Method for forecasting multiple sound scattering characteristics of underwater vehicle, electronic equipment and program product

Abstract

The invention discloses an underwater vehicle multi-time sound scattering characteristic forecasting method, which comprises the following steps: a preprocessing step: constructing a geometric model of an underwater vehicle for subsequent acoustic simulation and calculation; subdividing an underwater vehicle grid model, performing grid subdivision on the basis of the geometric model, and dividing the underwater vehicle model into smaller units so as to facilitate numerical calculation; and establishing an underwater vehicle multilayer grouping surface element model, and dividing the surface of the underwater vehicle into a plurality of surface elements to simulate the complex structure of the underwater vehicle. The solving step comprises: using an iterative algorithm to accelerate the physical acoustic simulation process; different-order scattering sound fields are calculated, and an accurate simulation result is obtained according to the multiple scattering phenomena of the underwater vehicle. The post-processing step comprises the step of analyzing and obtaining acoustic characteristics of the underwater vehicle in the underwater environment, wherein the acoustic characteristics comprise multiple acoustic scattering time domain echoes and target intensity of the underwater vehicle.

Description

Technical Field

[0001] The invention belongs to the technical field of underwater acoustic engineering, and in particular relates to a method for predicting multiple acoustic scattering characteristics of an underwater vehicle, electronic equipment and a program product. Background Art

[0002] As a key equipment for the development of marine resources, underwater submersibles are characterized by diverse types and complex shapes. Their detection and identification is one of the key development directions of underwater security work. Compared with optical, electromagnetic or passive acoustic detection, active sonar is a more effective means of detecting and identifying underwater unmanned submersibles. In order to improve the accuracy and detection efficiency of the active sonar detection system, it is necessary to have a certain understanding of the echo characteristics of the submersible. For example, ROV (Remote Operated Vehicle), a type of underwater submersible, has complex structures such as an extended operating chassis, a fairing, a propeller channel, a propeller duct, an electronic compartment and a hydraulic unit. The incident sound wave will be scattered multiple times on the surface of the above structure. This is because the electronic compartment and hydraulic unit of the ROV can be a combination, and the extended operating chassis carrying the combination uses sound-transmitting materials, so that the sound waves can penetrate the chassis. The thruster of the ROV can also be a combination. When the thruster channel is designed to be concave, it will also cause acoustic scattering of the incident wave.

[0003] Therefore, when underwater submersibles are assigned to perform complex tasks, they carry a variety of sensors and equipment. However, there is currently little research on the fine characteristics of echoes caused by multiple acoustic scattering caused by complex structure surfaces of such complex underwater submersibles because they carry a variety of equipment. There is also a lack of efficient prediction models for multiple acoustic scattering of complex submersibles, which cannot meet the needs of modern underwater security. Summary of the invention

[0004] One of the embodiments of the present disclosure is a method for quickly predicting multiple acoustic scattering characteristics of a complex underwater vehicle, comprising:

[0005] Preprocessing steps include:

[0006] Construct a geometric model of the submersible for subsequent acoustic simulation and calculation;

[0007] Divide the submersible mesh model. Based on the geometric model, divide the submersible model into smaller units to facilitate numerical calculations.

[0008] A multi-layer grouped facet model of a submersible is established, and the surface of the submersible is divided into multiple facets to simulate the complex structure of the submersible.

[0009] The solution steps include:

[0010] Use iterative algorithms to speed up the physical acoustics simulation process;

[0011] Calculate different order scattered sound fields and multiple scattering phenomena of submarines to obtain accurate simulation results;

[0012] Post-processing steps include:

[0013] The acoustic characteristics of the underwater vehicle in the underwater environment are analyzed and obtained, including the time domain echoes of multiple acoustic scatterings of the underwater vehicle and the target intensity. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0015] Figure 1 A flow chart of a method for predicting multiple acoustic scattering characteristics of an underwater vehicle according to one embodiment of the present invention.

[0016] Figure 2 A two-dimensional schematic diagram of the lth layer and the (l+1)th layer grouping according to one embodiment of the present invention.

[0017] Figure 3 A schematic diagram of a simplified ROV geometric model and multi-layer grouping of its facets according to one embodiment of the present invention.

[0018] in, Figure 3 -a is the geometric model, Figure 3 -b is the mesh model, Figure 3 -c is a multi-layer grouping model.

[0019] Figure 4 A schematic diagram of far-field pairing within a calculation area according to one embodiment of the present invention.

[0020] Figure 5 A schematic diagram of the time-angle spectrum of scattered echo of an operation-level ROV model according to one embodiment of the present invention. Figure 5 -a is the simulation result, Figure 5 -b is the test result.

[0021] Figure 6 Schematic diagram of the acoustic target intensity pointing distribution (80kHz) of an operational-level ROV according to one embodiment of the present invention.

[0022] Figure 7 Schematic diagram of the effect of the number of subdivided facets on the computation time of the proposed method according to one embodiment of the present invention. DETAILED DESCRIPTION

[0023] Acoustic scattering characteristics refer to the phenomenon that when sound waves propagate in a medium and encounter obstacles or medium inhomogeneities, the sound waves will scatter in all directions in a certain pattern. This scattering phenomenon is closely related to factors such as the physical properties, shape, size of the obstacle or medium, and the wavelength of the sound wave. In the study of underwater unmanned submersibles, acoustic scattering characteristics are particularly important because these characteristics affect the detection, positioning and communication capabilities of the submersible in water.

[0024] In the existing scheme, the research on the acoustic scattering characteristics of underwater unmanned submersibles mainly focuses on measuring the acoustic target intensity through experiments. However, since the experimental measurement has certain requirements for the test site, the model to be tested and the test personnel, it has the disadvantages of high economic cost and long test cycle, which is often unacceptable for rapid research and development and design. In addition, it is also difficult to achieve the acoustic scattering test measurement of non-cooperative underwater submersible targets. Therefore, the modeling research on the acoustic scattering characteristics of underwater complex submersible targets is of great significance.

[0025] The main modeling methods for the acoustic scattering characteristics of complex underwater targets can be roughly divided into two categories: low-frequency numerical methods and medium- and high-frequency approximate methods. Among them, low-frequency numerical methods represented by finite elements and boundary elements have the advantages of high accuracy and high stability, but as the target scale increases, it also has the disadvantages of high computational load and high storage requirements.

[0026] In recent years, most studies have turned to using high-frequency approximation methods to solve the acoustic scattering problem of large-scale complex targets. Among them, the sound ray bouncing method, when considering the coupling effect, essentially only considers the coupling effect caused by mirror reflection. For concave surface targets with large curvature, there is a problem of ray divergence, and the calculation accuracy is not high. The plate element method based on Kirchhoff approximation has high calculation efficiency, but does not consider multiple acoustic scattering effects, and the calculation accuracy of acoustic scattering of underwater complex targets is low.

[0027] Here, the high-frequency approximation method refers to a simplified calculation method used when the sound wave frequency is high. It is based on the assumption of geometric acoustics, that is, at high frequencies, the propagation and scattering of sound waves can be treated in an analogy with light, and sound waves mainly manifest as reflection and diffraction when encountering targets. This method is particularly useful when dealing with large-scale complex targets because it can reduce the computational complexity.

[0028] The ray bouncing method is a high-frequency approximate numerical calculation method that divides the incident sound wave into several sound beams and calculates the reflection direction and energy loss of each sound beam on the target surface based on the geometric acoustic method. This method takes into account the multiple reflections of the sound beam on the target surface and obtains the scattering field of the entire target by calculating the superposition of the scattering fields generated by all sound beams.

[0029] Kirchhoff approximation is a classic acoustic scattering theory that assumes that the incident wave and the scattered wave can be approximated by plane waves at every point on the target surface. The plate element method based on Kirchhoff approximation decomposes the target into many small plane elements, calculates the scattering of the sound wave by each element, and then superimposes these scattered fields to predict the acoustic scattering characteristics of the entire target.

[0030] Due to the limitations of the above-mentioned methods, the present invention proposes a method for quickly predicting the multiple acoustic scattering characteristics of complex underwater submersibles. This method uses an iterative physical acoustic multi-layer grouping model to approximate the multiple scattered sound fields of the submersible as the coherent superposition results of scattered sound fields of different orders of discrete surface elements within a non-empty group. This method has the advantages of clear physical meaning, high calculation accuracy and efficiency.

[0031] According to one or more embodiments, a method for quickly predicting the multiple acoustic scattering characteristics of a complex underwater submersible is generally divided into two parts: one is preprocessing, i.e., using an octree structure to establish a multi-layer grouped facet model of a complex underwater submersible; the other is using an iterative physical acoustic acceleration algorithm to calculate the ordered scattering sound field of discrete facets in non-empty groups, and then coherently superimpose the facet ordered scattering sound fields to obtain the target's overall ordered scattering sound field, and finally coherently superimpose different order scattering sound fields to obtain the target's overall total multiple scattering sound field. Combining the above schemes, the multiple acoustic scattering characteristics of a complex underwater submersible can be quickly predicted. Figure 1 shown.

[0032] Preprocessing includes:

[0033] Construct a geometric model of the submersible for subsequent acoustic simulation and calculation;

[0034] Divide the submersible mesh model. Based on the geometric model, divide the submersible model into smaller units to facilitate numerical calculations.

[0035] A multi-layer grouped facet model of a submersible is established, and the surface of the submersible is divided into multiple facets to simulate the complex structure of the submersible.

[0036] The further solution process includes:

[0037] Use iterative algorithms to speed up the physical acoustics simulation process;

[0038] Calculate scattered sound fields of different orders and target the multiple scattering phenomenon of submarines to obtain more accurate simulation results.

[0039] Further post-processing includes:

[0040] Analyze the acoustic characteristics of the submersible in the underwater environment, including the time domain echoes and target strength of multiple acoustic scattering of complex underwater submersibles.

[0041] From the construction of the geometric model to the calculation of acoustic properties, each step is to more accurately simulate and predict the acoustic behavior of the submersible in the underwater environment.

[0042] The method for constructing the multi-layer grouped facet model is to establish the multi-layer grouped facet model using an octree structure. The octree structure is a tree-like data structure for three-dimensional space data. It divides the three-dimensional space into multiple cubic units, each of which can be further subdivided into 8 smaller cubic units. This process can be performed recursively. The specific process is as follows:

[0043] For the three-dimensional case, the solution area is surrounded by a cube and then subdivided into 8 sub-cubes, which is recorded as the first layer.

[0044] Each sub-cube is further subdivided into 8 smaller sub-cubes to obtain the second layer.

[0045] The same logic is used to get finer layers. The number of sub-cubes in the first layer is 8. l , thus completing the multi-layer grouping of the octree of the target. Here, the grouping is based on:

[0046] First, the definitions of parent group and parent layer, sublayer and subgroup, and distant relative group are given. Let the current layer be the lth layer, and the finer layer subdivided by it be the (l+1)th layer. At this time, the lth layer is the parent layer of the (l+1)th layer, and the (l+1)th layer is the sublayer of the lth layer. The non-empty group on the parent layer is the parent group, and the non-empty group on its sublayer is the subgroup. If the two groups on the (l+1)th layer are near-field groups, and their parent group is also a far-field group, then they are distant relative groups, such as Figure 2 shown.

[0047] Here, the significance of grouping is to describe the physical properties at different levels through parent groups and child groups, including the scattering characteristics of sound waves in areas of different sizes. Distant relative groups are used to describe groups that interact at a longer distance to solve the problem of long-distance sound wave propagation. This grouping method allows the problem to be abstracted and simplified at different levels, thereby improving computational efficiency.

[0048] like Figure 2 As shown in the figure, a square is subdivided into smaller squares, each of which represents a group. These groups are marked as parent groups, child groups, or distant relatives to indicate their positions and relationships in the octree structure. The source group is the starting point for the calculation, and the face elements it contains are used for the calculation of the scattered sound field generated by the direct action of the incident sound wave; the main difference between the nearby group, distant relative group, and far-field group is the distance between them and the source group and the way they interact. The interaction between nearby groups needs to be calculated accurately, while the interaction between distant relative groups and far-field groups can be handled by approximate methods.

[0049] Further, if Figure 4 As shown, the criteria for the far-field group include:

[0050] The number of groups using the interval between the i-th group and the j-th group To determine whether the two groups are far-field groups, when When , the two groups are far-field groups, when When , the two groups are near-field groups. Here, is a positive integer and can be obtained by The value of is used to control the calculation accuracy of the algorithm. In general, the far-field criteria at different levels are:

[0051]

[0052] in, represents the ceiling function, Δ l Indicates the side length of the l-th layer group.

[0053] For example, because the geometric configuration of the ROV physical model is very complex, it is difficult to directly construct a multi-layer grouping surface element model for it. Therefore, on the basis of not affecting the acoustic scattering of the ROV model in general, the main framework and components are retained, and the simplified ROV model is as follows: Figure 3 (a) is shown. The main frames and components retained include: bow anti-collision rubber, protective plate, buoyancy block, power box, electronic compartment, multi-function compartment, 80L hydraulic source, 2 stern hydraulic thrusters, 8 power thrusters and telescopic tray. The simplified ROV model is discretized into 532378 triangular face elements, as shown in Figure 3 According to the method of the embodiment of the present disclosure, a seven-layer grouping method is adopted, and the third layer is selected as the coarsest layer. The multi-layer grouping of the face element is as follows: Figure 3 (c) as shown.

[0054] The iterative physical acoustic acceleration algorithm comprises:

[0055] Applying the Helmholtz integral formula, the scattered sound field at any point in space is:

[0056]

[0057] Φ (q) =G mul Φ (q-1) ,q≥2, (1b)

[0058] Among them, r A is the coordinate vector of the sound source A, r B is the coordinate vector of field point B, G mul is the coupled scattering kernel matrix, G radi External field radiation function matrix, G radi Φ(1) represents the first-order scattered field of the target, represents the sum of the high-order scattered fields of the target, Φ (q) represents the qth-order surface acoustic field of the target, Φ (q-1) Represents the (q-1)th order surface acoustic field of the target.

[0059] like Figure 4 As shown in the figure, assuming that the i-th group and the j-th group are far-field pairs, and the m-th face element and the n-th face element belong to the i-th group and the j-th group respectively, then the distance R between face elements m and n is nm It can be expressed as

[0060] R nm =|r cn -r cm |=|R ji +R nj -R mi |, (2)

[0061] Among them, R ji =r ci -r ci , R mi =r cm -r ci , R nj =r cn -r cj , r cm and r cn are the geometric center position vectors of the mth and nth face elements, r ci and r cj are the geometric center position vectors of the i-th group and the j-th group respectively. According to the Taylor series expansion, the distance R between the face elements m and n is nm α-th power It can be approximated as

[0062]

[0063] Among them, the power index α=1,-1,-2, is the distance between face n and the jth group center R n The α-th power term, is the distance between the face element m and the center of the i-th group R m Therefore, the coupled scattering kernel function of the mth and nth surface elements can be approximated as

[0064]

[0065] in, and represents the type I and type II clustering factors for the transfer of the mth facet center to the ith group geometric center, and Represents the type I and type II divergence factors for the transfer of the jth group geometric center to the nth facet center.

[0066] After grouping, the right side of formula (1b) can be approximately expressed in a multi-level form

[0067]

[0068] Among them, i l and j l represents the i-th group and the j-th group of the l-th layer, Indicates i l and j l The group is a near-field pair. Indicates i l and j l Groups are distant relatives, J is the number of non-empty groups, and V is the number of near-field groups. Formula (5) divides the coupling effect between the facets into two terms. The first term is represents the coupling effect between the elements in the finest layer near field group, and the second term represents the coupling effect between the distant relatives of each layer. Substituting formula (5) into formula (1a), we can get

[0069]

[0070] Formula (6) is the theoretical formula of the multi-layer group iterative physical acoustic acceleration algorithm. The first-order scattered sound field of the target is obtained by coherently superposing the first-order scattered sound field of the discrete surface elements in the non-empty group. The right-hand side term of equation (6a)

[0071]

[0072] The q(≥2)th order scattered sound field of the target is obtained by coherently superposing the q(≥2)th order scattered sound field of the discrete surface element in the non-empty group. The right-hand side term of equation (6a)

[0073]

[0074] It means that the multiple scattered sound fields of the target as a whole are obtained by coherently superimposing scattered sound fields of different orders. For a problem with N facets, the computational complexity of each layer of equation (6) is approximately N, and it can usually be divided into log N layers, so the total computational complexity is O(N log N).

[0075] To further illustrate the effect of the embodiment of the present disclosure, the following calculation example is given.

[0076] For example Figure 3The work-level ROV shown in the figure performs 0-360 degree horizontal omnidirectional time domain echo simulation, using a 4ms short pulse and a 60kHz-120kHz linear frequency modulation signal as the transmitting signal. The acoustic wave transmitting point and receiving point are 16.8m and 6.8m away from the acoustic center of the model, respectively.

[0077] Figure 5 (a) and (b) respectively give the simulation and experimental results of the time-angle distribution of the received echo pulse sequences in each azimuth direction, where the horizontal axis represents the incident azimuth angle, the vertical axis represents the echo pulse time, and the color represents the echo amplitude. i =0° corresponds to the stern incident, θ i =90° and 270° correspond to normal transverse incidence, θ i =180° corresponds to the bow incident. Figure 5 It can be seen that the echo structures of the test and simulation are quite consistent, both showing the external contour features of "WW". The bright spots of the target's multiple scattered echoes can be clearly observed in the echo, which shows that the disclosed method can accurately predict the bright spots of multiple scattered echoes of underwater complex submersibles and provide identifiable fine features for underwater target recognition.

[0078] For example Figure 3 The work-level ROV shown in the figure performs 0-360 degree horizontal omnidirectional target strength simulation. Figure 6 The experimental and simulation comparison results of the acoustic target intensity of the ROV model at a frequency of 80kHz are given, where θ i =0° corresponds to the stern incident, θ i =90° and 270° correspond to normal transverse incidence, θ i =180° corresponds to the bow incident. Figure 6 It can be seen that the test and simulation results are quite consistent, which verifies the effectiveness of the method proposed in this disclosure in calculating the scattered acoustic field of underwater complex submersibles. Both the test and simulation results show a "WW"-shaped azimuth feature, with peaks in the target intensity near the bow, stern and two transverse azimuths.

[0079] Continue with Figure 3 Taking a certain operation-level ROV shown in FIG. 1 as an example, the relationship between the calculation time of the method proposed in the present disclosure and the number of discrete bins N is analyzed, and the logarithmic proportional relationship curve between the required CPU time and the number of discrete bins N is shown in FIG. Figure 7 As shown. Figure 7 It can be seen that as the number of facets continues to increase, the computational advantage of the proposed fast method is fully reflected, and the relationship between its computational time and the number of facets is in the order of O(N log N), which is consistent with the previous analysis.

[0080] Therefore, the beneficial effects of the present disclosure include: using the method for rapidly predicting the multiple acoustic scattering characteristics of a complex underwater submersible proposed in the present disclosure, the omnidirectional time domain echo and target intensity of the multiple scattered echoes of the complex underwater submersible are rapidly predicted, providing theoretical support for predicting the fine characteristics of the multiple scattered echoes of the complex underwater submersible and improving the recognition capability of the underwater monitoring sonar for the submersible.

[0081] It should be understood that in the embodiments of the present invention, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0082] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0083] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0084] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for predicting multiple acoustic scattering characteristics of an underwater vehicle, characterized in that: The method comprises the following steps: Step 1, preprocessing step, specifically includes: Construct a geometric model of the submersible for subsequent acoustic simulation and calculation; Divide the submersible mesh model. Based on the geometric model, divide the submersible model into smaller units to facilitate numerical calculations. A multi-layer grouped facet model of a submersible is established, and the surface of the submersible is divided into multiple facets to simulate the complex structure of the submersible. Step 2, solution step, specifically includes: Use iterative algorithms to speed up the physical acoustics simulation process; Calculate different order scattered sound fields and multiple scattering phenomena of submarines to obtain accurate simulation results; Step 3, post-processing step, specifically includes: The acoustic characteristics of the underwater vehicle in the underwater environment are analyzed and obtained, including the time domain echoes of multiple acoustic scatterings of the underwater vehicle and the target intensity.

2. The method according to claim 1, characterized in that In step one, an octree structure is used to establish a multi-layer grouped facet model of the underwater vehicle.

3. The method according to claim 1, characterized in that: In step 2, an iterative physical acoustic acceleration algorithm is used to calculate the order scattered sound field of discrete surface elements in the non-empty group, and then the surface element order scattered sound field is coherently superimposed to obtain the order scattered sound field of the underwater vehicle.

4. The method according to claim 3, characterized in that In step three, different order scattered sound fields are coherently superimposed to obtain the total multiple scattered sound field of the underwater vehicle.

5. The method according to claim 2, characterized in that: The specific process of establishing the underwater vehicle multi-layer grouping facet model using the octree structure is as follows: Use a cube to surround the solution area, and then subdivide it into 8 sub-cubes. This layer is recorded as the first layer. Subdivide each sub-cube into 8 smaller sub-cubes to obtain the second layer; The same logic is used to get finer layers. The number of sub-cubes in the first layer is 8. l , thus completing the octree multi-layer grouping of the target.

6. The method according to claim 3, characterized in that The scattered sound field at any point in the underwater space is: F (q) =G mul F (q-1) ,q≥2, (1b) Among them, r A is the coordinate vector of the sound source A, r B is the coordinate vector of field point B, G mul is the coupled scattering kernel matrix, G radi External field radiation function matrix, G radi Φ (1) represents the first-order scattered field of the target, represents the sum of the high-order scattered fields of the target, Φ (q) represents the qth-order surface acoustic field of the target, Φ (q-1) Represents the (q-1)th order surface acoustic field of the target.

7. The method according to claim 1, characterized in that The underwater submersible comprises: a bow anti-collision rubber, a protection plate, a buoyancy block, a power box, an electronic cabin, a multifunctional cabin, a hydraulic source, a stern hydraulic thruster, a power thruster and a telescopic tray.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor runs the computer program to implement the method according to any one of claims 1 to 7.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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