Ship bottom bubble shunting control method for reducing sound wave interference

Abstract

The invention discloses a ship bottom bubble diversion control method capable of reducing sound wave interference, and relates to the technical field of ship engineering. Comprising the steps that S1, active bubble flow dividing devices are symmetrically arranged on the two sides of a ship body, and the active bubble flow dividing devices are located on the downstream of a ship bow wave breaking area and adjacent to a ship bottom acoustic window area; s2, acquiring navigation state parameters of the ship and bubble distribution information around a ship bottom acoustic window area in real time; active bubble flow dividing devices are symmetrically arranged on the two sides of a ship body, ship navigation state parameters obtained in real time and bubble distribution information around a ship bottom acoustic window are combined, a control instruction is generated through a flow field-bubble coupling prediction model, and the fluid jet direction, flow and pressure of the devices are independently adjusted to change a local flow field; bubbles can be actively guided, shunted or restrained from converging towards an acoustic window, and the problems that a traditional passive structure cannot dynamically adapt to changes of the navigational speed and the sea condition and is lack of active intervention capacity for formed bubble flow are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of marine engineering technology, and in particular to a method for controlling the flow of air bubbles at the bottom of a ship to reduce acoustic interference. Background Technology

[0002] As marine scientific research activities continue to deepen, shipborne acoustic detection systems play a crucial role in tasks such as seabed topographic mapping and hydrological surveys. These systems are typically installed in the central area of ​​the bottom of research vessels. They emit sound waves underwater and receive the reflected signals, using the sound wave propagation speed and round-trip time to accurately calculate parameters such as water depth. However, the stability of sound wave propagation in water is highly dependent on the homogeneity of the medium. Any factor that causes drastic fluctuations in water density will significantly interfere with the detection accuracy, thus affecting the reliability and scientific value of the research data.

[0003] For example, during ship navigation, the waves generated by the interaction between the bow and the water surface draw in a large amount of air, forming bubbles that are swept downwards along both sides of the hull and eventually converge in the acoustic window area in the middle of the hull. These bubbles, when mixed with water, form a heterogeneous medium, causing sound waves to encounter scattering, refraction, and energy attenuation in the initial stages of transmission, making it difficult for them to effectively penetrate to the seabed and form a clear echo. Especially at medium to high speeds, the continuous generation and stable downward sweep of bubbles results in a long-term deterioration of the acoustic detection environment, severely restricting the implementation conditions for high-precision surveying operations.

[0004] Existing technologies mostly employ passive fairings or local deflectors to reduce bubble intrusion. However, these structures struggle to dynamically adapt to changes in bubble trajectories under varying speeds and sea states, and lack the ability to actively intervene in established downward-sweeping bubble flows. Furthermore, traditional hull designs do not establish bubble isolation mechanisms for acoustically sensitive areas, leading to bubbles inevitably crossing the sound wave emission path. In addition, the lack of real-time control over the coupling relationship between bubble motion and the hull flow field means that existing solutions cannot fundamentally eliminate bubble interference with acoustic detection. Summary of the Invention

[0005] Purpose of the invention: To address the above-mentioned problems, the purpose of this invention is to provide a method for controlling the flow of air bubbles at the bottom of a ship to reduce acoustic interference. This method can actively guide, divert, and suppress the convergence of air bubbles at the bottom of the ship, thereby ensuring the stable operation of the acoustic detection device under complex navigation conditions.

[0006] Technical solution: A method for controlling bubble diversion at the bottom of a ship to reduce acoustic interference, comprising the following steps:

[0007] S1: Active bubble splitting devices are symmetrically installed on both sides of the hull. Both active bubble splitting devices are located downstream of the wave breaking zone at the bow and near the acoustic window area at the bottom of the ship.

[0008] S2: Real-time acquisition of the ship's navigation status parameters and bubble distribution information around the acoustic window area on the bottom of the ship;

[0009] S3: Generate control commands for the active bubble splitter based on navigation status parameters and bubble distribution information;

[0010] S4: According to the control command, independently adjust the operating parameters of the active bubble diverting devices on both sides of the hull to change the local flow field on both sides of the hull, thereby guiding, diverting or suppressing the convergence of bubbles towards the acoustic window area at the bottom of the hull; wherein, the operating parameters of the active bubble diverting devices include at least one of the following parameters: fluid injection direction, fluid injection flow rate and fluid injection pressure.

[0011] In addition, when the speed in the real-time acquired navigation status parameters is in the preset medium-high speed range, and the correlation database between ship speed and sea state level indicates that the current sea state level is higher than the preset calm level, the automatic control mode of the active bubble diverter is activated; in the automatic control mode, the steps of acquiring navigation status parameters, acquiring bubble distribution information, generating control commands, and adjusting operating parameters are executed periodically.

[0012] Furthermore, in step S2, the ship's real-time speed and heading angle are obtained through the ship navigation system as navigation state parameters; underwater real-time image sequences are acquired through an optical imaging sensor array installed around the acoustic window area on the bottom of the ship, and the underwater real-time image sequences are processed to identify and quantify the spatial distribution density and motion trajectory of bubbles as bubble distribution information.

[0013] The optimal acquisition of real-time underwater image sequences includes the following steps:

[0014] S21: Multiple optical imaging sensors are arranged in a ring array around the acoustic window, and the fields of view of adjacent sensors overlap.

[0015] S22: Synchronously trigger all optical imaging sensors to acquire images and obtain a set of spatially consistent real-time underwater images;

[0016] S23: Stitch and fuse a set of underwater real-time images to generate a panoramic underwater real-time image sequence covering the entire area around the acoustic window.

[0017] Ideally, obtaining bubble distribution information includes the following steps:

[0018] S24: Perform grayscale conversion and background subtraction on each frame of the underwater real-time image to extract the target region containing bubbles;

[0019] S25: Perform edge detection and connected component analysis on the target region to separate individual bubble targets;

[0020] S26: Calculate the motion vector of the same bubble target based on the position coordinates of the same bubble target in multiple consecutive frames of images;

[0021] S27: Count the number of bubble targets appearing within a preset spatial grid per unit time, and use it as the bubble spatial distribution density of that grid.

[0022] Furthermore, in step S3, the generation of control commands includes the following steps:

[0023] S31: Input the navigation state parameters and bubble distribution information into the preset flow field-bubble coupling prediction model;

[0024] S32: Using a flow field-bubble coupling prediction model, simulate the convergence trend of bubbles within a preset time window under the current navigation state without intervention;

[0025] S33: Determine the required flow field disturbance intensity and direction based on the convergence trend and the preset bubble isolation threshold;

[0026] S34: Maps the intensity and direction of flow field disturbance to the specific operating parameters of the active bubble splitter and generates control commands.

[0027] Ideally, the establishment of the flow field-bubble coupling prediction model includes the following steps:

[0028] S311: Based on computational fluid dynamics, establish a three-dimensional numerical model including the ship's geometry, active bubble splitting device, and surrounding water area.

[0029] S312: In the three-dimensional numerical model, bubbles are defined as discrete phases, and their initial particle size distribution and inlet conditions are set.

[0030] S313: By solving the Navier-Stokes equations and discrete phase motion equations, the bubble motion trajectory under different navigation states and different combinations of working parameters of the diversion device is simulated.

[0031] S314: The simulated bubble trajectory data is correlated with the corresponding navigation state parameters and the working parameters of the flow splitter to train a flow field-bubble coupling prediction model.

[0032] Ideally, in step S34, the control command includes:

[0033] When the predicted bubble convergence trend shows that the bubble density on the port side of the hull is higher than that on the port side, increase the fluid jet flow rate of the port active bubble splitter and adjust the fluid jet direction to deflect outwards.

[0034] When the predicted bubble convergence trend shows that the bubble density on the right side of the hull is higher than that on the left side, increase the fluid jet flow rate of the active bubble splitter on the right side and adjust the fluid jet direction to deflect outwards.

[0035] When the predicted bubble convergence trend shows that the bubble density on both sides of the hull exceeds the bubble isolation threshold, the fluid jet flow rate of the active bubble diverting devices on both sides is increased simultaneously, and the fluid jet direction is adjusted to deflect outwards.

[0036] Preferably, the active bubble diverting device includes a fluid supply unit, a direction adjustment mechanism, and a nozzle array; wherein, the fluid supply unit is used to provide pressurized fluid; the direction adjustment mechanism is connected to the nozzle array and is used to drive the nozzle array to rotate in the horizontal and vertical planes; the nozzle array is connected to the fluid supply unit and is used to spray the pressurized fluid into the water in a set direction.

[0037] Ideally, the fluid supply unit is a high-pressure plunger pump with a maximum output pressure of 0.5 MPa, the direction adjustment mechanism is a dual-axis servo-driven rotary support, which can achieve horizontal deflection of 0 to 30° and vertical deflection of 0 to 10° with a deflection accuracy of 0.1°, and the nozzle array consists of 8 fan-shaped nozzles with a nozzle spray angle of 30°.

[0038] Ideally, in step S4, the adjustment of the active bubble splitting devices on both sides of the hull includes:

[0039] Send a first angle signal to the direction adjustment mechanism of the active bubble splitter on the left side of the hull, causing its nozzle array to deflect to the first preset azimuth angle;

[0040] Send a first flow signal to the fluid supply unit of the active bubble diverter on the left side of the hull, so that it outputs pressurized fluid at a first preset flow rate;

[0041] Send a second angle signal to the direction adjustment mechanism of the active bubble splitter on the right side of the hull, so that its nozzle array deflects to the second preset azimuth angle;

[0042] A second flow signal is sent to the fluid supply unit of the active bubble diverter on the right side of the hull, causing it to output pressurized fluid at a second preset flow rate.

[0043] Beneficial effects: Compared with the prior art, the advantages of the present invention are:

[0044] This invention utilizes symmetrical active bubble diversion devices on both sides of the hull. By combining real-time acquired ship navigation parameters with bubble distribution information around the acoustic window at the bottom of the ship, and using a flow field-bubble coupling prediction model to generate control commands, the fluid injection direction, flow rate, and pressure of the devices are independently adjusted to change the local flow field. This actively guides, diverts, or inhibits bubbles from converging towards the acoustic window, effectively solving the problems of traditional passive structures being unable to dynamically adapt to changes in speed and sea state and lacking the ability to actively intervene in formed bubble flows. At the same time, by establishing a database linking speed and sea state levels to activate the automatic control mode, real-time regulation of bubble distribution is achieved, preventing bubbles from crossing the sound wave emission path, ensuring the uniformity of the acoustic detection medium, reducing sound wave scattering, refraction, and energy attenuation, and improving the detection stability and data reliability of the shipborne acoustic detection system. Furthermore, the device layout and process design are adaptable to different marine research vessels, demonstrating strong practicality and broad application prospects. Attached Figure Description

[0045] Figure 1 This is the overall control flowchart of the present invention;

[0046] Figure 2 A flowchart for generating intelligent control instructions according to the present invention;

[0047] Figure 3 This is a flowchart illustrating the adjustment of the active bubble diversion device on both sides of the hull according to the present invention. Detailed Implementation

[0048] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0049] For a method to reduce acoustic interference in hull bubble diversion control, please refer to [link / reference]. Figures 1-3 It includes the following steps:

[0050] First, implement S1, symmetrically installing active bubble diverters on both sides of the hull. These active bubble diverters are located downstream of the bow wave-breaking zone and near the acoustic window area on the hull bottom. In practical applications, the specific placement of the devices needs to be determined based on the hull's hull shape and the installation location of the acoustic window. Taking an 85m-class oceanographic research vessel as an example, its central acoustic window on the hull bottom has a diameter of 1.0m and is used to carry a multibeam echo sounder. The active bubble diverters need to be symmetrically arranged on both sides of the hull, 1.5m from the leading edge of the acoustic window and 3m downstream of the bow wave-breaking zone. This placement is based on the fact that bubbles generated by bow wave breaking sweep downwards along both sides of the hull; placing the devices in this area allows for effective intervention before the bubbles reach the acoustic window. The active bubble diverter includes a fluid supply unit, a direction adjustment mechanism, and a nozzle array. The fluid supply unit provides pressurized fluid. The direction adjustment mechanism, connected to the nozzle array, drives the nozzle array to rotate in both horizontal and vertical planes. The nozzle array, connected to the fluid supply unit, injects the pressurized fluid into the water in a set direction. For equipment selection, a high-pressure plunger pump is used for the fluid supply unit, with a maximum output pressure of 0.5 MPa, meeting the fluid injection pressure requirements under different sea conditions. The direction adjustment mechanism employs a dual-axis servo-driven rotating support, enabling horizontal deflection of 0–30° and vertical deflection of 0–10°, with a deflection accuracy of 0.1°. The nozzle array consists of eight fan-shaped nozzles with a 30° injection angle, creating a large-scale flow field disturbance area to ensure effective guidance of the bubble flow.

[0051] Next, step S2 is executed to acquire the ship's navigation status parameters and bubble distribution information around the acoustic window area on the hull in real time. This step is the foundation for the generation of subsequent control commands, and the real-time performance and accuracy of the data must be guaranteed. First, the ship's real-time speed and heading angle are acquired as navigation status parameters through the ship's navigation system. The navigation system selected is the Beidou dual-mode navigation system, with a sampling frequency set to 10Hz. The speed measurement accuracy can reach 0.1kn, and the heading angle measurement accuracy can reach 0.1°, which can provide high-precision navigation status data for subsequent model calculations. During data transmission, serial communication is used to transmit the navigation data to the central control unit in real time, with the transmission delay controlled within 10ms to ensure the real-time performance of the data.

[0052] Subsequently, the operation of acquiring real-time underwater image sequences using an array of optical imaging sensors installed around the acoustic window area on the hull was carried out. The specific implementation followed these steps: Multiple optical imaging sensors were arranged in a ring array around the outer perimeter of the acoustic window, with overlapping fields of view between adjacent sensors. In practical applications, eight high-definition underwater optical imaging sensors were selected, with a resolution of 1920×1080, a frame rate of 25fps, and an IP68 waterproof rating, suitable for complex environments at depths of up to 50m. The diameter of the ring array was set to 1.2m, and the overlap rate of the fields of view between adjacent sensors was 15%. This overlap rate was set to avoid blind spots and reduce the computational load for image stitching. All optical imaging sensors were simultaneously triggered to acquire images, obtaining a set of images with... Consistent underwater real-time images are generated through a high-precision clock synchronization module, with synchronization errors controlled within 1ms. This ensures that images acquired by multiple sensors remain consistent over time, laying the foundation for subsequent image stitching and bubble recognition. A set of underwater real-time images is stitched and fused to generate a panoramic underwater real-time image sequence covering the entire area around the acoustic window. The stitching and fusion algorithm adopts a panoramic stitching method based on SIFT feature point matching. First, SIFT feature points of each frame are extracted using the difference of Gaussian pyramid. Then, the RANSAC algorithm is used to complete feature point matching and extrinsic parameter calibration. Finally, a multi-resolution fusion algorithm is used to eliminate stitching seams. The generated panoramic image can completely cover an area with a radius of 0.8m around the acoustic window, ensuring the comprehensiveness of bubble distribution information.

[0053] After image acquisition, the underwater real-time image sequence needs to be processed to identify and quantify the spatial distribution density and motion trajectory of bubbles as bubble distribution information. This process includes several core processing steps: First, each frame of the underwater real-time image is subjected to grayscale conversion and background subtraction to extract the target region containing bubbles. The grayscale conversion uses a weighted average method, the mathematical expression of which is:

[0054] ;

[0055] in Represents the pixel after grayscale conversion grayscale value, , , These represent the red, green, and blue channel pixel values ​​of the original color image at that pixel. The weighting coefficients in this formula are set based on the human eye's sensitivity to different color channels, ensuring the contrast of the image after grayscale conversion. Background subtraction processing uses an adaptive background modeling method, and the background image update formula is:

[0056] ;

[0057] in The background weighting coefficients are updated, and their values ​​are determined based on the dynamic changes in the underwater environment. For example, when the sea state is relatively calm and the water disturbance is small... A value of 0.01 is used when complex sea conditions and a large amount of suspended matter in the water cause significant dynamic changes in the image. Setting it to 0.05, this formula enables real-time updates of the background image, preventing target extraction failure due to environmental changes. The difference image is then calculated.

[0058] ;

[0059] And set the grayscale difference threshold. To extract the target area of ​​the bubble, The setting method is to statistically analyze the average grayscale difference of 50 consecutive frames. and standard deviation ,Pick ,when When the threshold setting method is used, the pixel is determined to be a bubble target area pixel. This threshold setting method can effectively distinguish bubble targets from water noise.

[0060] After extracting the target region, edge detection and connected component analysis need to be performed on the target region to separate individual bubble targets. Edge detection uses the Canny operator, and its implementation process is as follows: First, the difference image is smoothed and denoised using Gaussian filtering. The filtering formula is:

[0061] ;

[0062] in The standard deviation of the Gaussian kernel is determined based on the image noise level; for example, when the image noise is low... When the noise level is high, a value of 0.8 is used. Take 1.2, The coordinates of the center pixel of the Gaussian kernel. This represents the convolution operation, which eliminates high-frequency noise while preserving bubble edge information; next, the magnitude and direction of the image gradient are calculated, with the gradient magnitude expressed as:

[0063] ;

[0064] The expression for the gradient direction is:

[0065] ;

[0066] in , The gradients in the x and y directions are used to calculate and locate the bubble's edge contour; subsequently, non-maximum suppression and dual-threshold filtering are performed, with the higher threshold value being selected from the lower threshold value. and low threshold The method involves plotting a histogram of gradient magnitudes and taking the magnitude with a cumulative probability of 90% as the value. , Pick The dual threshold setting can effectively preserve the complete edge of the bubble and remove false edges; the connected component analysis adopts a label-based connected component algorithm, which marks the connected regions in the binary image after edge detection as a single bubble target. Each connected component corresponds to an independent bubble, and the centroid coordinates of each connected component are calculated to provide a basis for subsequent motion trajectory calculation.

[0067] After separating a single bubble target, its motion vector needs to be calculated based on the position coordinates of the same bubble target in multiple consecutive frames of images. Let the i-th frame be the first bubble target. The centroid coordinates of a bubble target in the frame image are: , No. The centroid coordinates of the bubble target in the frame image are: Then the motion vector of the bubble The calculation formula is:

[0068] ;

[0069] in The time interval between two adjacent frames is determined by the sensor frame rate; for example, when the sensor frame rate is 25fps. To improve the accuracy of motion vector calculation, the Lucas-Kanade optical flow method is used to track the coordinates of the bubble's centroid. The basic constraint equation of the optical flow method is: ,in , , respectively, are the gray-level partial derivatives of the image in the x and y directions. The time partial derivative of the image grayscale is... , These are the velocity components in the x and y directions, respectively. By solving this equation, the precise motion vector of the bubble can be obtained, avoiding tracking failure caused by bubble deformation.

[0070] Finally, the number of bubble targets appearing per unit time within the preset spatial grid is counted and used as the bubble spatial distribution density of that grid. The area surrounding the acoustic window is then divided into... A uniform spatial grid, for example, taking , The grid has a side length of 0.1m. This grid size is set based on the detection range of the acoustic window and the particle size of the bubbles, ensuring the precision of density statistics. Let the nth bubble in a certain grid be... Time to The number of bubble targets appearing within a given time period is The bubble spatial distribution density of the grid is then... The calculation formula is:

[0071] ;

[0072] in For the area of ​​a single grid cell, For example, a statistical time window This formula can quantify the bubble distribution in each grid area, providing accurate data support for the generation of subsequent control commands.

[0073] After completing data acquisition and processing in step S2, the process proceeds to step S3, the core step of generating control commands for the active bubble splitter based on navigation state parameters and bubble distribution information. The key to this step lies in the construction and application of the flow field-bubble coupling prediction model: First, the model needs to be constructed. This process involves establishing a three-dimensional numerical model based on computational fluid dynamics methods, including the ship's geometry, the active bubble splitter, and the surrounding water area. The solution domain of the three-dimensional numerical model is set as a water area centered on the ship, extending forward and backward by 3 times the ship's length, laterally and laterally by 2 times the ship's width, and vertically and horizontally by 1 time the draft. The ship's geometry... Using 1:1 scale actual hull line data, the geometric model of the active bubble splitter includes the complete structure of the fluid supply unit, direction adjustment mechanism, and nozzle array. A structured mesh is used, with mesh refinement applied to the hull surface and the area near the nozzles. The mesh size in the refined region is 0.02m, and the mesh size in the far-field region is 0.5m. This meshing method ensures computational accuracy while controlling the computational load. In the three-dimensional numerical model, the bubbles are defined as discrete phases, and their initial particle size distribution and inlet conditions are set. The initial particle size distribution of the bubbles adopts the Rosin-Rammler distribution, with the distribution function being... ,in For particles smaller than The proportion of bubble volume, For example, characteristic particle size , For example, the distribution index This distribution function can accurately fit the particle size distribution characteristics of bubbles generated by wave breaking at the bow. The bubble inlet conditions are set in the wave breaking zone at the bow, and the inlet bubble concentration is set based on actual ship test data. For example, the inlet bubble concentration during medium- and high-speed navigation is... By solving the Navier-Stokes equations and the discrete phase motion equations, the bubble trajectory under different navigation conditions and different combinations of flow splitter operating parameters is simulated. The governing equations of the Navier-Stokes equations include a continuity equation and a momentum equation. The continuity equation is as follows: The momentum equation is:

[0074] ;

[0075] in For water density, For time, For fluid velocity vector, For pressure, The dynamic viscosity of water. It is the gravitational acceleration vector. The volume force vector includes the interaction force between the bubble and the water body. The discrete phase motion equation is:

[0076] ;

[0077] in The bubble velocity vector, Bubble density, The bubble size is... This is the drag coefficient. The relative Reynolds number is expressed as follows: , For other force vectors, including lift and virtual mass forces, the bubble trajectory data under different operating conditions can be obtained by solving the above equations. The simulated bubble trajectory data is then correlated with the corresponding navigation state parameters and the working parameters of the flow diversion device to train a flow field-bubble coupling prediction model. The model uses a BP neural network, with input layer parameters including speed, heading angle, bubble distribution density in each grid, and bubble motion vector in each grid. The number of hidden layer nodes is determined by trial and error, for example, 30. The output layer parameter is the bubble convergence density in the acoustic window region within a future preset time window. The training dataset contains 1000 sets of simulated data under different operating conditions. The mean squared error is used as the loss function during training, and the loss function expression is:

[0078] ;

[0079] in For the sample size, This represents the actual bubble aggregation density. To predict bubble aggregation density, when the loss function value is less than Training is stopped when the model is completed, and the model can accurately predict future bubble convergence trends.

[0080] After the model is built, the navigation state parameters and bubble distribution information are input into the preset flow field-bubble coupling prediction model. The flow field-bubble coupling prediction model is used to simulate the convergence trend of bubbles in the future preset time window under the current navigation state without intervention. The value of the future preset time window is based on the response time of the active bubble diversion device. For example, if the device response time is 0.5s, then the time window is 1s, which can ensure that the prediction results match the device response rhythm. By the bubble convergence density of each grid in the acoustic window area output by the model in the next 1s, the convergence trend of bubbles can be intuitively judged. For example, the bubble convergence density of the left grid is higher than that of the right grid, or the density of both grids exceeds the threshold.

[0081] Subsequently, based on the convergence trend and the preset bubble isolation threshold, the required flow field disturbance intensity and direction are determined, and the bubble isolation threshold is set. The setting method involves conducting acoustic detection tests on a real ship to determine the acoustic detection accuracy under different bubble densities. The bubble density corresponding to the acoustic detection accuracy dropping below a preset acceptable threshold is then determined. For example, when the water depth measurement error of acoustic detection exceeds 0.5m, the corresponding bubble density is... Then take This threshold ensures the basic accuracy requirements of acoustic detection; flow field disturbance intensity The calculation formula is: ,in This is the disturbance intensity coefficient, and its value is based on the maximum disturbance capability of the shunt device, for example... ,when For the predicted bubble aggregation density, The larger The larger the volume, the more the direction of flow field disturbance is determined by the main area where the bubbles converge. For example, if the bubbles mainly converge towards the left acoustic window, the disturbance direction is that the left device deflects outward, which can achieve precise guidance of the bubbles.

[0082] Finally, the flow field disturbance intensity and direction are mapped to the specific operating parameters of the active bubble splitter, generating control commands. The specific mapping rules are as follows: when the predicted bubble convergence trend indicates that the bubble density on the left side of the hull is higher than on the right, increase the fluid jet flow rate of the active bubble splitter on the left and adjust the fluid jet direction to deflect outwards; when the predicted bubble convergence trend indicates that the bubble density on the right side of the hull is higher than on the left, increase the fluid jet flow rate of the active bubble splitter on the right and adjust the fluid jet direction to deflect outwards; when the predicted bubble convergence trend indicates that the bubble density on both sides of the hull exceeds the bubble isolation threshold, simultaneously increase the fluid jet flow rate of the active bubble splitters on both sides and adjust the fluid jet direction to deflect outwards on both sides. To achieve accurate parameter mapping, the flow field disturbance intensity is set. With jet flow rate The mapping formula is ,in Based on the injection flow rate, for example Outer deflection angle of the injection direction The calculation formula is: ,in Based on the deflection angle, for example Injection pressure With traffic The relationship is ,in The flow-pressure coefficient is determined by the nozzle's flow characteristic curve, for example... Through the above mapping relationship, the flow field disturbance requirements can be transformed into specific device operating parameters, generating executable control commands.

[0083] After generating the control command, proceed to step S4, where the operating parameters of the active bubble diverting devices on both sides of the hull are independently adjusted according to the control command to change the local flow field on both sides of the hull, thereby guiding, diverting, or suppressing the convergence of bubbles towards the acoustic window area at the bottom of the hull. The operating parameters of the active bubble diverting device include at least one of fluid injection direction, fluid injection flow rate, and fluid injection pressure. The specific adjustment process involves sending a first angle signal to the direction adjustment mechanism of the active bubble diverting device on the left side of the hull, causing its nozzle array to deflect to a first preset azimuth angle; sending a first flow rate signal to the fluid supply unit of the active bubble diverting device on the left side of the hull, causing it to output pressurized fluid at a first preset flow rate; and sending a second angle signal to the direction adjustment mechanism of the active bubble diverting device on the right side of the hull, causing its nozzle array to deflect to a second preset azimuth angle. A second flow signal is sent to the fluid supply unit of the active bubble diverter on the right side of the hull, causing it to output pressurized fluid at a second preset flow rate. For example, when the bubble density on the left side is higher than that on the right side, the first preset azimuth angle is 20° (outer deflection), the first preset flow rate is 0.012 m³ / s, and the corresponding injection pressure is 7.2 × 10⁴ Pa. The second preset azimuth angle is 15°, the second preset flow rate is 0.01 m³ / s, and the corresponding injection pressure is 5 × 10⁴ Pa. During the adjustment process, the central control unit transmits control commands to the execution module of the device via the CAN bus. The response time of the execution module is controlled within 0.1s, which can realize a rapid response to the bubble convergence trend. Through the independent adjustment of the two devices, the speed and direction of the local flow field can be changed, which can guide and divert the bubble flow and prevent the bubbles from converging towards the acoustic window area.

[0084] In addition, the method also includes the activation and operation of the automatic control mode, that is, to establish a correlation database between ship speed and sea state level. When the ship speed in the real-time acquired navigation status parameters is in the preset medium-high speed range, and the correlation database indicates that the current sea state level is higher than the preset calm level, the automatic control mode of the active bubble diverter is activated. In the automatic control mode, the steps of acquiring navigation status parameters, acquiring bubble distribution information, generating control commands, and adjusting operating parameters are executed periodically. The database linking ship speed and sea state was established through ship trials and marine environmental data statistics. The sea state uses the internationally recognized Beaufort scale. For example, when the speed is 10-15 knots (medium-high speed range), Beaufort scale 2 and below is calm, while scale 3 and above is not calm. The medium-high speed range is set based on the speed range where bubble interference begins to be significant during ship trials. For example, if it is set to 10-18 knots, the calmness level is set to Beaufort scale 2. The execution cycle of the automatic control mode is set to 0.5 seconds. This cycle is determined based on the response time of bubble movement and the sensor sampling frequency, which ensures that the system can track changes in bubble distribution in real time and adjust the device parameters in a timely manner.

Claims

1. A method for controlling bubble diversion at the bottom of a ship to reduce acoustic interference, characterized in that... Includes the following steps: S1: Active bubble splitting devices are symmetrically installed on both sides of the hull. Both active bubble splitting devices are located downstream of the wave breaking zone at the bow and near the acoustic window area at the bottom of the ship. S2: Real-time acquisition of the ship's navigation status parameters and bubble distribution information around the acoustic window area on the bottom of the ship; S3: Generate control commands for the active bubble splitter based on navigation status parameters and bubble distribution information; S4: According to the control command, independently adjust the operating parameters of the active bubble diverting devices on both sides of the hull to change the local flow field on both sides of the hull, thereby guiding, diverting or suppressing the convergence of bubbles towards the acoustic window area at the bottom of the hull; wherein, the operating parameters of the active bubble diverting devices include at least one of the following parameters: fluid injection direction, fluid injection flow rate and fluid injection pressure.

2. The method for reducing acoustic interference by controlling bubble diversion at the bottom of a ship according to claim 1, characterized in that: In step S2, the ship's real-time speed and heading angle are obtained through the ship navigation system as navigation state parameters; underwater real-time image sequences are acquired through an optical imaging sensor array installed around the acoustic window area on the bottom of the ship, and the underwater real-time image sequences are processed to identify and quantify the spatial distribution density and motion trajectory of bubbles as bubble distribution information.

3. The method for reducing acoustic interference by controlling bubble diversion at the bottom of a ship according to claim 2, characterized in that, The acquisition of underwater real-time image sequences includes the following steps: S21: Multiple optical imaging sensors are arranged in a ring array around the acoustic window, and the fields of view of adjacent sensors overlap. S22: Synchronously trigger all optical imaging sensors to acquire images and obtain a set of spatially consistent real-time underwater images; S23: Stitch and fuse a set of underwater real-time images to generate a panoramic underwater real-time image sequence covering the entire area around the acoustic window.

4. A method for reducing acoustic interference by controlling bubble diversion at the bottom of a ship according to claim 2 or 3, characterized in that, Obtaining bubble distribution information includes the following steps: S24: Perform grayscale conversion and background subtraction on each frame of the underwater real-time image to extract the target region containing bubbles; S25: Perform edge detection and connected component analysis on the target region to separate individual bubble targets; S26: Calculate the motion vector of the same bubble target based on the position coordinates of the same bubble target in multiple consecutive frames of images; S27: Count the number of bubble targets appearing within a preset spatial grid per unit time, and use it as the bubble spatial distribution density of that grid.

5. The method for reducing acoustic interference by controlling bubble diversion at the bottom of a ship according to claim 1, characterized in that, In step S3, the generation of control commands includes the following steps: S31: Input the navigation state parameters and bubble distribution information into the preset flow field-bubble coupling prediction model; S32: Using a flow field-bubble coupling prediction model, simulate the convergence trend of bubbles within a preset time window under the current navigation state without intervention; S33: Determine the required flow field disturbance intensity and direction based on the convergence trend and the preset bubble isolation threshold; S34: Maps the intensity and direction of flow field disturbance to the specific operating parameters of the active bubble splitter and generates control commands.

6. The method for reducing acoustic interference by controlling bubble diversion at the bottom of a ship according to claim 5, characterized in that, The establishment of the flow field-bubble coupling prediction model includes the following steps: S311: Based on computational fluid dynamics, establish a three-dimensional numerical model including the ship's geometry, active bubble splitting device, and surrounding water area. S312: In the three-dimensional numerical model, bubbles are defined as discrete phases, and their initial particle size distribution and inlet conditions are set. S313: By solving the Navier-Stokes equations and discrete phase motion equations, the bubble motion trajectory under different navigation states and different combinations of working parameters of the diversion device is simulated. S314: The simulated bubble trajectory data is correlated with the corresponding navigation state parameters and the working parameters of the flow splitter to train a flow field-bubble coupling prediction model.

7. The method for reducing acoustic interference by controlling bubble diversion at the bottom of a ship according to claim 5, characterized in that, In step S34, the control command includes: When the predicted bubble convergence trend shows that the bubble density on the port side of the hull is higher than that on the port side, increase the fluid jet flow rate of the port active bubble splitter and adjust the fluid jet direction to deflect outwards. When the predicted bubble convergence trend shows that the bubble density on the right side of the hull is higher than that on the left side, increase the fluid jet flow rate of the active bubble splitter on the right side and adjust the fluid jet direction to deflect outwards. When the predicted bubble convergence trend shows that the bubble density on both sides of the hull exceeds the bubble isolation threshold, the fluid jet flow rate of the active bubble diverting devices on both sides is increased simultaneously, and the fluid jet direction is adjusted to deflect outwards.

8. The method for reducing acoustic interference by controlling bubble diversion at the bottom of a ship according to claim 1, characterized in that: The active bubble diverting device includes a fluid supply unit, a direction adjustment mechanism, and a nozzle array; wherein, the fluid supply unit is used to provide pressurized fluid; the direction adjustment mechanism is connected to the nozzle array and is used to drive the nozzle array to rotate in the horizontal and vertical planes; the nozzle array is connected to the fluid supply unit and is used to spray the pressurized fluid into the water in a set direction.

9. The method for reducing acoustic interference by controlling bubble diversion at the bottom of a ship according to claim 8, characterized in that: The fluid supply unit is a high-pressure plunger pump with a maximum output pressure of 0.5MPa. The direction adjustment mechanism is a dual-axis servo-driven rotating bracket that can achieve horizontal deflection of 0 to 30° and vertical deflection of 0 to 10° with a deflection accuracy of 0.1°. The nozzle array consists of 8 fan-shaped nozzles with a spray angle of 30°.

10. The method for reducing acoustic interference by controlling bubble diversion at the bottom of a ship according to claim 8, characterized in that, In step S4, the adjustment of the active bubble splitting devices on both sides of the hull includes: Send a first angle signal to the direction adjustment mechanism of the active bubble splitter on the left side of the hull, causing its nozzle array to deflect to the first preset azimuth angle; Send a first flow signal to the fluid supply unit of the active bubble diverter on the left side of the hull, so that it outputs pressurized fluid at a first preset flow rate; Send a second angle signal to the direction adjustment mechanism of the active bubble splitter on the right side of the hull, so that its nozzle array deflects to the second preset azimuth angle; A second flow signal is sent to the fluid supply unit of the active bubble diverter on the right side of the hull, causing it to output pressurized fluid at a second preset flow rate.

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