Statistical energy analysis based ship sonar self-noise prediction method and system

Abstract

The application discloses a ship sonar self-noise prediction method and system based on statistical energy analysis, and the method comprises the following steps: constructing a sonar self-noise statistical energy analysis prediction model according to ship drawing materials, constructing an external auxiliary acoustic cavity by locally refining a sonar array and a load area; determining the vibration acceleration level load of the connecting position, the mechanical noise source sound source level load and the like based on the external auxiliary acoustic cavity, and simultaneously determining the shell plate structure and the acoustic cavity loss factor; establishing a shell plate material structure microscopic acoustic analysis model to calculate material acoustic parameters; according to the passive sonar working frequency range, the statistical energy analysis method is used to perform ship sonar self-noise prediction analysis on the aforementioned load to obtain sonar cabin external self-noise prediction values; and the material acoustic parameters are used to calculate the sonar cabin internal sound pressure level, and the sonar cabin internal self-noise prediction values are calculated in the form of 1 / 3 octave. The method can effectively improve the efficiency and precision of ship sonar self-noise prediction.

Description

TECHNICAL FIELD

[0001] The present application relates to the underwater noise prediction technical field, in particular to a ship sonar self-noise prediction method and system based on statistical energy analysis. BACKGROUND

[0002] The working efficiency of sonar is closely related to the external acoustic interference (i.e. sonar self-noise). Reducing the sonar self-noise can improve the detection ability of the sonar and increase the action distance of the sonar. When the ship is sailing, in order to avoid the direct impact of the water flow on the sonar array, a sound-transmitting cover with good hydrodynamic performance is often configured outside the sonar array, which is called sonar cover. The sonar self-noise mainly comes from the hydrodynamic noise, mechanical noise and propeller noise. The actual ship test shows that, at high frequency band and high speed, the self-noise is mainly composed of hydrodynamic noise; at low speed and low frequency band, the self-noise is mainly composed of mechanical noise.

[0003] Using the statistical energy analysis method to predict the sonar self-noise has become a relatively mature sonar self-noise prediction method, which can divide the complex mechanical or acoustic system into different modal groups, and decompose the large system into several independent subsystems in a statistical sense, rather than accurately determine the response of each modal one by one. This method fully utilizes the modal intensity of vibration and sound radiation at high frequency band, and is an effective method to solve the wideband dynamics problem of complex systems.

[0004] Although the statistical energy method has been used for the calculation and analysis of sonar self-noise, such as related technology one: statistical energy analysis of sonar cavity self-noise under the excitation of spatially non-uniform turbulent boundary layer (China Shipboard Research, December 2007, No. 6); related technology two: calculation of hydrodynamic noise component of sonar self-noise by statistical energy method (Ship Mechanics, February 2004, No. 1); related technology three: prediction method and control technology of sonar self-noise of ship parts (Ship Mechanics, October 2002, No. 5); among them, related technology one uses the statistical energy analysis method, focuses on the calculation and analysis of the input power of the sonar cover under the excitation of spatially non-uniform turbulent boundary layer and the determination of the analysis parameters, which can provide a reference for engineering estimation, but there is no reasonable and specific self-noise prediction process, and the efficiency of the sonar self-noise prediction process is low; related technology two and three comprehensively summarize various prediction methods of sonar self-noise and analyze the influence of different parameters on sonar self-noise, but the model is simple and does not consider the construction of the sound cavity, and the prediction accuracy of the sonar self-noise is low. Therefore, a detailed and systematic sonar self-noise prediction process is urgently needed. SUMMARY

[0005] The present application aims to at least solve one of the technical problems in the related art to some extent.

[0006] To this end, one object of the present application is to provide a ship sonar self-noise prediction method based on statistical energy analysis.

[0007] Another object of the present application is to provide a ship sonar self-noise prediction system based on statistical energy analysis.

[0008] Still another object of the present application is to provide a computer device.

[0009] Still another object of the present application is to provide a non-transitory computer readable storage medium.

[0010] To achieve the above objects, in one aspect, the present application provides a ship sonar self-noise prediction method based on statistical energy analysis, comprising the following steps: step S1, constructing a sonar self-noise statistical energy analysis prediction model according to a preset ship drawing material; step S2, locally refining a sonar array and a load area in the sonar self-noise statistical energy analysis prediction model to construct an external auxiliary acoustic cavity; step S3, determining a vibration acceleration level load, a mechanical noise source sound source level load and a flow-induced load power spectral density function of a connection position of the external auxiliary acoustic cavity according to a preset first experimental test or formula; step S4, calculating the shell plate structure loss factor and the acoustic cavity loss factor of the external auxiliary acoustic cavity according to a preset second experimental test or theoretical formula; step S5, establishing a shell plate material structure microscopic acoustic analysis model according to the shell plate structure loss factor and the acoustic cavity loss factor by using a transfer matrix method or an acoustic boundary element method, and obtaining material acoustic parameters through sound insulation performance analysis; step S6, performing ship sonar self-noise prediction analysis on the vibration acceleration level load, the mechanical noise source sound source level load and the flow-induced load power spectral density function by using a statistical energy analysis method according to a passive sonar operating frequency range, to obtain sonar cabin external self-noise prediction values; and step S7, calculating the internal sound pressure level of the sonar cabin by using the material acoustic parameters, and obtaining sonar cabin internal self-noise prediction values in the form of 1 / 3 octave calculation.

[0011] The ship sonar self-noise prediction method based on statistical energy analysis of the present application has the characteristics of detailed improvement and complete system, and can provide method support for sonar self-noise prediction and control, effectively improve the efficiency and accuracy of ship sonar self-noise prediction, and be used for ship cabin, offshore platform noise prediction and control.

[0012] In addition, the ship sonar self-noise prediction method based on statistical energy analysis according to the above embodiments of the present application can also have the following additional technical features:

[0013] Further, in one embodiment of the present application, the step S2 specifically comprises: step S201, based on the sonar self-noise statistical energy analysis prediction model, refining the inside structure of the dome at a scale of 0.1m, 0.2m and 0.3m respectively at the sonar array, and constructing the acoustic cavity subsystem according to the refined sonar cabin structure; step S202, based on the sonar self-noise statistical energy analysis prediction model, connecting the acoustic cavity subsystem with the shell plate subsystem, the auxiliary acoustic cavity and the dome shell subsystem with the semi-infinite flow field at the connection between the sonar cabin and the hull, to obtain the external auxiliary acoustic cavity.

[0014] Further, in one embodiment of the present application, the step S3 specifically comprises: step S301, obtaining the mechanical noise sound source level load in the external auxiliary acoustic cavity and the vibration acceleration level load at the connection between the sonar cabin and the hull through the preset first experimental test or formula; step S302, calculating the power spectral density function of the flow-induced load in the external auxiliary acoustic cavity; step S303, applying the mechanical noise sound source level load to the auxiliary acoustic cavity on the side connected with the hull, and applying the vibration acceleration level load to the shell plate subsystem at the connection part.

[0015] Optionally, in one embodiment of the present application, the step S4 specifically comprises: step S401, obtaining the shell plate structure loss factor of the dome in the external auxiliary acoustic cavity through the second experimental test or theoretical formula; step S402, solving the acoustic cavity loss factor of the external auxiliary acoustic cavity based on the center circular frequency of the frequency band and the internal reverberation time of the acoustic cavity.

[0016] To achieve the above object, another aspect of the embodiment of the present application provides a ship sonar self-noise prediction system based on statistical energy analysis, comprising: a construction module configured to construct a sonar self-noise statistical energy analysis prediction model according to a preset ship drawing material; a local refinement module configured to locally refine a sonar array and a load area in the sonar self-noise statistical energy analysis prediction model to construct an external auxiliary acoustic cavity; an acoustic cavity external load solving module configured to determine vibration acceleration level load, mechanical noise source sound source level load and flow-induced load power spectral density function of a connection position of the external auxiliary acoustic cavity according to a preset first experimental test or formula; an acoustic cavity internal loss solving module configured to calculate a shell plate structure loss factor and an acoustic cavity loss factor of the external auxiliary acoustic cavity according to a preset second experimental test or theoretical formula; a material acoustic parameter solving module configured to establish a shell plate material structure microscopic acoustic analysis model according to the shell plate structure loss factor and the acoustic cavity loss factor by using a transfer matrix method or an acoustic boundary element method, and obtain material acoustic parameters through sound insulation performance analysis; a sonar cabin external self-noise prediction module configured to perform ship sonar self-noise prediction analysis on the vibration acceleration level load, the mechanical noise source sound source level load and the flow-induced load power spectral density function according to a passive sonar operating frequency range by using a statistical energy analysis method, and obtain a sonar cabin external self-noise prediction value; and a sonar cabin internal self-noise prediction module configured to calculate a sonar cabin internal sound pressure level by using the material acoustic parameters, and obtain a sonar cabin internal self-noise prediction value in a 1 / 3 octave form.

[0017] The ship sonar self-noise prediction system based on statistical energy analysis has the characteristics of detailed perfection and system integrity, can provide method support for sonar self-noise prediction and control, and can effectively improve the efficiency and precision of ship sonar self-noise prediction, and can also be used for ship cabin, offshore platform noise prediction and control.

[0018] In addition, the ship sonar self-noise prediction system based on statistical energy analysis according to the above embodiment of the present application can also have the following additional technical features:

[0019] Further, in an embodiment of the present application, the local refinement module is specifically configured to: a construction unit configured to refine an inside structure of a fairing at a scale of 0.1 m, 0.2 m and 0.3 m respectively at the sonar array based on the sonar self-noise statistical energy analysis prediction model, and construct an acoustic cavity subsystem according to the refined sonar cabin structure; and a connection unit configured to connect the acoustic cavity subsystem, a shell plate subsystem, an auxiliary acoustic cavity and a fairing shell subsystem with a semi-infinite domain flow field at a connection position of the sonar cabin and a hull to obtain the external auxiliary acoustic cavity based on the sonar self-noise statistical energy analysis prediction model.

[0020] Further, in one embodiment of the present application, the external load solving module of the auxiliary acoustic cavity specifically comprises: an external first calculation unit, configured to obtain the mechanical noise source level load in the external auxiliary acoustic cavity and the vibration acceleration level load at the connection between the sonar cabin and the hull body by the preset first experimental test or formula; an external second calculation unit, configured to calculate the power spectral density function of the flow-induced load in the external auxiliary acoustic cavity; and an application unit, configured to apply the mechanical noise source level load to the auxiliary acoustic cavity connected to the hull body and apply the vibration acceleration level load to the shell plate subsystem at the connection position.

[0021] Further, in one embodiment of the present application, the internal loss solving module of the auxiliary acoustic cavity specifically comprises: a first calculation unit, configured to obtain the shell plate structure loss factor of the fairing in the external auxiliary acoustic cavity by the second experimental test or theoretical formula; and a second calculation unit, configured to solve the acoustic cavity loss factor of the external auxiliary acoustic cavity based on the center circular frequency of the frequency band and the internal reverberation time of the acoustic cavity.

[0022] To achieve the above object, another aspect of the present application provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the ship sonar self-noise prediction method based on statistical energy analysis as described in the above embodiment.

[0023] To achieve the above object, another aspect of the present application provides a non-transitory computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the ship sonar self-noise prediction method based on statistical energy analysis as described in the above embodiment.

[0024] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0025] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description, taken in conjunction with the accompanying drawings, in which:

[0026] Figure 1 Flow chart of the ship sonar self-noise prediction method based on statistical energy analysis according to one embodiment of the present application;

[0027] Figure 2 Execution flow chart of the ship sonar noise prediction according to one embodiment of the present application;

[0028] Figure 3This is a partial detail optimization diagram of a sonar compartment according to an embodiment of the present invention, wherein A is an auxiliary acoustic cavity at the sonar compartment connection, B is a refined acoustic cavity at the sonar array, and C is the fairing shell.

[0029] Figure 4 This is a power level spectrum curve of a mechanical noise source according to an embodiment of the present invention.

[0030] Figure 5 This is a spectrum curve of vibration acceleration level at a connection point according to an embodiment of the present invention.

[0031] Figure 6 This is a spectrum curve of the loss factor inside the acoustic cavity of a sonar compartment according to an embodiment of the present invention.

[0032] Figure 7 This is a sound insulation spectrum curve of the guide shield material according to an embodiment of the present invention;

[0033] Figure 8 This is a numerical spectrum curve of the self-noise prediction of a sonar compartment according to an embodiment of the present invention;

[0034] Figure 9 This is a schematic diagram of a ship sonar self-noise prediction system based on statistical energy analysis according to an embodiment of the present invention. Detailed Implementation

[0035] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0036] The following describes, with reference to the accompanying drawings, a method for predicting ship sonar self-noise based on statistical energy analysis according to an embodiment of the present invention. First, the method and system for predicting ship sonar self-noise based on statistical energy analysis according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0037] Figure 1 This is a flowchart of a ship sonar self-noise prediction method based on statistical energy analysis according to an embodiment of the present invention.

[0038] like Figure 1 and 2 As shown, the ship sonar self-noise prediction method based on statistical energy analysis includes the following steps:

[0039] In step S1, a sonar self-noise statistical energy analysis and prediction model is constructed based on the preset ship drawing materials.

[0040] In step S2, the sonar array and the load area in the sonar self-noise statistical energy analysis prediction model are locally refined to construct an external auxiliary acoustic cavity.

[0041] Further, in an embodiment of the present application, step S2 specifically comprises:

[0042] Step S201, based on the sonar self-noise statistical energy analysis prediction model, the inside structure of the dome is refined at the sonar array with scales of 0.1m, 0.2m and 0.3m respectively, and the acoustic cavity subsystem is constructed according to the refined sonar cabin structure;

[0043] Step S202, based on the sonar self-noise statistical energy analysis prediction model, at the connection between the sonar cabin and the hull, the acoustic cavity subsystem is connected with the shell plate subsystem, the auxiliary acoustic cavity and the dome shell subsystem, and the semi-infinite flow field, to obtain the external auxiliary acoustic cavity.

[0044] Specifically, as shown in Figure 3 , for the sonar array, the inside structure of the dome is refined with scales of 0.1m, 0.2m and 0.3m respectively, and the acoustic cavity subsystem is constructed according to the refined sonar cabin structure; at the connection between the sonar cabin and the hull, the acoustic cavity subsystem, the shell plate subsystem and the auxiliary acoustic cavity are connected with the semi-infinite flow field.

[0045] In step S3, the vibration acceleration level load, the mechanical noise source sound level load and the flow-induced load power spectrum density function of the connection position of the external auxiliary acoustic cavity are determined according to a preset first experimental test or formula.

[0046] Further, in an embodiment of the present application, step S3 specifically comprises:

[0047] Step S301, the mechanical noise sound source level load in the external auxiliary acoustic cavity and the vibration acceleration level load at the connection between the sonar cabin and the hull are obtained by a preset first experimental test or formula;

[0048] Step S302, the power spectrum density function of the flow-induced load in the external auxiliary acoustic cavity is calculated;

[0049] Step S303, the mechanical noise source sound level load is applied to the auxiliary acoustic cavity on the side connected with the hull, and the vibration acceleration level load is applied to the shell plate subsystem at the connection position.

[0050] Specifically, as shown in Figure 4 and 5 , the ship mechanical noise source sound level load and the vibration acceleration level load at the connection between the sonar cabin and the hull are obtained by testing, as follows:

[0051]

[0052] wherein a i is the vibration acceleration frequency domain result obtained by test measurement, a0 is a reference value of vibration acceleration in the medium, and a0 is 2x10 -5 .

[0053] The required calculation parameters of the turbulent flow excitation load include: U0, p, v, c0, X0, and the power spectrum density function S p (f) of the flow excitation load is:

[0054]

[0055]

[0056]

[0057] wherein U0 represents a free flow speed, p represents a fluid density, v represents a kinematic viscosity of the fluid, c0 represents a speed of sound wave propagation in the fluid, X0 represents a distance from a turbulent boundary layer leading edge to a center of the subsystem, the coefficients A=0.9, B=2, and C=0.346 in the adhesion state, and the coefficients A=0.83, B=2.15, and C=0.1 in the separation state;

[0058] Then, the mechanical noise source sound source level load is applied to the auxiliary sound cavity connected to one side of the hull, and the vibration acceleration level load is applied to the shell plate subsystem at the connection position.

[0059] In step S4, the shell plate structure loss factor of the external auxiliary sound cavity and the sound cavity loss factor are determined according to a preset second experimental test or a theoretical formula.

[0060] Further, in an embodiment of the present application, step S4 specifically includes:

[0061] Step S401: obtaining the shell plate structure loss factor of the external auxiliary sound cavity through a second experimental test or a theoretical formula;

[0062] Step S402: solving the sound cavity loss factor of the external auxiliary sound cavity based on the frequency band center circular frequency and the sound cavity internal reverberation time.

[0063] For example, as shown in FIG. 1, the sonar cabin dome shell plate is commonly made of glass steel or steel, and the loss factors of the two kinds of shell plate structures obtained through experimental tests are respectively: glass steel 0.01-0.05 and steel 0.001-0.003; and the sound cavity internal loss factor is calculated through the following formula, as follows: Figure 6

[0064]

[0065] ​wherein ω is the center circular frequency of the frequency band, T 60 is the internal reverberation time of the sound cavity, and f is the frequency.

[0066] In step S5, a shell plate material structure microscopic acoustic analysis model is established according to the shell plate structure loss factor and the sound cavity loss factor by using the transfer matrix method or the acoustic boundary element method, and the material acoustic parameters are obtained through the sound insulation performance analysis.

[0067] Specifically, as shown in Figure 7 The microscopic energy transfer between the shell plate material structures can be represented by the following formula:

[0068] Z1=TZ0

[0069] wherein Z0 is the initial state variable, Z1 is the terminal state variable, T=e A is the transfer matrix, in which A is the coefficient matrix item, and the loss factor item obtained by the test measurement is brought into the matrix A, so that the energy transfer in the plate shell structure can be obtained.

[0070] Further, the sound insulation performance of the material can be calculated by the following formula:

[0071] R=14.51lgmf-26

[0072] wherein R is the actual sound insulation of the panel, m is the surface density of the bulkhead, and f is the frequency.

[0073] In step S6, according to the passive sonar working frequency range, the statistical energy analysis method is used to perform ship sonar self-noise prediction analysis on the vibration acceleration level load, the mechanical noise source sound source level load and the flow excitation load power spectrum density function, and the sonar cabin outside self-noise prediction value is obtained.

[0074] wherein the calculation frequency range is set to 100Hz-10kHz.

[0075] Specifically, a ship sonar cabin plate shell model is established and the material performance is assigned, the sonar cabin is divided into multiple space regions and a sound cavity subsystem is established, an auxiliary sound cavity subsystem is established outside the sonar cabin, a semi-infinite domain is established outside the sonar cabin and a non-reflecting boundary condition is set; the obtained sound cavity loss factor is applied to the sound cavity subsystem, the plate shell structure vibration acceleration load is applied to the cabin plate in the form of acceleration level, the mechanical noise sound source level load is applied to the sound cavity subsystem in the form of air vibration acceleration, the energy distribution of each sound cavity subsystem is calculated according to the statistical energy principle, and is converted into sound pressure level; then the sound pressure measuring point is arranged outside the sonar cabin, and the sound pressure calculation result of the point, i.e. the sonar cabin outside self-noise prediction value, is extracted.

[0076] In step S7, the sound pressure level inside the sonar cabin is calculated by using the material acoustics parameters, and the self-noise prediction value in the sonar cabin is calculated in the form of 1 / 3 octave.

[0077] Specifically, as shown in step S6, after the sonar cabin noise distribution is calculated, the average sound pressure level of the sound cavity subsystem in the sonar cabin at each frequency point is extracted, and the sound pressure octave calculation result is converted into the 1 / 3 octave result, i.e., the self-noise prediction value in the sonar cabin, according to the following formula: Figure 8

[0078]

[0079] Wherein, Lp is the octave sound pressure level of the center frequency f c , Δf is the octave bandwidth of the center frequency f c , L P1 , L P2 , L P3 are respectively the 1 / 3 octave sound pressure level of the center frequency f c1 , f c2 , f c3 , Δf1, Δf2, Δf3 are respectively the 1 / 3 octave bandwidth of the center frequency f c1 , f c2 , f c3 .

[0080] Further, Δf1, Δf2, Δf3 have the following relationship with Δf:

[0081]

[0082] According to the ship sonar self-noise prediction method based on statistical energy analysis provided in the embodiments of the present application, the statistical energy analysis prediction model of the sonar cabin self-noise is established according to the ship drawing data, and the local details of the sonar array area and the load application area are optimized; the vibration acceleration level load of the connecting part of the ship body and the mechanical noise source sound source level load are determined through experimental test or theoretical formula; the acoustic characteristic parameters of the shell plate and the loss factor in the sound cavity are determined through experiment or formula calculation; the calculation frequency band is 100Hz-10kHz, the statistical energy analysis method is used for sonar self-noise prediction analysis; the sound pressure level inside the sonar cabin is calculated, and the calculation result is processed in the form of 1 / 3 octave. The prediction process can effectively improve the efficiency and accuracy of the ship sonar self-noise prediction, and can also be used for ship cabin, offshore platform noise prediction and control.

[0083] Secondly, the ship sonar self-noise prediction system based on statistical energy analysis provided in the embodiments of the present application is described with reference to the accompanying drawings.

[0084] Figure 9 ​This is a schematic diagram of a ship sonar self-noise prediction system based on statistical energy analysis according to an embodiment of the present invention.

[0085] like Figure 9 As shown, the system 10 includes: a construction module 100, a local refinement module 200, a cavity external load solving module 300, a cavity internal loss solving module 400, a material acoustic parameter solving module 500, a sonar cabin external self-noise prediction module 600, and a sonar cabin internal self-noise prediction module 700.

[0086] The system comprises the following modules: Module 100 constructs a sonar self-noise statistical energy analysis and prediction model based on pre-set ship drawings and materials. Module 200 refines the sonar array and load regions within the sonar self-noise statistical energy analysis and prediction model to construct an external auxiliary acoustic cavity. Module 300 determines the vibration acceleration level load, mechanical noise source sound source level load, and flow-induced load power spectral density function at the connection location of the external auxiliary acoustic cavity based on a pre-set first experimental test or formula. Module 400 calculates and determines the shell structure loss factor and acoustic cavity loss factor of the external auxiliary acoustic cavity based on a pre-set second experimental test or theoretical formula. Module 500 uses the transfer matrix method or acoustic boundary element method to establish a microscopic acoustic analysis model of the shell material structure based on the shell structure loss factor and acoustic cavity loss factor, and obtains the material acoustic parameters through sound insulation performance analysis. The external sonar cabin self-noise prediction module 600 is used to perform shipboard sonar self-noise prediction analysis based on the operating frequency range of the passive sonar and using statistical energy analysis methods to analyze the power spectral density functions of vibration acceleration level load, mechanical noise source sound source level load, and flow-induced load, thereby obtaining the predicted value of the external sonar cabin self-noise. The internal sonar cabin self-noise prediction module 700 is used to calculate the sound pressure level inside the sonar cabin using material acoustic parameters and calculates it in 1 / 3 octave band form to obtain the predicted value of the internal sonar cabin self-noise.

[0087] Furthermore, in one embodiment of the present invention, the local refinement module 200 is specifically used for:

[0088] The building unit is used to refine the internal structure of the fairing at the sonar array at scales of 0.1m, 0.2m, and 0.3m, respectively, based on the statistical energy analysis and prediction model of sonar self-noise, and to construct the acoustic cavity subsystem according to the refined sonar cabin structure.

[0089] The connecting unit is used to connect the acoustic cavity subsystem, the shell plate subsystem, the auxiliary acoustic cavity, and the fairing shell subsystem to the semi-infinite domain flow field at the connection between the sonar compartment and the hull, based on the sonar self-noise statistical energy analysis and prediction model, so as to obtain the external auxiliary acoustic cavity.

[0090] Furthermore, in one embodiment of the present invention, the acoustic cavity external load solving module 300 specifically includes:

[0091] The external first calculation unit is used to obtain the mechanical noise source level load in the external auxiliary acoustic cavity and the vibration acceleration level load at the connection between the sonar compartment and the hull through a preset first experimental test or formula.

[0092] The external second calculation unit is used to calculate the power spectral density function of the flow-induced load in the external auxiliary acoustic cavity;

[0093] The application unit is used to apply the mechanical noise source level load to the auxiliary acoustic cavity on the side connected to the hull, and to apply the vibration acceleration level load to the shell plate subsystem at the connection point.

[0094] Furthermore, in one embodiment of the present invention, the cavity internal loss solving module 400 is specifically used for:

[0095] The first calculation unit is used to obtain the shell structure loss factor of the shroud in the external auxiliary acoustic cavity through the second experimental test or theoretical formula.

[0096] The second calculation unit is used to solve the cavity loss factor of the external auxiliary cavity based on the center circular frequency of the frequency band and the reverberation time inside the cavity.

[0097] It should be noted that the foregoing explanation of the embodiment of the ship sonar self-noise prediction method based on statistical energy analysis also applies to the system of this embodiment, and will not be repeated here.

[0098] The ship sonar self-noise prediction system based on statistical energy analysis proposed in this invention establishes a statistical energy analysis prediction model for the sonar compartment's self-noise based on ship drawings and data, and performs local detail optimization on the sonar array region and the load application region; determines the vibration acceleration level load of the hull connection parts and the sound source level load of the mechanical noise source through experimental testing or theoretical formulas; determines the acoustic characteristic parameters of the loss factor in the shell plate and acoustic cavity through experimental or formula calculations; the calculation frequency band is 100Hz-10kHz, and the sonar self-noise prediction analysis is performed using statistical energy analysis methods; the sound pressure level inside the sonar compartment is calculated, and the calculation results are processed in 1 / 3 octave band form. This prediction process can effectively improve the efficiency and accuracy of ship sonar self-noise prediction, and can also be used for noise prediction and control in ship compartments and offshore platforms.

[0099] To implement the above embodiments, the present invention also proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the ship sonar self-noise prediction method based on statistical energy analysis as described in the foregoing embodiments.

[0100] In order to achieve the above-mentioned embodiments, the application further provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the statistical energy analysis based ship sonar self-noise prediction method according to the above-mentioned embodiments.

[0101] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or N embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0102] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0103] Any process or method descriptions in flow charts or described elsewhere herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for performing specific logic functions (or steps) in the process, and that the various embodiments of the application can include additional or fewer processes, steps, or codes modules, and that the method description or associated description herein can not be limited to a single process, flow, or method. Furthermore, it should be understood that the various embodiments of the application could be implemented in hardware, software, or a combination thereof.

[0104] The logic and / or steps represented in flow diagrams or otherwise described herein, for example, can be considered as a sequence of instructions to implement logic functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a machine-readable storage device (e.g., magnetic, optical or other) a machine-readable storage diskette (e.g., floppy, flexible or other), a machine-readable storage card (e.g., ROM, EEPROM, flash memory or other), a machine- readable storage tape (e.g., magnetic, optical or other), a machine-readable storage medium (e.g., a portable electronic device, a computer diskette, a computer memory, a programmable logic device, an application-specific integrated circuit, a programmable logic controller, a digital signal processor, a microprocessor, a microprocessor array or other), or a machine- readable interface device (e.g., a wired or wireless interface device). The computer-readable medium can also be paper or other suitable material upon which the program is printed, as the program can be electronically captured, for example by optically scanning the paper or other suitable medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and stored in a computer memory.

[0105] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0106] Those of skill in the art could readily implement the above described example methods with all or a subset of the recited steps carried out with a program of instructions executed by associated hardware, the program of instructions being stored in a computer readable storage medium that, when executed, includes one or a combination of the steps of the method embodiments.

[0107] In addition, each function unit in each embodiment of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module. When the integrated module is realized in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0108] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for predicting ship sonar self-noise based on statistical energy analysis, characterized in that, Includes the following steps: Step S1: Construct a sonar self-noise statistical energy analysis and prediction model based on the preset ship drawings and materials; Step S2: The sonar array and payload region in the sonar self-noise statistical energy analysis and prediction model are locally refined to construct an external auxiliary acoustic cavity; Step S3: Determine the vibration acceleration level load, mechanical noise source sound source level load, and flow-induced load power spectral density function of the connection position of the external auxiliary acoustic cavity according to the preset first experimental test or formula; Step S4: Calculate and determine the shell structure loss factor and acoustic cavity loss factor of the external auxiliary acoustic cavity according to the preset second experimental test or theoretical formula. Step S5: Using the transfer matrix method or acoustic boundary element method, a microscopic acoustic analysis model of the shell material structure is established based on the loss factor of the shell structure and the loss factor of the acoustic cavity, and the acoustic parameters of the material are obtained through sound insulation performance analysis. Step S6: Based on the operating frequency range of the passive sonar, statistical energy analysis is used to perform ship sonar self-noise prediction analysis on the power spectral density function of the vibration acceleration level load, the mechanical noise source sound source level load, and the flow-induced load, so as to obtain the self-noise prediction value outside the sonar cabin. Step S7: Calculate the sound pressure level inside the sonar cabin using the acoustic parameters of the material, and calculate the self-noise prediction value inside the sonar cabin in the form of 1 / 3 octave band.

2. The method for predicting ship sonar self-noise based on statistical energy analysis according to claim 1, characterized in that, Step S2 specifically includes: Step S201: Based on the sonar self-noise statistical energy analysis and prediction model, refine the internal structure of the fairing at the sonar array at scales of 0.1m, 0.2m, and 0.3m respectively, and construct the acoustic cavity subsystem according to the refined sonar cabin structure; Step S202: Based on the sonar self-noise statistical energy analysis and prediction model, at the connection between the sonar cabin and the hull, the acoustic cavity subsystem, the shell plate subsystem, the auxiliary acoustic cavity, and the fairing shell subsystem are all connected to the semi-infinite domain flow field to obtain the external auxiliary acoustic cavity.

3. The method for predicting ship sonar self-noise based on statistical energy analysis according to claim 1, characterized in that, Step S3 specifically includes: Step S301: Obtain the mechanical noise source load and the vibration acceleration load at the connection between the sonar compartment and the hull in the external auxiliary acoustic cavity through the preset first experimental test or formula. Step S302: Calculate the power spectral density function of the flow-induced load in the external auxiliary acoustic cavity; Step S303: Apply the mechanical noise source level load to the auxiliary acoustic cavity on the side connected to the hull, and apply the vibration acceleration level load to the shell plate subsystem at the connection point.

4. The method for predicting ship sonar self-noise based on statistical energy analysis according to claim 1, characterized in that, Step S4 specifically includes: Step S401: Obtain the shell structure loss factor of the shroud in the external auxiliary acoustic cavity through the second experimental test or theoretical formula; Step S402: Solve the cavity loss factor of the external auxiliary cavity based on the center circular frequency of the frequency band and the reverberation time inside the cavity.

5. A ship sonar self-noise prediction system based on statistical energy analysis, characterized in that, include: The construction module is used to build a statistical energy analysis and prediction model for sonar self-noise based on preset ship drawings and materials; The local refinement module is used to locally refine the sonar array and payload regions in the sonar self-noise statistical energy analysis and prediction model in order to construct an external auxiliary acoustic cavity. The external load solution module for acoustic cavity is used to determine the vibration acceleration level load, mechanical noise source sound source level load and flow-induced load power spectral density function of the connection position of the external auxiliary acoustic cavity according to the preset first experimental test or formula. The acoustic cavity internal loss calculation module is used to calculate and determine the shell plate structure loss factor and acoustic cavity loss factor of the external auxiliary acoustic cavity based on a preset second experimental test or theoretical formula. The material acoustic parameter solving module is used to establish a microscopic acoustic analysis model of the shell material structure based on the shell structure loss factor and the acoustic cavity loss factor using the transfer matrix method or the acoustic boundary element method, and obtain the material acoustic parameters through sound insulation performance analysis. The sonar cabin external self-noise prediction module is used to perform ship sonar self-noise prediction analysis on the power spectral density function of the vibration acceleration level load, the mechanical noise source sound source level load and the flow-induced load according to the operating frequency range of the passive sonar, and to obtain the external self-noise prediction value of the sonar cabin. The sonar cabin self-noise prediction module is used to calculate the sound pressure level inside the sonar cabin using the acoustic parameters of the material, and to calculate the self-noise prediction value inside the sonar cabin in the form of 1 / 3 octave band.

6. The ship sonar self-noise prediction system based on statistical energy analysis according to claim 5, characterized in that, The local refinement module is specifically used for: The construction unit is used to refine the internal structure of the fairing at the sonar array at scales of 0.1m, 0.2m, and 0.3m, respectively, based on the sonar self-noise statistical energy analysis and prediction model, and to construct the acoustic cavity subsystem according to the refined sonar cabin structure. The connection unit is used to connect the acoustic cavity subsystem, the shell plate subsystem, the auxiliary acoustic cavity, and the fairing shell subsystem to the semi-infinite domain flow field at the connection between the sonar cabin and the hull, based on the sonar self-noise statistical energy analysis and prediction model, so as to obtain the external auxiliary acoustic cavity.

7. The ship sonar self-noise prediction system based on statistical energy analysis according to claim 5, characterized in that, The acoustic cavity external load solving module specifically includes: An external first calculation unit is used to obtain the mechanical noise source level load and the vibration acceleration level load at the connection between the sonar compartment and the hull in the external auxiliary acoustic cavity through the preset first experimental test or formula. An external second computing unit is used to calculate the power spectral density function of the flow-induced load in the external auxiliary acoustic cavity; An application unit is used to apply the mechanical noise source level load to the auxiliary acoustic cavity on the side connected to the hull, and to apply the vibration acceleration level load to the shell plate subsystem at the connection point.

8. The ship sonar self-noise prediction system based on statistical energy analysis according to claim 5, characterized in that, The acoustic cavity internal loss calculation module is specifically used for: The first calculation unit is used to obtain the shell structure loss factor of the shroud in the external auxiliary acoustic cavity through the second experimental test or theoretical formula. The second calculation unit is used to solve the cavity loss factor of the external auxiliary cavity based on the center circular frequency of the frequency band and the reverberation time inside the cavity.

9. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the ship sonar self-noise prediction method based on statistical energy analysis as described in any one of claims 1-4.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the ship sonar self-noise prediction method based on statistical energy analysis as described in any one of claims 1-4.

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