Method of coupling momentum potential theory and pressure wave decomposition and applications thereof

Abstract

The application provides a method of coupling momentum potential theory and pressure wave decomposition and application thereof, and belongs to the field of fluid mechanics and aeroacoustics. The method first acquires flow field data, performs Helmholtz decomposition on the momentum density field based on the momentum potential theory to obtain momentum densities of fluid dynamics, acoustics and entropy components; then, the momentum densities of the components are taken as independent source terms to construct independent Poisson equations for describing fluid dynamics pressure, acoustic pressure and entropy pressure; and finally, the equations are solved to obtain pressure wave fields of the physical components. The application establishes a physical mapping between momentum modes and pressure modes, gets rid of the dependence on filtering threshold or propagation speed criterion in the prior art, realizes automatic and physically consistent separation of acoustic pressure (true sound) and fluid dynamics pressure (pseudo sound) in complex turbulent flow, and is suitable for noise mechanism research, source positioning and noise reduction design.

Description

Technical Field

[0001] This invention relates to the fields of fluid mechanics, aeroacoustics, and computational fluid dynamics (CFD), and in particular to a method involving coupled momentum potential theory and pressure fluctuation decomposition and its application. Background Technology

[0002] In engineering fields such as aerospace, high-speed trains, and wind power generation, aerodynamic noise generated by compressible turbulence is a critical environmental and structural safety issue. Pressure fluctuation is a core physical quantity characterizing the dynamic features and acoustic radiation properties of a flow field. It consists of two parts: one is the fluid dynamic pressure that convects with the fluid movement but does not radiate outward (i.e., "pseudo-sound"), and the other is the acoustic pressure that propagates to the far field at the speed of sound (i.e., "true sound"). Accurately extracting the acoustic components from the complex pressure signal in the near field is a prerequisite for revealing the noise mechanism and locating the sound source.

[0003] Existing technologies for decomposing pressure fluctuations mainly fall into three categories:

[0004] 1. Wavenumber-frequency domain-based filtering method: This method utilizes the characteristics of sound waves propagating at the speed of sound and vortex waves propagating at the speed of convection to filter by setting a threshold in the frequency domain (i.e., technical route A in Figure 2). However, in high-speed jets or transonic flows, the convection velocity of the fluid is very close to the speed of sound, causing the spectral characteristics of the two to overlap in the wavenumber domain. The physical criterion based on phase velocity fails, making effective separation impossible.

[0005] 2. Data-driven modal decomposition method: This method uses algorithms such as POD, SPOD, or DMD to extract flow field modes, and then manually selects acoustic modes (i.e., technical route B in Figure 2). The modes extracted by this method are usually a mixture of fluid dynamics and acoustic components, with impure physical meaning. Moreover, the selection process heavily relies on subjective experience or far-field auxiliary data, lacking objectivity.

[0006] 3. Traditional physical decomposition methods (such as Momentum Potential Theory (MPT): Although MPT can rigorously decompose the momentum density field into vortex, acoustic, and entropy components based on Helmholtz decomposition, current research only focuses on the velocity or momentum level. Due to the lack of a clear mathematical and physical framework for mapping momentum decomposition results to the pressure field, this theory has previously been unable to be directly used to obtain independent acoustic pressure fields.

[0007] In summary, existing technologies lack an automated decomposition scheme that can directly act on the pressure field, does not rely on empirical thresholds, and is physically self-consistent. Summary of the Invention

[0008] This invention provides a method and its application that couples momentum potential theory and pressure wave decomposition. It addresses the problems of existing pressure decomposition techniques, such as the failure of criteria when the convection velocity is close to the sound velocity (filtering method), or the inability to automatically and objectively separate pure acoustic pressure components in complex compressible turbulence due to the mixture of modal physical meanings and reliance on subjective screening (data-driven method).

[0009] The core technology of this invention is to propose a method for coupling momentum potential theory and pressure fluctuation decomposition. The core of this method is to use momentum potential theory (MPT) to perform Helmholtz decomposition on momentum density, and use the fluid dynamics, acoustic and entropy momentum density components obtained from the decomposition as independent source terms to construct and solve the corresponding Poisson equation, thereby achieving automatic and accurate decoupling of the pressure fluctuation field in terms of physical mechanism.

[0010] In a first aspect, the present invention provides a method for coupling momentum potential theory and pressure fluctuation decomposition, the method comprising the following steps:

[0011] Acquire transient flow field data of the target flow field, which includes at least density, velocity vector and pressure;

[0012] Based on momentum potential theory, the momentum density vector field of the target flow field is decomposed into modes to separate the momentum density of the fluid dynamic component, the momentum density of the acoustic component, and the momentum density of the entropy component.

[0013] Based on the divergence form of the flow field momentum equation, pressure Poisson equations that independently describe fluid dynamic pressure fluctuations, acoustic pressure fluctuations, and entropy pressure fluctuations are constructed respectively; where the source terms of the pressure Poisson equations are respectively constructed from the momentum densities of the fluid dynamic components, acoustic components, and entropy components.

[0014] Solving the pressure Poisson equation yields the fluid dynamic pressure fluctuation field, the acoustic pressure fluctuation field, and the entropy pressure fluctuation field, respectively.

[0015] Furthermore, the steps for modal decomposition of the momentum density vector field based on momentum potential theory specifically include:

[0016] Calculate the momentum density vector field of the target flow field;

[0017] The Reynolds decomposition of the flow field density is performed, and the density Poisson equation is solved using the time derivative of the fluctuating density as the source term to obtain the total fluctuating scalar potential.

[0018] The pressure in the flow field is decomposed by Reynolds decomposition, and the pressure Poisson equation is solved using the time derivative of the fluctuating pressure as a function of the average sound velocity as the source term to obtain the acoustic scalar potential.

[0019] Based on the Helmholtz decomposition theorem, the momentum densities of the hydrodynamic component and the acoustic component are calculated using the total pulsating scalar potential and the acoustic scalar potential.

[0020] Furthermore, the modal decomposition steps also include:

[0021] Based on thermodynamic relations, the entropy scalar potential is calculated by subtracting the acoustic scalar potential from the total pulsating scalar potential.

[0022] The momentum density of the entropy component is calculated using the gradient of the entropy scalar potential.

[0023] Furthermore, the momentum density of each component is determined according to the following formula:

[0024] The momentum density of the hydrodynamic components includes the average quantity and pulsation quantity Among them, pulsation quantity Subtract the average quantity from the momentum density vector field The acoustic momentum density and entropy momentum density are then obtained;

[0025] The momentum density of the acoustic component is the negative gradient of the acoustic scalar potential. ;

[0026] The momentum density of the entropy component is the negative gradient of the entropy scalar potential. .

[0027] Furthermore, the source terms of the pressure Poisson equation are composed of first-order linear source terms generated by the corresponding momentum density component, or higher-order nonlinear terms generated by the self-interaction of the corresponding momentum density component and its coupling with other components.

[0028] When constructing the pressure Poisson equation, the first-order linear source term is retained as the dominant mechanism.

[0029] Furthermore, the construction of the pressure Poisson equation satisfies the following relationship:

[0030] Source term function of pressure fluctuation in fluid dynamics At least with the momentum density of the hydrodynamic component Related;

[0031] Source term function of acoustic pressure fluctuation At least with the momentum density of the acoustic component and the time derivative of the acoustic scalar potential Related;

[0032] Source term function of entropy pressure fluctuation At least the momentum density of the entropy component and the time derivative of the entropy scalar potential Related.

[0033] Furthermore, the specific form of the pressure Poisson equation is:

[0034] Poisson's equation for pressure in fluid dynamics: ;

[0035] Acoustic pressure Poisson equation: ;

[0036] Entropy-pressure Poisson equation: ;

[0037] in, , , These are the fluid dynamic pressure fluctuations, acoustic pressure fluctuations, and entropy pressure fluctuations to be solved, respectively. , , It is a higher-order nonlinear term; Let be the flow field density.

[0038] Furthermore, the steps to solve the pressure Poisson equation include:

[0039] Set boundary conditions based on the physical properties of the flow field boundary;

[0040] For solid wall boundaries, acoustic pressure fluctuations and entropy pressure fluctuations are set to zero;

[0041] For the far-field boundary, the normal gradients of the hydrodynamic pressure fluctuations, acoustic pressure fluctuations, and entropy pressure fluctuations are set to zero to simulate the non-reflective outflow condition.

[0042] Furthermore, it also includes:

[0043] The acoustic pressure fluctuation field is output, and the spatial distribution, wave packet structure, or spectral characteristics of the noise source are analyzed based on the acoustic pressure fluctuation field to identify the dominant noise generation mechanism.

[0044] In a second aspect, the present invention provides a readable storage medium storing a computer program, the computer program including program code for controlling a process to execute the process, the process including the method according to the above.

[0045] The main contributions and innovations of this invention are as follows:

[0046] 1. Physical Consistency and Rigor: This invention establishes for the first time a rigorous mathematical bridge between momentum modes and pressure modes. Unlike the "mathematical statistical approximation" of data-driven methods, this method is based on the first principles of the Navier-Stokes equations, ensuring that the decomposed hydrodynamic pressure (pseudo-sound) and acoustic pressure (true sound) are highly self-consistent and pure in terms of physical mechanism.

[0047] 2. Fully Automated and Objective: This method eliminates the subjective reliance on filter thresholds, reference velocities, or modal selection found in traditional techniques. The entire process only requires input of basic flow field data, and automatically outputs decomposition results by solving the Poisson equations, eliminating errors introduced by human factors and ensuring the objectivity and repeatability of the analysis results.

[0048] 3. Wide applicability and robustness: This method overcomes the limitation of phase velocity-based filtering methods failing in high-speed shear flows (high subsonic or supersonic). Regardless of the variation in the convective velocity of the flow field, this method can achieve effective separation through the physical differences of the source terms, making it particularly suitable for the analysis of highly nonlinear and unsteady compressible turbulence.

[0049] 4. Improved accuracy of noise source localization: Since the separated acoustic pressure field eliminates high-intensity hydrodynamic pseudo-sound interference, the sound source characteristic analysis (such as wave packet structure and spectral characteristics) and source localization based on this result will be more accurate, providing a more reliable theoretical basis and data support for low-noise aerodynamic design.

[0050] Details of one or more embodiments of the present invention are set forth in the following drawings and description, so that other features, objects and advantages of the invention will be more readily understood. Attached Figure Description

[0051] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0052] Figure 1 This is a flowchart of a method for decomposing coupled momentum potential theory and pressure fluctuation according to an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram illustrating the principle differences between the present invention and existing typical methods. Detailed Implementation

[0054] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0055] It should be noted that the steps of the corresponding methods are not necessarily performed in the order shown and described in this specification in other embodiments. In some other embodiments, the methods may include more or fewer steps than described in this specification. Furthermore, a single step described in this specification may be broken down into multiple steps in other embodiments; and multiple steps described in this specification may be combined into a single step in other embodiments.

[0056] Example 1

[0057] This invention proposes a noise source decomposition method that couples Momentum Potential Theory (MPT) and pressure fluctuation decomposition. The core idea of ​​this method is as follows: First, based on momentum potential theory, a physical mode decomposition (Helmholtz decomposition) is performed on the momentum density vector field of the flow field to obtain the momentum densities of the fluid dynamics (vortex), acoustic, and entropy components. Then, these momentum density components are used as source terms to construct and solve the corresponding independent Poisson equations, thereby decomposing the total pressure field into fluid dynamic pressure (pseudo-sound), acoustic pressure (true sound), and entropy pressure.

[0058] like Figure 1 The diagram shown is an overall flowchart of the method of the present invention; as follows: Figure 2 The diagram shows a comparison between the technical approach of this invention and existing technical approaches. The specific steps of this embodiment are as follows:

[0059] Step S1: Acquire target flow field data

[0060] First, transient spatiotemporal evolution data of the target flow field needs to be obtained through computational fluid dynamics (CFD) numerical simulations (such as Large Eddy Simulation (LES) or Direct Numerical Simulation (DNS)) or high-precision flow field experimental measurements (such as PIV combined with pressure measurements). This data should contain time series of at least the following physical quantities:

[0061] density ;

[0062] velocity vector ;

[0063] pressure ;

[0064] Furthermore, according to the equation of state (for ideal gases) The local sound velocity of the flow field can be further calculated. These data form the basis for subsequent physical decomposition.

[0065] Step S2: Perform momentum density mode decomposition based on momentum potential theory

[0066] This step aims to separate the flow field disturbance into physically meaningful components at the momentum level. The specific operation is as follows:

[0067] 1. Calculate the momentum density field: using the density obtained in step S1 and velocity vector Calculate the momentum density vector field .

[0068] 2. Reynolds decomposition: for density ρ ( x , t Reynolds decomposition was performed to obtain the average density. ( x ) and pulsation density ( x , t ); for pressure field p ( x , t Reynolds classification was performed to obtain the mean pressure. ( x ) and pulsating pressure ( x , t ).

[0069] 3. Solve for the total pulsating scalar potential Construct a Poisson equation for the total pulsating scalar potential, whose source term is derived from the pulsation density. time derivative ρ ' / t constitute:

[0070]

[0071] in, The time derivative is calculated using the central difference method. The equation is solved on the computational grid using the finite volume method, yielding the following results. The time series of the field. That is, using numerical methods (such as the finite difference method or spectral method) to solve the equation, and obtaining the total fluctuating scalar potential. .

[0072] 4. Solving for the acoustic scalar potential For the pressure field, the time-averaged sound velocity of the flow field is first obtained. (This value is usually determined by the mean temperature field and is a constant over time, or can be approximated as a global constant in low Mach number flows).

[0073] Subsequently, a Poisson equation for the acoustic scalar potential is constructed. Its source term is derived from pulsating pressure. time derivative p ' / t With average speed of sound The reciprocals of the squares together constitute:

[0074]

[0075] in This represents the average speed of sound.

[0076] Solving the equation, we get The time series of the field yields the acoustic scalar potential. .

[0077] 5. Obtain the entropy scalar potential According to thermodynamic relations, the total pulsating scalar potential Acoustic scalar potential and entropy scalar potential The sum. Therefore, the entropy scalar potential can be obtained by linear subtraction:

[0078]

[0079] Among them, the total pulsating scalar potential It can be linearly decomposed into the acoustic scalar potential corresponding to the isentropic process. and the entropy scalar potential of the corresponding isobaric process ,Right now .

[0080] 6. Calculate the momentum density of each component: based on the Helmholtz decomposition theorem. The momentum density vector field is decomposed into a rotational irrational component (vortex component) and a divergent irrational component (potential component). Here, B is the irrational component (…). ).

[0081] Acoustic momentum density: defined as the negative gradient of the acoustic scalar potential, i.e. .

[0082] Entropy momentum density: defined as the negative gradient of the entropy scalar potential, i.e. .

[0083] Hydrodynamic (vortex) momentum density: obtained by subtracting the two potential components mentioned above from the total momentum density. From this, the vortex component (without divergence) can be calculated, i.e., the hydrodynamic (vortex) momentum density. .in For the average part, It represents the pulsating fluid dynamics (vortex) momentum density.

[0084] Through the above steps, a precise physical decomposition of the momentum density field was achieved:

[0085]

[0086] in, The average part of the hydrodynamic (vortex) momentum density. The momentum density is the vortex momentum density in pulsating fluid dynamics. Acoustic momentum density, It is the entropy momentum density.

[0087] Step S3: Construct independent Poisson equations for each pressure component.

[0088] This is a key step in the invention. Based on the fluid momentum equation, its divergence can be used to derive the Poisson equation for pressure. The fluid dynamics (eddy) momentum density obtained in step S2 through MPT physical decomposition... Acoustic momentum density Entropy and Momentum Density This directly and naturally becomes the fluid dynamics (vortex) pressure component constructed in step S3. Acoustic pressure component and entropy pressure components The physical source term corresponding to the Poisson equation is used to realize the mechanism connection from "momentum mode" to "pressure mode".

[0089] When constructing the equations, the source term S typically contains both linear and nonlinear terms. In this preferred embodiment, to highlight the dominant physical mechanism and simplify calculations, linear source terms are primarily considered, while higher-order nonlinear mixed terms are ignored.

[0090] 1. Poisson equation for pressure (pseudo-sound) in fluid dynamics: Constructing a description of pressure fluctuations in fluid dynamics The equation:

[0091]

[0092] in, The fluid dynamics (vortex) pressure component to be solved; S H It is determined by the momentum density of the hydrodynamic (vortex) The resulting first-order linear dominant source term is the average hydrodynamic (eddy) momentum density. Pulsating fluid dynamics (vortex) momentum density and density The function formed together; S NLH Due to the momentum density of the hydrodynamic (vortex) Self-interaction or with other pulsating quantities (acoustic momentum density) and entropy momentum density The higher-order nonlinear terms generated by nonlinear coupling.

[0093] 2. Acoustic Pressure (True Sound) Poisson Equation: Constructing a description of acoustic pressure fluctuations The equation:

[0094]

[0095] in, The acoustic pressure component to be solved; S A It is determined by acoustic momentum density The resulting first-order linear dominant source term is the average hydrodynamic (eddy) momentum density. Acoustic momentum density The time derivative of acoustic scalar potential and and density The function formed together; S NLA Due to acoustic momentum density Self-interaction or other pulsating quantities (hydrodynamic (vortex) momentum density) and entropy momentum density The higher-order nonlinear terms generated by nonlinear coupling.

[0096] 3. Entropy-Pressure Poisson Equation: Constructing a description of entropy-pressure fluctuations The equation:

[0097]

[0098] in, The entropy pressure component to be solved; S T It is determined by entropy and momentum density The resulting first-order linear dominant source term is the average hydrodynamic (eddy) momentum density. Entropy and Momentum Density The time derivative of entropy scalar potential energy and density The function formed together; S NLT Due to entropy momentum density Self-interaction or with other pulsating quantities (acoustic momentum density) and fluid dynamics (vortex) momentum density The higher-order nonlinear terms generated by nonlinear coupling.

[0099] In this embodiment, the principle behind constructing the Poisson equation for the pressure component in this step is as follows:

[0100] Starting from the momentum equation:

[0101]

[0102] in, It is the momentum density vector. To control the circulation volume, Represents the viscous stress tensor. This represents the net change in momentum flux. p For pressure.

[0103] The decomposition obtained in step S2 Substituting into the above momentum equation, we can obtain:

[0104]

[0105] Taking the divergence of the momentum equation, we can derive the relationship between the total pressure and the equation. Poisson's equation:

[0106]

[0107] Among them, total source item It includes various contributions from momentum density corresponding to fluid dynamics (vortices), acoustics, and entropy modes. Based on the physical independence of momentum density decomposition, the total source term... It can be further separated into momentum densities corresponding to hydrodynamic (vortex) forces. Acoustic momentum density and entropy momentum density Contributions:

[0108]

[0109] in, S H , S A and S T These are related to the momentum density of fluid dynamics (vortex). Acoustic momentum density and entropy momentum density The directly related first-order linear dominant source term is the core mechanism for generating the corresponding pressure components; S NLH , S NLA and S NLT These are the self-interactions of the momentum densities of the aforementioned modes, or higher-order nonlinear terms generated by nonlinear coupling with other pulsating quantities; S Hmean It is a time-independent source term, representing the average pressure field contribution determined by the average flow field; S V Represents the viscous stress tensor The source term that caused it.

[0110] In general high Reynolds number free shear flow, viscous stress source term S V Generally, this can be ignored, and since the mean field of the irrotational components (sound, entropy) is more than two orders of magnitude smaller than their respective pulsations, it can also be ignored in specific decompositions. Therefore, the above equation can be simplified to a function relating pressure pulsations. The expression:

[0111]

[0112] At this point, the total pressure pulsation... It can be considered as a linear superposition of three physically independent pressure components:

[0113]

[0114] in, For fluid dynamics (vortex) pressure components (i.e., "pseudo-sound") The acoustic pressure component (i.e., "true sound") This is the entropy pressure component.

[0115] Step S4: Solve the pressure Poisson equations.

[0116] The three equations constructed in step S3 are solved numerically. To ensure the uniqueness and physical correctness of the solutions, appropriate boundary conditions must be set:

[0117] 1. Solid wall boundary (Dirichlet boundary condition):

[0118] Acoustic pressure Entropy pressure On rigid walls, the non-penetration condition is usually met, and the gradient can be approximated as zero or zero (depending on the acoustic impedance of the wall). In this embodiment, for rigid walls, let... and This means directly specifying the value of the pressure component on the boundary.

[0119] For fluid dynamic pressure Its boundary conditions are obtained by subtracting the acoustic and entropy parts from the total pressure boundary conditions.

[0120] 2. Far-field boundary (Neumann boundary condition):

[0121] To simulate sound wave radiation to infinity and avoid boundary reflections contaminating the computational domain, a non-reflective boundary condition is required. In this embodiment, acoustic pressure... Entropy pressure The Neumann boundary condition is used, which specifies that the normal gradient is zero. , , To simulate outflow with no reflection or full development.

[0122] After setting the boundary conditions, the equations for S3 can be solved using either the Algebraic Multigrid (AMG) solver or the Fast Fourier Transform (FFT) solver to obtain the hydrodynamic pressure field. Acoustic pressure field Entropy pressure field The time series.

[0123] Step S5: Noise Source Characteristic Analysis

[0124] Based on the "pure" acoustic pressure fluctuation field obtained from step S4. To conduct in-depth analysis of the noise sources. Because By eliminating high-intensity fluid dynamics "pseudo-sound" interference, the analysis results can more accurately reflect the physical nature of noise radiation.

[0125] The specific analysis includes:

[0126] 1. Source characteristic analysis: for Spatiotemporal analysis is performed, including its spatial distribution, wave packet structure, spectral characteristics (through FFT or wavelet transform), and modal energy distribution (through intrinsic orthogonal decomposition POD or spectral intrinsic orthogonal decomposition SPOD), to identify the frequency bands, modes, and evolution of the dominant noise.

[0127] 2. Source region localization: through analysis The spatial intensity (such as root mean square distribution) or its cross-correlation / coherence function with the sound pressure signal at a fixed point in the far field can be calculated to determine the physical spatial region that contributes the most to the far field noise, thereby achieving accurate spatial positioning of the noise source.

[0128] 3. Modeling and Prediction: The extracted pure sound source field As input, simplified noise prediction models can be built by combining acoustic analogies (such as Lighthill or Powell vortex acoustics theory) or computational acoustic methods to quickly assess the impact of changes in flow parameters on noise or guide low-noise design.

[0129] Example Effect Description

[0130] Compared with existing technologies (such as) Figure 2As shown, this invention does not rely on the difference between "convection velocity" and "sound velocity" for filtering (existing technical route A), nor does it rely on manual selection of data-driven modes (existing technical route B). This invention automatically decomposes the pressure field through rigorous derivation of physical equations. In a high-subsonic jet test at Mach number 0.9, this invention successfully separated the sound source structure hidden within the strongly turbulent region, and the decomposed acoustic pressure field showed a high degree of agreement with the results measured by the microphone array in the far field, verifying the accuracy and physical consistency of the method.

[0131] Example 2

[0132] This embodiment also provides a readable storage medium storing a computer program, the computer program including program code for controlling a process to execute the process, the process including the noise source decomposition method according to Embodiment 1.

[0133] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0134] Generally, various embodiments can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects of the invention can be implemented in hardware, while others can be implemented by firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, by way of non-limiting example, these blocks, apparatuses, systems, techniques, or methods described herein can be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0135] Embodiments of the present invention can be implemented by computer software, which may be executable by a data processor of a mobile device, such as a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets, and / or macros can be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. The computer program product may include one or more computer-executable components configured to perform the embodiments when the program is run. The one or more computer-executable components may be at least one piece of software code or a portion thereof. Additionally, it should be noted in this respect that, as Figure 1Any box in the logical flow can represent a program step, or interconnected logic circuits, boxes and functions, or a combination of program steps and logic circuits, boxes and functions. Software can be stored on physical media such as memory chips or blocks of storage implemented within a processor, magnetic media such as hard disks or floppy disks, and optical media such as DVDs and their data variants, CDs, etc. The physical medium is a non-transient medium.

[0136] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0137] The above embodiments are merely illustrative of several implementations of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims

1. A method of coupling a momentum potential theory and a pressure fluctuation decomposition, characterized by, Includes the following steps: Acquire transient flow field data of the target flow field, which includes at least density, velocity vector and pressure; Based on momentum potential theory, the momentum density vector field of the target flow field is modally decomposed to separate the momentum density of the fluid dynamic component, the momentum density of the acoustic component, and the momentum density of the entropy component. Based on the divergence form of the flow momentum equation, pressure Poisson equations independently describing fluid dynamic pressure fluctuations, acoustic pressure fluctuations, and entropy pressure fluctuations are constructed. The source terms of these pressure Poisson equations are constructed from the momentum densities of the fluid dynamic components, acoustic components, and entropy components, respectively. The source terms are either first-order linear source terms generated by the corresponding momentum density components, or higher-order nonlinear terms generated by the self-interaction of the corresponding momentum density components and their coupling with other components. When constructing the pressure Poisson equations, the first-order linear source terms are retained as the dominant mechanism. The specific form of the pressure Poisson equations is as follows: Poisson's equation for pressure in fluid dynamics: ; Acoustic pressure Poisson equation: ; Entropy-pressure Poisson equation: ; in, , , These are the fluid dynamic pressure fluctuations, acoustic pressure fluctuations, and entropy pressure fluctuations to be solved, respectively. This is the source term function for pressure fluctuations in fluid dynamics; This is the source term function for acoustic pressure fluctuations; It is the source term function of entropy pressure fluctuation; The flow field density; It is the average momentum density of the fluid dynamic components; The momentum density pulsation is a component of the fluid dynamics. Acoustic momentum density; It is the entropy-momentum density; The time derivative of the acoustic scalar potential; The time derivative of the entropy scalar potential energy; S NLH Due to momentum density pulsation Self-interaction or acoustic momentum density and entropy momentum density Higher-order nonlinear terms generated by nonlinear coupling; Due to acoustic momentum density Self-interaction or momentum density pulsation and entropy momentum density Higher-order nonlinear terms generated by nonlinear coupling; Due to entropy momentum density Self-interaction or acoustic momentum density and momentum density pulsation Higher-order nonlinear terms generated by nonlinear coupling; Solving the pressure Poisson equation yields the fluid dynamic pressure fluctuation field, the acoustic pressure fluctuation field, and the entropy pressure fluctuation field, respectively.

2. The method as described in claim 1, characterized in that, The steps for modal decomposition of the momentum density vector field based on momentum potential theory specifically include: Calculate the momentum density vector field of the target flow field; The Reynolds decomposition of the flow field density is performed, and the density Poisson equation is solved using the time derivative of the fluctuating density as the source term to obtain the total fluctuating scalar potential. The pressure in the flow field is decomposed by Reynolds decomposition, and the pressure Poisson equation is solved using the time derivative of the fluctuating pressure as a function of the average sound velocity as the source term to obtain the acoustic scalar potential. Based on the Helmholtz decomposition theorem, the momentum density of the hydrodynamic component and the momentum density of the acoustic component are calculated using the total pulsating scalar potential and the acoustic scalar potential.

3. The method as described in claim 2, characterized in that, The steps of mode decomposition also include: Based on thermodynamic relations, the entropy scalar potential is calculated by subtracting the acoustic scalar potential from the total pulsating scalar potential. The momentum density of the entropy component is calculated using the gradient of the entropy scalar potential.

4. The method as described in claim 3, characterized in that, The momentum density of each component is determined according to the following formula: The momentum density of the fluid dynamic component includes the average momentum density. and momentum density pulsation Among them, momentum density pulsation quantity Subtract the average momentum density from the momentum density vector field Acoustic momentum density and entropy momentum density It was obtained later.

5. The method as described in claim 1, characterized in that, The steps to solve the pressure Poisson equation include: Set boundary conditions based on the physical properties of the flow field boundary; For solid wall boundaries, acoustic pressure fluctuations and entropy pressure fluctuations are set to zero; For the far-field boundary, the normal gradients of the hydrodynamic pressure fluctuations, acoustic pressure fluctuations, and entropy pressure fluctuations are set to zero to simulate the non-reflective outflow condition.

6. The method as described in claim 1, characterized in that, Also includes: The acoustic pressure fluctuation field is output, and the spatial distribution, wave packet structure, or spectral characteristics of the noise source are analyzed based on the acoustic pressure fluctuation field to identify the dominant noise generation mechanism.

7. A readable storage medium, characterized in that, The readable storage medium stores a computer program, the computer program including program code for controlling a process to execute the process, the process including the method according to any one of claims 1 to 6.

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