Aircraft body surface acoustic load time domain prediction method and system

Abstract

The invention discloses an aircraft body surface acoustic load time domain prediction method and system, and belongs to the technical field of aircraft detection. In order to solve the problem of high-precision solving calculation of the sound scattering problem of the aircraft, an aircraft flow field calculation grid and a sound field grid are established; the grids are initialized; calculating a Jacobian determinant and grid cell measurement, and completing grid coordinate system conversion; judging the convergence of the numerical solution of the grid unit; a Runge-Kutta method is adopted to iteratively solve a linear Euler equation under each time step; obtaining flow field data and sound field data of each grid unit; filtering processing is carried out by adopting a spatial filtering format, numerical pseudo waves are eliminated, and flow field data and sound field data of each grid unit after filtering processing are obtained; and performing time updating iteration on the flow field data and the sound field data until the obtained acoustic load data is stable, and outputting aircraft surface acoustic load time domain data, a watershed sound field cloud picture and a flow field cloud picture. The method gives consideration to calculation efficiency and precision.

Description

Technical Field

[0001] The present invention belongs to the technical field of aircraft detection, and in particular relates to a time-domain prediction method and system for the acoustic load on the surface of an aircraft body. Background Art

[0002] Propeller noise is one of the primary acoustic sources in turboprop aircraft. Due to the propeller's close proximity to the airframe, the aircraft scatters the noise strongly, affecting the acoustic field distribution. Therefore, accurately predicting the propeller noise scattering effect of the airframe is crucial for studying the noise characteristics of turboprop aircraft. Airframe scattering can be addressed using traditional CFD methods, rapid estimation, or a hybrid approach (CFD + CAA). CFD methods require a sufficiently large computational domain and high mesh resolution to accurately capture the high-intensity blade pass frequencies. Furthermore, the complex structure of rotating components and the airframe significantly increases the computational effort required at the boundaries. Rapid estimation methods focus on specific structural components and cannot explain the mechanistic characteristics of noise generation and propagation, resulting in a more targeted approach. Hybrid methods overcome the geometric limitations of rapid estimation while reducing computational effort and improving efficiency. They offer an effective prediction method that balances high accuracy and efficiency. However, the magnitude of acoustic pulsation is significantly different from that of the macroscopic scale of flow field changes (such as vortex structure and boundary layer structure). The acoustic quantities involved in the sound field are usually 5 to 6 orders of magnitude smaller than the fluid dynamics quantities. In addition, sound often propagates over long distances. If not handled properly, it may be masked by numerical errors and dissipate the real acoustic signal. In addition, the magnitude of sound intensity from the hearing threshold to the pain threshold spans 10 14 , with a frequency range of 20–20 kHz. Simulating motion at all these scales is extremely difficult. Therefore, there is an urgent need to develop an acoustic field solution method with high precision, low dispersion, and low loss, capable of accurately capturing acoustic waves, to meet the practical engineering needs of turboprop aircraft acoustic load prediction. Summary of the Invention

[0003] The problem to be solved by the present invention is to perform high-precision calculation and solution to the problem of aircraft sound scattering, and propose a time-domain prediction method and system for the acoustic load on the surface of an aircraft body.

[0004] To achieve the above object, the present invention is implemented through the following technical solutions:

[0005] A method for predicting the acoustic load on the surface of an aircraft body in the time domain comprises the following steps:

[0006] S1 establishes the aircraft flow field calculation grid and acoustic field grid, reads the aircraft parameter file, the aircraft flow field calculation grid and acoustic field grid file and flow field data file, initializes the information transfer interface, and establishes connectivity between the grid cells;

[0007] S2. Calculate boundary parameters based on the boundary condition type in the aircraft parameter file obtained in step S1 and initialize the boundary conditions, including the aircraft wall using an unbonded wall boundary condition and the aircraft far field using a combination of mesh stretching and an absorbing layer;

[0008] S3. Calculate the Jacobian determinant and grid cell metrics, complete the grid coordinate system conversion, and transform the linear Euler equation into an equation in general curvilinear coordinates;

[0009] S4. Traverse all grid cells at each time step, calculate the maximum value of the convergence condition judgment number at each time step, and judge the convergence of the numerical solution of the grid cells;

[0010] S5. Use the Runge-Kutta method to iteratively solve the linear Euler equations at each time step. This involves enforcing boundary conditions at each grid cell and performing MPI communication. A high-order, low-dissipation, low-dispersion difference scheme is used to calculate spatial derivatives. This, combined with the source term parameters, solves the linear Euler equations for the entire regional grid cell, obtaining flow and acoustic field data for each grid cell.

[0011] S6. The flow field data and acoustic field data of each grid unit obtained in step S5 are filtered using a spatial filtering format to eliminate numerical artifacts to obtain the flow field data and acoustic field data of each grid unit after filtering;

[0012] S7. Repeat steps S4 to S6 to iterate the flow field data and acoustic field data until the acquired acoustic load data reaches stability. Output the aircraft surface acoustic load time domain data, flow domain acoustic field cloud map, and flow field cloud map.

[0013] Furthermore, the specific implementation method of step S1 includes the following steps:

[0014] S1.1. Use a structured grid to establish the aircraft flow field computational grid and the acoustic field grid. Use a multi-block meshing technique to divide the overall area into multiple sub-areas. Establish a separate structured grid for each sub-area, maintaining consistent grid distribution at the boundaries of adjacent sub-areas.

[0015] S1.2. Read the aircraft parameter file, mesh file, and flow field data file;

[0016] The aircraft parameter file includes Mach number, temperature, density, angle of attack, monitoring point coordinates, grid information, sound source information and flow field information. Among them, the grid information is used to identify and process the grid file and establish the connectivity of grids in different areas; the sound source information includes the sound source type, sound source amplitude and sound source frequency; the flow field information is used to identify the flow field data file and allocate data space for each flow field data.

[0017] S1.3. Initialize the information transmission interface and establish connectivity between grid cells.

[0018] Furthermore, the specific implementation method of step S2 includes the following steps:

[0019] S2.1. The vehicle wall adopts the unbonded wall boundary condition, that is, the normal velocity and its partial derivative with respect to time are zero;

[0020] All physical points in the boundary area are divided into three categories, including internal points, boundary points and virtual points. The internal points are obtained by solving the control equation; the boundary points are specified by the boundary conditions and satisfy the control equation; the virtual points are obtained by specifying the pressure value. To simulate the solid wall boundary condition;

[0021] S2.2. Aircraft far field conditions using a combination of mesh stretching and absorbing layers;

[0022] The absorption layer is to add three virtual grid layers outside the calculation area grid. In the absorption layer, the outward propagating wave is attenuated by the attenuation factor function. Decay to a specified value, that is, explicitly decay the solution vector after each time step iteration:

[0023]

[0024] in, is the initial solution vector after the n+1th iterative time step, U targent is the target attenuation solution vector, U n+1 is the explicit decay solution vector after the n+1th iterative time step, is the attenuation factor;

[0025] The decay factor function transitions smoothly in the following way, expressed as:

[0026]

[0027] Where L is the width of the absorption layer, is the maximum value of the attenuation factor, is a constant of the decay factor function;

[0028] The shape of the decay function is determined by manual setting;

[0029] While using the absorption layer method, the mesh stretching method is used simultaneously to reduce the mesh density in the far field to attenuate the propagation of sound waves.

[0030] Furthermore, the calculation formula for solving the CFL number in step S4 is:

[0031]

[0032] in, is the flow field at each time step, is the time step, is the spatial step length;

[0033] Determine the convergence of the numerical solution of the grid unit. If the numerical solution of the grid unit converges, proceed to the next step. If the numerical solution of the grid unit does not converge, adjust the grid read in step S1 until the convergence condition is met.

[0034] Furthermore, the specific implementation method of step S5 includes the following steps:

[0035] S5.1. Apply boundary conditions to each converged grid cell obtained in step S5 and perform MPI communication.

[0036] S5.2. Use a high-order, low-dissipation, low-dispersion difference scheme to calculate spatial derivatives. Combined with the source term parameters, solve the linear Euler equations for the entire regional grid cell, obtaining flow and acoustic field data for each grid cell.

[0037] S5.2.1. Use the linearized Euler equation to solve the acoustic load on the airframe surface. Set the variables in the calculation to be dimensionless according to the following reference values: , the reference speed is , the reference density is , the reference pressure is , the reference time is ;

[0038] S5.2.2. Spatial derivatives are calculated using the high-order, low-dissipation, low-dispersion OPCS format, decomposing the derivative into the sum of forward and backward derivatives. A third-order, four-point explicit template is used at the outer boundaries of the grid blocks, and a fourth-order, eleven-point explicit template is used at the block interfaces, forming a recursive system in the form of a fourth-order OPCS matrix. The OPCS matrix is a bidiagonal matrix, and a forward sweep is used to solve the lower diagonal matrix, while a backward sweep is used to solve the upper diagonal matrix. In a three-dimensional flow domain of dimensions (Nx, Ny, Nz), a total of Ny × Nz upper and lower diagonal matrices are solved.

[0039] S5.2.3. Time discretization uses an explicit multi-intermediate-step Runge-Kutta method, alternating between 4-step and 6-step LDDRK formats during time marching. Coefficients for the alternating formats are optimized, ensuring that the marching format maintains low loss and dispersion characteristics while achieving fourth-order accuracy at each time step and enabling low-memory applications.

[0040] S5.2.4. The sound source term is given by the sound source information converted from the computational fluid dynamics solution or the analytical sound source model. The sound source information converted from the computational fluid dynamics solution is calculated by CFD to obtain the unsteady turbulent velocity, and the sound source information is obtained by taking its derivative.

[0041] For the sound scattering problem, the sound source is simplified and given by the classical sound source model, that is, it is composed of the superposition of rotating monopoles or dipoles;

[0042] S5.2.5. Combine the sound source term in step S5.2.4 and solve the energy equation, momentum equation, and continuity equation by spatial discretization and temporal discretization to obtain sound field data.

[0043] Furthermore, the spatial filtering format of step S6 is an explicit spatial filtering using a classic tenth-order format, and the expression is:

[0044]

[0045] in, is the sound field or flow field after the i-th filter, is the i-th original and unfiltered flow field or sound field.

[0046] A time-domain prediction system for aircraft body surface acoustic loads is implemented based on a method for time-domain prediction of aircraft body surface acoustic loads. The system includes a processor and a computer-readable storage medium communicatively connected to the processor. The computer-readable storage medium further includes a data list for each grid cell and stores computer instructions. When the computer instructions are executed by the processor, the method for time-domain prediction of aircraft body surface acoustic loads is implemented.

[0047] Beneficial effects of the present invention:

[0048] The present invention describes a method for predicting aircraft surface acoustic loads in the time domain. This method employs a high-order format to achieve high-precision numerical simulation of noise propagation, combined with spatial filtering to improve computational stability. For time discretization, an explicit low-dispersion, low-loss time format is employed to increase the time step and reduce computational effort. Furthermore, a non-reflecting boundary condition is introduced in the far field to minimize acoustic reflections. Overall, this method improves acoustic wave resolution, accurately simulates the acoustic scattering of rotating sound sources while maintaining computational efficiency and accuracy, and enhances the accuracy of time-domain predictions of aircraft surface acoustic loads.

[0049] The proposed method for predicting aircraft surface acoustic loads in the time domain can account for complex aircraft geometry and, through high-order numerical formats and filtering, achieve high-precision numerical simulation of turboprop aircraft surface acoustic loads. This method also exhibits low dispersion and dissipation, balancing computational efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of a method for predicting the acoustic load on the surface of an aircraft body in the time domain according to the present invention;

[0051] Figure 2 A full-scale wing-body assembly model and a schematic diagram of the solution domain;

[0052] Figure 3 The grid of a full-size wing-body assembly;

[0053] Figure 4 It is the boundary point division method of the near-wall area;

[0054] Figure 5 This is a comparison chart of the numerical analytical wavenumbers in the difference format;

[0055] Figure 6 The comparison diagrams of dispersion error and dissipation error of different time discretization formats are shown in Figure 1, where (a) is the dispersion error comparison diagram and (b) is the dissipation error comparison diagram;

[0056] Figure 7 The sound field cloud map before and after filtering, where (a) is the sound field cloud map before filtering, and (b) is the sound field cloud map after filtering;

[0057] Figure 8 is the instantaneous sound pressure cloud map of the turboprop plane;

[0058] Figure 9 It is the curve of sound pressure level distribution on the fuselage surface. DETAILED DESCRIPTION

[0059] In order to make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present invention and are not intended to limit the present invention. That is, the specific embodiments described herein are only some embodiments of the present invention, not all embodiments. Generally, the components of the specific embodiments of the present invention described and illustrated in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0060] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected specific embodiments of the present invention. All other specific embodiments obtained by those skilled in the art based on the specific embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0061] In order to further understand the content, features and effects of the present invention, the following specific embodiments are given as examples, and the attached Figure 1 -Attached Figure 9 The detailed instructions are as follows:

[0062] Example 1:

[0063] A method for predicting the acoustic load on the surface of an aircraft body in the time domain comprises the following steps:

[0064] S1 establishes the aircraft flow field calculation grid and acoustic field grid, reads the aircraft parameter file, the aircraft flow field calculation grid and acoustic field grid file and flow field data file, initializes the information transfer interface, and establishes connectivity between the grid cells;

[0065] Furthermore, the specific implementation method of step S1 includes the following steps:

[0066] S1.1. Use a structured grid to establish the aircraft flow field computational grid and the acoustic field grid. Use a multi-block meshing technique to divide the overall area into multiple sub-areas. Establish a separate structured grid for each sub-area, maintaining consistent grid distribution at the boundaries of adjacent sub-areas.

[0067] Furthermore, the flow field grid considers the influence of the boundary layer, that is, the thickness of the first boundary layer is calculated according to the actual working conditions of the aircraft:

[0068]

[0069] in, is the dynamic viscosity coefficient, is the friction coefficient, The value is 1;

[0070] S1.2. Read the aircraft parameter file, mesh file, and flow field data file;

[0071] The aircraft parameter file includes Mach number, temperature, density, angle of attack, monitoring point coordinates, grid information, sound source information and flow field information. Among them, the grid information is used to identify and process the grid file and establish the connectivity of grids in different areas; the sound source information includes the sound source type, sound source amplitude and sound source frequency; the flow field information is used to identify the flow field data file and allocate data space for each flow field data.

[0072] Furthermore, in order to accurately simulate the near field, it is necessary to consider the scattering, refraction, and absorption of sound waves by turbulence and shear flow, that is, to solve the linearized Euler equation based on the non-uniform background flow field. (pressure), (density), 、 、 (speed in three directions);

[0073] S1.3. Initialize the information transmission interface and establish connectivity between grid cells.

[0074] Furthermore, taking the propeller noise scattering calculation on a full-scale fuselage as an example, Figure 2 A schematic diagram of the model and calculation domain is given. The model consists of two parts: the wing and the fuselage. The propeller plane is perpendicular to the fuselage, and both propellers rotate in a counterclockwise direction. The background flow Mach number is 0.2. The present invention uses the finite difference method to solve the linearized Euler equations, and uses structured grids for division in acoustic calculations. In order to consider the influence of the boundary layer in the flow field calculation, it is necessary to set a boundary layer grid close to the aircraft. Both the acoustic grid and the flow field calculation grid are in the form of a structured grid to reduce interpolation errors. At the same time, the multi-block docking grid technology is used to partition the geometry. The grid size is adjusted at the interface of the partitioned grid to ensure the continuity of the grid measurement. As Figure 3 The mesh of a full-size wing-body assembly is given, and the acoustic mesh density PPW is 7. Smooth transition meshes are set at the nose and tail, and O-type meshes are used at the leading and trailing edges of the wing to improve the mesh quality.

[0075] S2. Calculate boundary parameters based on the boundary condition type in the aircraft parameter file obtained in step S1 and initialize the boundary conditions, including the aircraft wall using an unbonded wall boundary condition and the aircraft far field using a combination of mesh stretching and an absorbing layer;

[0076] Furthermore, the specific implementation method of step S2 includes the following steps:

[0077] S2.1. The vehicle wall adopts the unbonded wall boundary condition, that is, the normal velocity and its partial derivative with respect to time are zero, expressed as:

[0078] ;

[0079] in is the modulus of the normal vector on the solid wall, 、 、 are the horizontal variables, longitudinal variables and vertical variables in the computational domain; 、 、 , 、 、 , 、 and are the partial derivatives of the horizontal variable with respect to the x-axis, the partial derivative of the horizontal variable with respect to the y-axis, the partial derivative of the horizontal variable with respect to the z-axis, the partial derivative of the vertical variable with respect to the x-axis, the partial derivative of the vertical variable with respect to the y-axis, the partial derivative of the vertical variable with respect to the z-axis, the partial derivative of the vertical variable with respect to the x-axis, the partial derivative of the vertical variable with respect to the y-axis, and the partial derivative of the vertical variable with respect to the z-axis; 、 and are the x-axis component, y-axis component, and z-axis component of the wall normal vector;

[0080] like Figure 4 , all physical points in the boundary area are divided into three categories, including internal points, boundary points and virtual points. Among them, internal points are obtained by solving the control equation; boundary points are specified by boundary conditions and satisfy the control equation; virtual points are obtained by specifying pressure values so that To simulate the solid wall boundary condition;

[0081] S2.2. Aircraft far field conditions using a combination of mesh stretching and absorbing layers;

[0082] The absorption layer is to add three virtual grid layers outside the calculation area grid. In the absorption layer, the outward propagating wave is attenuated by the attenuation factor function. Decay to a specified value, that is, explicitly decay the solution vector after each time step iteration:

[0083]

[0084] in, is the initial solution vector after the n+1th iterative time step, U targent is the target attenuation solution vector, U n+1 is the explicit decay solution vector after the n+1th iterative time step, is the attenuation factor;

[0085] The decay factor function transitions smoothly in the following way, expressed as:

[0086]

[0087] Where L is the width of the absorption layer, is the maximum value of the attenuation factor, is a constant of the decay factor function;

[0088] The shape of the attenuation function is determined by manual setting; Figure 2, the computational domain is a cuboid, and absorption layers (outflow buffers) are set on the six faces of the cuboid;

[0089] While using the absorption layer method, the grid stretching method is used synchronously to reduce the grid density in the far field to attenuate the propagation of sound waves. The far field adopts a combination of grid stretching and absorption layer to reduce the size of the physical domain while avoiding reflections caused by artificial boundaries as much as possible. When the average background flow field is not a uniform field or the acoustic disturbance does not propagate in the direction perpendicular to the wall, the traditional non-reflecting boundary condition cannot play a role in attenuating the propagation of sound waves. The absorption layer boundary condition is used to attenuate the sound wave propagation through the attenuation function. Attenuate the outward propagating wave to a target value, thereby reducing boundary spurious reflections;

[0090] S3. Calculate the Jacobian determinant and grid cell metrics, complete the grid coordinate system conversion, and transform the linear Euler equation into an equation in general curvilinear coordinates;

[0091] Furthermore, the specific implementation method of step S3 includes the following steps:

[0092] S3.1. The expression of the Jacobian determinant is:

[0093]

[0094] in, is the Jacobian determinant;

[0095] S3.2. According to the standard rule of inverse matrix, we get the expression:

[0096] ;

[0097] S3.3. Compare the corresponding elements of the two matrices in the equation of step S3.2 to obtain the direct metric relationship of the inverse metric representation as follows:

[0098] .

[0099] S4. Traverse all grid cells at each time step, calculate the maximum value of the convergence condition judgment number at each time step, and judge the convergence of the numerical solution of the grid cells;

[0100] Furthermore, the calculation formula for solving the CFL number in step S4 is:

[0101]

[0102] in, is the flow field at each time step, is the time step, is the spatial step length;

[0103] Determine the convergence of the numerical solution of the grid unit. If the numerical solution of the grid unit converges, proceed to the next step. If the numerical solution of the grid unit does not converge, adjust the grid read in step S1 until the convergence condition is met.

[0104] S5. Use the Runge-Kutta method to iteratively solve the linear Euler equations at each time step. This involves enforcing boundary conditions at each grid cell and performing MPI communication. A high-order, low-dissipation, low-dispersion difference scheme is used to calculate spatial derivatives. This, combined with the source term parameters, solves the linear Euler equations for the entire regional grid cell, obtaining flow and acoustic field data for each grid cell.

[0105] Furthermore, the specific implementation method of step S5 includes the following steps:

[0106] S5.1. Apply boundary conditions to each converged grid cell obtained in step S5 and perform MPI communication.

[0107] S5.2. Use a high-order, low-dissipation, low-dispersion difference scheme to calculate spatial derivatives. Combined with the source term parameters, solve the linear Euler equations for the entire regional grid cell, obtaining flow and acoustic field data for each grid cell.

[0108] S5.2.1. Use the linearized Euler equation to solve the acoustic load on the airframe surface. The calculation formula is:

[0109]

[0110] in, , and Respectively 、 、 The component velocity of the direction, and represent density and pressure respectively, represents specific heat, which is set to be steady, and the specific heat of air is 1.4. The superscript ′ represents the disturbance field, and the subscript 0 represents the background flow field; arrive They are the 1st source term, the 2nd source term, the 3rd source term, the 4th source term, and the 5th source term respectively;

[0111] The variables in the calculation are set to be dimensionless according to the following reference values: The reference length is , the reference speed is , the reference density is , the reference pressure is , the reference time is ;

[0112] S5.2.2. Spatial derivatives are calculated using the high-order, low-dissipation, low-dispersion OPCS format, decomposing the derivative into the sum of forward and backward derivatives. A third-order, four-point explicit template is used at the outer boundaries of the grid blocks, and a fourth-order, eleven-point explicit template is used at the block interfaces, forming a recursive system in the form of a fourth-order OPCS matrix. The OPCS matrix is a bidiagonal matrix, and a forward sweep is used to solve the lower diagonal matrix, while a backward sweep is used to solve the upper diagonal matrix. In a three-dimensional flow domain of dimensions (Nx, Ny, Nz), a total of Ny × Nz upper and lower diagonal matrices are solved.

[0113] Furthermore, the fourth-order OPCS along the x-direction can be given by a recursive system:

[0114]

[0115] in, 、 ,and denote the original derivative, forward derivative and backward derivative at the grid points respectively, represents the known original field. The coefficient 、 、 、 、 、 、 and For the outer boundary of the grid block, a third-order four-point explicit template is used (after combining forward and backward):

[0116]

[0117] At the interface of the blocks, a fourth-order eleven-point explicit template (combined forward and backward) is used:

[0118]

[0119] in, 、 and are all fixed coefficients, and the above formula forms a recursive system. It can be expressed in matrix form as:

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] Similarly, along and The fourth-order OPCS of the direction can be respectively changed by and Come and find a solution. Figure 5 By comparing the wave numbers that can be resolved by different formats, it can be seen that the OPCS format applied in the present invention can increase the maximum resolvable wave number of the format.

[0128] S5.2.3. Time discretization uses an explicit multi-intermediate-step Runge-Kutta method, alternating between 4-step and 6-step LDDRK formats during time marching. Coefficients for the alternating formats are optimized, ensuring that the marching format maintains low loss and dispersion characteristics while achieving fourth-order accuracy at each time step and enabling low-memory applications.

[0129] Furthermore, the low-storage P-step LDDRK format can be expressed as:

[0130] cycle :

[0131] ;

[0132] Incremental overlay:

[0133] ;

[0134] in is the intermediate step increment, is the solution vector, is a constant coefficient, calculated according to the following formula:

[0135]

[0136] Figure 6 By comparing the dispersion error and dissipation error of different time discrete formats, it can be seen that the method used in the present invention has higher accuracy and stability.

[0137] S5.2.4. The sound source term is given by the sound source information converted from the computational fluid dynamics solution or the analytical sound source model. The sound source information converted from the computational fluid dynamics solution is obtained by CFD calculation to obtain the unsteady turbulent velocity, and the sound source information is obtained by taking its derivative:

[0138]

[0139] in, is the speed of the i-th sound source information at time t, is the average speed of the i-th sound source information at time t, is the i-th sound source information, j is the grid point position;

[0140] For the sound scattering problem, the sound source is simplified and given by the classical sound source model, that is, it is composed of rotating monopoles or dipoles superimposed, and the coordinate of the center of the rotation axis is , perpendicular to the yoz plane, the position of the i-th rotating sound source is as follows:

[0141]

[0142] in, represents the radius of the ring, Indicates the relative position of the sound source, represents the angular velocity of rotation;

[0143] The speed of the sound source is expressed as:

[0144]

[0145] Set the monopole strength to , the dipole strength is , the expression of the sound source term is rewritten as:

[0146]

[0147] S5.2.5. Combine the sound source term in step S5.2.4 and solve the energy equation, momentum equation, and continuity equation by spatial discretization and temporal discretization to obtain sound field data.

[0148] S6. The flow field data and acoustic field data of each grid unit obtained in step S5 are filtered using a spatial filtering format to eliminate numerical artifacts to obtain the flow field data and acoustic field data of each grid unit after filtering;

[0149] Furthermore, the spatial filtering format of step S6 is an explicit spatial filtering using a classic tenth-order format, and the expression is:

[0150]

[0151] in, is the sound field or flow field after the i-th filter, is the i-th original and unfiltered flow field or sound field;

[0152] Figure 7 The filtering effect is analyzed using a point sound source example. It can be seen that the numerical calculation results after filtering suppress the non-physical numerical oscillations caused by the numerical calculation, and the contour map of the isobars is smoother and closer to the true value.

[0153] S7. Repeat steps S4 to S6 to iterate the flow field data and acoustic field data until the acquired acoustic load data reaches stability. Output the aircraft surface acoustic load time domain data, flow domain acoustic field cloud map, and flow field cloud map.

[0154] Further, Figure 8 The instantaneous sound pressure cloud diagram of the turboprop plane is given. The counterclockwise rotation of the sound pressure of the turboprop plane over time indicates the counterclockwise rotation of the sound source. The calculation results of the present invention can clearly calculate the sound scattering effect of the fuselage on the propeller. Figure 9 Comparing the sound pressure level distribution curves of uniform incoming flow and RANS, the present invention can take into account the refraction effect of the incoming flow on the propeller sound wave and predict the time domain results of the acoustic load on the aircraft body surface.

[0155] Example 2:

[0156] A time-domain prediction system for aircraft body surface acoustic loads is implemented based on a time-domain prediction method for aircraft body surface acoustic loads described in Example 1. The system includes a processor and a computer-readable storage medium communicatively connected to the processor. The computer-readable storage medium further includes a data list for each grid unit and stores computer instructions. When the computer instructions are executed by the processor, the above-mentioned time-domain prediction method for aircraft body surface acoustic loads is implemented.

[0157] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0158] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and components may be substituted with equivalents without departing from the scope of the present application. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of these combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions within the scope of the claims.

Claims

1. A method for predicting the acoustic load on the surface of an aircraft body in the time domain, characterized in that: The steps include: S1 establishes the aircraft flow field calculation grid and acoustic field grid, reads the aircraft parameter file, the aircraft flow field calculation grid and acoustic field grid file and flow field data file, initializes the information transfer interface, and establishes connectivity between the grid cells; S2. Calculate boundary parameters based on the boundary condition type in the aircraft parameter file obtained in step S1 and initialize the boundary conditions, including the aircraft wall using an unbonded wall boundary condition and the aircraft far field using a combination of mesh stretching and an absorbing layer; S3. Calculate the Jacobian determinant and grid cell metrics, complete the grid coordinate system conversion, and transform the linear Euler equation into an equation in general curvilinear coordinates; S4. Traverse all grid cells at each time step, calculate the maximum value of the convergence condition judgment number at each time step, and judge the convergence of the numerical solution of the grid cells; S5. Use the Runge-Kutta method to iteratively solve the linear Euler equations at each time step. This involves enforcing boundary conditions at each grid cell and performing MPI communication. A high-order, low-dissipation, low-dispersion difference scheme is used to calculate spatial derivatives. This, combined with the source term parameters, solves the linear Euler equations for the entire regional grid cell, obtaining flow and acoustic field data for each grid cell. S6. The flow field data and acoustic field data of each grid unit obtained in step S5 are filtered using a spatial filtering format to eliminate numerical artifacts to obtain the flow field data and acoustic field data of each grid unit after filtering; S7. Repeat steps S4 to S6 to iterate the flow field data and acoustic field data until the acquired acoustic load data reaches stability. Output the aircraft surface acoustic load time domain data, flow domain acoustic field cloud map, and flow field cloud map.

2. The method for predicting the acoustic load on the surface of an aircraft body in the time domain according to claim 1, characterized in that: The specific implementation method of step S1 includes the following steps: S1.

1. Use a structured grid to establish the aircraft flow field computational grid and the acoustic field grid. Use a multi-block meshing technique to divide the overall area into multiple sub-areas. Establish a separate structured grid for each sub-area, maintaining consistent grid distribution at the boundaries of adjacent sub-areas. S1.

2. Read the aircraft parameter file, mesh file, and flow field data file; The aircraft parameter file includes Mach number, temperature, density, angle of attack, monitoring point coordinates, grid information, sound source information and flow field information. Among them, the grid information is used to identify and process the grid file and establish the connectivity of grids in different areas; the sound source information includes the sound source type, sound source amplitude and sound source frequency; the flow field information is used to identify the flow field data file and allocate data space for each flow field data. S1.

3. Initialize the information transmission interface and establish connectivity between grid cells.

3. A method for predicting aircraft body surface acoustic loads in time domain according to claim 1 or 2, characterized in that: The specific implementation method of step S2 includes the following steps: S2.

1. The vehicle wall adopts the unbonded wall boundary condition, that is, the normal velocity and its partial derivative with respect to time are zero, expressed as: ； in, is the modulus of the normal vector on the solid wall, 、 、 are the horizontal variables, longitudinal variables and vertical variables in the computational domain; 、 、 , 、 、 , 、 and are the partial derivatives of the horizontal variable with respect to the x-axis, the partial derivative of the horizontal variable with respect to the y-axis, the partial derivative of the horizontal variable with respect to the z-axis, the partial derivative of the vertical variable with respect to the x-axis, the partial derivative of the vertical variable with respect to the y-axis, the partial derivative of the vertical variable with respect to the z-axis, the partial derivative of the vertical variable with respect to the x-axis, the partial derivative of the vertical variable with respect to the y-axis, and the partial derivative of the vertical variable with respect to the z-axis; 、 and are the x-axis component, y-axis component, and z-axis component of the wall normal vector; All physical points in the boundary area are divided into three categories, including internal points, boundary points and virtual points. The internal points are obtained by solving the control equation; the boundary points are specified by the boundary conditions and satisfy the control equation; the virtual points are obtained by specifying the pressure value. To simulate the solid wall boundary condition; S2.

2. Aircraft far field conditions using a combination of mesh stretching and absorbing layers; The absorption layer is to add three virtual grid layers outside the calculation area grid. In the absorption layer, the outward propagating wave is attenuated by the attenuation factor function. Decay to a specified value, that is, explicitly decay the solution vector after each time step iteration: ； in, is the initial solution vector after the n+1th iterative time step, U targent is the target attenuation solution vector, U n+1 is the explicit decay solution vector after the n+1th iterative time step, is the attenuation factor; The decay factor function transitions smoothly in the following way, expressed as: ； Where L is the width of the absorption layer, is the maximum value of the attenuation factor, is a constant of the decay factor function; The shape of the decay function is determined by manual setting; While using the absorption layer method, the mesh stretching method is used simultaneously to reduce the mesh density in the far field to attenuate the propagation of sound waves.

4. The method for predicting the acoustic load on the surface of an aircraft body in the time domain according to claim 3, characterized in that: The specific implementation method of step S3 includes the following steps: S3.

1. The expression of the Jacobian determinant is: ； in, is the Jacobian determinant; S3.

2. According to the standard rule of inverse matrix, we get the expression: ； S3.

3. Compare the corresponding elements of the two matrices in the equation of step S3.2 to obtain the direct metric relationship of the inverse metric representation as follows: 。 5. The method for predicting the acoustic load on the surface of an aircraft body in the time domain according to claim 4, characterized in that: Step S4 solves the calculation formula of the CFL number as follows: in, is the flow field at each time step, is the time step, is the spatial step length; Determine the convergence of the numerical solution of the grid unit. If the numerical solution of the grid unit converges, proceed to the next step. If the numerical solution of the grid unit does not converge, adjust the grid read in step S1 until the convergence condition is met.

6. The method for predicting the acoustic load on the surface of an aircraft body in time domain according to claim 5, characterized in that: The specific implementation method of step S5 includes the following steps: S5.

1. Apply boundary conditions to each converged grid cell obtained in step S5 and perform MPI communication. S5.

2. Use a high-order, low-dissipation, low-dispersion difference scheme to calculate spatial derivatives. Combined with the source term parameters, solve the linear Euler equations for the entire regional grid cell, obtaining flow and acoustic field data for each grid cell. S5.2.

1. Use the linearized Euler equation to solve the acoustic load on the airframe surface. The calculation formula is: ； in, , and Respectively 、 、 The component velocity of the direction, and represent density and pressure respectively, represents specific heat, which is set to be steady, and the specific heat of air is 1.

4. The superscript ′ represents the disturbance field, and the subscript 0 represents the background flow field; arrive They are the 1st source term, the 2nd source term, the 3rd source term, the 4th source term, and the 5th source term respectively; The variables in the calculation are set to be dimensionless according to the following reference values: The reference length is , the reference speed is , the reference density is , the reference pressure is , the reference time is ; S5.2.

2. Spatial derivatives are calculated using the high-order, low-dissipation, low-dispersion OPCS format, decomposing the derivative into the sum of forward and backward derivatives. A third-order, four-point explicit template is used at the outer boundaries of the grid blocks, and a fourth-order, eleven-point explicit template is used at the block interfaces, forming a recursive system in the form of a fourth-order OPCS matrix. The OPCS matrix is a bidiagonal matrix, and a forward sweep is used to solve the lower diagonal matrix, while a backward sweep is used to solve the upper diagonal matrix. In a three-dimensional flow domain of dimensions (Nx, Ny, Nz), a total of Ny × Nz upper and lower diagonal matrices are solved. S5.2.

3. Time discretization uses an explicit multi-intermediate-step Runge-Kutta method, alternating between 4-step and 6-step LDDRK formats during time marching. Coefficients for the alternating formats are optimized, ensuring that the marching format maintains low loss and dispersion characteristics while achieving fourth-order accuracy at each time step and enabling low-memory applications. S5.2.

4. The sound source term is given by the sound source information converted from the computational fluid dynamics solution or the analytical sound source model. The sound source information converted from the computational fluid dynamics solution is obtained by CFD calculation to obtain the unsteady turbulent velocity, and the sound source information is obtained by taking its derivative: ； in, is the speed of the i-th sound source information at time t, is the average speed of the i-th sound source information at time t, is the i-th sound source information, j is the grid point position; For the sound scattering problem, the sound source is simplified and given by the classical sound source model, which is composed of rotating monopoles or dipoles superimposed, and the coordinate of the center of the rotation axis is , perpendicular to the yoz plane, the position of the i-th rotating sound source is as follows: ； in, represents the radius of the ring, Indicates the relative position of the sound source, represents the angular velocity of rotation; The speed of the sound source is expressed as: ； Set the monopole strength to , the dipole strength is , the expression of the sound source term is rewritten as: ； S5.2.

5. Combine the sound source term in step S5.2.4 and solve the energy equation, momentum equation, and continuity equation by spatial discretization and temporal discretization to obtain sound field data.

7. The method for predicting the acoustic load on the surface of an aircraft body in time domain according to claim 6, characterized in that: The spatial filtering format of step S6 is explicit spatial filtering using the classic tenth-order format, and the expression is: ； in, is the sound field or flow field after the i-th filter, is the i-th original and unfiltered flow field or sound field.

8. A time-domain prediction system for aircraft body surface acoustic loads, implemented based on a time-domain prediction method for aircraft body surface acoustic loads according to any one of claims 1 to 7, characterized in that: The system includes a processor and a computer-readable storage medium communicatively connected to the processor. The computer-readable storage medium further includes a data list for each grid unit and stores computer instructions. When the computer instructions are executed by the processor, the above-mentioned time-domain prediction method for the acoustic load on the surface of the aircraft body is implemented.

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