Method, device and computer readable storage medium for controlling jet far-field noise

Abstract

The application discloses a jet far-field noise control method and device and a computer readable storage medium. The control method comprises the following steps: obtaining all sample points covering the whole design space by using a test design method; performing far-field sound pressure level calculation according to a physical simplified companion Green function to obtain jet far-field noise target functions of all sample points; establishing a correlation model of the jet far-field noise and the inner and outer curve wall surface shape according to the jet far-field noise target functions of all sample points; and searching for an optimal value of the jet far-field noise on the established correlation model with the jet pipe aerodynamic performance as a constraint to control the jet far-field noise. The application can control the jet far-field noise size by modifying the inner and outer curve wall surface without affecting the jet pipe aerodynamic performance, and automatically designs an optimal inner and outer curve configuration without interference, so that the jet far-field noise is minimized, thereby significantly reducing the jet far-field noise evaluation and noise reduction measure design time.

Description

Technical Field

[0001] This invention relates to the field of noise reduction and control technology, and in particular to a method, device, and computer-readable storage medium for controlling far-field noise of jet streams. Background Technology

[0002] In the early 1950s, Lighthill, using an analogy with classical acoustics, introduced the concept of "pseudo-sound sources," laying the theoretical foundation for aeroacoustics. Simultaneously, he developed the basic theory of jet noise, establishing physical and mathematical models of it, pointing out that this noise, caused by turbulence, possesses quadrupole sound source characteristics and is proportional to the eighth power of the velocity. Lighthill's theory played a significant role in guiding the reduction of jet engine noise. In the 1960s, theoretical prediction methods for jet noise became increasingly in-depth and mature. Jet noise is caused by the rapid mixing of high-speed airflow with the surrounding relatively stationary medium, resulting in strongly pulsating turbulence. Its physical mechanism is highly complex, involving many aspects such as turbulence and stability, and has always been a hot topic and a challenge in aeroacoustic noise research. Reducing jet noise has significant practical implications in many areas, and research on its suppression methods has become a crucial topic in contemporary acoustics research.

[0003] Noise issues encompass the noise generation area, near-field sound propagation, and far-field radiation. Generally, methods for predicting jet noise can be categorized into engineering empirical prediction methods, semi-empirical models, and numerical simulation methods.

[0004] Numerical Simulation Methods for Jet Noise: In the initial acoustic analogy theory, Lighthill likened jet noise sources to quadrupole sources. However, simply representing the sound source using acoustic analogy limits its application to specific ideal conditions. Lilley proposed transferring the representation of acoustic-flow interaction from the source term to the propagation operator, thus improving the limitations of Lighthill's theory in acoustic-flow interactions such as refraction effects. The Lilley equation, as the governing equation for noise prediction, requires solving the source model and the sound propagation model separately.

[0005] Currently, the most popular jet noise prediction models in engineering are semi-empirical models based on RANS (Reynolds-averaged equations). The main calculation idea is to establish a relationship between the sound field and the flow field, and then use the RANS turbulence model to calculate the relevant parameters of the flow field to obtain the properties of the radiated sound field.

[0006] Existing methods for reducing jet noise include increasing airflow mixing between the inner and outer bypass pipes, changing the direction of jet noise propagation, and reducing the radiation of jet noise to the ground. However, these methods can affect the aerodynamic performance of the nozzle and make it susceptible to various interferences, thus failing to effectively meet the requirements for reducing jet noise. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to overcome the defects of existing technologies in which reducing jet noise affects the aerodynamic performance of the nozzle and cannot effectively meet the higher requirements for reducing jet noise. The present invention provides a method, device and computer-readable storage medium for controlling far-field noise of jet.

[0008] The present invention solves the above-mentioned technical problems through the following technical solution:

[0009] As one embodiment, a method for controlling far-field noise of a jet is provided, comprising:

[0010] Experimental design was used to obtain all sample points covering the entire design space;

[0011] The far-field sound pressure level is calculated by performing a physical simplification of the accompanying Green's function to obtain the objective function of the jet far-field noise for all sample points;

[0012] Based on the objective function of the jet far-field noise at all sample points, a correlation model between the inner and outer curve wall shape and the jet far-field noise is established.

[0013] Using the nozzle aerodynamic performance as a constraint, the optimal value of the far-field noise of the jet is searched on the established correlation model in order to control the far-field noise of the jet.

[0014] Optionally, before the step of obtaining all sample points covering the entire design space using experimental design, the control method further includes:

[0015] Set upper and lower limits for design variables that control changes in shape;

[0016] The objective function for jet far-field noise is defined based on the far-field sound pressure level spectrum.

[0017] Optionally, the step of performing far-field sound pressure level calculation based on the physically simplified accompanying Green's function includes:

[0018] The far-field sound pressure level is calculated by expanding and deriving the second-order homogeneous ordinary differential equation of the associated Green's function by setting a maximum of nine cylindrical wavenumbers m.

[0019] Optionally, in the step of obtaining the objective function of the jet far-field noise for all sample points,

[0020] The objective function for the far-field noise of the jet was obtained by using a high-order acoustic spatial scheme and a time-progression scheme.

[0021] Optionally, the step of establishing a correlation model between the inner and outer bypass curve wall shapes and the jet far-field noise based on the objective function of the jet far-field noise at all sample points includes:

[0022] By associating the variables of all sample points with the objective function value of the jet far-field noise, a correlation model is established between the inner and outer curve wall shape and the jet far-field noise.

[0023] Optionally, after finding the optimal value of the far-field noise of the jet, the control method further includes:

[0024] The optimized sample points are added to the existing sample points and repeated calculations and searches are performed to achieve the convergence criterion.

[0025] Optionally, the step of obtaining all sample points covering the entire design space using an experimental design approach includes:

[0026] An optimized Latin square experimental design was used to obtain all sample points covering the entire design space.

[0027] Optionally, the step of searching for the optimal value of the far-field noise of the jet on the established correlation model includes:

[0028] The optimal value of the far-field noise of the jet is searched on the established correlation model using a multi-island genetic algorithm.

[0029] As another embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the jet far-field noise control method as described above.

[0030] As another embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the jet far-field noise control method described above.

[0031] Based on common knowledge in the field, the preferred conditions described can be combined arbitrarily to obtain various preferred embodiments of the present invention.

[0032] The positive and progressive effects of this invention are as follows:

[0033] The present invention provides a method, device, and computer-readable storage medium for controlling far-field noise of a jet stream. By controlling the shape of the inner and outer bypass curve walls, the far-field noise of the jet stream can be effectively reduced. Without affecting the aerodynamic performance of the nozzle, the magnitude of the far-field noise of the jet stream can be controlled by modifying the inner and outer bypass curve walls. The optimal inner and outer bypass curve configurations are automatically designed without interference to minimize the far-field noise of the jet stream, thereby significantly reducing the time required for jet stream far-field noise assessment and noise reduction measure design. Attached Figure Description

[0034] The features and advantages of the present invention will be better understood after reading the following detailed description of embodiments of the present disclosure in conjunction with the accompanying drawings. In the drawings, components are not necessarily drawn to scale, and components having similar related properties or features may have the same or similar reference numerals.

[0035] Figure 1 This is a flowchart illustrating a preferred embodiment of the jet far-field noise control method of the present invention.

[0036] Figure 2 This is a flowchart illustrating the efficient calculation method for far-field noise of jets according to a preferred embodiment of the present invention.

[0037] Figure 3 A schematic diagram illustrating the effect of different cylindrical wave numbers on the far-field noise of the jet.

[0038] Figure 4 This is a schematic diagram illustrating the calculation time corresponding to different numbers of cylindrical wave numbers.

[0039] Figure 5 A comparative schematic diagram showing the shape of the inner and outer duct surfaces established using the shape function transformation method.

[0040] Figure 6 This is a schematic diagram of an electronic device for implementing a method for controlling far-field noise of a jet according to another preferred embodiment of the present invention. Detailed Implementation

[0041] The present invention will be further illustrated by way of embodiments below, but the present invention is not limited to the scope of the embodiments described herein.

[0042] Tam and Auriault proposed a method for predicting turbulent mixing noise that is entirely different from acoustic analogies. They applied the adjoint Green's function to solve the sound propagation model, enabling the prediction of turbulent mixing noise perpendicular to the jet axis. The Tam and Auriault computational model (hereinafter referred to as the TA model) extracts turbulence parameters from the RANS flow field, calculates the sound sources using the model, and uses the adjoint Green's function to calculate far-field sound radiation. One advantage of the adjoint Green's function is that it only requires a single calculation to represent the sound field generated by all sound sources radiating sound waves in a given direction, significantly reducing computational complexity. Another advantage is that the adjoint Green's function has no singularities within the jet, allowing for numerical solutions using axial and radial average flow gradients. This model is not frequency-limited, achieves good accuracy over a wide frequency band, and is computationally fast.

[0043] This embodiment develops an engineering simplification method based on the traditional TA model, which balances accuracy and computational efficiency. It proposes a physically simplified accompanying Green's function, which reduces the evaluation time of a single jet noise example.

[0044] To overcome existing deficiencies, this embodiment provides a method for controlling far-field noise of a jet stream, comprising: obtaining all sample points covering the entire design space using an experimental design approach; performing far-field sound pressure level calculations based on the physically simplified accompanying Green's function to obtain the objective function for far-field noise of the jet stream at all sample points; establishing a correlation model between the inner and outer bypass curve wall shapes and the far-field noise of the jet stream based on the objective function for far-field noise of the jet stream at all sample points; and searching for the optimal value of the far-field noise of the jet stream on the established correlation model, with the aerodynamic performance of the nozzle as a constraint, in order to control the far-field noise of the jet stream.

[0045] Jet noise is one of the main sound sources of aero engines, especially during takeoff, where it constitutes a major portion of engine noise. Far-field noise refers to the noise that propagates to a distant observation point. Based on the physical processes of noise, aerodynamic noise can be divided into three parts: the sound source, near-field sound propagation, and far-field noise. Noise reduction measures are methods to reduce noise, typically including active and passive control methods. Passive methods are often used because they have less impact on the existing system.

[0046] In this embodiment, the evaluation time of a single jet noise case is effectively reduced by the jet far-field noise control method. A correlation model between the inner and outer bypass curve wall shapes and the jet far-field noise is established. The magnitude of the jet far-field noise can be controlled by modifying the inner and outer bypass curve walls. Moreover, the optimal inner and outer bypass curve configurations can be automatically designed without affecting the aerodynamic performance of the nozzle, so as to minimize the jet far-field noise.

[0047] As a preferred embodiment, such as Figure 2 As shown, this embodiment also provides an efficient method for calculating far-field noise of a jet, which mainly includes the following steps:

[0048] Step 201: Construct the linear Euler equations in cylindrical coordinates;

[0049] Step 202: Derive the governing equations containing the adjoint Green's function;

[0050] Step 203: Expand the adjoint Green's function into a trigonometric series with a maximum of 9 cylindrical wave numbers m to obtain the physically simplified adjoint Green's function;

[0051] Step 204: Through detailed mathematical derivation, the physical simplified second-order homogeneous ordinary differential equation with Green's function is obtained;

[0052] Step 205: Perform calculations using a high-order acoustic spatial discretization and time-progression scheme;

[0053] Step 206: Obtain the solution of the physically simplified accompanying Green's function;

[0054] Step 207: Model the jet noise source term using a two-point correlation function;

[0055] Step 208: Perform spatial volume integration to obtain the far-field noise spectrum of the jet;

[0056] Step 209: Calculate the far-field sound pressure level at various angles;

[0057] Step 210: Obtain the far-field noise solution of the jet stream that balances efficiency and accuracy.

[0058] Specifically, the following explains the process of simplifying the Green's function and solving the governing equations with high precision.

[0059] 1) Accompanying Green's function simplification process

[0060] Within the jet stream region, the assumption of local parallel flow is as follows: Therefore, the governing equations of the Green's function are simplified to the following equations 1-4:

[0061]

[0062]

[0063]

[0064]

[0065] The adjoint Green's function that satisfies the adjoint equation is:

[0066]

[0067] Where x, y, z represent three spatial coordinates, x0 is the far-field observation point, and the superscript (a) indicates the associated Green's function. Let be the average velocity in the three coordinate directions of the flow field. and Let represent the mean pressure and mean density, respectively. u, v, w, and p are the three velocity and pressure components associated with the Green's function. i represents a complex number, γ represents the specific heat rate constant, and δ represents the Dirac function. a This represents the accompanying Green's function, where π is the constant of pi.

[0068] The accompanying Green's function can be expanded as a Fourier cosine sequence of φ:

[0069]

[0070] Substituting equation (6) into equation (5) and expressing it in cylindrical coordinates, a second-order homogeneous ordinary differential equation can be derived through complex mathematical derivation:

[0071]

[0072] The accompanying Green's function f a Expanding the trigonometric series yields the following equation:

[0073]

[0074] In Equation 6-8, a spherical coordinate system is established with the jet axis (x-axis) as the polar axis. R represents the radial distance of the observation point, θ represents the elevation angle of the observation point, Θ represents the polar angle, and φ represents the azimuth angle obtained from the (x,y) plane. Without loss of generality, let... The spherical coordinates are (R, Θ, φ = 0), m is the cylindrical wave number, y is represented by r and φ, i.e., y = rcosφ, ω represents the complex space, and a ∞ This represents the ambient sound speed value.

[0075] The accompanying Green's function f a A trigonometric series expansion was performed based on the azimuth angle φ. To efficiently solve the governing equations, the influence of the number of cylindrical wavenumbers *m* on the far-field noise of the jet was investigated. The far-field noise was calculated using 1, 9, 45, and 95 cylindrical wavenumbers, respectively. The calculated far-field noise results are as follows: Figure 3 As shown ( Figure 3 The horizontal axis represents frequency, and the vertical axis represents sound pressure level (SPL). `max(m)` refers to the maximum number of values ​​for `m`. The computation time is as follows: Figure 4 As shown ( Figure 4 The horizontal axis represents max(m), which indicates the maximum number of m, and the vertical axis represents the calculation time (time(s)).

[0076] refer to Figure 3 and Figure 4 It can be seen that when the cylindrical wavenumber m is 1, the calculation accuracy is insufficient and the error is large. When the cylindrical wavenumber m is 9, the calculation result is very close to that when the cylindrical wavenumber m is 45 and 95, indicating that the accuracy requirement is met when the cylindrical wavenumber m is 9. Furthermore, the time required is much shorter than when the cylindrical wavenumber m is 45 and 95. Based on this calculation result, the accompanying Green's function f... a With the maximum number of cylindrical wavenumbers m being 9, a trigonometric series expansion is performed as shown in Equation 9, yielding the physically simplified associated Green's function.

[0077]

[0078] 2) High-precision solution of governing equations

[0079] Compared to aerodynamic problems, for aeroacoustic problems, it is insufficient to consider only stability issues; sound dissipation and dispersion errors must also be taken into account. To solve the governing equations with high accuracy, this embodiment employs high-order acoustic discretization schemes (e.g., the 4th-order Dispersion Relation Preservation (DRP) scheme and compact scheme developed by Tam and Webb) to perform spatial discretization and time advancement to obtain a noisy solution. The time-advancing algorithm can use the 4th-order 4 / 6-level low-dissipation, low-dispersion Runge-Kutta method (LDDRK) proposed by Hu or the leapfrog scheme.

[0080] Specifically, as a preferred embodiment, such as Figure 1 As shown, the jet far-field noise control method provided in this embodiment mainly includes the following steps:

[0081] Step 101: Set the upper and lower limits of the design variables that control the shape changes.

[0082] Specifically, the following describes the modeling method for the surface shape of the inner duct and outer duct.

[0083] The surface modeling of the inner and outer culvert curves is performed using the class function / shape function transformation method proposed by Kulfan et al. The geometric profiles of the inner and outer culverts can be represented as the product of the class function D(x) and the shape function S(x), i.e.:

[0084] y(x)=D(x)·S(x) (Equation 10)

[0085] Where x is the abscissa value, y is the ordinate value, D(x) is a class function, and the trailing edge thickness term is x·Δy. te Equation 10 becomes:

[0086]

[0087] S(x) is the shape function, where the coefficient a i As an optimization design variable, Kulfan et al. used a weighted sum of Bernstein polynomials to construct a shape function, namely:

[0088]

[0089] in It is an nth-order Bernstein polynomial. Therefore, the shape of the inner and outer duct surfaces parameterized using the CST method can be expressed as:

[0090]

[0091] Where a i For design variables, Δy te This represents the trailing edge thickness, where n is the polynomial order.

[0092] The value of the shape function S(x) at x = 0 can be expressed as:

[0093]

[0094] Among them, R le Let be the leading edge radius.

[0095] The value of the shape function S(x) at x = 1 can be expressed as:

[0096] S(1)=tanα+Δy te (Equation 15)

[0097] Where, Δy te Let α be the trailing edge thickness and α be the trailing edge angle.

[0098] After transforming Equation 12, we get:

[0099]

[0100] The shape function distribution of the basic surface can be obtained using equations 14, 15, and 16.

[0101] Assuming there are m coordinate points for the basic shape, then from Equation 12, we obtain the shape function S(x) and the Bernstein polynomial. The relationship is as follows:

[0102]

[0103] Right now:

[0104]

[0105] Suppose that the matrix and vector in the above formula are represented as follows:

[0106]

[0107]

[0108]

[0109] Equation 13 then becomes:

[0110] Aa=S (Formula 18)

[0111] Its canonical equation system is as follows:

[0112] A T Aa=A T S (Equation 19)

[0113] The superscript T indicates the matrix transpose operation.

[0114] The coefficient 'a' can be obtained from the above formula, and the coefficient 'a' is the optimization design variable.

[0115] Figure 5 This is a comparison diagram of the top and bottom surface shapes of the bypass duct, parameterized using the CST method, with the original reference shape. The horizontal and vertical axes represent the x and y coordinates of the top and bottom surface shapes of the bypass duct, respectively. The Bernstein polynomial used is of order 12. Figure 5 The upper part of the curve family represents the design sample of the outer bypass duct's upper surface shape. Figure 5 The lower family of curves represents the design samples of the lower surface profile of the bypass duct. By employing the CST method, a large number of upper and lower surface profile samples of the bypass duct were obtained, providing a rich parametric design space that can represent detailed designs of local details on the upper and lower surfaces, supporting noise reduction design of the bypass duct surface.

[0116] In this step, the design variable is the coefficient of the cubic spline curve, which is a. i To control the design variables for shape changes, the upper and lower limits of the coefficient [-2, 2] are first determined.

[0117] Step 102: Define the objective function for the far-field noise of the jet.

[0118] In this step, the objective function for the far-field noise of the jet is defined as the sum of the sound pressure levels at observation angles of 0-150 degrees with 10-degree intervals.

[0119] We obtain the sound source term and the physically simplified accompanying Green's function f. a After obtaining the numerical solution, the far-field sound pressure level spectrum can be obtained by applying Wiener-Khinchin's theorem according to the following formula.

[0120]

[0121] In Equation 20, Vjet Γ(υ) represents the volume of the jet wake region, and υ value simulates the attenuation rate of the acoustic source correlation function in the thermal jet. τ represents the intensity of small-scale turbulent kinetic energy fluctuations, where c is a constant coefficient, typically less than 1. s It is the time scale of the pulsation. Parameter l s and τ s These are the spatial scale parameter and the temporal scale parameter in the spatiotemporal correlation function of the sound source model, respectively.

[0122] Converting Equation 20 above to the form of decibel-Strouhal number, i.e., sound pressure level:

[0123]

[0124] Where S(x, ω) represents the far-field noise spectrum, p ref D represents the reference sound pressure level. j ,u j These represent the nozzle diameter and the jet velocity, respectively.

[0125] The objective function for far-field noise of the jet is defined as follows:

[0126]

[0127] in, This represents the sound pressure level, and the objective function is the sum of polar angles from 0 to 150 degrees.

[0128] Step 103: Obtain sample point data using an optimized experimental design.

[0129] In this step, as a preferred embodiment, an optimized Latin square experimental design is used to obtain sample points. Compared with the conventional Latin square experimental design, the optimized Latin square experimental design allows for more points and more sample combinations to study the influence of each factor. It requires fewer simulations while maintaining the orthogonality and uniformity of the experimental design. Preferably, the number of sample points is between 60 and 100, but it is not limited to this.

[0130] Step 104: Expand the accompanying Green's function according to the maximum of 9 cylindrical wave numbers m.

[0131] Step 105: Calculate the far-field sound pressure level using the efficient calculation method for jet far-field noise.

[0132] Step 106: Obtain the objective function of the jet far-field noise for all sample points.

[0133] Specifically, the jet far-field noise of all sample points is calculated using the above 9th-order formula. The associated Green's function is expanded according to a maximum of 9 cylindrical wavenumbers m to obtain the physically simplified associated Green's function. The jet far-field noise is then solved using a higher-order acoustic spatial scheme and a time-progression scheme.

[0134] Step 107: Establish a correlation model between the inner and outer curve wall shapes and the far-field noise of the jet.

[0135] In this step, the variables and objective function values ​​of all sample points are correlated to establish a correlation model between the inner and outer curve wall shape and the far-field noise of the jet.

[0136] The far-field noise of the jet was calculated for all sample points, and the design variable 'a' controlling the shape change was obtained. i And the value of the objective function obj. Based on this, a correlation model is established between the inner and outer bypass curve wall shape and the far-field noise of the jet.

[0137] Step 108: Using the nozzle aerodynamic performance as a constraint, search for the optimal value on the associated model.

[0138] In this step, as a preferred embodiment, the nozzle aerodynamic performance is used as a constraint (e.g., thrust loss). To avoid the problem that traditional genetic algorithms are prone to getting stuck in local optimization points, a multi-island genetic algorithm is used to search for the optimal value on the association model.

[0139] Step 109: Determine whether the convergence criterion is met. If yes, proceed to step 111; otherwise, proceed to step 110.

[0140] Step 110: Add the optimized points obtained from the optimization to the existing sample points.

[0141] In this step, the optimized points obtained are added to the original sample point set, and steps 103 to 108 are repeated until the convergence criterion is met (e.g., the optimized values ​​do not change significantly between the two steps).

[0142] Step 111: Control the far-field noise of the jet.

[0143] In this step, the far-field noise of the jet is controlled with an optimized value; the specific control process will not be described in detail here.

[0144] In this embodiment, a 9th-order approximate expression for the physically simplified accompanying Green's function is proposed, and a second-order homogeneous ordinary differential equation expression containing the 9th-order physically simplified accompanying Green's function is derived. This develops an efficient method for calculating far-field noise of jets, effectively reducing the evaluation time for a single far-field noise example of a jet.

[0145] In this embodiment, the 9th-order approximate expansion of the adjoint Green's function is mainly performed to establish an approximate physically simplified adjoint Green's function model. The expression of the second-order homogeneous ordinary differential equation containing the 9th-order physically simplified adjoint Green's function is derived and solved efficiently and with high accuracy. The shape function transformation method is used to establish the surface shape of the inner and outer ducts. The objective function of the far-field noise of the jet is defined. With the aerodynamic performance of the nozzle as a constraint, the optimal value is searched on the correlation model using a multi-island genetic algorithm.

[0146] The jet far-field noise control method provided in this embodiment effectively reduces jet far-field noise. It can control the magnitude of jet far-field noise by modifying the inner and outer bypass curve walls without affecting the aerodynamic performance of the nozzle. It automatically designs the optimal inner and outer bypass curve configurations to minimize jet far-field noise, thereby significantly reducing the time required for jet far-field noise assessment and noise reduction measure design.

[0147] Figure 6 This is a schematic diagram of an electronic device according to another embodiment of the present invention. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the jet far-field noise control method as described in the above embodiment. Figure 6 The electronic device 30 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0148] like Figure 6 As shown, the electronic device 30 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0149] Bus 33 includes a data bus, an address bus, and a control bus.

[0150] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0151] The memory 32 may also include a program / utility 325 having a set (at least one) of program modules 324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0152] The processor 31 executes various functional applications and data processing by running computer programs stored in the memory 32, such as the jet far-field noise control method of the present invention as described in the above embodiment.

[0153] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, the model-generating device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 36. Figure 6 As shown, network adapter 36 communicates with other modules of the model-generated device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0154] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0155] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the jet far-field noise control method as described in the above embodiment.

[0156] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0157] In a possible implementation, the present invention can also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform steps in the jet far-field noise control method as described in the above embodiments.

[0158] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0159] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but all such changes and modifications fall within the scope of protection of the present invention.

Claims

1. A method of controlling the noise of a jet in the far field, characterized in that, The control method comprises the following steps: acquiring all sample points covering the entire design space by using a design of experiment method; calculating a far-field sound pressure level according to a physically simplified companion Green function to obtain a jet far-field noise target function of all sample points; establishing a correlation model of the inner and outer curved wall surface shape and the jet far-field noise according to the jet far-field noise target function of all sample points; searching for an optimal value of the jet far-field noise on the established correlation model by using a multi-island genetic algorithm, taking the jet aerodynamic performance as a constraint, to control the jet far-field noise; wherein, the step of calculating the far-field sound pressure level according to the physically simplified companion Green function comprises the following steps: expanding the companion Green function according to a cylindrical wave number m, m is less than or equal to 9, and deriving a second-order homogeneous ordinary differential equation of the physically simplified companion Green function to perform the far-field sound pressure level calculation; in the step of obtaining the jet far-field noise target function of all sample points, a high-order acoustic space format and a time advancing format are used to calculate and solve to obtain the jet far-field noise target function of all sample points; the step of establishing the correlation model of the inner and outer curved wall surface shape and the jet far-field noise according to the jet far-field noise target function of all sample points comprises the following step: establishing the correlation model of the inner and outer curved wall surface shape and the jet far-field noise by correlating the variables of all sample points and the values of the jet far-field noise target function; the step of correlating the variables of all sample points and the values of the jet far-field noise target function comprises the following step: calculating and obtaining jet far-field noise examples corresponding to all sample points, so as to obtain the design variables for controlling the changes of the inner and outer curved wall surface shape and the values of the jet far-field noise target function.

2. The control method according to claim 1, characterized by, Before the step of acquiring all sample points covering the entire design space by using the design of experiment method, the control method further comprises the following steps: setting upper and lower limit values of the design variables for controlling the shape changes; defining the jet far-field noise target function according to the far-field sound pressure level spectrum.

3. The control method according to claim 1, characterized by, After searching for the optimal value of the jet far-field noise, the control method further comprises the following steps: adding the searched optimal sample point to the existing sample points to perform repeated calculation and searching to achieve a convergence criterion.

4. The control method according to claim 1, characterized by, The step of acquiring all sample points covering the entire design space by using the design of experiment method comprises the following step: acquiring all sample points covering the entire design space by using an optimized Latin square design of experiment method.

5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the control method of the jet far-field noise according to any one of claims 1-4.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the control method of the jet far-field noise according to any one of claims 1-4.

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