A low-flow-noise submersible carrier design method and submersible carrier

Abstract

This invention relates to the field of marine observation technology, specifically to a design method and carrier for low-current noise submersible mooring systems. The method includes: 1) constructing a CST model to describe the meridional profile of the mooring mooring and control its shape changes; 2) selecting design variables and precisely adjusting shape parameters to achieve directional optimization and rapid convergence of current noise; 3) establishing an optimization objective function, balancing the optimization weights of different frequency bands, and matching noise reduction targets with detection performance requirements; 4) selecting an optimizer to solve for the optimal shape parameters, constructing an automated optimization closed loop from shape design to noise assessment; 5) introducing a temperature-salinity transition disturbance factor to construct a depth-density transition intensity probability distribution model, solidifying the required damping structure parameters and carrier shape parameters; 6) determining whether the optimization iteration meets the convergence conditions; and 7) generating the three-dimensional geometric shape of the low-current noise submersible mooring system to obtain a mooring mooring system that meets the requirements. Through continuous and smooth control of the shape, the flexibility and targeting of shape adjustments are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of marine observation technology, specifically to a design method for a low-current noise underwater mooring carrier and the mooring carrier itself. Background Technology

[0002] Submarine mooring systems are core equipment for long-term, fixed-point, and continuous observation of the marine environment. They typically consist of a surface buoy, a mooring line, and a mooring carrier that sits on the bottom or is suspended. The mooring carrier carries various acoustic, hydrological, and biochemical sensors. In practical applications, ocean currents acting on the surface of the mooring carrier can cause vortex shedding and flow separation, resulting in periodic or broadband current-induced noise. This current noise can severely interfere with the detection performance of the acoustic sensors inside the carrier (such as hydrophones and acoustic Doppler current profilers), reduce the signal-to-noise ratio, and even drown out weak target signals, becoming a key bottleneck restricting the accuracy of submarine mooring acoustic observations.

[0003] Traditional underwater mooring vehicles often employ cylindrical, spherical, or yurt-shaped designs, which generally suffer from sensitivity to the direction of incoming currents, severe flow separation, and intense vortex shedding. This results in wide-bandwidth and high-intensity flow noise, making it difficult to meet the requirements of high-precision acoustic observation for low-noise platforms. Among these, cylindrical structures are simple to manufacture but generate the most flow noise; spherical or yurt-shaped structures are isotropic but suffer from severe flow separation at the tail; conventional streamlined designs are sensitive to the direction of incoming currents, and their noise reduction effect is unstable in complex and variable deep-sea current fields. Furthermore, when underwater mooring vehicles operate in complex, stratified sea areas, changes in temperature and salinity gradients cause localized acceleration of surface flow and sudden changes in pressure pulsations, easily inducing narrow-band high-frequency flow noise howling. Therefore, traditional designs often rely on experience or simple shape imitation, lacking a systematic optimization method based on noise targets, and failing to achieve optimal synergy between shape and acoustic performance under multiple constraints. For this reason, those skilled in the art urgently need a design method and a mooring vehicle for low-flow noise underwater mooring vehicles to solve the problems existing in the prior art. Summary of the Invention

[0004] To address the problems existing in the prior art, the purpose of this invention is to provide a method and a submerged mooring carrier for mathematical description of the shape of the mooring carrier by adopting the CST parametric modeling method, constructing a meridional contour expression system with the product of class function and shape function as the core, realizing continuous and smooth control of the shape, enabling designers to independently adjust the leading edge curvature, maximum thickness position and arc contraction characteristics, significantly improving the flexibility and targeted design of the shape adjustment of the low-flow noise mooring carrier.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is: a design method for a low-flow-noise underwater buoy carrier, comprising the following steps: Step 1: Using the CST parameterization method, define the ratio of dimensionless coordinates to the contour radius of the mooring carrier, construct the CST parameterization model, and describe the contour of the mooring meridian by the product of the class function and the shape function to control the shape change; Step 2: Select design variables, and through precise control of key shape parameters, achieve directional optimization and rapid convergence of flow noise. Set geometric and physical constraints to balance low noise performance with the actual installation space requirements of internal equipment. Set static water stability constraints to enable the buoy to have good attitude self-recovery capability in a multi-directional flow environment. Step 3: Establish an optimization objective function with minimizing the total sound pressure level and the sound pressure level of a specific frequency band as the core, introduce weighting coefficients to balance the optimization weights of noise in different frequency bands, set a penalty term to meet the design feasibility, and accurately match the noise reduction target with the detection performance requirements; Step 4: Select an optimizer based on optimization algorithms, combine unsteady RANS flow field simulation with FW-H acoustic analogy method, automatically iteratively solve the optimal shape parameters, calculate the optimization objective function value for each iteration, and construct a closed loop of fully automated optimization from shape design to noise assessment. Step 5: In the optimization iteration process, a temperature-salinity jump perturbation factor is introduced. Based on the historical temperature-salinity profile data of the target deployment area, a probability distribution model of the depth-density jump intensity is constructed to identify the critical diameter-to-length ratio range that is prone to induce local flow acceleration and pressure pulsation abrupt change. In the neighborhood of the corresponding shape parameters in this range, a groove damping structure with a spiral distribution is implanted to suppress the flow instability and narrow-band high-frequency howling caused by density abrupt change, and to solidify the damping structure parameters and carrier shape parameters that meet the requirements. Step 6: Determine whether the optimization iteration meets the convergence condition. If it does not converge, update the design variables and continue the iteration. Generate a new generation of design variable population according to the update rules of the optimization algorithm, and return to step 4 to continue the flow field and acoustic calculations and solve the optimization objective function value. If it converges, output the optimal design parameter set that satisfies all constraints, and verify whether the optimal design parameter set fully satisfies the geometric and physical constraints set in step 2. Step 7: Substitute the optimal design parameter set into the CST parameterized model to generate the three-dimensional geometric shape of the low-flow-noise underwater glider carrier, thus obtaining an underwater glider carrier that meets the requirements.

[0006] The above-mentioned low-flow-noise underwater buoy design method, step 1 includes: Step 1-1: Establish a CST parametric model based on the CST parametric method, and define the dimensionless longitudinal coordinates of the meridional profile of the glider carrier. and the ratio of the outline radius ,in, For axial coordinates, For the chord length of the carrier outline, For the local radius, Establish a basic coordinate system with parameterized description for the maximum radius; Step 1-2: Construct class functions ,in, , An exponential parameter used to control the sharpness of the leading edge and the fullness of the trailing edge; Steps 1-3: Constructing shape functions ,in, These are the Bernstein polynomial coefficients. The coefficients are binomial coefficients. The order of the polynomial; Steps 1-4: Multiply the class function and the shape function and add the arc terms. , Using the relative radius of the arc, we obtain the complete meridional plane profile expression. .

[0007] The aforementioned low-flow-noise underwater buoy design method further includes, in step 1: Steps 1-5: Define the design parameter vector ,in, The coefficients are Bernstein polynomials, obtained by adjusting the design parameter vector. The parameters in the model are used to perform continuous and smooth deformation of the shape of the underwater glider carrier. Steps 1-6: Determine the order of the Bernstein polynomial The value range is 4 to 8. By increasing the order, the fitting accuracy of the shape function to the local contour is controlled, while maintaining the mathematical smoothness of the overall curve. Steps 1-7: Fix the first and last control coefficients and To satisfy the condition that the radius of the leading edge point is zero and the radius of the arc point is a specified value. The geometric boundary conditions ensure that the beginning and end positions of the meridian plane profile are precisely closed.

[0008] In the aforementioned low-noise underwater buoy design method, step 2 includes key parameters such as the maximum diameter-to-length ratio, the location of maximum thickness, and the trailing edge radius contraction angle. The geometric and physical constraints include the internal equipment compartment dimensions and hydrostatic stability. Step 2 includes: Select the maximum diameter-to-length ratio from the design parameter vector P. Location of maximum thickness Trailing edge radius contraction angle As a core design variable, the trailing edge radius contraction angle is one of them. The angle between the line connecting the location of maximum thickness and the center of the trailing edge and the center line of the target body; Set the minimum diameter of the internal equipment compartment. With minimum volume As geometric constraints, this makes it practically feasible for the underwater buoy carrier to accommodate batteries, a data collector, and acoustic sensors. Set still water stability constraints, including the positional relationship between the height of the buoyancy center and the height of the center of gravity, so that the underwater buoy carrier maintains the set attitude angle range, and the buoy body can rotate and automatically correspond to the incoming flow according to the direction of the water flow.

[0009] In the aforementioned low-flow-noise underwater buoy design method, step 3 includes: Step 3-1: The total sound pressure level, reflecting the overall noise level, is obtained by integrating the broadband noise induced by the flow on the carrier surface. Establish based on total sound pressure level The objective function for flow noise as a fundamental optimization term; Step 3-2: Introduce a summation term for sound pressure level in a specific frequency band into the objective function. Construct a complete optimization objective function ,in, , These are weighting coefficients. As a penalty item, Corresponding to the key operating frequency bands of acoustic sensors, the detection signal-to-noise ratio is controlled by specifically suppressing noise in sensitive frequency bands, ensuring an effective balance between noise reduction targets, sensitive frequency band suppression, and engineering feasibility.

[0010] In the aforementioned low-flow-noise underwater buoy design method, step 4 includes: Step 4-1: Select the multi-island genetic algorithm as the optimizer, set the initial population size, crossover probability, mutation probability and maximum number of iterations, and use the design variables as individual gene encodings to perform global optimization; Step 4-2: In each iteration, the CST parameterized shape is generated and the computational grid is constructed based on the current design variables. The flow field is simulated using the unsteady Reynolds-averaged Navier-Stokes equations to accurately capture complex flow characteristics and obtain flow details on the carrier surface and near the wake, including transient pressure fluctuations, providing a high-fidelity flow field data foundation for noise analysis. Step 4-3: Based on the FW-H acoustic analogy method, the transient pressure pulsation obtained from the flow field simulation is used as the sound source term to calculate the sound pressure level distribution in the far field or on the surface of the carrier, thereby obtaining the optimization objective function F value under the current shape, and returning it to the optimization algorithm as the fitness evaluation basis to assess the impact of shape changes on radiated noise and drive the optimization to evolve towards lower noise.

[0011] In the aforementioned low-flow-noise underwater buoy design method, step 5 includes: Step 5-1: Obtain historical temperature and salinity profile data of the target deployment area, construct a probability distribution model of depth-density jump intensity, identify the critical jump depth range where the density gradient exceeds the set threshold, and determine the critical range of diameter-to-length ratio that is prone to induce local flow acceleration and pressure pulsation changes in the critical jump environment through parameter sensitivity analysis, and use it as the target shape neighborhood for damping structure implantation. Step 5-2: Generate the local curved surface of the mooring carrier based on the shape parameters corresponding to the critical range of diameter-to-length ratio. According to the local boundary layer thickness and pressure gradient distribution, plan a micron-level groove damping structure with a spiral distribution on the surface of the mooring carrier. The groove depth is set to 0.2 to 0.5 times the boundary layer thickness, and the ratio of groove spacing to depth is controlled between 2 and 4. The helix angle is adaptively adjusted according to the local streamline deflection angle to make the groove direction consistent with the near-wall flow direction. Step 5-3: Correct the groove geometry parameters using the local pressure gradient coefficient, increase the groove depth and density in the reverse pressure gradient region to enhance flow stability, and verify the damping structure’s suppression effect on the wall pressure pulsation amplitude through CFD simulation. If the sound pressure level reduction in a specific frequency band does not reach the preset target, locally densify the groove distribution or fine-tune the helix angle until the narrowband howling suppression requirements in the stratified environment are met.

[0012] In the aforementioned low-flow-noise underwater buoy design method, step 6 includes: Step 6-1: Determine whether the current iteration has reached the preset maximum number of iterations, or determine whether the relative change in the objective function F value over multiple consecutive iterations is less than a set threshold, as a convergence criterion; Step 6-2: If the convergence condition is not met, generate a new generation of design variable population according to the update rules of the optimization algorithm, return to step 4 to continue the flow field and acoustic calculations and solve the objective function value, drive the population to continue to evolve, and gradually approach the global optimal solution region; Step 6-3: If the convergence condition is met, select the individual with the smallest objective function F value from the last generation of the population as the optimal design parameter set. And verify whether the optimal design parameter set fully satisfies the geometric and physical constraints set in step 2.

[0013] In the aforementioned low-flow-noise underwater buoy design method, step 7 includes: Step 7-1: Output the optimal design parameter set Substituting into the established CST parameterized model, the discrete point set of the optimal meridional contour curve is reconstructed through the product operation of the class function and the shape function, and the optimized geometric shape is accurately reproduced. Step 7-2: Based on the optimal meridional plane contour line, rotate 360° around the central axis to generate a three-dimensional geometric model of the mooring carrier, and smooth the surface to eliminate the small curvature fluctuations generated during the parameterization process, ensure the mathematical smoothness of the outer surface, eliminate local curvature abrupt changes, and ensure the continuity and stability of surface flow. Step 7-3: Perform final verification on the generated three-dimensional geometric model of the underwater mooring carrier, extracting data including the maximum diameter-to-length ratio. Location of maximum thickness and trailing edge radius contraction angle The design parameters were confirmed to fall within the set design variable range, thus forming a three-dimensional model of a low-flow noise underwater buoy carrier that can be used for engineering manufacturing.

[0014] A low-flow-noise underwater mooring carrier, based on a low-flow-noise underwater mooring carrier design method as described above, includes a base and an underwater mooring carrier, wherein the underwater mooring carrier and the base are rotatable relative to each other, and the underwater mooring carrier is a smooth rotationally symmetric body, wherein its meridional profile is formed by a leading edge segment, a maximum thickness segment and a tail arc smoothly connected. The leading edge segment has a large radius of curvature. With the chord length of the underwater glider The proportions satisfy: ; The tail arc is a slender tail vertebra, and its tail edge arc contraction angle is... Between 5° and 12°; The maximum thickness segment of the meridional plane profile is located at 1 / 4 to 1 / 2 of the total chord length from the leading edge, with a diameter-to-length ratio of... =0.15~0.25, the distance from the maximum thickness segment to the leading edge segment is 0.30~0.45 of the profile chord length; The outer surface of the underwater buoy carrier is a mathematically smooth curved surface, and all sensor openings and watertight connectors are integrated with the main curved surface through a flow guide or flush design.

[0015] The beneficial effects of the low-flow-noise underwater mooring carrier design method of the present invention are as follows: by adopting the CST parametric modeling method to mathematically describe the shape of the underwater mooring carrier, a meridional contour expression system with the product of class function and shape function as the core is constructed, so as to realize continuous and smooth control of the shape, enabling designers to independently adjust the leading edge curvature, the maximum thickness position and the arc contraction characteristics, significantly improving the flexibility and pertinence of shape adjustment.

[0016] An optimization objective function with minimizing flow noise as its core is constructed. The total sound pressure level and the sound pressure level of a specific frequency band are weighted and summed. A penalty term is introduced to handle geometric and physical constraints. This function can comprehensively reflect the acoustic stealth performance of the underwater glider in the actual working environment, while taking into account the internal equipment compartment volume and still water stability requirements, ensuring that the optimization results have both low flow noise characteristics and engineering feasibility.

[0017] An automated iterative process coupled with optimization algorithms, unsteady flow field simulation, and acoustic analogy calculation methods is adopted to realize a forward design mode that directly drives the shape design from the noise target. In each iteration, the algorithm automatically completes geometric reconstruction, mesh generation, flow field simulation, and acoustic calculation, continuously driving the population to evolve towards low flow noise, significantly improving design efficiency and optimization accuracy, and overcoming the limitations of traditional experience-based design that relies on trial and error and has a long cycle.

[0018] By introducing a thermo-salinity gradient disturbance factor into the optimization iteration, a probability distribution model of depth-density gradient intensity based on measured historical data was constructed. This model accurately identified the critical diameter-to-length ratio range that is prone to inducing local flow acceleration and pressure pulsation abrupt changes under density abrupt changes. Based on this, a micron-sized groove damping structure with a spiral distribution was implanted in the corresponding shape region to actively suppress flow instability under gradient conditions. This significantly enhanced the flow stability of the mooring carrier in complex ocean density gradients, effectively curbed narrowband high-frequency howling induced by density abrupt changes, and greatly reduced flow noise interference in specific frequency bands. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the workflow of the low-flow-noise underwater buoy carrier design method in an embodiment of the present invention. Figure 2 This is a schematic flowchart of the low-flow-noise underwater buoy carrier design method in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the low-flow noise underwater buoy carrier in an embodiment of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be described below in conjunction with specific embodiments and accompanying drawings.

[0021] Example 1 like Figures 1-2 As shown, a design method for a low-flow-noise underwater buoy carrier includes the following steps.

[0022] Step 1: Establish the CST parameterized model.

[0023] The CST parametric method is adopted to define the ratio of dimensionless coordinates to contour radius, construct a CST parametric model, and describe the contour of the submersible meridional surface by the product of class function and shape function, control the shape change, realize the continuous smooth deformation of the shape, and ensure the flexibility and accuracy of geometric description.

[0024] A CST parametric model is established based on the CST parametric method, and the dimensionless longitudinal coordinates of the meridional profile of the glider carrier are defined. and the ratio of the outline radius ,in, The vertical coordinate is dimensionless. For axial coordinates, For the carrier chord length, For the local radius, To achieve the maximum radius, a basic coordinate system for parametric description is established, decoupling the shape description from the absolute size, which facilitates unified optimization of underwater buoys of different scales.

[0025] Constructing class functions ,in, , To control the exponential parameters of leading-edge sharpness and trailing-edge fullness, adjustments are made... , It enables independent control of the leading edge curvature radius and arc contraction characteristics, and independent adjustment of the geometric features of the head and tail, thereby improving the flexibility of shape adjustment.

[0026] Constructing shape functions ,in, These are the Bernstein polynomial coefficients. The coefficients are binomial coefficients. To determine the order of the polynomial, multiply the class function and the shape function and then add the circular arc terms. , Using the relative radius of the arc, we obtain the complete meridional plane profile expression. This enables the mathematical smooth representation of complex shape curves, ensuring surface continuity and reconstruction accuracy.

[0027] It should be noted that when establishing the CST parametric model, it is first necessary to define a dimensionless coordinate system for the meridional profile of the glider carrier. In practice, this is done using the carrier chord length. As a feature scale, the axial coordinates Normalized to a dimensionless vertical coordinate, with values ​​ranging from [0, 1], and the local radius is also... With maximum radius The ratio is defined as the contour radius ratio, thus establishing a parametric description basis that does not depend on absolute dimensions. The establishment of the dimensionless coordinate system decouples the shape description from the actual size of the carrier, which facilitates the unified optimization of underwater buoys of different scales in the future.

[0028] Operators need to map the physical coordinate system to dimensionless space based on the chord length and expected maximum radius of the underwater glider.

[0029] In practical applications, by setting the exponent parameter and Construct class functions, where Controlling the curvature characteristics of the leading edge region Control the fullness of the curved area.

[0030] During operation, when it is necessary to increase the leading edge radius of curvature to mitigate water flow impact, appropriately reduce... Value selection; when it is necessary to adjust the degree of arc contraction to control flow separation, adjust by... This allows designers to independently adjust the geometric features of the head and tail of the underwater glider without interference, significantly improving the flexibility and specificity of shape adjustments.

[0031] Selecting the order of the Bernstein polynomial The value ranges from 4 to 8. The higher the order, the stronger the local contour fitting ability. Then, the coefficients of each order are determined. Combined with binomial coefficients Construct shape functions .

[0032] Finally, the class function and the shape function are multiplied together, and the arc terms are added to obtain the complete meridional contour expression. Operators must ensure the first and last coefficients are correct. and The boundary conditions of zero radius at the leading edge point and a specified radius at the arc point are met, thus ensuring that the generated meridional profile curve is precisely closed at both ends.

[0033] Specifically, define the design parameter vector. ,in, The relative radius of the arc is determined by adjusting the design parameter vector. The parameters in the model are used to perform continuous and smooth deformation of the shape of the underwater glider, thereby achieving a parameterized and unified description and flexible control of the shape features.

[0034] Setting the order of the Bernstein polynomial The value range is from 4 to 8. By increasing the order, the fitting accuracy of the shape function to the local contour is controlled, while maintaining the mathematical smoothness of the overall curve, thus balancing the dual needs of fine local control and overall curve smoothness.

[0035] Fixed first and last control coefficients and To satisfy the condition that the radius of the leading edge point is zero and the radius of the arc point is a specified value. The geometric boundary conditions ensure that the beginning and end positions of the meridional plane profile are precisely closed, guaranteeing the geometric closure and reconstruction consistency of the meridional plane profile curve.

[0036] It should be noted that in practice, designers construct a complete design parameter vector. This serves as the mathematical basis for optimizing the shape of the underwater glider carrier. and These are exponential parameters for class functions, which independently control the leading edge curvature characteristics and the fullness of the arc. to The coefficients of the Bernstein polynomial determine the specific form of the shape function; For the carrier chord length, The two factors together determine the basic size ratio of the underwater mooring, with the maximum radius as the reference point. The relative radius of the arc is defined as the ratio of the radius at the arc's point to its maximum radius.

[0037] In the actual optimization process, designers select all or part of the parameter vector as design variables according to the noise reduction requirements, while keeping the remaining parameters fixed. By adjusting each parameter, continuous and smooth deformation of the shape is achieved, ensuring that the geometric model generated each time maintains mathematical smoothness.

[0038] Designers will The value range of is set to 4 to 8 to achieve a balance between fitting accuracy and computational efficiency. When the value is small, the shape function provides a smoother description of the contour, which is suitable for preliminary exploration of the design space.

[0039] When fine-tuning of local areas such as shoulder curvature or trailing edge radius contraction angle is required, designers may appropriately increase... Value, improves the fitting accuracy of shape function to local contours, regardless of The CST parameterization method itself guarantees the mathematical smoothness of the generated curve, i.e., it has at least first-order continuity, avoiding curvature abrupt changes introduced by local adjustments, regardless of the value chosen.

[0040] In practice, designers need to select an appropriate order in the initial optimization stage based on the specific requirements of the underwater glider design and keep it fixed throughout the optimization process to ensure the consistency of the design space. Designers also need to control the initial and final order coefficients. and Specific constraints are applied to ensure that the generated meridional profile meets the basic geometric closure requirements.

[0041] Specifically, by fixing The value of is chosen such that the contour radius at the leading edge point is greater than . Zero corresponds to the sharp or slightly blunted starting point of the mooring head, achieved through fixing. And combined with the relative radius of the arc Make the radius of the outline at the arc point greater than equal That is, the actual radius of the arc point is accurate to and The product of these ensures the precise closure of the meridional profile curve at both ends, avoiding geometric discontinuities caused by parameter fluctuations.

[0042] In the actual parametric modeling process, designers will and Excluding it from the design variables and making it a fixed constraint in each shape reconstruction ensures that all generated buoy carrier models have complete geometric closure and meet the geometric integrity requirements of engineering manufacturing.

[0043] Step 2: Set design variables and constraints.

[0044] Key parameters, including the maximum diameter-to-length ratio, the location of the maximum thickness, and the tail edge arc contraction angle, were selected as design variables. Geometric and physical constraints covering the internal equipment compartment dimensions and hydrostatic stability were set to ensure that the optimization results simultaneously meet the requirements of low flow noise performance and engineering feasibility.

[0045] Select the maximum diameter-to-length ratio from the design parameter vector P. Location of maximum thickness Trailing edge radius contraction angle As a core design variable, the trailing edge radius contraction angle is one of them. The angle between the line connecting the maximum thickness position and the center of the trailing edge and the center line of the target body is defined. By precisely adjusting key shape parameters, the directional optimization and rapid convergence of flow noise can be achieved.

[0046] Set the minimum diameter of the internal equipment compartment. With minimum volume As a geometric constraint, the underwater glider carrier is made practically engineering feasible to accommodate the battery, data collector and acoustic sensor, ensuring that the optimization results take into account both low noise performance and the actual installation space requirements of the internal equipment.

[0047] Set static water stability constraints, with a total height of H for the buoy and a center of buoyancy height of H'. f With center of gravity height H g The positional relationship satisfies H g =0.6H f H f ≤0.6H, which allows the underwater buoy to maintain the set attitude angle range, and the buoy body can rotate and automatically correspond to the direction of the water flow, ensuring that the underwater buoy has good attitude self-recovery ability in multi-directional flow environment.

[0048] It should be noted that the maximum diameter-to-length ratio is set in the range of 0.15-0.25. This range is determined based on the consideration of both the internal equipment compartment volume requirements and low flow noise characteristics.

[0049] The maximum thickness position is controlled between 0.30 and 0.45 (i.e., 30%-45% of the chord length from the leading edge) to ensure that the flow attachment point is located at the front of the carrier, delaying the occurrence of flow separation. The determination of the trailing edge arc contraction angle needs to be calculated in conjunction with the relative radius of the arc and the shape function coefficient of the trailing region. Its value range is limited to between 5° and 15°. This angle range can effectively guide the boundary layer to merge smoothly and suppress the shedding of large-scale vortices.

[0050] When selecting the above variables, designers must ensure the coordination between the parameters to avoid the destruction of the streamlined features of the overall shape due to extreme values ​​of a single parameter.

[0051] Based on the typical configuration requirements of deep-sea long-term observation equipment, the minimum diameter of the internal equipment compartment is set at 0.6 meters, and the minimum volume is set at 1.5 cubic meters. This size can accommodate standardized energy modules and electronic compartment units, while reserving the necessary space for sensor installation. During the parametric modeling process, the designers transformed the geometric constraints into the maximum diameter-to-length ratio and the contour radius ratio. The indirect limitations are mitigated by establishing a spatial mapping relationship between the internal compartments and the external curved surfaces, ensuring that the generated shape can meet the equipment loading requirements each time, and avoiding engineering problems such as insufficient internal space due to excessive pursuit of low flow noise.

[0052] Based on the density distribution of the carrier material and the mass configuration of each component in the equipment compartment, the overall center of gravity position is calculated. Simultaneously, based on the drainage volume distribution determined by the curved surface, the center of buoyancy position is calculated. The stability constraint requires that the height of the center of buoyancy be higher than the height of the center of gravity, and the vertical distance between the two should not be less than 5% of the total height of the carrier, to ensure that the mooring has sufficient restoring torque under the action of the horizontal inflow and maintains the attitude angle within ±5°. In the actual parameter optimization process, the designers incorporated the stability requirement as a penalty term into the optimization objective function. When the shape generated by the design variables cannot meet the stability criterion, the penalty term is given a maximum value, guiding the optimization algorithm to automatically eliminate infeasible design schemes.

[0053] Step 3: Construct the objective function for optimization.

[0054] An optimization objective function is established with minimizing the total sound pressure level and the sound pressure level in a specific frequency band as the core. Weighting coefficients are introduced to balance the optimization weights of noise in different frequency bands, and a penalty term is set to meet the design feasibility, so as to achieve a precise match between the noise reduction target and the detection performance requirements.

[0055] Establish based on total sound pressure level The objective function for stream noise as a fundamental optimization term The overall noise level is obtained by integrating the broadband noise induced by the flow on the carrier surface. This allows for a comprehensive assessment of the overall radiated noise level of the underwater buoy, ensuring its acoustic stealth performance.

[0056] Introducing a summation term for sound pressure level in a specific frequency band into the objective function. ,in, By targeting the key operating frequency bands of acoustic sensors and controlling the detection signal-to-noise ratio by specifically suppressing noise in sensitive frequency bands, the noise in the sensor's sensitive frequency bands can be accurately suppressed, significantly improving the target detection capability.

[0057] Construct a complete optimization objective function ,in, , The weighting coefficients and , As a penalty term, it is assigned a maximum value when the design variable violates the constraints of step 2, which can achieve synergistic optimization of noise reduction target and engineering feasibility, and ensure the effectiveness of design.

[0058] It should be noted that, based on surface pressure pulsation data obtained from unsteady flow field simulations, the designers set up an array of observation points in acoustic calculation software, covering the carrier surface and the near-wake region. They then converted the time-domain pressure signal into a frequency-domain sound pressure level distribution using Fourier transform, and finally integrated the results to obtain... The value, as a basic optimization item, can comprehensively reflect the overall radiated noise level of the underwater buoy carrier under different flow direction conditions.

[0059] In practical engineering applications, designers set calculation conditions based on the typical current velocity range of the sea area where the mooring is deployed, selecting a current velocity range of 0.2 m / s to 1.0 m / s to ensure that the optimization results cover the actual working environment. The introduction of this technology directly links the optimization of the target with the acoustic stealth performance of the buoy; The construction of the underwater glider is specifically designed with the operating frequency band of the acoustic sensors installed inside the glider in mind.

[0060] In practice, designers extract the sensitive frequency band range from the technical specifications of the hydrophone or acoustic Doppler current profiler as... The value selection is based on a long-term deep-sea observation mission as an example. A 1 / 3 octave bandwidth is used to calculate the sound pressure level at each frequency point, and the sum of the three values ​​is taken as the final value. In the actual calculation process, the designers ensured that the sound pressure level extraction location was consistent with the actual installation location of the sensor. The projection area of ​​the internal bulkhead of the carrier corresponding to the external surface was selected as the sound pressure level monitoring point. By introducing a specific frequency band summation term, the algorithm was optimized to suppress the frequency band noise that has the most significant impact on the detection performance, thus avoiding the problem of noise residue in sensitive frequency bands while simply reducing the total sound pressure level.

[0061] In actual parameter settings, designers determine the weight allocation based on the primary and secondary requirements of the underwater mooring mission. When the mission focuses on broadband background noise control, the weight should be appropriately increased. Value selection; when there is a need to detect weak target signals in a specific frequency band, increase... Weight, The penalty term is introduced to handle constraint violations. In practice, designers convert the minimum diameter of the internal equipment compartment (0.6 meters), the minimum volume (1.5 cubic meters), and the hydrostatic stability criterion into numerical judgment conditions. When the shape generated by the design variables fails to meet any constraint, the penalty term is assigned a value of 10. 6 The maximum value of the order of magnitude ensures that the individual is automatically eliminated in the optimized population, thus ensuring an effective balance between noise reduction objectives, sensitive frequency band suppression, and engineering feasibility.

[0062] Step 4: Initialize the population and set the genetic algorithm parameters.

[0063] An optimizer based on optimization algorithms is selected, and unsteady RANS flow field simulation and FW-H acoustic analogy method are combined to automatically iteratively solve for the optimal shape parameters, calculate the optimization objective function value for each iteration, and construct a closed-loop automated optimization process from shape design to noise assessment.

[0064] The multi-island genetic algorithm is selected as the optimizer. The initial population size, crossover probability, mutation probability and maximum number of iterations are set. The design variables are used as individual gene codes to perform global optimization, which ensures that the optimization process efficiently explores the global design space and avoids getting trapped in local optima.

[0065] In each iteration, the CST parameterized shape is generated based on the current design variables and a computational grid is constructed. The flow field is simulated using the unsteady Reynolds-averaged Navier-Stokes equations to obtain flow details on the carrier surface and near the wake, accurately capture complex flow characteristics, and provide a high-fidelity flow field data foundation for noise analysis.

[0066] Based on the FW-H acoustic analogy method, the transient pressure pulsation obtained from flow field simulation is used as the sound source term to calculate the sound pressure level distribution in the far field or on the carrier surface. This allows for the acquisition of the optimization objective function F value under the current shape, accurately assessing the impact of shape changes on radiated noise, and driving optimization towards lower noise levels.

[0067] It should be noted that the initial population size should be set to balance computational resources and the breadth of the design space, ideally between 50 and 100 individuals, to allow the algorithm to fully explore the feasible domain of morphological parameters in the initial stage. The crossover probability controls the frequency of gene information exchange between individuals, and should be set between 0.7 and 0.9 to promote the combination and propagation of superior gene fragments. The mutation probability is used to maintain population diversity and avoid getting trapped in local optima too early, with a typical value of 0.01 to 0.1. The maximum number of iterations should be set in conjunction with the limitations of computational resources and the requirements of convergence speed, ideally between 50 and 200 generations, to ensure that the algorithm has enough iterations to complete the transition from global search to local refinement. The selection of each parameter should be completed before the design task starts and kept fixed throughout the optimization process to ensure consistency in the optimization process.

[0068] Before each iteration begins, the designers call the CST parametric model to reconstruct the three-dimensional geometry of the buoy carrier based on the design variable values ​​corresponding to each individual in the current population, and automatically generate a high-quality mesh for fluid calculation. The mesh generation focuses on the mesh density of the boundary layer region on the carrier surface to ensure accurate capture of pressure gradients and flow separation phenomena. At the same time, the mesh in the wake region is appropriately refined to resolve vortex structures.

[0069] Subsequently, the unsteady Reynolds-averaged Navier-Stokes equations were used to simulate the flow field. The incoming flow velocity was set to 0.2 m / s to 1.0 m / s based on the typical working conditions of the buoy deployment area. The time step was selected to satisfy the CFL condition (CFL condition refers to the Courant-Friedrichs-Lewy condition, which is a fundamental concept in CFD and computational mathematics) and ensure that the key vortex shedding frequency could be resolved. The calculation continued until the flow field reached a statistical steady state, and sufficient time of surface pressure pulsation data was saved for subsequent acoustic analysis.

[0070] Based on transient pressure fluctuation data obtained from flow field simulation, designers used the FW-H acoustic analogy method to calculate flow noise. The sound source surface was selected from key locations on the carrier surface and near the wake region. Observation points were set according to actual needs on the projection area of ​​the carrier surface at the far field or the corresponding internal sensor installation location. The time-domain pressure signal was converted into a frequency-domain sound pressure level distribution through Fourier transform, and then integrated to obtain the total sound pressure level under the current shape. The sound pressure level corresponding to the three center frequencies within the range of 10-100Hz in the 1 / 3 octave band was extracted as... The summation term, combined with the pre-set weighting coefficients and penalty term, calculates the optimization objective function F value of the current individual, and returns it to the optimization algorithm as the basis for fitness evaluation, driving the population to evolve towards low flow noise.

[0071] Step 5: Generate the current shape parameter set.

[0072] During the optimization iteration process, a thermo-salinity jump perturbation factor is introduced. Based on the historical thermo-salinity profile data of the target deployment area, a probability distribution model of the depth-density jump intensity is constructed to identify the critical diameter-to-length ratio range that is prone to induce local flow acceleration and pressure pulsation abrupt changes. In the neighborhood of the shape parameters corresponding to this range, a groove damping structure with a spiral distribution is implanted.

[0073] The depth, spacing, and helix angle of the groove damping structure are adaptively adjusted according to the local boundary layer thickness and pressure gradient to suppress flow instability and narrow-band high-frequency whistling caused by abrupt density changes.

[0074] Historical temperature and salinity profile data of the target deployment area were obtained, and a probability distribution model of the intensity of the depth-density jump was constructed. The critical jump depth range where the density gradient exceeds the set threshold was identified. Through parameter sensitivity analysis, the critical range of the diameter-to-length ratio that is prone to induce local flow acceleration and pressure pulsation in the critical jump environment was determined as the target shape neighborhood for the implantation of the damping structure.

[0075] Based on the shape parameters corresponding to the critical range of diameter-to-length ratio, the local curved surface of the mooring carrier is generated. According to the local boundary layer thickness and the distribution of the compressive gradient, a micron-level groove damping structure with a spiral distribution is planned on the carrier surface. The groove depth is set to 0.2 to 0.5 times the boundary layer thickness, and the ratio of groove spacing to depth is controlled between 2 and 4. The helix angle is adaptively adjusted according to the local streamline deflection angle so that the groove direction is consistent with the near-wall flow direction.

[0076] The local pressure gradient coefficient is used to correct the groove geometry parameters. In the reverse pressure gradient region, the groove depth and density are increased to enhance flow stability. At the same time, the damping structure is verified to suppress the amplitude of wall pressure pulsation through CFD simulation. If the sound pressure level reduction in a specific frequency band does not reach the preset target, the groove distribution is locally densified or the helical rise angle is finely adjusted until the narrowband howling suppression requirements in the layered environment are met.

[0077] It should be noted that in the initial stage of optimization and iteration, designers must first obtain historical temperature and salinity profile data for the target deployment area. Specifically, by accessing global ocean databases or using pre-deployment CTD profile data, the vertical distribution of temperature and salinity under different seasons and tidal cycles is extracted. Based on the seawater state equation, the seawater density at each depth is calculated, thereby constructing a depth-density gradient curve. The density jump intensity is represented by the density change rate per unit depth. When the density gradient exceeds 0.5 kg·m³, the density gradient is considered significant. -3 When the density reaches / m, it is defined as the critical depth range of the strong stratus. Within this range, a sudden change in seawater density will significantly affect the pressure distribution on the surface of the mooring vessel.

[0078] For the identified critical gradient depth range, the designers set a perturbation factor in the optimization algorithm, superimposed a density jump forced perturbation on the current population shape parameters, and used unsteady RANS to simulate the flow characteristics in the corresponding water depth environment. By monitoring the power spectral density of the pressure pulsation on the carrier surface, the diameter-to-length ratio range corresponding to the narrowband high-frequency howling peak was identified and determined as the target shape neighborhood for the damping structure implantation.

[0079] After determining the critical range of the diameter-to-length ratio for the damping structure implantation, the designers selected typical individuals with typical shapes within this range from the optimized population, extracted their local surface geometry information, and used computational fluid dynamics to obtain the local boundary layer thickness δ on the carrier surface based on boundary layer theory. They focused on the axial position of the carrier corresponding to the critical sag depth, which is located downstream of the maximum thickness segment to the beginning of the arc contraction segment. The groove depth was set to 0.2δ to 0.5δ, and the specific value was dynamically adjusted according to the local Reynolds number and pressure gradient coefficient.

[0080] The ratio of groove spacing to depth is controlled between 2 and 4 to ensure that a stable spanwise vortex structure can be formed within the groove without inducing additional turbulent kinetic energy. The grooves are arranged on the curved surface using a spiral path planning. The spiral angle is determined based on the direction of the wall's limiting streamlines when no grooves are added. The local flow direction angle is obtained by extracting the wall shear stress vector field, so that the deviation between the groove direction and the near-wall flow direction is controlled within ±5°, ensuring the maximum suppression effect of the grooves on the turbulent quasi-sequential structure.

[0081] To address the potential adverse pressure gradient region in critical hydrostatic environments, designers introduced a local pressure gradient coefficient to correct the trench geometry. The local pressure gradient coefficient is defined as the ratio of the velocity gradient along the flow direction at the outer edge of the boundary layer to the local velocity. A positive local pressure gradient coefficient exceeding 0.02m is considered a positive gradient. -1 When the region is identified as a strong adverse pressure gradient region, the trench depth w is adjusted to the upper limit of 0.4δ to 0.5δ, and the trench spacing s is correspondingly narrowed to the ratio of trench spacing s to depth w s / w≈2, in order to enhance flow stability and delay flow separation.

[0082] After completing the preliminary design, the large eddy simulation combined with the FW-H acoustic analogy method was used to compare the wall pressure pulsation amplitude with and without the damping structure. If the reduction in a specific frequency band did not meet the preset target, the original design was improved by locally densifying the grooves in the densified area by 20% to 30%, or by fine-tuning the helix angle by ±2°. The verification was repeated until the damping structure could effectively suppress narrowband howling in a multi-layered environment. Finally, the damping structure parameters that met the requirements and the carrier shape parameters were used together as the design result and solidified.

[0083] Step 6: Determine whether the optimization iteration meets the convergence condition.

[0084] If convergence is not achieved, the design variables are updated and the iteration continues; if convergence is achieved, the optimal set of design parameters that satisfies all constraints is output, ensuring an effective balance between global search and computational efficiency in the optimization process.

[0085] The algorithm determines whether the current iteration has reached the preset maximum number of iterations, or whether the relative change in the objective function F value across multiple generations is less than a set threshold, as a convergence criterion. This ensures that the optimization process obtains stable and reliable optimization results within reasonable computational resources. If the convergence condition is not met, a new generation of design variable population is generated according to the update rules of the optimization algorithm. The algorithm then returns to step 4 to continue flow field and acoustic calculations and solve for the objective function value, driving the population to continuously evolve and gradually approach the global optimum. If the convergence condition is met, the individual with the smallest objective function F value from the last generation of the population is selected as the optimal design parameter set. And verify whether the optimal design parameter set fully meets the geometric and physical constraints set in step 2, so as to ensure that the final design is both feasible in engineering and meets the noise reduction target.

[0086] It should be noted that, in actual operation, a dual convergence determination mechanism is adopted.

[0087] Firstly, to reach the preset maximum number of iterations, which is set to 50 to 200 generations based on computing resources and the complexity of the optimization task; secondly, if the relative change in the objective function F value of continuous optimization over multiple generations is less than a set threshold, typically ranging from 0.1% to 1%, it indicates that the algorithm has converged to a stable optimal region.

[0088] After each iteration, the optimization algorithm automatically compares the F-value of the best individual in the current population with historical records. When the relative change in the F-value of individuals across five consecutive generations is below a threshold, the convergence condition is considered met. This dual criterion ensures that the algorithm will not terminate prematurely due to computational resource limitations, nor will it result in invalid iterations due to slow convergence, achieving a balance between global search capability and computational efficiency. If the convergence condition is not met, the optimization algorithm generates a new generation of design variable populations according to preset update rules.

[0089] Taking the multi-island genetic algorithm as an example, a selection operation is performed based on the fitness values ​​of each individual in the current population. Individuals with higher fitness are more likely to be selected as parents. Then, gene fragments are exchanged with a crossover probability of 0.7 to 0.9 to generate offspring individuals. At the same time, some gene positions of the offspring individuals are randomly perturbed with a mutation probability of 0.01 to 0.1 to maintain the diversity of the population and avoid getting trapped in local optima. The generated new generation population contains 50 to 100 individuals, covering a design space breadth comparable to the previous generation. Designers do not need to intervene manually. The algorithm automatically uses the design variable values ​​of the new population to start a new round of flow field and acoustic calculations, forming a complete automated optimization closed loop, continuously driving the shape to evolve towards low flow noise.

[0090] Once the convergence condition is met, the designers select the individual with the smallest objective function F value from the last generation of the population as the optimal design parameter set. This optimal design parameter set contains complete CST modeling parameters, including class function exponents. , Bernstein coefficient to Carrier chord length Maximum radius and the relative radius of the arc Then, constraint compliance verification is performed. Substitute the internal compartment space mapping relationship to calculate the actual usable equipment compartment diameter and volume, and confirm whether the geometric constraints of minimum diameter 0.6 meters and minimum volume 1.5 cubic meters are met.

[0091] Simultaneously, the positions of the center of buoyancy and center of gravity are calculated based on the external curved surface and mass distribution. The stability criterion that the height of the center of buoyancy is higher than the height of the center of gravity and the vertical distance is not less than 5% of the total height of the carrier is verified. If any constraint is not met, the verification is backtracked to the second-best individual in the last generation to ensure the final output. It simultaneously meets the optimization goals of low flow noise and the requirements of engineering feasibility.

[0092] Step 7: Output the 3D model of the low-flow-noise underwater buoy carrier.

[0093] By substituting the optimal design parameter set into the CST parametric model, the three-dimensional geometry of the low-flow-noise underwater glider is generated, resulting in an underwater glider that meets the requirements, significantly reduces broadband flow noise, is suitable for multi-directional flow environments, and ultimately obtains an underwater glider product that combines excellent acoustic stealth performance with engineering practicality.

[0094] The output of the optimal design parameter set Substituting into the established CST parameterized model, the discrete point set of the optimal meridional contour curve is reconstructed through the product operation of the class function and the shape function, ensuring that the optimized geometric shape is accurately reproduced and the noise reduction performance is highly consistent with the theoretical design.

[0095] Based on the optimal meridional plane contour, a three-dimensional geometric model of the mooring carrier is generated by rotating 360° around the central axis. The surface is then smoothed to eliminate minor curvature fluctuations generated during parameterization, ensuring the mathematical smoothness of the surface shape, eliminating local curvature abrupt changes, and guaranteeing the continuity and stability of surface flow.

[0096] The generated three-dimensional geometric model of the underwater mooring carrier is finally verified, including its shape parameters and constraints, and the extraction of parameters such as the maximum diameter-to-length ratio. Location of maximum thickness and trailing edge radius contraction angle The design parameters are confirmed to fall within the set design variable range, forming a three-dimensional model of a low-flow noise underwater buoy carrier that can be used for engineering manufacturing, ensuring that the final product meets both noise reduction performance and engineering manufacturing requirements.

[0097] It should be noted that the designers determined this based on the optimization convergence. , , and The value is taken in a dimensionless coordinate system. Within the range, samples are taken at intervals of 0.001 to generate a sufficient number of discrete points to ensure the reconstruction accuracy of the contour curve. The carrier chord length and maximum radius adopt the absolute dimensions from the optimization results, and the dimensionless contour radius ratio is used. Restore to the actual radius value This forms a complete meridional plane outline, and the reconstruction process fully reproduces the geometric shape finally determined by the optimization iteration, ensuring that the noise reduction performance is consistent with the design goals.

[0098] Designers import discrete point sets of the contour lines into 3D modeling software, generate smooth meridional curves through spline curve fitting, and then construct a 3D solid model of the carrier using the surface of revolution generation function. For minor curvature fluctuations generated during parametricization, a surface smoothing algorithm is used to process them, focusing on checking the smoothness of the leading edge transition region, near the maximum thickness, and the arc contraction section. This ensures continuous curvature changes in all directions, eliminating local distortions introduced by minor fluctuations in the Bernstein coefficient. The smoothing process is based on not changing the basic shape of the contour lines, only adjusting curvature abrupt change points and maintaining the position of the maximum thickness. and trailing edge radius contraction angle The original design values ​​are used to obtain a mathematically smooth three-dimensional surface model.

[0099] Designers measured the actual diameter-to-length ratio and the location of maximum thickness from the three-dimensional geometric model of the underwater glider. and the trailing edge radius contraction angle, confirm its relationship with The corresponding theoretical value is consistent: It falls within the range of 0.15 to 0.25. Within the chord length range of 0.30 to 0.45, The angle is between 5° and 15°. At the same time, it is verified that the diameter of the internal chamber is not less than 0.6 meters and the volume is not less than 1.5 cubic meters. The distance between the center of buoyancy and the center of gravity meets the stability requirements. After the verification is passed, a three-dimensional model of a low-flow noise underwater buoy carrier that can be used for engineering manufacturing is output. The model data is saved in a standard format and can be directly used for mold processing or composite material molding manufacturing.

[0100] Example 2 like Figure 3As shown, based on Example 1, this example provides a low-flow-noise underwater buoy carrier.

[0101] Based on the low-flow-noise underwater glider design method described in Example 1, a low-flow-noise underwater glider is obtained, including a base and an underwater glider, which can rotate relative to each other. The underwater glider is a smooth rotationally symmetric body, and its meridional profile is smoothly formed by the connection of a leading edge segment, a maximum thickness segment, and a tail arc, ensuring that incoming flows from all directions can flow smoothly around it and improving multi-directional adaptability.

[0102] Radius of curvature R of the leading edge segment q The ratio of the length of the carrier chord L to the following satisfies: This allows for a smooth introduction of the water flow front, delaying the initiation point of flow separation.

[0103] The tail is rounded into a slender caudal vertebra, with a tapering angle at the tail edge. (i.e., the angle between the tangent of the contour line and the central axis) is between 5° and 12°, which guides the boundary layer to merge smoothly and inhibits the shedding of large-scale vortices.

[0104] Maximum diameter of underwater buoy carrier The ratio of the length of the carrier chord L to the following satisfies: It takes into account both the internal equipment compartment volume and the overall streamlined shape requirements.

[0105] The maximum thickness of the meridional profile is located at 1 / 4 to 1 / 2 of the total chord length from the leading edge, ensuring that the flow attachment point is forward and maintaining a stable laminar boundary layer, with a maximum diameter-to-length ratio. =0.15~0.25, balancing the slenderness of the carrier with the utilization rate of internal space, the distance from the maximum thickness position to the leading edge segment is 0.30~0.45 of the profile chord length, optimizing the pressure distribution, reducing the reverse pressure gradient intensity, and reducing the vortex generation intensity.

[0106] In addition, the outer surface of the mooring carrier is a mathematically smooth curved surface without any abrupt steps, bolts, or pits, eliminating local flow disturbance sources and avoiding additional noise generation. All sensor openings and watertight connectors are integrated with the main curved surface through flow guides or flush designs to ensure the continuity of surface flow, maintain the integrity of the shape, and ensure that the flow field around the openings is not distorted. The mooring carrier is made of fiberglass or carbon fiber composite material in one piece to ensure the smoothness and precision of the curved surface, while meeting the requirements for pressure resistance and corrosion resistance.

[0107] Example 3 This embodiment is based on Embodiment 1 and Embodiment 2, and is applied in a specific way.

[0108] Given that the chord length L of the carrier profile is 2.0 meters, the design according to this invention is as follows: chord length to maximum diameter ratio It is 0.2; Location of maximum thickness It is 0.35L; Trailing edge radius contraction angle It is 8°.

[0109] Flow field and acoustic analogue analysis were performed using computational fluid dynamics (CFD) software. Compared to cylindrical moorings of the same size, the wake region behind the carrier of this invention is narrower and more stable, with large-scale vortex structures almost disappearing. Only small-scale, high-frequency vortices are generated at the end of the tail cone, and the corresponding flow noise sound pressure level is significantly reduced in the main frequency band, significantly improving the detection signal-to-noise ratio of the internal acoustic sensors.

[0110] The above embodiments are merely illustrative of the structural concept and features of the present invention, intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A design method for a low-flow-noise underwater buoy carrier, characterized in that, Includes the following steps: Step 1: Using the CST parameterization method, define the ratio of dimensionless coordinates to the contour radius of the mooring carrier, construct the CST parameterization model, and describe the contour of the mooring meridian by the product of the class function and the shape function to control the shape change; Step 2: Select design variables, and through precise control of key shape parameters, achieve directional optimization and rapid convergence of flow noise. Set geometric and physical constraints to balance low noise performance with the actual installation space requirements of internal equipment. Set static water stability constraints to enable the buoy to have good attitude self-recovery capability in a multi-directional flow environment. Step 3: Establish an optimization objective function with minimizing the total sound pressure level and the sound pressure level of a specific frequency band as the core, introduce weighting coefficients to balance the optimization weights of noise in different frequency bands, set a penalty term to meet the design feasibility, and accurately match the noise reduction target with the detection performance requirements; Step 4: Select an optimizer based on optimization algorithms, combine unsteady RANS flow field simulation with FW-H acoustic analogy method, automatically iteratively solve the optimal shape parameters, calculate the optimization objective function value for each iteration, and construct a closed loop of fully automated optimization from shape design to noise assessment. Step 5: In the optimization iteration process, a temperature-salinity jump perturbation factor is introduced. Based on the historical temperature-salinity profile data of the target deployment area, a probability distribution model of the depth-density jump intensity is constructed to identify the critical diameter-to-length ratio range that is prone to induce local flow acceleration and pressure pulsation abrupt change. In the neighborhood of the corresponding shape parameters in this range, a groove damping structure with a spiral distribution is implanted to suppress the flow instability and narrow-band high-frequency howling caused by density abrupt change, and to solidify the damping structure parameters and carrier shape parameters that meet the requirements. Step 6: Determine whether the optimization iteration meets the convergence condition. If it does not converge, update the design variables and continue the iteration. Generate a new generation of design variable population according to the update rules of the optimization algorithm, and return to step 4 to continue the flow field and acoustic calculations and solve the optimization objective function value. If it converges, output the optimal design parameter set that satisfies all constraints, and verify whether the optimal design parameter set fully satisfies the geometric and physical constraints set in step 2. Step 7: Substitute the optimal design parameter set into the CST parameterized model to generate the three-dimensional geometric shape of the low-flow-noise underwater glider carrier, thus obtaining an underwater glider carrier that meets the requirements.

2. The low-flow-noise underwater buoy carrier design method according to claim 1, characterized in that, Step 1 includes: Step 1-1: Establish a CST parametric model based on the CST parametric method, and define the dimensionless longitudinal coordinates of the meridional profile of the glider carrier. and the ratio of the outline radius ,in, For axial coordinates, For the carrier chord length, For the local radius, Establish a basic coordinate system with parameterized description for the maximum radius; Step 1-2: Construct class functions ,in, , An exponential parameter used to control the sharpness of the leading edge and the fullness of the trailing edge; Steps 1-3: Constructing shape functions ,in, These are the Bernstein polynomial coefficients. The coefficients are binomial coefficients. The order of the polynomial; Steps 1-4: Multiply the class function and the shape function and add the arc terms. , Using the relative radius of the arc, we obtain the complete meridional plane profile expression. .

3. The low-flow-noise underwater buoy carrier design method according to claim 2, characterized in that, Step 1 further includes: Steps 1-5: Define the design parameter vector ,in, The coefficients are Bernstein polynomials, obtained by adjusting the design parameter vector. The parameters in the model are used to perform continuous and smooth deformation of the shape of the underwater glider carrier. Steps 1-6: Determine the order of the Bernstein polynomial The value range is 4 to 8. By increasing the order, the fitting accuracy of the shape function to the local contour is controlled, while maintaining the mathematical smoothness of the overall curve. Steps 1-7: Fix the first and last control coefficients and To satisfy the condition that the radius of the leading edge point is zero and the radius of the arc point is a specified value. The geometric boundary conditions ensure that the beginning and end positions of the meridian plane profile are precisely closed.

4. The low-flow-noise underwater buoy carrier design method according to claim 3, characterized in that, In step 2, the design variables include key parameters such as the maximum diameter-to-length ratio, the location of the maximum thickness, and the trailing edge radius contraction angle. The geometric and physical constraints include the internal equipment compartment dimensions and hydrostatic stability. Step 2 includes: Select the maximum diameter-to-length ratio from the design parameter vector P. Location of maximum thickness Trailing edge radius contraction angle As a core design variable, the trailing edge radius contraction angle is one of them. The angle between the line connecting the location of maximum thickness and the center of the trailing edge and the center line of the target body; Set the minimum diameter of the internal equipment compartment. With minimum volume As geometric constraints, the underwater glider carrier is feasible in practical engineering to accommodate batteries, a data collector and an acoustic sensor. Set still water stability constraints, including the positional relationship between the height of the buoyancy center and the height of the center of gravity, so that the underwater buoy carrier maintains the set attitude angle range, and the buoy body can rotate and automatically correspond to the incoming flow according to the direction of the water flow.

5. The low-flow-noise underwater buoy carrier design method according to claim 1, characterized in that, Step 3 includes: Step 3-1: The total sound pressure level, reflecting the overall noise level, is obtained by integrating the broadband noise induced by the flow on the carrier surface. Establish based on total sound pressure level The objective function for flow noise as a fundamental optimization term; Step 3-2: Introduce a summation term for sound pressure level in a specific frequency band into the objective function. Construct a complete optimization objective function ,in, , These are weighting coefficients. As a penalty item, Corresponding to the key operating frequency bands of acoustic sensors, the detection signal-to-noise ratio is controlled by specifically suppressing noise in sensitive frequency bands, ensuring an effective balance between noise reduction targets, sensitive frequency band suppression, and engineering feasibility.

6. The low-flow-noise underwater buoy carrier design method according to claim 5, characterized in that, Step 4 includes: Step 4-1: Select the multi-island genetic algorithm as the optimizer, set the initial population size, crossover probability, mutation probability and maximum number of iterations, and use the design variables as individual gene encodings to perform global optimization; Step 4-2: In each iteration, the CST parameterized shape is generated and the computational grid is constructed based on the current design variables. The flow field is simulated using the unsteady Reynolds-averaged Navier-Stokes equations to accurately capture complex flow characteristics and obtain flow details on the carrier surface and near the wake, including transient pressure fluctuations, providing a high-fidelity flow field data foundation for noise analysis. Step 4-3: Based on the FW-H acoustic analogy method, the transient pressure pulsation obtained from the flow field simulation is used as the sound source term to calculate the sound pressure level distribution in the far field or on the surface of the carrier, thereby obtaining the optimization objective function F value under the current shape, and returning it to the optimization algorithm as the fitness evaluation basis to assess the impact of shape changes on radiated noise and drive the optimization to evolve towards lower noise.

7. The low-flow-noise underwater buoy carrier design method according to claim 6, characterized in that: Step 5 includes: Step 5-1: Obtain historical temperature and salinity profile data of the target deployment area, construct a probability distribution model of depth-density jump intensity, identify the critical jump depth range where the density gradient exceeds the set threshold, and determine the critical range of diameter-to-length ratio that is prone to induce local flow acceleration and pressure pulsation changes in the critical jump environment through parameter sensitivity analysis, and use it as the target shape neighborhood for damping structure implantation. Step 5-2: Generate the local curved surface of the mooring carrier based on the shape parameters corresponding to the critical range of diameter-to-length ratio. According to the local boundary layer thickness and pressure gradient distribution, plan a micron-level groove damping structure with a spiral distribution on the surface of the mooring carrier. The groove depth is set to 0.2 to 0.5 times the boundary layer thickness, and the ratio of groove spacing to depth is controlled between 2 and 4. The helix angle is adaptively adjusted according to the local streamline deflection angle to make the groove direction consistent with the near-wall flow direction. Step 5-3: Correct the groove geometry parameters using the local pressure gradient coefficient, increase the groove depth and density in the reverse pressure gradient region to enhance flow stability, and verify the damping structure’s suppression effect on the wall pressure pulsation amplitude through CFD simulation. If the sound pressure level reduction in a specific frequency band does not reach the preset target, locally densify the groove distribution or fine-tune the helix angle until the narrowband howling suppression requirements in the stratified environment are met.

8. The design method for low-flow-noise underwater buoy carrier according to claim 7, characterized in that: Step 6 includes: Step 6-1: Determine whether the current iteration has reached the preset maximum number of iterations, or determine whether the relative change in the objective function F value over multiple consecutive iterations is less than a set threshold, as a convergence criterion; Step 6-2: If the convergence condition is not met, generate a new generation of design variable population according to the update rules of the optimization algorithm, return to step 4 to continue the flow field and acoustic calculations and solve the objective function value, drive the population to continue to evolve, and gradually approach the global optimal solution region; Step 6-3: If the convergence condition is met, select the individual with the smallest objective function F value from the last generation of the population as the optimal design parameter set. And verify whether the optimal design parameter set fully satisfies the geometric and physical constraints set in step 2.

9. The design method for a low-flow-noise underwater buoy carrier according to claim 8, characterized in that: Step 7 includes: Step 7-1: Output the optimal design parameter set Substituting into the established CST parameterized model, the discrete point set of the optimal meridional contour curve is reconstructed through the product operation of the class function and the shape function, and the optimized geometric shape is accurately reproduced. Step 7-2: Based on the optimal meridional plane contour line, rotate 360° around the central axis to generate a three-dimensional geometric model of the mooring carrier, and smooth the surface to eliminate the small curvature fluctuations generated during the parameterization process, ensure the mathematical smoothness of the outer surface, eliminate local curvature abrupt changes, and ensure the continuity and stability of surface flow. Step 7-3: Perform final verification on the generated three-dimensional geometric model of the underwater mooring carrier, extracting data including the maximum diameter-to-length ratio. Location of maximum thickness and trailing edge radius contraction angle The design parameters were confirmed to fall within the set design variable range, thus forming a three-dimensional model of a low-flow noise underwater buoy carrier that can be used for engineering manufacturing.

10. A low-flow-noise underwater buoy carrier, characterized in that: Based on the design method of a low-flow noise underwater mooring carrier as described in any one of claims 1-9, the method includes a base and an underwater mooring carrier, wherein the underwater mooring carrier and the base are rotatable relative to each other, and the underwater mooring carrier is a smooth rotationally symmetric body, wherein its meridional profile is smoothly connected by a leading edge segment, a maximum thickness segment and a tail arc. The leading edge segment has a large radius of curvature. The ratio of the chord length L of the underwater glider carrier's outline satisfies: ; The tail arc is a slender tail vertebra, and its tail edge arc contraction angle is... Between 5° and 12°; The maximum thickness segment of the meridional plane profile is located at 1 / 4 to 1 / 2 of the total chord length from the leading edge, with a diameter-to-length ratio of... =0.15~0.25, and the distance from the maximum thickness segment to the leading edge point accounts for 0.30~0.45 of the profile chord length; The outer surface of the underwater glider carrier is a mathematically smooth curved surface, and all sensor openings and watertight connectors are integrated with the main curved surface through a flow guide or flush design.

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