is a schematic diagram of ANC simulation of 2D pipe acoustics. Figure 2 After turning on ANC, the ANC system uses the time-series sound pressure data of the reverse sound waves of the reference microphone, error microphone and speaker to generate reverse sound waves based on the input, which significantly reduces the sound pressure near the error microphone. Figure 3 To compare the sound pressure cloud map with ANC turned on and without ANC turned on, it can be seen from the figure that after turning on ANC, the sound pressure at the tail of the pipe is significantly reduced, which has a noise reduction effect.

[0040] Example 2: 3D pipeline ANC simulation, see Figure 5-7 ; like Figure 4The figure is a schematic diagram of the ANC simulation of 3D pipe acoustics. The pipe inlet is a plane wave with an amplitude of 1Pa and a frequency of 1000Hz. The system is simulated. Figure 5 This is a schematic diagram of 3D pipe acoustic ANC simulation. Figure 5 After turning on ANC, the ANC system uses the time-series sound pressure data of the reverse sound waves of the reference microphone, error microphone and speaker to generate reverse sound waves based on the input, which significantly reduces the sound pressure near the error microphone. Figure 6 This is the sound pressure contour of a certain cross section after ANC is turned on. It can be seen from the figure that a low sound pressure area is formed between the speaker and the error microphone.

[0041] Example 3: For ANC simulation of a certain range hood model, please refer to Figure 8-10 ; like Figure 7 This is an ANC simulation model diagram of a complex range hood. The sound source is the noise generated by the range hood turbine when it is working. By installing an ANC system in the range hood, the noise is blocked. Figure 8 This is a simulation diagram of the ANC of a range hood. Figure 8 After turning on ANC, the time series sound pressure data of the reverse sound waves of the reference microphone, error microphone and speaker are obtained. Figure 8 It can be seen that the generated reverse sound wave will reduce the noise at the error microphone. Figure 9 The sound pressure distribution cloud diagram of a certain cross section of the range hood at different times, Figure 9 It can be seen that in certain periods of time, the sound pressure in the speaker area is significantly reduced.

[0042] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. The discontinuous Galerkin finite element and active noise reduction embedded fusion simulation system is characterized by: include: The DG-FEM acoustic solver module is used to perform acoustic simulation calculations and includes mesh topology construction, boundary condition construction, and a DG-FEM acoustic solver. The mesh topology construction is used to establish the connection relationships between nodes, cells, faces, and edges based on the read mesh information, and to map parameter coordinates to physical coordinates and construct the Jacobian matrix. The boundary condition construction is used to set the basic acoustic boundary and the ANC boundary. The ANC boundary includes the specified loudspeaker boundary, reference microphone position, and error microphone position. The DG-FEM acoustic solver is used to perform discretization and time discretization based on the linear Euler acoustic control equation to obtain a solution vector of the acoustic equation; An ANC control module, embedded in the underlying numerical framework of the DG-FEM acoustic solver, is used to implement active noise control and includes an LMS algorithm unit and an FxLMS algorithm unit; the LMS algorithm unit is used to train the secondary path and find a set of filter coefficients that minimizes the mean square error between the filter output and the desired signal; the FxLMS algorithm unit is used to introduce an estimation model for the secondary path, perform filter preprocessing on the reference signal, and generate an inverse sound wave; A closed-loop control coupling interface is used to implement real-time data exchange between the DG-FEM acoustic solution module and the ANC control module, input the sound field solution obtained by the DG-FEM acoustic solution module into the ANC control module, and simultaneously input the reverse sound wave generated by the ANC control module into the DG-FEM acoustic solution module in the form of boundary conditions.

2. The embedded fusion simulation system of discontinuous Galerkin finite element and active noise reduction according to claim 1 is characterized by: The linear Euler acoustic governing equation is: The governing equations to be solved are as follows: ; is the acoustic conservation variable vector, is the flux vector, is the source term vector; Discretize and construct the mass matrix , stiffness matrix , flux etc., and its discrete form is: ； Among them, u is the matrix of variables to be solved, k is the number of discrete units, t For time.

3. The embedded fusion simulation system of discontinuous Galerkin finite element and active noise reduction according to claim 2 is characterized by: The time discretization uses the explicit forward Euler format, and the time derivative is approximated as: ;in, u n+1 、 u n They are n +1 moment and n The solution variable matrix at time Δ t is the time step; Substituting the above formula into the discretized control equation, we can get: ； Solving the above equation can obtain the solution vector of the acoustic equation.

4. The embedded fusion simulation system of discontinuous Galerkin finite element and active noise reduction according to claim 1 is characterized by: In the LMS algorithm unit: The input signal vector is ; The filter coefficient vector is ; The filter output is ; The error signal is ,in, L is the filter order, d ( n ) is the expected signal.

5. The embedded fusion simulation system of discontinuous Galerkin finite element and active noise reduction according to claim 1 is characterized by: In the FxLMS algorithm: The reference microphone signal is ; Primary noise: (the original noise to be canceled); The adaptive filter weights are ; The secondary path transfer function is S ( z ) (acoustic path from filter output to error microphone); The error signal is ,in, is the actual effect of the anti-noise after passing through the secondary path, M is the adaptive filter order, d ( n ) is the primary noise.

6. A discontinuous Galerkin finite element and active noise reduction embedded fusion simulation method, applied to the system according to any one of claims 1 to 5, characterized in that: The steps include: Step S1, constructing a DG-FEM acoustic simulation model, establishing the connection relationship between nodes, units, faces, and edges based on the read mesh information through mesh topology construction, and performing mapping of parameter coordinates to physical coordinates and constructing the Jacobian matrix; Step S2, setting a basic acoustic boundary and an ANC boundary by constructing boundary conditions, wherein the ANC boundary includes a specified loudspeaker boundary, a reference microphone position, and an error microphone position; Step S3, using the DG-FEM acoustic solver to perform discretization and time discretization based on the linear Euler acoustic governing equation to obtain a solution vector of the acoustic equation; Step S4: The LMS algorithm unit in the ANC control module trains the secondary path to find a set of filter coefficients that minimizes the mean square error between the filter output and the desired signal. Step S5: The FxLMS algorithm unit in the ANC control module introduces an estimation model of the secondary path, performs filtering preprocessing on the reference signal, and generates a reverse sound wave; In step S6, the sound field solution obtained in step S3 is input to the ANC control module through the closed-loop control coupling interface, and the reverse sound wave generated in step S5 is input to the DG-FEM acoustic solution module in the form of boundary conditions to achieve real-time coupled simulation.

7. The embedded fusion simulation method of discontinuous Galerkin finite element and active noise reduction according to claim 6 is characterized by: In step S3, the linear Euler acoustic governing equation is: The governing equations to be solved are as follows: ; is the acoustic conservation variable vector, is the flux vector, is the source term vector; Discretize and construct the mass matrix , stiffness matrix , flux etc., and its discrete form is: ； Among them, u is the matrix of variables to be solved, k is the number of discrete units, t For time.

8. The embedded fusion simulation method of discontinuous Galerkin finite element and active noise reduction according to claim 6 is characterized by: In step S3, the explicit forward Euler format used for time discretization is: The time derivative is approximated by: ;in, u n+1 、 u n They are n +1 moment and n The solution variable matrix at time Δ t is the time step; Substituting the above formula into the discretized control equation, we can get: ； Solving the above equation can obtain the solution vector of the acoustic equation.

9. The embedded fusion simulation method of discontinuous Galerkin finite element and active noise reduction according to claim 6 is characterized by: In step S4, in the LMS algorithm unit: S4.1 Assume that the input signal vector is ; S4.2 Let the filter coefficient vector be ; S4.3 calculates the filter output as ; S4.4 calculates the error signal as ,in, L is the filter order, d ( n ) is the expected signal.

10. The embedded fusion simulation method of discontinuous Galerkin finite element and active noise reduction according to claim 6 is characterized by: In step S5, the FxLMS algorithm unit obtains the reference microphone signal, primary noise, adaptive filter weight, secondary path transfer function and error signal: The reference microphone signal is ; Primary noise: (the original noise to be canceled); The adaptive filter weights are ; The secondary path transfer function is S ( z ) (acoustic path from filter output to error microphone); The error signal is ,in, is the actual effect of the anti-noise after passing through the secondary path, M is the adaptive filter order, d ( n ) is the primary noise.

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