Method for predicting characteristics of multiple acoustic scattering of underwater vehicle, electronic device and program product

Abstract

Disclosed in the present invention is a method for predicting characteristics of multiple acoustic scattering of an underwater vehicle, comprising a preprocessing step: constructing an underwater vehicle geometric model for subsequent acoustic simulation and calculation, generating a underwater vehicle mesh model: on the basis of the geometric model, generating meshes to divide the model of the underwater vehicle into smaller units so as to facilitate numerical calculation, and establishing an underwater vehicle multi-layer grouped surface element model: dividing the surface of the underwater vehicle into a plurality of surface elements so as to simulate a complex structure of the underwater vehicle; a solving step comprising: using an iterative algorithm for accelerating a physical acoustic simulation process, and calculating scattering sound fields of different orders so as to obtain an accurate simulation result for multiple scattering phenomena of the underwater vehicle; and a post-processing step comprising: analyzing acoustic characteristics, comprising multiple acoustic scattering time domain echoes and target strength of the underwater vehicle, of the underwater vehicle in an underwater environment.

Description

A method, electronic equipment, and software product for predicting the multiple acoustic scattering characteristics of underwater vehicles. Technical Field

[0001] This invention belongs to the field of underwater acoustic engineering technology, and specifically relates to a method, electronic equipment and program products for predicting the multiple acoustic scattering characteristics of underwater vehicles. Background Technology

[0002] As key equipment for marine resource development, underwater vehicles (UVs) exhibit diverse types and complex shapes, making their detection and identification a key development direction for underwater security. Compared to optical, electromagnetic, or passive acoustic detection methods, active sonar is a more effective means of detecting and identifying unmanned underwater vehicles (UUVs). To improve the accuracy and efficiency of active sonar detection systems, a certain understanding of the echo characteristics of UUVs is necessary. For example, ROVs (Remote Operated Vehicles), a type of underwater vehicle, have complex structures such as extended operating chassis, fairings, propeller channels, propeller ducts, electronics compartments, and hydraulic units. Incident sound waves undergo multiple acoustic scattering on the surfaces of these structures. This is because the ROV's electronics compartment and hydraulic unit can be a combined unit, and the extended operating chassis supporting this combination uses acoustically transparent materials, allowing sound waves to penetrate the chassis. Similarly, the ROV's propulsion system can also be a combined unit; when the propulsion channel is designed with a concave shape, it will also cause acoustic scattering of incident waves.

[0003] Therefore, when underwater vehicles are assigned to perform complex tasks, they carry multiple sensors and equipment. However, there is currently limited research on the fine echo characteristics caused by multiple acoustic scattering due to the complex surface structure of such complex underwater vehicles due to the multiple devices they carry, and there is a lack of efficient multiple acoustic scattering prediction models for complex underwater vehicles, which cannot meet the needs of modern underwater security. Summary of the Invention

[0004] One embodiment of this disclosure provides a method for rapid prediction of multiple acoustic scattering characteristics of complex underwater vehicles, comprising:

[0005] The preprocessing steps specifically include:

[0006] Construct a geometric model of the submersible for subsequent acoustic simulations and calculations;

[0007] The underwater vehicle mesh model is divided into smaller units based on the geometric model to facilitate numerical calculations.

[0008] A multi-layer grouped surface element model of the submersible was established, dividing the surface of the submersible into multiple surface elements to simulate the complex structure of the submersible;

[0009] The solution steps specifically include:

[0010] Use iterative algorithms to accelerate the physical acoustic simulation process;

[0011] Calculate the scattered sound fields of different orders to obtain accurate simulation results for the multiple scattering phenomenon of underwater vehicles;

[0012] Post-processing steps specifically include:

[0013] The acoustic characteristics of the underwater vehicle in the underwater environment were obtained through analysis, including the time-domain echo of multiple acoustic scatterings of the underwater vehicle and the target intensity. Attached Figure Description

[0014] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example, not limitation, in which:

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

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

[0017] Figure 3 shows a simplified ROV geometric model and its multi-layer grouping of face elements according to one embodiment of the present invention.

[0018] Figure 4-a shows the geometric model, Figure 4-b shows the mesh model, and Figure 4-c shows the multi-layer grouping model.

[0019] Figure 4 is a schematic diagram of far-field pairing within a computational region according to one embodiment of the present invention.

[0020] Figure 5 is a schematic diagram of the time-angle spectrum of the scattered echo from an operational ROV model according to one embodiment of the present invention. Figure 4-a shows the simulation results, and Figure 4-b shows the experimental results.

[0021] Figure 6 is a schematic diagram of the acoustic target intensity direction distribution (80kHz) of an operational ROV according to one embodiment of the present invention.

[0022] Figure 7 is a schematic diagram illustrating the effect of the number of meshing elements on the computation time of the proposed method according to one embodiment of the present invention. Detailed Implementation

[0023] Sound scattering refers to the phenomenon where sound waves scatter in various directions according to a certain pattern when they encounter obstacles or inhomogeneities in a medium. This scattering phenomenon is closely related to factors such as the physical properties, shape, and size of the obstacle or medium, as well as the wavelength of the sound wave. Sound scattering characteristics are particularly important in the research of underwater unmanned vehicles (UUVs) because these characteristics affect the UUV's detection, positioning, and communication capabilities in water.

[0024] Current research on the acoustic scattering characteristics of underwater unmanned vehicles (UUVs) mainly focuses on experimentally measuring the acoustic target intensity. However, this approach is costly and time-consuming due to the high requirements for test sites, test models, and personnel, making it unacceptable for rapid research and development. Furthermore, conducting acoustic scattering measurements on non-cooperative UUV targets is also difficult. Therefore, modeling the acoustic scattering characteristics of complex underwater UUV targets is of significant importance.

[0025] The main modeling methods for the acoustic scattering characteristics of complex underwater targets can be broadly classified into two categories: low-frequency numerical methods and mid-to-high-frequency approximation methods. Among them, low-frequency numerical methods, represented by the finite element method and the boundary element method, have the advantages of high accuracy and high stability. However, as the target size increases, they also suffer from the disadvantages of high computational load and high storage requirements.

[0026] In recent years, most research has shifted towards using high-frequency approximation methods to solve the acoustic scattering problem of large-scale complex targets. Among them, the ray bouncing method, when considering coupling effects, essentially only considers the coupling effects caused by mirror reflections. For concave targets with large curvatures, there is a problem of ray divergence, resulting in low computational accuracy. The plate element method based on the Kirchhoff approximation has high computational efficiency, but it does not consider multiple acoustic scattering effects, resulting in lower accuracy for acoustic scattering calculations of complex underwater targets.

[0027] Here, the high-frequency approximation method refers to a simplified calculation method used when sound waves have high frequencies. It is based on the assumption of geometrical acoustics, that at high frequencies, the propagation and scattering of sound waves can be treated analogously to light rays, with sound waves primarily exhibiting reflection and diffraction when encountering a target. This method is particularly useful when dealing with large-scale, complex targets because it reduces computational complexity.

[0028] The acoustic bounce method is a high-frequency approximation 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 using geometric acoustics. This method considers multiple reflections of the sound beams on the target surface and obtains the scattered field of the entire target by calculating the superposition of the scattered fields produced by all the sound beams.

[0029] The Kirchhoff approximation, a classic acoustic scattering theory, assumes that every point on the target surface, both incident and scattered, can be approximated by a plane wave. The plate-element method, based on the Kirchhoff approximation, decomposes the target into many small planar elements, calculates the scattering of sound waves 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 methods, this disclosure proposes a rapid prediction method for the multiple acoustic scattering characteristics of complex underwater vehicles. This method uses an iterative physical acoustic multilayer grouping model to approximate the multiple scattering sound field of the underwater vehicle as the coherent superposition result of the scattering sound fields of different orders of discrete surface elements in a non-empty group. It has the advantages of clear physical meaning, high computational accuracy and efficiency.

[0031] According to one or more embodiments, a rapid prediction method for the multiple acoustic scattering characteristics of complex underwater vehicles generally consists of two parts: first, preprocessing, which involves establishing a multi-layer grouped surface model of the complex underwater vehicle using an octree structure; second, using an iterative physical acoustic acceleration algorithm to calculate the graded-order scattering sound fields of discrete surface elements within the non-empty group, then coherently superimposing the graded-order scattering sound fields of the surface elements to obtain the overall graded-order scattering sound field of the target, and finally coherently superimposing the scattering sound fields of different orders to obtain the overall multiple scattering sound field of the target. Combining the above scheme, the multiple acoustic scattering characteristics of complex underwater vehicles can be rapidly predicted. (See Figure 1.)

[0032] Preprocessing includes:

[0033] Construct a geometric model of the submersible for subsequent acoustic simulations and calculations;

[0034] The underwater vehicle mesh model is divided into smaller units based on the geometric model to facilitate numerical calculations.

[0035] A multi-layer grouped surface element model of the submersible was established, dividing the surface of the submersible into multiple surface elements to simulate the complex structure of the submersible.

[0036] The further solution process includes:

[0037] Use iterative algorithms to accelerate the physical acoustic simulation process;

[0038] Calculate the scattered sound field of different orders, and take into account the multiple scattering phenomenon of the submarine to obtain more accurate simulation results.

[0039] Further post-processing includes:

[0040] The acoustic characteristics of underwater vehicles in underwater environments are analyzed, including time-domain echoes of multiple acoustic scattering from complex underwater vehicles and target intensity.

[0041] From constructing the geometric model to calculating the acoustic properties, every step is aimed at more accurately simulating and predicting the acoustic behavior of underwater vehicles in the underwater environment.

[0042] The method for constructing the multi-layer grouped surface model is to use an octree structure. An octree is a tree-like data structure used for 3D spatial data. It divides the 3D space into multiple cubic units, each of which can be further subdivided into eight smaller cubic units. This process can be performed recursively. The specific process is as follows:

[0043] In the three-dimensional case, the solution domain is enclosed by a cube, and then subdivided into 8 sub-cubes, which is called the first layer.

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

[0045] This process is repeated to obtain finer layers, with the number of sub-cubes in the l-th layer being 8. l This completes the multi-level grouping of the target octree. Here, the grouping is based on:

[0046] First, we define parent groups and parent layers, child layers and child groups, and distant relatives. Let the current layer be layer l, then the subdivided layers are layer (l+1). In this case, layer l is the parent layer of layer (l+1), and layer (l+1) is the child layer of layer l. Non-empty groups on the parent layer are parent groups, and non-empty groups on its child layers are child groups. If two groups in layer (l+1) are near-field groups, and their parent group is a far-field group, then they are distant relatives, as shown in Figure 2.

[0047] Here, the significance of grouping lies in describing physical properties at different levels through parent and child groups, including the scattering characteristics of sound waves over regions of different sizes. Distant parent groups describe groups interacting at longer distances, thus addressing the problem of long-distance sound wave propagation. This grouping method allows for abstraction and simplification of the problem at different levels, thereby improving computational efficiency.

[0048] As shown in Figure 2, a square is subdivided into smaller squares, each representing a group. These groups are labeled as parent, child, or distant relative groups to indicate their position and relationship within the octree structure. The source group serves as the starting point for computation, containing surface elements used to calculate the scattered sound field generated by the direct interaction of the incident sound wave. The main differences between the nearby, distant relative, and far-field groups lie in their distance from the source group and their interaction patterns. The interactions between nearby groups require precise calculation, while the interactions between distant relative and far-field groups can be handled using approximation methods.

[0049] Furthermore, as shown in Figure 4, the criteria for the far-field group include:

[0050] Using the number of groups between the i-th and j-th groups To determine whether the two groups are far-field groups, when At that time, the two groups were the far-field groups. At that time, both groups are near-field groups. It is a positive integer, which can be obtained through The value of controls the computational precision of the algorithm. Generally, the far-field criteria for different levels are:

[0051] in, Denotes the floor function, Δ l This represents the side length of the l-th layer group.

[0052] For example, because the geometric configuration of the ROV physical model is very complex, it is difficult to directly construct a multi-layer grouped surface element model for it. Therefore, without significantly affecting the sound scattering of the ROV model, the main framework and components are retained, and the simplified ROV model is shown in Figure 3(a). The retained main framework and components include: bow anti-collision rubber, protective plate, buoyancy block, power box, electronics compartment, multi-functional compartment, 80L hydraulic power source, 2 stern hydraulic thrusters, 8 power thrusters, and telescopic tray. The simplified ROV model is discretized into 532,378 triangular surface elements, as shown in Figure 3(b). According to the method of this embodiment, a seven-layer grouping method is adopted, and the third layer is selected as the coarsest layer, and the multi-layer grouping of surface elements is shown in Figure 3(c).

[0053] The iterative physical acoustic acceleration algorithm includes:

[0054] Applying the Helmholtz integral formula, the scattered sound field at any point in space is: Φ (q) =G mul Φ (q-1) ,q≥2, (1b)

[0055] Where, r A Let r be the coordinate vector of sound source A. B Let G be the coordinate vector of field point B. mul It is the coupled scattering kernel function matrix, G radi External radiation function matrix, G radi Φ (1) This represents the first-order scattering field of the target. Φ represents the sum of the higher-order scattered fields of the target. (q) Let Φ represent the q-th order surface acoustic field of the target. (q-1) Let represent the (q-1)th order surface acoustic field of the target.

[0056] As shown in Figure 4, assume that the i-th group and the j-th group are far-field pairs, and the m-th and n-th face elements belong to the i-th and j-th groups respectively. At this time, the distance R between face elements m and n is... nm It can be represented as R nm =|r cn -r cm |=|R ji +R nj -R mi |, (2)

[0057] Among them, R ji =r cj -r ci R mi =r cm -r ci R nj =r cn -r cj r cm and r cn These are the geometric center position vectors of the m-th and n-th face elements, respectively, r ci and r cj These are the geometric center position vectors of the i-th and j-th groups, respectively. According to the Taylor series expansion, the distance R between surface elements m and n is... nm α-th power term It can be approximated as

[0058] Where the power exponent α = 1, -1, -2, The related term R is the distance between the nth face element and the jth group of centers. n The α-th power term, The related term R is the distance between the surface element m and the center of the i-th group. m The α-th term. Therefore, the coupling scattering kernel function of the m-th and n-th surface elements can be approximated as...

[0059] in, and The type I and type II aggregation factors represent the transfer of the m-th surface element center to the i-th geometric center. and The type I and type II divergence factors represent the shift of the geometric center of the j-th group to the center of the n-th surface element.

[0060] After grouping, the right-hand side of equation (1b) can approximate a multi-level form.

[0061] Among them, i l and j l N represents the i-th and j-th groups in the l-th layer. jlIndicate i l and j l The group is a near-field pair, R jl Indicate i l and j l The groups are distantly related, J is the number of non-empty groups, and V is the number of near-field groups. Equation (5) divides the coupling effect between surface elements into two terms, the first term... The second term represents the coupling effect between facets in the finest near-field group. This represents the coupling effect between face elements in each layer of distant relatives. Substituting equation (5) into equation (1a) yields...

[0062] Equation (6) is the theoretical formula for the multi-layer grouped iterative physical acoustic acceleration algorithm. In Equation (6a), the terms on the right-hand side... The first-order scattered sound field of the target is obtained by coherently superimposing the first-order scattered sound fields of discrete surface elements within a non-empty group, as shown in the right-hand side of equation (6a).

[0063] The q-th (≥2)-th order scattered sound field of the target is obtained by coherently superimposing the scattered sound fields of discrete surface elements within a non-empty group. In the right-hand side of equation (6a)...

[0064] This indicates that the multiple scattered sound fields of the target as a whole are obtained by coherently superimposing the scattered sound fields of different orders. For a problem with N surface elements, 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).

[0065] To further illustrate the effects of the embodiments of this disclosure, the following calculation examples are provided.

[0066] A 0-360 degree horizontal omnidirectional time-domain echo simulation was performed on a certain operational ROV shown in Figure 3. A 4ms short pulse and a 60kHz–120kHz linear frequency modulated signal were used as the transmission signal. The sound wave transmission point and the receiving point were 16.8m and 6.8m away from the sound center of the model, respectively.

[0067] Figures 5(a) and (b) show the simulation and experimental results of the time-angle distribution of the received echo pulse sequences at various azimuths, respectively. The horizontal axis represents the incident azimuth angle, the vertical axis represents the echo pulse time, and the color represents the echo amplitude. θ i =0° corresponds to stern incidence, θ i = 90° and 270° correspond to normal transverse incidence, θ i=180° corresponds to the bow incidence. As shown in Figure 5, the echo structures of the experiment and simulation are quite consistent, both exhibiting the external contour features of "WW". The distribution of multiple scattering echo bright spots of the target can be clearly observed in the echo, which indicates that the method disclosed in this paper can accurately predict the distribution of multiple scattering echo bright spots of complex underwater vehicles, providing identifiable and fine features for underwater target identification.

[0068] A 0-360 degree horizontal omnidirectional target intensity simulation was performed on a certain operational ROV shown in Figure 3. Figure 6 shows the experimental and simulation comparison results of the acoustic target intensity of the ROV model at a frequency of 80kHz, where θ i =0° corresponds to stern incidence, θ i = 90° and 270° correspond to normal transverse incidence, θ i =180° corresponds to the bow incidence. As shown in Figure 6, the experimental and simulation results are in good agreement, verifying the effectiveness of the method proposed in this disclosure for calculating the scattered sound field of complex underwater vehicles. Both experimental and simulation results exhibit a "WW" shaped azimuth feature, with target intensity peaking near the bow, stern, and two positive transverse azimuths.

[0069] Continuing with the example of a job-level ROV shown in Figure 3, we analyze the relationship between the computation time of the proposed method and the number of discrete facets N. The logarithmic proportional relationship between the required CPU time and the number of discrete facets N is shown in Figure 7. As can be seen from Figure 7, as the number of facets increases, the computational advantage of the proposed fast method is fully demonstrated. The relationship between its computation time and the number of facets is on the order of O(N log N), consistent with the previous analysis.

[0070] Therefore, the beneficial effects of this disclosure include: using the rapid prediction method for the multiple acoustic scattering characteristics of complex underwater vehicles proposed in this disclosure, the omnidirectional time-domain echo and target intensity of the multiple scattering echoes of complex underwater vehicles can be predicted rapidly. This provides theoretical support for predicting the fine characteristics of the multiple scattering echoes of complex underwater vehicles and improving the identification capability of underwater monitoring sonar for underwater vehicles.

[0071] It should be understood that in the embodiments of the present invention, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, the character " / " in this document generally indicates that the preceding and following associated objects have an "or" relationship.

[0072] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0073] If the integrated unit is implemented as 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, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting the multiple acoustic scattering characteristics of an underwater vehicle, characterized in that, The method includes the following steps: Step 1, preprocessing, specifically includes: Construct a geometric model of the submersible for subsequent acoustic simulations and calculations; The underwater vehicle mesh model is divided into smaller units based on the geometric model to facilitate numerical calculations. A multi-layer grouped surface element model of the submersible was established, dividing the surface of the submersible into multiple surface elements to simulate the complex structure of the submersible; Step two, the solution steps, specifically include: Use iterative algorithms to accelerate the physical acoustic simulation process; Calculate the scattered sound fields of different orders to obtain accurate simulation results for the multiple scattering phenomenon of underwater vehicles; Step three, post-processing steps, specifically include: The acoustic characteristics of the underwater vehicle in the underwater environment were obtained through analysis, including the time-domain echo 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 surface model of the underwater vehicle.

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

4. The method according to claim 3, characterized in that, In step three, the scattered sound fields of different orders 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 multi-layer grouped element model of the underwater vehicle using an octree structure is as follows: Enclose the solution domain with a cube, and then subdivide it into 8 sub-cubes. This layer is called the first layer. Each sub-cube is further subdivided into 8 smaller sub-cubes to obtain the second layer; This process is repeated to obtain finer layers, with the number of sub-cubes in the l-th layer being 8. l This completes the multi-level grouping of the target octree.

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

7. The method according to claim 1, characterized in that, The underwater vehicle includes: bow anti-collision rubber, protective plate, buoyancy block, power box, electronics compartment, multi-functional compartment, hydraulic source, stern hydraulic thruster, power thruster, and telescopic tray.

8. An electronic device comprising 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 method as described in 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, it implements the method as described in any one of claims 1 to 7.

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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