Method for analyzing and predicting high-frequency noise of pod propulsor

Abstract

The invention discloses an analysis and prediction method for high-frequency noise of a pod propeller, and the method comprises the following steps: S1, obtaining flow field original data and corresponding high-frequency noise monitoring data of the pod propeller in a working state, and carrying out the preprocessing; and S2, constructing a flow field-noise coupling analysis framework based on the separation vortex model, and setting a time step length and a calculation frequency matched with actual working conditions. The invention relates to the technical field of ship propeller noise analysis and prediction. According to the pod propulsor high-frequency noise analysis and prediction method, flow field vortex structure characteristics can be captured by constructing a flow field-noise coupling analysis framework and processing the preprocessed flow field data, and the contribution degree of each vortex unit to the high-frequency noise is accurately calculated based on a vortex structure and noise generation correlation mechanism, so that the high-frequency noise analysis and prediction method is provided for the high-frequency noise analysis and prediction of the pod propulsor. The vortex units leading the noise source are screened, dynamic positioning of the high-frequency noise source is achieved by combining the space-time position information of the vortex units, and the source of noise generation can be deeply known.

Description

Technical Field

[0001] This invention relates to the field of ship propulsion noise analysis and prediction, and more specifically, to a method for analyzing and predicting high-frequency noise in podded propulsion systems. Background Technology

[0002] In marine propulsion systems, podded propulsion systems are widely used in various types of ships due to their unique structural advantages, such as good maneuverability and high propulsion efficiency. However, podded propulsion systems generate complex flow fields during operation, and the vortex structure motion within these flow fields is a significant source of high-frequency noise. High-frequency noise not only causes hearing damage to crew members, affecting their working and living environment, but also interferes with the normal operation of electronic equipment on board, reducing the ship's stealth and combat capabilities, and also has a certain negative impact on the marine ecological environment.

[0003] Currently, existing methods for analyzing noise from podded propulsion systems have several limitations. On the one hand, some methods focus only on simple noise monitoring and statistics, lacking in-depth analysis of the vortex structure of the noise-generating flow field. This makes it difficult to accurately capture the location and dynamic characteristics of high-frequency noise sources, resulting in inaccurate noise predictions. On the other hand, while some methods consider the correlation between the flow field and noise, the analytical models used are not precise enough to accurately simulate complex separated flow fields. This leads to inaccurate quantification of the correlation between vortex structures and high-frequency noise, thus affecting the effectiveness of noise control optimization schemes. Furthermore, existing methods also lack real-time performance, failing to meet the real-time prediction and control requirements of high-frequency noise during actual ship operation. Therefore, developing a method capable of accurately analyzing and predicting high-frequency noise from podded propulsion systems and providing effective control optimization schemes is of significant practical importance. Summary of the Invention

[0004] The purpose of this invention is to provide a method for analyzing and predicting high-frequency noise in podded propulsion systems. This method addresses the shortcomings of existing methods, which often focus solely on simple noise monitoring and statistics, lacking in-depth analysis of the vortex structure of the noise-generating flow field. This makes it difficult to accurately capture the location and dynamic characteristics of high-frequency noise sources, resulting in inaccurate noise prediction results. Furthermore, the analytical models used are not precise enough to accurately simulate complex separated flow fields, leading to inaccurate quantification of the correlation between vortex structures and high-frequency noise. Consequently, the effectiveness of noise control optimization schemes is affected, and the methods fail to meet usage requirements.

[0005] This invention achieves the above objective through the following technical solution: a method for analyzing and predicting high-frequency noise in a podded propulsion system, the method comprising the following steps: S1. Acquire raw flow field data and corresponding high-frequency noise monitoring data of the podded thruster under working conditions, and perform preprocessing. S2. Construct a flow field-noise coupling analysis framework based on the separated vortex model, and set the time step and calculation frequency to match the actual working conditions; S3. The preprocessed flow field data is solved dynamically in real time through the analysis framework to capture the spatiotemporal evolution characteristics of the vortex structure and locate the dynamic distribution of high-frequency noise sources. S4. Quantify the correlation between vortex structure changes and high-frequency noise intensity to generate a dynamic influence spectrum of noise sources; S5. Based on the dynamic influence map, optimize vortex reduction by adjusting the flow field structure parameters, suppress non-physical field noise, and output the real-time prediction results of high-frequency noise and the noise control optimization scheme.

[0006] Furthermore, the process of acquiring the raw flow field data and corresponding high-frequency noise monitoring data of the podded thruster in its operating state in step S1, and performing preprocessing, includes: Acquire multi-dimensional flow field raw data of the podded thruster under different operating conditions and high-frequency noise time-domain signal data under the corresponding operating conditions. The multi-dimensional flow field raw data includes data related to flow velocity, pressure, and vorticity distribution. Outlier removal and normalization were performed on the raw flow field data; The high-frequency noise time-domain signal is filtered and denoised to retain the noise signal components in the target frequency band, thus obtaining the preprocessed flow field data and noise data.

[0007] Furthermore, the normalization process is achieved through a preset normalization expression, which is based on the original, minimum, and maximum values ​​of the parameters corresponding to each monitoring point to obtain the normalized flow field parameter values.

[0008] Furthermore, step S2, which involves constructing a flow field-noise coupled analysis framework based on the separated vortex model and setting a time step and calculation frequency that match the actual working conditions, includes: A core flow field calculation method based on the separated vortex model is constructed, and shear stress transport equations and vortex viscosity correction terms are introduced; A coupling mapping relationship between flow field parameters and high-frequency noise was established, and vortex kinetic energy and specific dissipation rate were selected as key intermediate variables for noise generation. Based on the actual operating speed of the podded thruster and the dynamic characteristics of the flow field, a time step that meets the preset conditions is set. Set the calculation frequency to ensure that it is synchronized with the noise monitoring frequency.

[0009] Furthermore, the time step satisfy ,in The highest frequency of the target high-frequency noise.

[0010] Furthermore, step S3 involves using an analysis framework to dynamically solve the preprocessed flow field data in real time, capturing the spatiotemporal evolution characteristics of the vortex structure, and locating the dynamic distribution of high-frequency noise sources. This process includes: The preprocessed flow field data is input into the separated vortex model, and the flow field control equations are solved in real time using numerical calculation methods to obtain the vortex structure distribution data at each time step. Based on the correlation mechanism between vortex structure and noise generation, the contribution of each vortex unit to high-frequency noise is calculated. Based on the noise contribution threshold, the vortex units that dominate the noise source are selected, and combined with their spatiotemporal location information, a dynamic localization map of the high-frequency noise source is generated.

[0011] Furthermore, the vortex structure distribution data includes: Vortex core location, vortex intensity, and vortex evolution trajectory; The contribution is calculated using a preset contribution calculation formula, which is based on the kinetic energy and specific dissipation rate of each vortex unit.

[0012] Furthermore, step S4, which quantifies the correlation between vortex structure changes and high-frequency noise intensity to generate a dynamic influence map of the noise source, includes: A time-series correlation model between the characteristic parameters of the vortex structure and the intensity of high-frequency noise was established, with the vortex intensity change rate and vortex core migration velocity as input variables and the high-frequency noise sound pressure level as output variable. The correlation model is trained and optimized based on vortex structure data and corresponding noise monitoring data at different time steps. Based on the optimized correlation model, a dynamic spectrum of the impact of vortex structure changes on high-frequency noise under different operating conditions is generated.

[0013] Furthermore, the temporal correlation model is as follows: ,in, The high-frequency noise sound pressure level is a variable output by the model. The vortex intensity change rate is a model input variable; The vortex core migration velocity is a model input variable. These are model correction coefficients, determined by fitting experimental data, and are model correction variables.

[0014] Furthermore, step S5, based on the dynamic influence map, optimizes vortex reduction by adjusting flow field structure parameters, suppresses non-physical field noise, and outputs real-time prediction results of high-frequency noise and noise control optimization schemes, including: Based on the dynamic impact map of the noise source, key areas are identified and the flow field structure adjustment parameters are determined; Establish a parameter optimization objective function and solve for the optimal parameter combination using an optimization algorithm; The optimal parameter combination is input into the flow field-noise coupling analysis framework to simulate the vortex reduction effect and the corresponding noise change, and output the real-time predicted value of high-frequency noise. By combining the prediction results with the parameter adjustment scheme, a complete noise control scheme is generated.

[0015] The beneficial effects of this invention are as follows: 1. By constructing a flow field-noise coupling analysis framework, the preprocessed flow field data can be processed to capture the characteristics of the flow field vortex structure. Based on the correlation mechanism between vortex structure and noise generation, the contribution of each vortex unit to high-frequency noise can be accurately calculated, the vortex unit that dominates the noise source can be screened, and the dynamic location of the high-frequency noise source can be realized by combining its spatiotemporal location information. This helps to understand the root cause of noise generation and provides a clear target for subsequent noise control.

[0016] 2. When quantifying the correlation between vortex structure and high-frequency noise, a time-series correlation model between the characteristic parameters of the vortex structure and the intensity of high-frequency noise is established. This model can accurately reflect the impact of the dynamic changes of the vortex structure on high-frequency noise, providing a reliable theoretical basis for noise prediction and control.

[0017] 3. The shear stress transport equation and vortex viscosity correction term were introduced when constructing the analysis framework, which improved the accuracy of the separated flow field simulation. At the same time, the calculation parameters were reasonably set so that the time step matched the highest frequency of the target high-frequency noise and the calculation frequency was synchronized with the noise monitoring frequency, realizing real-time matching of flow field and noise data. The flow field control equations were solved dynamically in real time through numerical calculation methods, which can obtain accurate vortex structure distribution data and evolution characteristics, thereby outputting real-time prediction results of high-frequency noise, meeting the real-time requirements of actual ship operation.

[0018] 4. With the goal of minimizing high-frequency noise sound pressure level, and combined with the constraints of flow field dynamic performance loss, the optimal combination of flow field structural parameters is solved through optimization algorithms. The optimal parameter combination is then input into the analysis framework to simulate the vortex reduction effect and corresponding noise changes. This not only outputs real-time predicted values ​​of high-frequency noise, but also generates a complete noise control scheme by combining parameter adjustment schemes. This effectively reduces the high-frequency noise generated by podded propulsion, improves the ship's working environment, enhances the ship's stealth and combat capabilities, and reduces the impact on the marine ecological environment. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a flowchart of the preprocessing module of the present invention; Figure 3 This is a flowchart illustrating the construction of the analytical framework for this invention. Figure 4 The flowchart for the optimized control module of this invention is shown below. Detailed Implementation

[0020] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0021] Example 1: Please refer to Figures 1-4 This invention provides a technical solution: a method for analyzing and predicting high-frequency noise in a podded propulsion system, the method comprising: S1. Acquire raw flow field data and corresponding high-frequency noise monitoring data of the podded thruster under working conditions, and perform preprocessing. Among them, the podded propulsion system is a propulsion device that places the propulsion motor outside the hull and directly drives the propeller. It has advantages such as high propulsion efficiency and good maneuverability and is widely used in the marine industry. The raw flow field data consists of various physical quantities describing the fluid motion state under the working conditions of the podded propulsion system, such as velocity, pressure, and temperature. These data reflect the distribution and changes of the fluid around the propulsion system. The high-frequency noise monitoring data is high-frequency noise signal data collected by specialized noise monitoring equipment during the operation of the podded propulsion system. It is used to analyze the high-frequency noise characteristics generated by the propulsion system. Preprocessing involves a series of processing operations on the acquired raw flow field data and high-frequency noise monitoring data, including data cleaning, data normalization, and data filtering, to improve the quality and usability of the data and prepare it for subsequent analysis. S2. Based on the SST k-Omega separated vortex model, a flow field-noise coupling analysis framework is constructed, and the time step and calculation frequency are set to match the actual working conditions. Among them, the SST k-Omega separated vortex model is a turbulent model used to simulate fluid flow, where SST represents shear stress transport, k is turbulent kinetic energy, and Omega is turbulent specific dissipation rate; the flow field-noise coupling analysis framework is a comprehensive analysis system that combines flow field simulation and noise analysis. Through this framework, the influence of the physical characteristics of the flow field on noise generation and propagation, as well as the feedback effect of noise on the flow field, can be considered simultaneously, thereby more accurately predicting and analyzing the high-frequency noise generated by the podded propulsion system; the time step is the time interval used to discretize continuous time in numerical simulation. An appropriate time step is crucial to ensuring the stability and accuracy of numerical simulation and needs to be set according to actual working conditions and simulation requirements to accurately capture the changes in flow field and noise over time; the calculation frequency is the frequency of calculation and updating during numerical simulation, which determines the temporal resolution of the simulation results. The higher the calculation frequency, the more details of the dynamic changes in flow field and noise can be captured, but it will also increase the computational load and computation time; S3. The preprocessed flow field data is solved dynamically in real time through the analysis framework to capture the spatiotemporal evolution characteristics of the vortex structure and locate the dynamic distribution of high-frequency noise sources. Among these features, real-time dynamic solution utilizes a pre-constructed flow field-noise coupled analysis framework to continuously calculate and solve pre-processed flow field data over time. By constantly updating the flow field state, it simulates the dynamic changes of the flow field during the operation of the podded thruster. Vortex structures are rotating flow regions formed by fluid movement and are an important feature of the flow field. Around the podded thruster, due to the viscosity and complex geometry of the fluid, vortex structures of various sizes and shapes are generated. The generation, development, and dissipation of these vortex structures have a significant impact on noise generation. Spatiotemporal evolution characteristics refer to the changes in vortex structures in time and space, including the vortex generation location, trajectory, size changes, and intensity changes. By capturing these characteristics, we can gain a deeper understanding of the relationship between vortex structures and high-frequency noise. The dynamic distribution location of high-frequency noise sources changes with time and flow field variations during the operation of the podded thruster. By solving the analysis framework, the location of these noise sources can be located in real time, providing a basis for noise control. S4. Quantify the correlation between vortex structure changes and high-frequency noise intensity to generate a dynamic influence spectrum of noise sources; Among them, the quantitative correlation is determined by mathematical methods and statistical analysis to determine the quantitative relationship between vortex structure changes and high-frequency noise intensity; the noise source dynamic influence map is a graphical representation of the impact of vortex structure changes on high-frequency noise intensity, as well as the dynamic distribution of noise sources at different times and spatial locations. This map can intuitively reflect the noise generation mechanism and propagation path, providing an intuitive reference for the formulation of noise control strategies. S5. Based on the dynamic influence map, optimize vortex reduction by adjusting the flow field structure parameters, suppress non-physical field noise, and output the real-time prediction results of high-frequency noise and the noise control optimization scheme. Among them, flow field structure parameters are various parameters that affect the characteristics of the flow field, such as the geometry of the podded propeller, the shape and angle of the propeller blades, and the installation position of the propeller. Changes in these parameters directly affect the distribution of the flow field and the formation of vortex structures, thus affecting the generation of high-frequency noise. Vortex reduction is achieved by adjusting the flow field structure parameters to change the flow characteristics of the flow field, reducing or suppressing the generation and development of vortex structures, thereby reducing noise caused by vortices. Non-physical field noise is noise that may be generated in numerical simulations due to imperfections in the model and unreasonable boundary conditions. By optimizing the flow field structure parameters, these non-physical field noises can be suppressed, improving the accuracy of noise prediction. The real-time prediction result of high-frequency noise is based on the dynamic influence spectrum and the optimized flow field structure to predict the intensity of high-frequency noise generated by the podded propeller at different times during operation, obtaining real-time noise prediction data. The noise control optimization scheme is based on the noise prediction results and the analysis of the vortex structure, proposing control measures and improvement suggestions for the high-frequency noise of the podded propeller, such as optimizing the design parameters of the propeller and using noise reduction materials, to reduce the noise level generated by the propeller.

[0022] It should be noted that acquiring and preprocessing raw data during use ensures data quality and lays the foundation for subsequent analysis. The coupled framework built upon the SST k-Omega separated vortex model can accurately simulate complex flow fields. Matching the time step and calculation frequency to actual working conditions improves the accuracy and practicality of the simulation. Real-time dynamic solving captures the spatiotemporal changes of vortex structures, accurately locates the dynamic distribution of noise sources, and provides a deeper understanding of noise generation mechanisms. Quantifying correlations and generating dynamic influence maps visually presents the laws of noise generation and propagation. Finally, adjusting parameters based on the maps optimizes vortex reduction, effectively suppressing non-physical field noise. It not only outputs real-time prediction results, providing a basis for noise monitoring, but also offers optimization schemes to reduce propeller noise and improve its operational performance and comfort. The overall design is scientific, reasonable, comprehensive, and effective.

[0023] In one embodiment, raw flow field data and corresponding high-frequency noise monitoring data of the podded thruster under operating conditions are acquired and preprocessed, including: The original multi-dimensional flow field data of the podded thruster under different operating conditions is obtained. The original multi-dimensional flow field data includes velocity, pressure, vorticity distribution data, and high-frequency noise time-domain signal data under the corresponding operating conditions. The flow field data dimension is M×T, where M represents the number of monitoring points and T represents the time series length. The noise data is a 1×T time-domain signal sequence. Outlier removal and normalization are performed on the raw flow field data. The normalization expression is as follows: ,in This represents the original value of the flow field parameters at time t for the m-th monitoring point. and These are the minimum and maximum values ​​of the parameter corresponding to the m-th monitoring point, respectively. These are the normalized flow field parameter values; The high-frequency noise time-domain signal is filtered and denoised to retain the noise signal components within the target frequency band, thus obtaining the preprocessed noise data.

[0024] This design acquires multi-dimensional raw flow field data and high-frequency noise time-domain signal data of the podded thruster. Outlier removal and normalization are performed on the flow field data, and noise signals are filtered and denoised. The multi-dimensional data comprehensively reflects the thruster's operating status, providing rich information for subsequent analysis. Outlier removal and normalization eliminate data bias, unify the data range, and improve data quality and comparability. Filtering and denoising retain the target frequency band noise signal, reducing interference and making the noise data purer and more accurate. Preprocessing lays a solid foundation for subsequent data-based analysis and prediction, improving the reliability and accuracy of the overall method.

[0025] In one embodiment, a flow field-noise coupling analysis framework is constructed based on the SST k-Omega separated vortex model, and a time step and calculation frequency are set to match the actual working conditions, including: A core flow field calculation method based on the SST k-Omega separated vortex model is constructed, and shear stress transport equations and vortex viscosity correction terms are introduced to enhance the simulation accuracy of separated flow fields. A coupling mapping relationship between flow field parameters and high-frequency noise was established, with vortex kinetic energy k and specific dissipation rate Omega used as key intermediate variables for noise generation. The time step is set based on the actual operating speed of the podded thruster and the dynamic characteristics of the flow field. ,satisfy ,in The highest frequency of the target high-frequency noise; Set calculation frequency ,make sure It keeps synchronized with the noise monitoring frequency to achieve real-time matching of flow field and noise data.

[0026] This design constructs a core flow field calculation system based on the SST k-Omega separated vortex model, introducing relevant equations and correction terms to enhance simulation accuracy. It establishes a coupling mapping relationship between the flow field and noise, sets appropriate time steps and calculation frequencies, and enhances simulation accuracy to more accurately represent complex flow fields, providing reliable flow field data for noise analysis. The coupling mapping relationship clearly defines key variables in noise generation, facilitating analysis. Appropriate time steps and calculation frequencies ensure simulation-to-real-time matching, achieving real-time synchronization of flow field and noise data. This enables subsequent analysis to accurately capture the dynamic relationship between the two, improving the timeliness and accuracy of noise prediction.

[0027] In one embodiment, the preprocessed flow field data is dynamically solved in real time using an analysis framework to capture the spatiotemporal evolution characteristics of the vortex structure and locate the dynamic distribution of high-frequency noise sources, including: The preprocessed flow field data is input into the SST k-Omega separated vortex model, and the flow field control equations are solved in real time using the finite volume method to obtain the vortex structure distribution data at each time step, including vortex core position, vortex intensity and vortex evolution trajectory. Based on the correlation mechanism between vortex structure and noise generation, the contribution of each vortex element to high-frequency noise is calculated. The contribution calculation formula is as follows: ,in For the first The noise contribution of each vortex unit. and The first The kinetic energy and specific dissipation rate of each vortex element. This represents the total number of vortex units; Based on the noise contribution threshold, the vortex units that dominate the noise source are selected, and combined with their spatiotemporal location information, a dynamic localization map of the high-frequency noise source is generated to achieve real-time tracking of the noise source.

[0028] This design inputs preprocessed data into the model to obtain vortex structure data, calculates the noise contribution of each vortex unit, selects the dominant noise sources, and generates a dynamic location map. Through real-time dynamic solving, it can accurately capture the spatiotemporal evolution of the vortex structure, understand its changing patterns, quantify the impact of each vortex unit on noise by calculating the noise contribution, focus on key factors by selecting the dominant noise sources, and enable real-time tracking of noise sources, intuitively presenting changes in the location of noise sources. This provides clear targets for subsequent noise control and helps to formulate targeted noise reduction measures.

[0029] In one embodiment, the correlation between vortex structure changes and high-frequency noise intensity is quantified to generate a dynamic influence map of the noise source, including: A time-series correlation model between the characteristic parameters of the vortex structure and the intensity of high-frequency noise is established, using the vortex intensity change rate as the basis. and vortex core migration speed Using the high-frequency noise sound pressure level (SPL) as the input variable and the high-frequency noise sound pressure level (SPL) as the output variable, a mapping model is constructed: ,in These are the model correction coefficients, determined through fitting experimental data. Based on vortex structure data and corresponding noise monitoring data at different time steps, the correlation model is trained and optimized, the model prediction error is calculated and iteratively corrected. Based on the optimized correlation model, a dynamic spectrum of the impact of vortex structure changes on high-frequency noise under different operating conditions is generated, which intuitively presents the variation law of noise source intensity with vortex evolution.

[0030] This design establishes a time-series correlation model between vortex structure characteristic parameters and noise intensity. The model is trained and optimized using experimental data to generate a dynamic influence map. The correlation model clarifies the quantitative relationship between vortex structure changes and noise intensity, providing a theoretical basis for a deeper understanding of noise generation mechanisms. Training and optimization with experimental data makes the model more realistic and improves prediction accuracy. The dynamic influence map visually presents the variation law of noise source intensity with vortex evolution, allowing researchers to clearly grasp the noise change trend and providing an intuitive reference for the formulation of noise control strategies.

[0031] In one embodiment, based on the dynamic influence map, vortex reduction is optimized by adjusting the flow field structure parameters to suppress non-physical field noise, and real-time prediction results of high-frequency noise and noise control optimization scheme are output, including: Based on the dynamic impact spectrum of the noise source, the key areas where vortex aggregation leads to the enhancement of non-physical field noise are identified, and the flow field structure adjustment parameters are determined, including the guide structure angle, the propeller blade angle of attack, and the fluid medium flow velocity. A parameter optimization objective function was established, with the goal of minimizing the high-frequency noise sound pressure level, and the constraint that the loss of flow field dynamic performance is ≤5%. The optimal parameter combination was solved by a genetic algorithm. The optimal parameter combination is input into the flow field-noise coupling analysis framework to simulate the vortex reduction effect and the corresponding noise change, and output the real-time predicted value of high-frequency noise. By combining the prediction results with the parameter adjustment scheme, a complete noise control scheme is generated, which includes noise source location information, prediction accuracy and optimization parameters, so as to realize real-time prediction and dynamic suppression of high-frequency noise.

[0032] This design identifies key regions based on dynamic influence maps, determines adjustment parameters, establishes an objective function and solves it using a genetic algorithm, simulates the optimization effect, outputs predicted values, and generates a complete solution. Identifying key regions allows for precise location of the root cause of the problem, determining adjustment parameters provides direction for optimizing the flow field, and establishing an objective function and solving for the optimal parameter combination effectively reduces noise while ensuring minimal loss of flow field dynamic performance. The simulated optimization effect outputs predicted values, allowing for advance understanding of the noise reduction effect, and generating a complete solution that covers multiple aspects of information. This enables real-time prediction and dynamic suppression of high-frequency noise, improving the thruster's operating performance and comfort.

[0033] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0034] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for analyzing and predicting high-frequency noise in a podded thruster, characterized in that, The method includes the following steps: S1. Obtain raw flow field data and corresponding high-frequency noise monitoring data under the working state of the podded thruster, and perform preprocessing; S2. Construct a flow field-noise coupling analysis framework based on the separated vortex model, and set the time step and calculation frequency to match the actual working conditions; S3. The preprocessed flow field data is solved dynamically in real time through the analysis framework to capture the spatiotemporal evolution characteristics of the vortex structure and locate the dynamic distribution of high-frequency noise sources. S4. Quantify the correlation between vortex structure changes and high-frequency noise intensity to generate a dynamic influence spectrum of noise sources; S5. Based on the dynamic influence map, optimize vortex reduction by adjusting the flow field structure parameters, suppress non-physical field noise, and output the real-time prediction results of high-frequency noise and the noise control optimization scheme. In step S3, the process of performing real-time dynamic solution on the preprocessed flow field data through the analysis framework, capturing the spatiotemporal evolution characteristics of the vortex structure, and locating the dynamic distribution location of the high-frequency noise source includes: inputting the preprocessed flow field data into the separated vortex model, performing real-time dynamic solution on the flow field control equation through numerical calculation methods, and obtaining the vortex structure distribution data at each time step. Based on the correlation mechanism between vortex structure and noise generation, the contribution of each vortex unit to high-frequency noise is calculated. Based on the noise contribution threshold, the vortex units that dominate the noise source are selected, and combined with their spatiotemporal location information, a dynamic localization map of the high-frequency noise source is generated.

2. The method for analyzing and predicting high-frequency noise of a podded thruster according to claim 1, characterized in that, The process of acquiring raw flow field data and corresponding high-frequency noise monitoring data under the working state of the podded thruster in step S1, and performing preprocessing, includes: Acquire multi-dimensional flow field raw data of the podded thruster under different operating conditions and high-frequency noise time-domain signal data under the corresponding operating conditions. The multi-dimensional flow field raw data includes data related to flow velocity, pressure, and vorticity distribution. Outlier removal and normalization were performed on the raw flow field data; The high-frequency noise time-domain signal is filtered and denoised to retain the noise signal components in the target frequency band, thus obtaining the preprocessed flow field data and noise data.

3. The method for analyzing and predicting high-frequency noise of a podded thruster according to claim 2, characterized in that: The normalization process is achieved through a preset normalization expression, which is based on the original, minimum, and maximum values ​​of the parameters corresponding to each monitoring point to obtain the normalized flow field parameter values.

4. The method for analyzing and predicting high-frequency noise of a podded thruster according to claim 1, characterized in that, Step S2, which involves constructing a flow field-noise coupling analysis framework based on the separated vortex model and setting a time step and calculation frequency that match actual working conditions, includes: A core flow field calculation method based on the separated vortex model is constructed, and shear stress transport equations and vortex viscosity correction terms are introduced; A coupling mapping relationship between flow field parameters and high-frequency noise was established, and vortex kinetic energy and specific dissipation rate were selected as key intermediate variables for noise generation. Based on the actual operating speed of the podded thruster and the dynamic characteristics of the flow field, a time step that meets the preset conditions is set. Set the calculation frequency to ensure that it is synchronized with the noise monitoring frequency.

5. The method for analyzing and predicting high-frequency noise of a podded thruster according to claim 4, characterized in that: The time step satisfy ,in The highest frequency of the target high-frequency noise.

6. The method for analyzing and predicting high-frequency noise of a podded thruster according to claim 1, characterized in that, The vortex structure distribution data includes: Vortex core location, vortex intensity, and vortex evolution trajectory; The contribution is calculated using a preset contribution calculation formula, which is based on the kinetic energy and specific dissipation rate of each vortex unit.

7. The method for analyzing and predicting high-frequency noise of a podded thruster according to claim 1, characterized in that, Step S4, which quantifies the correlation between vortex structure changes and high-frequency noise intensity to generate a dynamic influence map of the noise source, includes: A time-series correlation model between the characteristic parameters of the vortex structure and the intensity of high-frequency noise was established, with the vortex intensity change rate and vortex core migration velocity as input variables and the high-frequency noise sound pressure level as output variable. The correlation model is trained and optimized based on vortex structure data and corresponding noise monitoring data at different time steps. Based on the optimized correlation model, a dynamic spectrum of the impact of vortex structure changes on high-frequency noise under different operating conditions is generated.

8. The method for analyzing and predicting high-frequency noise of a podded thruster according to claim 7, characterized in that... The time-series correlation model is as follows: ,in, The high-frequency noise sound pressure level is a variable output by the model. The vortex intensity change rate is a model input variable; The vortex core migration velocity is a model input variable. These are model correction coefficients, determined by fitting experimental data, and are model correction variables.

9. The method for analyzing and predicting high-frequency noise of a podded thruster according to claim 1, characterized in that, Step S5, which involves optimizing vortex reduction and suppressing non-physical noise by adjusting flow field structure parameters based on the dynamic influence map, and outputting real-time prediction results for high-frequency noise and noise control optimization schemes, includes the following steps: Based on the dynamic impact map of the noise source, key areas are identified and the flow field structure adjustment parameters are determined; Establish a parameter optimization objective function and solve for the optimal parameter combination using an optimization algorithm; The optimal parameter combination is input into the flow field-noise coupling analysis framework to simulate the vortex reduction effect and the corresponding noise change, and output the real-time predicted value of high-frequency noise. By combining the prediction results with the parameter adjustment scheme, a complete noise control scheme is generated.

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