Underwater vehicle mechanical noise simulation finite element model correction method and system

Abstract

The embodiment of the invention provides an underwater vehicle mechanical noise simulation finite element model correction method and system, and the method comprises the steps: building an initial underwater vehicle mechanical noise simulation finite element model, and carrying out the parameter region division according to the structural design parameters and vibration isolation system design parameters involved in the model; forming a design parameter area; setting to-be-optimized target parameters and corresponding parameter value ranges for each design parameter area to form a target parameter group, and setting constraint conditions of model correction according to tolerance requirements of model quality changes; obtaining vibration acceleration data measured in a land vibration test, and performing real spectrum estimation based on the vibration acceleration data to obtain an optimization target of model correction; and performing iterative optimization on each target parameter group according to a constraint condition and an optimization target, and determining a corrected underwater vehicle mechanical noise simulation model when a preset iteration termination condition is reached.

Description

Technical Field

[0001] This application relates to the field of underwater noise prediction technology, and more specifically, to a method and system for correcting a finite element model of mechanical noise simulation for underwater vehicles. Background Technology

[0002] When predicting underwater noise from ships, the first step is to establish an accurate structural finite element model. Due to various simplifying assumptions and uncertainties, the established finite element model usually contains errors, which can be attributed to the following three aspects:

[0003] 1) Discrete algorithm error: The algorithm error caused by discretizing the continuous structure model during finite element calculation;

[0004] 2) Model structure error: In the modeling process, the actual model structure is usually simplified (such as the simplification of boundary conditions and connection conditions), ignoring some uncertainties in establishing the model's governing equations;

[0005] 3) Modular parameter error: Due to reasons such as processing and manufacturing and environmental changes, the model parameters may be inaccurate, which in turn causes errors in the finite element model parameters.

[0006] Due to the influence of the above errors, the finite element analysis results often cannot fully and realistically reflect the system response, so it is necessary to correct the finite element simulation model.

[0007] Traditional methods for correcting finite element simulation models in ship dynamics are typically based on single-parameter optimization theory. This involves constructing an objective function with hull structural parameters as variables and using iterative algorithms to continuously adjust the parameter values ​​to approximate simulation results with test data under specific conditions. However, these methods only consider the influence of hull structural design parameters, neglecting the impact of internal vibration isolation system design parameters, leading to inadequate finite element simulation model correction. Furthermore, the optimization objective in the model correction process is usually derived from the spectral analysis of test signals. Since actual engineering tests inevitably involve various measurement noise interferences, and traditional spectral analysis has limited noise suppression capabilities, the optimization objective may be subject to deviations.

[0008] All of the above reasons will lead to significant errors in the corrected simulation model when used for mechanical noise prediction. Therefore, it is necessary to explore an innovative finite element simulation model correction scheme that comprehensively considers the design parameters of the hull structure and internal vibration isolation system, and can effectively suppress the impact of measurement noise on the optimization target, so as to improve the accuracy of underwater mechanical noise prediction for ships. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a method and system for correcting the finite element model of underwater vehicle mechanical noise simulation, which addresses the shortcomings of the prior art.

[0010] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for correcting a finite element model of underwater vehicle mechanical noise simulation, comprising the following steps:

[0011] S1. Establish an initial finite element model for simulating the mechanical noise of an underwater vehicle, and divide the parameter regions according to the structural design parameters and vibration isolation system design parameters involved in the model to form the corresponding design parameter regions;

[0012] S2. Set the target parameters to be optimized and the corresponding parameter value range for each of the design parameter regions to form a target parameter group, and set the constraints for model correction according to the tolerance requirements of model quality changes.

[0013] S3. Obtain the vibration acceleration data measured in the land vibration test, and perform true spectrum estimation based on the vibration acceleration data to obtain the optimization target for model correction;

[0014] S4. Iterate and optimize each target parameter group according to the constraints and optimization objectives, and when the preset iteration termination condition is reached, determine the corrected underwater vehicle mechanical noise simulation model based on the optimal parameter group obtained by optimization.

[0015] Furthermore, in step S1, establishing the initial underwater vehicle mechanical noise simulation finite element model includes: establishing the initial underwater vehicle mechanical noise simulation model according to the preset design specifications and requirements.

[0016] Furthermore, in step S2, the target parameters to be optimized include the material equivalent density, equivalent modulus, and material damping parameters of the corresponding structural design parameter region, as well as the triaxial stiffness value and damping ratio of the corresponding vibration isolation system design parameter region; the constraint conditions include setting the total mass change rate of the model to be less than a preset threshold to ensure the physical rationality of the model correction process and prevent the simulation results from deviating from the actual working conditions due to excessive parameter adjustment.

[0017] Furthermore, in step S3, the estimation of the true spectrum based on the vibration acceleration data includes:

[0018] S31. Select the measurement point signals around the vibration source equipment as reference signals, and use the remaining measurement point signals as response signals;

[0019] S32. Calculate the self-bispectrum of the reference signal and its mutual bispectrum with each response signal based on the bispectral analysis method;

[0020] S33. Based on the ratio of mutual bispectral and self bispectral, determine the estimated transfer function between each response signal and the reference signal;

[0021] S34. Perform true spectrum estimation based on the product of the transfer function estimate and the reference signal spectrum.

[0022] Furthermore, in step S4, a sequential linear programming algorithm is used to iteratively optimize each set of objective parameters, including the following steps:

[0023] S41. Set the initial values ​​of each target parameter group in the parameter design space, and construct a linear approximate model based on the objective function, constraints and optimization objectives at the current design point;

[0024] S42. Based on the linear approximation model, construct a linear programming subproblem, solve it to obtain the parameter update amount, and generate a new design point accordingly;

[0025] S43. Calculate the objective function value and constraint violation amount at the new design point, and determine whether the preset iteration termination condition is met. If it is met, output the optimal parameter set; otherwise, return to step S41 to continue iteration.

[0026] Furthermore, the method also includes:

[0027] S5. The mechanical noise prediction is performed based on the modified underwater vehicle mechanical noise simulation model, and the corresponding prediction results are output.

[0028] Furthermore, in step S5, the mechanical noise prediction based on the modified underwater vehicle mechanical noise simulation model and the output of the corresponding prediction results include:

[0029] S51. Based on the modified underwater vehicle mechanical noise simulation model, predict mechanical noise and organize the prediction results according to frequency band, operating condition and spatial location.

[0030] S52. Output the processed forecast results according to the multi-dimensional visualization template, where:

[0031] A three-dimensional sound pressure level cloud map is generated and the annotation results of key noise sources are superimposed to intuitively locate the high-frequency radiation area;

[0032] The total noise level at different speeds and / or depths is displayed using radar charts to provide a visual comparison of noise performance under multiple operating conditions.

[0033] By comparing simulation and measured data and using color gradations to represent the degree of deviation, high-error operating conditions and frequency bands are marked to guide the priority of model correction.

[0034] Secondly, this application discloses a finite element model correction system for simulating mechanical noise of underwater vehicles. The system includes a design parameter region partitioning module, a parameter group and constraint configuration module, a true spectrum estimation module, and a design parameter iterative optimization module, wherein:

[0035] The design parameter region division module is used to establish an initial finite element model for simulating the mechanical noise of an underwater vehicle, and to divide the parameter regions according to the structural design parameters and vibration isolation system design parameters involved in the model, thereby forming the corresponding design parameter regions.

[0036] The parameter group and constraint configuration module is used to set the target parameters to be optimized and the corresponding parameter value range for each of the design parameter regions, forming a target parameter group, and to set the constraint conditions for model correction according to the tolerance requirements of model quality changes.

[0037] The true spectrum estimation module is used to acquire vibration acceleration data measured in the land vibration test, and to perform true spectrum estimation based on the vibration acceleration data to obtain the optimization target for model correction.

[0038] The design parameter iterative optimization module is used to iteratively optimize each target parameter group according to the constraints and the optimization objectives, and when the preset iteration termination condition is reached, determine the corrected underwater vehicle mechanical noise simulation model based on the optimal parameter group obtained by optimization.

[0039] Thirdly, this application discloses a readable storage medium, which includes a program for correcting a finite element model of underwater vehicle mechanical noise simulation. When the program for correcting a finite element model of underwater vehicle mechanical noise simulation is executed by a processor, it implements the steps of the method described in any of the preceding claims.

[0040] The beneficial effects of this invention are:

[0041] 1) The design parameters of the hull structure and the internal vibration isolation system are taken into account at the same time. By decoupling and partitioning the structural design parameters and the vibration isolation system parameters, fine modeling can be carried out for different physical characteristics, avoiding the accumulation of errors caused by parameter coupling, so as to obtain a more scientific mechanical noise simulation model.

[0042] 2) When processing the test signal, a bispectral method in a higher-order spectrum with better noise suppression capability was used to generate the optimization target, which can effectively suppress noise interference in the test signal. By ensuring that the model correction direction is consistent with the actual physical response, the deviation between simulation and actual measurement is reduced.

[0043] 3) By using model quality tolerance constraints, we ensure that the model always conforms to the actual engineering situation during the parameter optimization process, thus avoiding the generation of spurious solutions. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a method for correcting a finite element model of mechanical noise simulation for underwater vehicles, as disclosed in this invention.

[0045] Figure 2 This is a finite element model (hidden shell) for simulating the mechanical noise of an underwater vehicle in an embodiment of the present invention.

[0046] Figure 3 The above are the acceleration test spectrum results of typical measuring points in the land vibration test in this embodiment of the invention;

[0047] Figure 4 The above are the calculation results of the mechanical noise of the underwater vehicle in the embodiments of the present invention;

[0048] Figure 5 This is a schematic diagram of the structure of a finite element model correction system for simulating mechanical noise of an underwater vehicle, as disclosed in this invention.

[0049] Figure 6 This is a schematic diagram of the structure of a readable storage medium disclosed in this invention. Detailed Implementation

[0050] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0051] like Figure 1 As shown, a method for correcting a finite element model of mechanical noise simulation for underwater vehicles includes the following steps:

[0052] Step S1: Establish an initial finite element model for simulating the mechanical noise of the underwater vehicle, and divide the parameter regions according to the structural design parameters and vibration isolation system design parameters involved in the model to form the corresponding design parameter regions.

[0053] Step S2: Set the target parameters to be optimized and the corresponding parameter value range for each of the design parameter regions to form a target parameter group, and set the constraints for model correction according to the tolerance requirements of model quality changes.

[0054] Step S3: Obtain the vibration acceleration data measured in the land vibration test, and perform true spectrum estimation based on the vibration acceleration data to obtain the optimization target for model correction.

[0055] Step S4: Iteratively optimize each target parameter group according to the constraints and optimization objectives, and when the preset iteration termination condition is reached, determine the corrected underwater vehicle mechanical noise simulation model based on the optimal parameter group obtained by optimization.

[0056] As can be seen from the above, the finite element model correction method for underwater vehicle mechanical noise simulation disclosed in this application simultaneously considers the influence of the hull structure and internal vibration isolation system design parameters. By decoupling and partitioning the structural design parameters and vibration isolation system parameters, it can perform refined modeling for different physical characteristics, avoiding error accumulation caused by parameter coupling, thus obtaining a more scientific mechanical noise simulation model. When processing test signals, it uses a bispectral method in a higher-order spectrum with better noise suppression capabilities to generate optimization targets, which can effectively suppress noise interference from test signals. By ensuring that the model correction direction is consistent with the actual physical response, it reduces the deviation between simulation and measurement. Through model quality tolerance constraints, it ensures that the model always conforms to engineering reality during parameter optimization, avoiding the generation of spurious solutions.

[0057] In one embodiment, step S1, establishing an initial underwater vehicle mechanical noise simulation finite element model, includes: establishing an initial underwater vehicle mechanical noise simulation model according to preset design specifications and requirements.

[0058] Specifically, considering that the design drawings define the underwater vehicle's geometry, material properties, and assembly relationships in detail, this application, based on the design drawings, performs necessary geometric simplifications or uses equivalent substitutions to model the base, deck, ribs, frame, motor, and other structures. In the vibration isolation system, RBE3 and CBUSH elements are used to simulate the vibration isolation units. The final initial finite element model for simulating the underwater vehicle's mechanical noise is shown below. Figure 2 As shown. By Figure 2 As can be seen, the initial finite element model accurately reproduces the overall geometry of the underwater vehicle through a multi-layered spiral compartment layout. Each compartment contains distributed nested bases, decks, ribs, and frames, among other key structures. A modular design using color differentiation (e.g., blue frames represent the main load-bearing structure, and yellow elements represent the vibration isolation system) visually reflects material properties and assembly relationships. It should be noted that this model employs geometric simplification and equivalent substitution strategies. While preserving the structural dynamics, it parametrically reconstructs complex surfaces and uses RBE3 elements to simulate rigid connections and CBUSH elements to simulate the nonlinear characteristics of the vibration isolators. This ensures that the frequency response characteristics of the vibration isolation system are consistent with the actual device, laying a reliable foundation for subsequent mechanical noise transmission path analysis.

[0059] It should be noted that, considering the differences in the characteristics of mechanical noise propagation in different structural sections of the underwater vehicle, this application divides the underwater vehicle into two structural design parameter areas—cylindrical section and conical section—and one internal vibration isolation system design parameter area according to its structural form.

[0060] In one embodiment, in step S2, the target parameters to be optimized include the material equivalent density, equivalent modulus, and material damping parameters of the corresponding structural design parameter region, as well as the triaxial stiffness value and damping ratio of the corresponding vibration isolation system design parameter region; the constraint conditions include setting the total mass change rate of the model to be less than a preset threshold to ensure the physical rationality of the model correction process and prevent the simulation results from deviating from the actual working conditions due to excessive parameter adjustment.

[0061] Specifically, the aforementioned target parameters to be optimized are dynamically adjusted within a range of ±20% of the initial design values, where the material equivalent density of the cylindrical / conical segment is... , With equivalent modulus , The floating range and material damping parameters are set according to the characteristics of the hull structure materials. , The triaxial stiffness values ​​of the vibration isolation system are determined using the Rayleigh damping model. With damping ratio The allowable correction range is given based on the physical characteristics of the vibration isolator. During the parameter optimization process, the rate of change of the total mass of the model is calculated in real time. , ensure | The hard constraint of ≤0.1% always holds. When parameter adjustment triggers the quality constraint, the current optimization direction is paused first and the parameter rollback mechanism is started. A new parameter combination that meets the constraint is selected for iteration.

[0062] In one embodiment, step S3, which involves estimating the true spectrum based on the vibration acceleration data, includes:

[0063] Step S31: Select the measurement point signals around the vibration source device as reference signals, and use the remaining measurement point signals as response signals.

[0064] Step S32: Calculate the self-bispectrum of the reference signal and its mutual bispectrum with each response signal based on the bispectral analysis method.

[0065] Specifically, this application uses the following formula to calculate the self-bispectrum of the reference signal and its mutual bispectrum with each response signal:

[0066] ;

[0067] ;

[0068] Where E represents the mathematical expectation, and r(f) represents the coefficients of the Fourier transform of the reference signal r(t) at frequency f. Indicates the i-th response signal The coefficients of the Fourier transform at frequency f.

[0069] Step S33: Based on the ratio of mutual bispectral and self bispectral, determine the estimated transfer function between each response signal and the reference signal.

[0070] Specifically, the transfer function estimation formula is as follows: This formula eliminates the influence of the reference signal phase by using the ratio of higher-order statistics, directly extracts the nonlinear / linear coupling characteristics of the response signal relative to the reference signal, and realizes blind estimation of the system's transfer characteristics.

[0071] Step S34: Perform true spectrum estimation based on the product of the transfer function estimate and the reference signal spectrum.

[0072] Specifically, the measurement points selected in this application are located at locations that can reflect the overall vibration state of the structure, such as the equipment base mounting point and near the shell ring ribs. Typical acceleration test spectrum results at these measurement points are shown below. Figure 3 As shown. By Figure 3 It can be seen that the vibration acceleration spectrum of the measuring points near the equipment base and shell ring ribs exhibits significant multi-peak characteristics in the range of 20-315 Hz.

[0073] In one embodiment, step S4 involves using a sequential linear programming algorithm to iteratively optimize each set of target parameters, including the following steps:

[0074] Step S41: Set the initial values ​​of each target parameter group in the parameter design space, and construct a linear approximate model based on the objective function, constraints and optimization objectives at the current design point.

[0075] Step S42: Construct a linear programming subproblem based on the linear approximation model, solve it to obtain the parameter update amount, and generate a new design point accordingly.

[0076] Step S43: Calculate the objective function value and constraint violation amount at the new design point, and determine whether the preset iteration termination condition is met. If it is met, output the optimal parameter set; otherwise, return to step S41 to continue iteration.

[0077] In one embodiment, the method further includes:

[0078] Step S5: Based on the modified underwater vehicle mechanical noise simulation model, predict the mechanical noise and output the corresponding prediction results.

[0079] In one embodiment, the mechanical noise prediction based on the modified underwater vehicle mechanical noise simulation model and the output of the corresponding prediction results include:

[0080] Step S51: Based on the modified underwater vehicle mechanical noise simulation model, perform mechanical noise prediction and organize the prediction results according to frequency band, operating condition and spatial location.

[0081] Specifically, this application categorizes noise data into low-frequency (10-100Hz), mid-frequency (100-1kHz), and high-frequency (1-10kHz) categories to facilitate the location of key noise sources. Furthermore, this application organizes data according to sailing speed (low-speed cruising / high-speed maneuvering), depth (shallow / deep sea), and equipment operating status (main propulsion operation / auxiliary engine start / stop) to cover typical mission scenarios. Finally, this application also marks the noise radiation locations (such as the bow, stern, and equipment compartments), visually displaying high-radiation areas through the generated 3D noise cloud map.

[0082] Step S52: Output the processed forecast results according to the multi-dimensional visualization template, including: generating a three-dimensional sound pressure level cloud map and overlaying the key noise source annotation results to intuitively locate the high-frequency radiation area; displaying the total noise level at different speeds and / or depths using radar charts to intuitively compare the noise performance under multiple operating conditions; comparing simulation and measured data, and using color gradations to indicate the degree of deviation, and marking high-error operating conditions and frequency bands to guide the priority of model correction.

[0083] For details, please refer to Figure 4 (This figure shows the distribution characteristics of the sound pressure level (SPL) of the mechanical noise of underwater vehicles as a function of frequency (20-315 Hz), by Figure 4 It can be seen that the modified simulation model can accurately capture the low-frequency dominant characteristics of underwater vehicle mechanical noise, especially showing a significant peak (close to 68dB) near 93 Hz, which is consistent with the theoretical prediction of the fundamental frequency of the propulsion system or the vibration characteristics of key mechanical components. At the same time, the fluctuation trend of the high-frequency band (>200 Hz) is consistent with the experimental data or theoretical attenuation law, verifying the model's ability to cover multi-frequency noise. Therefore, it can be determined that the modified underwater vehicle mechanical noise simulation model can perform mechanical noise prediction well and can provide reliable data support for subsequent noise suppression measures (such as vibration isolation design and structural optimization).

[0084] Please refer to Figure 5 This application discloses a finite element model correction system for simulating mechanical noise of underwater vehicles. The system includes a design parameter region partitioning module, a parameter group and constraint configuration module, a true spectrum estimation module, and a design parameter iterative optimization module, wherein:

[0085] The design parameter region division module is used to establish an initial finite element model for simulating the mechanical noise of an underwater vehicle, and to divide the parameter regions according to the structural design parameters and vibration isolation system design parameters involved in the model, thus forming the corresponding design parameter regions.

[0086] The parameter group and constraint configuration module is used to set the target parameters to be optimized and the corresponding parameter value range for each of the design parameter regions, forming a target parameter group, and to set the constraints for model correction according to the tolerance requirements of model quality changes.

[0087] The true spectrum estimation module is used to acquire vibration acceleration data measured in the land vibration test, and to perform true spectrum estimation based on the vibration acceleration data to obtain the optimization target for model correction.

[0088] The design parameter iterative optimization module is used to iteratively optimize each target parameter group according to the constraints and the optimization objectives, and when the preset iteration termination condition is reached, determine the corrected underwater vehicle mechanical noise simulation model based on the optimal parameter group obtained by optimization.

[0089] In one embodiment, the above modules are also used to implement a finite element model correction method for underwater vehicle mechanical noise simulation as described in any of the foregoing method embodiments, and this application does not limit this.

[0090] As can be seen from the above, the underwater vehicle mechanical noise simulation finite element model correction system disclosed in this application simultaneously considers the influence of the hull structure and internal vibration isolation system design parameters. By decoupling and partitioning the structural design parameters and vibration isolation system parameters, it can perform refined modeling for different physical characteristics, avoiding error accumulation caused by parameter coupling, thus obtaining a more scientific mechanical noise simulation model. When processing test signals, it uses a bispectral approach from a higher-order spectrum with better noise suppression capabilities to generate optimization targets, which can effectively suppress noise interference from test signals. By ensuring that the model correction direction is consistent with the actual physical response, it reduces the deviation between simulation and measurement. Through model quality tolerance constraints, it ensures that the model always conforms to engineering reality during parameter optimization, avoiding the generation of spurious solutions.

[0091] Please refer to Figure 6 This application discloses a readable storage medium, which includes a program for correcting a finite element model of underwater vehicle mechanical noise simulation. When the program is executed by a processor, it implements the steps of the method described in any of the preceding claims.

[0092] As can be seen from the above, the readable storage medium disclosed in this application simultaneously takes into account the influence of the design parameters of the hull structure and the internal vibration isolation system. By decoupling and partitioning the structural design parameters and the vibration isolation system parameters, it can perform refined modeling for different physical characteristics, avoid the accumulation of errors caused by parameter coupling, and thus obtain a more scientific mechanical noise simulation model. When processing test signals, it uses a bispectral approach from a higher-order spectrum with better noise suppression capabilities to generate optimization targets, which can effectively suppress noise interference from test signals. By ensuring that the model correction direction is consistent with the actual physical response, it reduces the deviation between simulation and measurement. Through model quality tolerance constraints, it ensures that the model always conforms to engineering reality during parameter optimization, avoiding the generation of spurious solutions.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for correcting a finite element model of mechanical noise simulation for underwater vehicles, characterized in that, Includes the following steps: S1. Establish an initial finite element model for simulating the mechanical noise of an underwater vehicle, and divide the parameter regions according to the structural design parameters and vibration isolation system design parameters involved in the model to form the corresponding design parameter regions; S2. Set the target parameters to be optimized and the corresponding parameter value range for each of the design parameter regions to form a target parameter group, and set the constraints for model correction according to the tolerance requirements of model quality changes. S3. Obtain the vibration acceleration data measured in the land vibration test, and perform true spectrum estimation based on the vibration acceleration data to obtain the optimization target for model correction; S4. Iterate and optimize each target parameter group according to the constraints and optimization objectives, and when the preset iteration termination condition is reached, determine the corrected underwater vehicle mechanical noise simulation model based on the optimal parameter group obtained by optimization.

2. The method according to claim 1, characterized in that, In step S1, establishing the initial underwater vehicle mechanical noise simulation finite element model includes: establishing the initial underwater vehicle mechanical noise simulation model according to the preset design specifications and requirements.

3. The method according to claim 1, characterized in that, In step S2, the target parameters to be optimized include the material equivalent density, equivalent modulus and material damping parameters of the corresponding structural design parameter region, as well as the triaxial stiffness value and damping ratio of the corresponding vibration isolation system design parameter region. The constraints include setting the total mass change rate of the model to be less than a preset threshold to ensure the physical rationality of the model correction process and prevent the simulation results from deviating from the actual working conditions due to excessive parameter adjustment.

4. The method according to claim 1, characterized in that, In step S3, the estimation of the true spectrum based on the vibration acceleration data includes: S31. Select the measurement point signals around the vibration source equipment as reference signals, and use the remaining measurement point signals as response signals; S32. Calculate the self-bispectrum of the reference signal and its mutual bispectrum with each response signal based on the bispectral analysis method; S33. Based on the ratio of mutual bispectral and self bispectral, determine the estimated transfer function between each response signal and the reference signal; S34. Perform true spectrum estimation based on the product of the transfer function estimate and the reference signal spectrum.

5. The method according to claim 1, characterized in that, In step S4, a sequential linear programming algorithm is used to iteratively optimize each set of objective parameters, including the following steps: S41. Set the initial values ​​of each target parameter group in the parameter design space, and construct a linear approximate model based on the objective function, constraints and optimization objectives at the current design point; S42. Based on the linear approximation model, construct a linear programming subproblem, solve it to obtain the parameter update amount, and generate a new design point accordingly; S43. Calculate the objective function value and constraint violation amount at the new design point, and determine whether the preset iteration termination condition is met. If it is met, output the optimal parameter set; otherwise, return to step S41 to continue iteration.

6. The method according to claim 1, characterized in that, The method further includes: S5. Based on the modified underwater vehicle mechanical noise simulation model, predict the mechanical noise and output the corresponding prediction results.

7. The method according to claim 6, characterized in that, In step S5, the mechanical noise prediction based on the modified underwater vehicle mechanical noise simulation model is performed, and the corresponding prediction results are output, including: S51. Based on the modified underwater vehicle mechanical noise simulation model, predict mechanical noise and organize the prediction results according to frequency band, operating condition and spatial location. S52. Output the processed forecast results according to the multi-dimensional visualization template, where: A three-dimensional sound pressure level cloud map is generated and the annotation results of key noise sources are superimposed to intuitively locate the high-frequency radiation area; The total noise level at different speeds and / or depths is displayed using radar charts to provide a visual comparison of noise performance under multiple operating conditions. By comparing simulation and measured data and using color gradations to represent the degree of deviation, high-error operating conditions and frequency bands are marked to guide the priority of model correction.

8. A finite element model correction system for simulating mechanical noise of underwater vehicles, characterized in that, The system includes a design parameter region partitioning module, a parameter group and constraint configuration module, a true spectrum estimation module, and a design parameter iterative optimization module, wherein: The design parameter region division module is used to establish an initial finite element model for simulating the mechanical noise of an underwater vehicle, and to divide the parameter regions according to the structural design parameters and vibration isolation system design parameters involved in the model, thereby forming the corresponding design parameter regions. The parameter group and constraint configuration module is used to set the target parameters to be optimized and the corresponding parameter value range for each of the design parameter regions, forming a target parameter group, and to set the constraint conditions for model correction according to the tolerance requirements of model quality changes. The true spectrum estimation module is used to acquire vibration acceleration data measured in the land vibration test, and to perform true spectrum estimation based on the vibration acceleration data to obtain the optimization target for model correction. The design parameter iterative optimization module is used to iteratively optimize each target parameter group according to the constraints and the optimization objectives, and when the preset iteration termination condition is reached, determine the corrected underwater vehicle mechanical noise simulation model based on the optimal parameter group obtained by optimization.

9. A readable storage medium, characterized in that, The readable storage medium includes a program for correcting the finite element model of underwater vehicle mechanical noise simulation. When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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