A Wave Impact Boundary Force Simulation and Analysis Method Based on SPH

Abstract

This invention relates to the field of fluid dynamics analysis technology, specifically to a wave impact boundary force simulation and analysis method based on SPH (Self-Proofing and Propagation). The invention obtains a divergence index based on the energy accumulation efficiency of each wave from initiation to breakup, the spatiotemporal distribution characteristics of energy release during breakup, and the wave height. All waves are divided into different clusters. The critical time step is adjusted according to the dispersion and divergence index of waves within each cluster to obtain an optimized critical duration. The interference-sensitive zone of each wave is identified, and the modal coupling coefficient is obtained based on the correlation strength and energy distribution of the pressure signal in the time-frequency domain within the interference-sensitive zone. The baseline artificial viscosity coefficient is adjusted based on the optimized critical duration and modal coupling coefficient to obtain an optimized artificial viscosity coefficient. The SPH algorithm is then used for wave impact boundary force simulation and analysis. This invention effectively improves the balance between accuracy and efficiency in multi-scale wave impact boundary force simulation.

Description

Technical Field

[0001] This invention relates to the field of fluid dynamics analysis technology, specifically to a wave impact boundary force simulation analysis method based on SPH. Background Technology

[0002] In the field of marine engineering, accurately understanding the boundary force characteristics generated when waves impact marine structures is crucial for assessing structural safety and optimizing structural design. Smoothed Particle Hydrodynamics (SPH), a highly promising meshless numerical simulation method, demonstrates significant advantages in simulating complex flow problems such as wave impact due to its ability to handle large deformations of free surfaces and fluid-structure interaction without requiring mesh construction. Compared to traditional mesh-based methods such as the finite element method and finite volume method, the SPH algorithm can more efficiently capture dynamic processes such as wave breaking and water splashing, providing more intuitive visualizations and richer quantitative data for in-depth exploration of the interaction mechanism between waves and marine structures.

[0003] However, in actual wave impact boundary force simulations, the wave scale varies significantly in the ocean, ranging from small-scale nearshore breaking waves to large-scale offshore giant waves, resulting in vastly different energy transfer and force distribution characteristics impacting structures. Existing SPH methods typically employ fixed particle resolution and computational parameters, making it difficult to adapt to the impact characteristics of waves at different scales. When simulating small-scale waves, insufficient particle resolution can lead to the omission of details such as local pressure concentrations; when simulating large-scale waves, high resolution results in a dramatic increase in the number of particles, significantly increasing computational costs and even making long-term simulations impossible, severely impacting simulation accuracy and engineering applicability. Summary of the Invention

[0004] To address the technical problem of low accuracy in multi-scale wave impact boundary force simulation due to fixed parameters in the SPH method, the present invention aims to provide a wave impact boundary force simulation and analysis method based on SPH. The specific technical solution adopted is as follows:

[0005] This invention proposes a wave impact boundary force simulation and analysis method based on SPH (Self-Pulse Hysteresis), the method comprising:

[0006] The pressure and strain signals of test points on the surface of the structure were acquired under each wave impact, and the waves had different scales.

[0007] Based on the energy accumulation efficiency of each wave from its initiation to its breaking, the spatiotemporal distribution characteristics of energy release during breaking, and the wave height, the divergence index of each wave is obtained; all waves are divided into different clusters; based on the degree of disorder in the frequency domain energy distribution of the pressure signal of each wave within its cluster and the degree of dispersion of the structural deformation relative to the pressure delay, and the aforementioned divergence index, the critical time step is adjusted to obtain the optimized critical duration of each wave.

[0008] Based on the fluctuation of the pressure signal at each test point on the structure surface under each wave impact, the interference sensitive area of ​​each wave is obtained; based on the correlation strength and energy distribution of the pressure signal in the time-frequency domain of each wave's interference sensitive area, the modal coupling coefficient of each wave is obtained.

[0009] Obtain the baseline artificial viscosity coefficient for each wave scale; adjust the baseline artificial viscosity coefficient according to the optimized critical duration and the modal coupling coefficient to obtain the optimized artificial viscosity coefficient for each wave scale, and use the SPH algorithm to perform wave impact boundary force simulation analysis.

[0010] Furthermore, obtaining the divergence index for each wave includes:

[0011] Acquire wave images for each wave at each moment during its test period, perform edge detection on the wave images to extract wave boundary lines, and calculate the fractal dimension of the wave images based on the spatial distribution of the wave boundary lines.

[0012] For each wave during its test period, the ratio of the difference in fractal dimension between the next moment and the previous moment to the time interval is taken as the dimension change rate. Curve fitting is performed on all dimension change rates to obtain the dimension curve. The peak point and the first maximum point of the dimension curve are recorded as the breaking moment and the starting moment of each wave, respectively.

[0013] Obtain the wave height values ​​of each wave at the moment of initiation and the moment of breakup, as well as the arrival time of each wave after breakup;

[0014] The ratio of the wave height at the moment of initiation to the moment of breaking is recorded as the cumulative energy value.

[0015] The ratio of the fractal dimension of the wave image at the moment of breakup to the arrival time after breakup is denoted as the complex potential energy index.

[0016] The ratio of the wave height of each wave at the breaking moment to the average wave height of all waves at the corresponding breaking moment is denoted as the abnormal wave probability.

[0017] Based on the energy accumulation value, the complex potential energy index, and the probability of the distorted wave, the divergence index of each wave is obtained.

[0018] Furthermore, the division of all waves into different clusters includes:

[0019] Calculate the mean pressure signal and mean strain signal of all test points on the surface of the structure under each wave impact, and record them as the overall pressure signal and overall strain signal of the corresponding wave, respectively.

[0020] Wavelet packet decomposition is performed on the overall pressure signal to obtain the wavelet packet decomposition coefficients corresponding to each decomposition frequency band; the sum of squares of all wavelet packet decomposition coefficients in each decomposition frequency band is calculated as the energy characterization value; the ratio of the energy characterization value of each decomposition frequency band to the sum of the energy characterization values ​​of all decomposition frequency bands is denoted as the energy proportion of each decomposition frequency band; the information entropy of the energy proportion of all decomposition frequency bands corresponding to the overall pressure signal is obtained and denoted as the modal energy entropy.

[0021] The times corresponding to the maximum points of the overall pressure signal and the overall strain signal of the same wave are respectively recorded as the pressure analysis time and the deformation analysis time. The time interval between each pressure analysis time and the next adjacent deformation analysis time is obtained, and the ratio of the time intervals corresponding to all pressure analysis times is taken as the deformation hysteresis time.

[0022] The mean time interval between all adjacent maxima on the overall pressure signal is calculated as the wave period; the ratio of the deformation lag time to the wave period is used as the lag coefficient; the modal energy entropy and the lag coefficient constitute the feature tuple for each wave; based on the Euclidean distance of the feature tuples of different waves, all waves are clustered to obtain several clusters.

[0023] Furthermore, obtaining the optimal critical duration for each wave includes:

[0024] Obtain the critical time step; use the ratio of the modal energy entropy to the hysteresis coefficient as the force structure coupling factor for each wave; obtain the coefficient of variation of the force structure coupling factors of all waves within the cluster of each wave;

[0025] The time adjustment coefficient of each wave is obtained by negatively correlating the product of the coefficient of variation of each wave and the divergence index of each wave when it reaches the structure and normalizing it.

[0026] The critical time step is weighted using the aforementioned time adjustment coefficient to obtain the optimized critical duration for each wave.

[0027] Furthermore, the acquisition of the interference-sensitive area includes:

[0028] The pressure signals of all test points on the surface of the structure under each wave impact are interpolated and fused to generate a spatial pressure field; the spatial pressure field is numerically differentiated to obtain the pressure gradient field; the Poincaré exponent of the singularities in the pressure gradient field is calculated, and the singularities with non-zero exponents are denoted as force field singularities.

[0029] Based on the Euclidean distance between force field singularities, all force field singularities are divided into several clusters. The region enclosed by the convex hull boundary of the cluster corresponding to the maximum number of singularities within the cluster is selected as the disturbance sensitive area of ​​each wave.

[0030] Further, obtaining the modal coupling coefficients includes:

[0031] The coupled pressure signal of the interference sensitive area of ​​each wave is decomposed into different component signals. The mutual information entropy between each component signal and all preset single-scale wave reference signals is calculated and normalized. The component signal with a mutual information entropy normalization result greater than or equal to the preset threshold is selected as the analysis signal.

[0032] The energy contribution value of each analytical signal is the ratio of the integral of the square of the amplitude of each analytical signal to the sum of the integrals of the squares of the amplitudes of all analytical signals.

[0033] The coupled pressure signal in the interference-sensitive area is decomposed to obtain component signals of different frequencies. The two component signals with the largest frequency difference are selected as target signals, and the two target signals are converted to the time-frequency domain to obtain wavelet coefficients. Based on the wavelet coefficients, the cross-wavelet spectrum between the two target signals and the wavelet power spectrum of each target signal are calculated. The wave coherence coefficient is obtained by taking the ratio of the square of the modulus of the cross-wavelet spectrum to the product of the wavelet power spectra of the two target signals.

[0034] The two analytical signals are linearly superimposed in the time domain to generate a composite signal; the ratio obtained by taking the integral of the square of the amplitude of the composite signal and the sum of the energy contribution values ​​of the two target signals as the numerator and the sum of the energy contribution values ​​of the two target signals as the denominator, is used as the coupling ratio.

[0035] Based on the coupling ratio and the wave correlation coefficient, the modal coupling coefficient of each wave is obtained.

[0036] Furthermore, obtaining the optimized artificial viscosity coefficient for each wave scale includes:

[0037] The product of the ratio of the optimized critical duration to the critical time step and the modal coupling coefficient for each wave is used as the duration correction coefficient for each wave; the average of the duration correction coefficients for waves of the same scale is recorded as the final correction coefficient.

[0038] By using the sum of constant 1 and the final correction coefficient, the baseline artificial viscosity coefficient of waves at each scale is weighted to obtain the optimized artificial viscosity coefficient of waves at the corresponding scale.

[0039] Furthermore, the method for decomposing the coupled pressure signal into different component signals is a variational mode decomposition algorithm.

[0040] Furthermore, the coupled pressure signal of the interference sensitive area of ​​each wave is the average pressure signal of all test points within the interference sensitive area of ​​each wave under the corresponding wave impact.

[0041] Furthermore, the preset threshold is 0.3.

[0042] The present invention has the following beneficial effects:

[0043] In this embodiment of the invention, the feature dispersion of all waves within the cluster of each wave represents the stability of the coupling between force and structure at the wave scale, reflecting the significance of the time difference in force transmission between waves. The divergence index reflects the non-uniform and dynamic distribution of boundary forces on the structural surface. By combining the above two factors, the time step in the SPH algorithm is dynamically adjusted, so that a fine step size is automatically used to accurately capture the instantaneous force peak during small-scale wave impacts, and a relaxed step size is used to efficiently simulate the cumulative effect during large-scale wave impacts, thus resolving the contradiction between missing key features at small scales and consuming resources at large scales under a fixed step size. The interference-sensitive area provides accurate spatial targets for interference correction. The pressure signal of the interference-sensitive area of ​​each wave in the time-frequency domain... The correlation strength and energy distribution analysis of waves reveal the cascading of wave energy, accurately quantifying the intensity of nonlinear interference to guide the dynamic correction of inter-particle forces. In cases of weak coupling, the analysis simplifies to linear superposition to avoid over-adjustment. Based on the optimized critical duration and modal coupling coefficient, the baseline artificial viscosity coefficient is adjusted, and the optimized artificial viscosity coefficient for each wave scale is adaptively determined. This ensures that small-scale transient details are not lost due to increased viscosity, and that large-scale energy transfer is not distorted due to increased step size. Ultimately, a full-chain optimization of "scale identification - time adaptation - interference elimination" is formed within the SPH framework, resolving the contradiction between accuracy and efficiency in multi-scale wave impact boundary force simulation, and providing high-fidelity and high-reliability numerical support for wave-resistant design of marine structures. Attached Figure Description

[0044] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1A flowchart illustrating the steps of a wave impact boundary force simulation and analysis method based on SPH, provided in an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of a computer device for simulating and analyzing wave impact boundary forces based on SPH, as provided in one embodiment of the present invention. Detailed Implementation

[0047] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a wave impact boundary force simulation and analysis method based on SPH proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0049] The following description, in conjunction with the accompanying drawings, details the specific scheme of the wave impact boundary force simulation and analysis method based on SPH provided by this invention.

[0050] Example 1:

[0051] This invention proposes a wave impact boundary force simulation and analysis method based on SPH. Please refer to [link / reference]. Figure 1 The diagram illustrates a flowchart of a wave impact boundary force simulation analysis method based on SPH provided in an embodiment of the present invention. The method includes:

[0052] Step S1: Obtain the pressure and strain signals of the test points on the surface of the structure under each wave impact. Waves exist at different scales.

[0053] By combining wave flue experiments with on-site ocean observations, a high-speed camera was installed on a fixed platform to ensure coverage of the target water area. Simultaneously, a wave height array and an acoustic Doppler current meter were deployed in the target water area. The testing period for a single wave was from the moment the wave leading edge entered the field of view of the high-speed camera until the moment the wave trailing edge completely left the field of view.

[0054] During the test period, a high-speed camera continuously captured wave images at a sampling rate of 1000 frames per second. A hardware-triggered synchronization device ensured strict alignment of the sampling times of the high-speed camera, wave height meter array, and acoustic Doppler current meter, enabling synchronous acquisition of multiple parameters. Specifically, this included: acquiring the wave surface elevation at each moment using the wave height meter array, recording it as the wave height value; based on the wave height values ​​at all moments during the test period, the average time interval between all adjacent maximum points was taken as the wave period, and the average horizontal distance between adjacent wave crests in the wave height meter's spatial distribution was recorded as the wavelength; the wave propagation speed was measured using the acoustic Doppler current meter, and the propagation speed was equal to the ratio of wavelength to wave period. Each wave corresponds to a wave period, propagation speed, and wavelength.

[0055] In one implementation of this invention, the wavelength of small-scale waves is less than 5 meters, the wavelength of large-scale waves is greater than 20 meters, and the wavelength of mesoscale waves is between 5 meters and 20 meters. At least 30 independent samples are collected for each wave scale.

[0056] A miniature pressure sensor array is deployed on the surface of the structure, with each sensor location serving as a test point. Based on high-speed camera image sequences, the movement trajectories of the leading and trailing edges of waves are tracked using optical flow or feature point matching to determine the precise moment the waves arrive at the structure. Upon wave arrival, pressure and strain time histories are simultaneously acquired at each test point under each wave impact. The pressure time histories cover the time interval from the onset of each wave impact to the complete dissipation of pressure. The strain time histories are triggered synchronously with the pressure time histories and extended until the structure's vibration decays to a steady state, ensuring the entire dynamic response of the structure is encompassed.

[0057] In one implementation of this invention, the sampling frequency of the pressure sensor is set to 1000 Hz.

[0058] Step S2: Based on the energy accumulation efficiency of each wave from its initiation to its breaking, the spatiotemporal distribution characteristics of energy release during breaking, and the wave height, obtain the divergence index of each wave; divide all waves into different clusters; based on the degree of dispersion and divergence index of the frequency domain energy distribution disorder of the pressure signal of each wave within its cluster and the degree of delay of structural deformation relative to pressure, adjust the critical time step to obtain the optimized critical duration of each wave.

[0059] When high-energy waves impact structures, their kinetic and potential energy is transformed into more complex fluid motions. Energy diffuses in three-dimensional space through turbulent eddies and wave breaking, and boundary forces are no longer concentrated locally. The high complexity of the interface leads to the generation of more micro-scale eddies during wave breaking, dispersing boundary forces over a wider surface area. Waves with large heights are usually accompanied by long wavelengths, enhancing diffraction effects and resulting in a more dispersed force field distribution. Therefore, by combining energy accumulation efficiency, the spatiotemporal distribution characteristics of energy release during breaking, and wave height values, the dispersion of wave boundary forces on the surface of the structure can be analyzed to obtain a divergence index.

[0060] Waves within the same cluster correspond to waves of a certain scale. The dispersion of features among all waves in each cluster represents the stability of the coupling between force and structure at the wave scale, reflecting the significance of the temporal differences in force transmission between waves. To avoid smoothing or missing detections, the time step should be as small as possible. The divergence index reflects the non-uniform and dynamic distribution of boundary forces on the structural surface. When the distribution changes rapidly, a coarse time scale cannot capture the instantaneous transfer of spatial loads, so the time step should be as small as possible. By combining the above two factors, the time step in the SPH algorithm is dynamically adjusted so that a fine step size is automatically used to accurately capture the instantaneous force peak during small-scale wave impacts, and a wider step size is used to efficiently simulate the cumulative effect during large-scale wave impacts. This resolves the contradiction between missing key features at small scales and consuming resources at large scales under a fixed step size.

[0061] Step S3: Based on the fluctuation of the pressure signal at each test point on the structure surface under each wave impact, obtain the interference sensitive area of ​​each wave; according to the correlation strength and energy distribution of the pressure signal in the time-frequency domain of each wave's interference sensitive area, obtain the modal coupling coefficient of each wave.

[0062] The time step update is used for a fine-grained characterization of the force-structure coupling of single-scale waves. However, in reality, complex marine environments involve multiple wave scales simultaneously. For example, nearshore areas are affected by both long-period offshore waves and short-period nearshore fracturing waves. Existing SPH algorithms lack effective multi-scale coupling mechanisms, making it difficult to accurately simulate the mutual interference and boundary force superposition effects of waves of different scales impacting structures. For instance, when waves of different scales impact together, it's not a simple addition of forces; they interfere with each other, like large waves carrying smaller waves, making the forces more complex. The interference-sensitive zone provides precise spatial targets for interference correction. The correlation strength and energy distribution of the pressure signal in the interference-sensitive zone of each wave in the time-frequency domain analyze the energy cascading of waves. Precisely quantifying the nonlinear interference intensity guides the dynamic correction of inter-particle forces. That is, in strong coupling, nonlinear interactions are strengthened to restore the energy cascading effect, while in weak coupling, it is simplified to linear superposition to avoid over-adjustment.

[0063] Step S4: Obtain the baseline artificial viscosity coefficient for each wave scale; adjust the baseline artificial viscosity coefficient according to the optimized critical duration and modal coupling coefficient to obtain the optimized artificial viscosity coefficient for each wave scale, and use the SPH algorithm to perform wave impact boundary force simulation analysis.

[0064] Based on the interference quantification results, the particle interaction parameters in the SPH algorithm are dynamically adjusted to achieve accurate simulation of boundary forces. In order to match the coupling mechanism of actual wave scale, a coordinated mechanism of time step and coupling coefficient is established, and the artificial viscosity coefficient is adjusted in conjunction to ensure that small-scale transient details are not lost due to increased viscosity and large-scale energy transfer is not distorted due to widening of the step size. Finally, a full-chain optimization of "scale identification - time adaptation - interference elimination" is formed within the SPH framework, which solves the contradiction between accuracy and efficiency in multi-scale wave impact boundary force simulation and provides high-fidelity and high-reliability numerical support for wave-resistant design of marine structures.

[0065] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the divergence index includes: acquiring wave images of each wave at each moment within its test period; performing edge detection on the wave images to extract wave boundary lines; calculating the fractal dimension of the wave images based on the spatial distribution of the wave boundary lines; for each wave within its test period, taking the ratio of the difference in fractal dimension between the later moment and the earlier moment to the time interval as the dimension change rate, performing curve fitting on all dimension change rates to obtain a dimension curve; and sequentially recording the peak point and the moment corresponding to the first maximum point of the dimension curve as each... The system calculates the breaking and initiation times of each wave; obtains the wave height values ​​at the initiation and breaking times, as well as the arrival time after breaking; records the ratio of the wave height values ​​at the initiation and breaking times as the energy accumulation value; records the ratio of the fractal dimension of the wave image at the breaking time to the arrival time after breaking as the complex potential energy index; records the ratio of the wave height value at the breaking time to the mean of the wave height values ​​at the corresponding breaking times of all waves as the abnormal wave probability; and obtains the divergence index of each wave based on the energy accumulation value, the complex potential energy index, and the abnormal wave probability. In this embodiment, the box counting method is used to calculate the fractal dimension; random walk or difference box counting methods can also be used.

[0066] It should be noted that fractal dimension reflects the complexity of the boundary line between the wave and the air, while the rate of change of dimension reflects the dynamic characteristics of the boundary line morphology evolution. When a wave enters the critical breaking state, the movement of water particles at the wave crest accelerates dramatically, leading to a sudden change in the topology of the contact surface, and the rate of change of fractal dimension increases significantly. Therefore, the rate of change of dimension is a key characteristic value for judging the transition of the wave breaking stage. The specific method for obtaining the dimension curve is as follows: construct a two-dimensional space with time as the horizontal axis and the rate of change of dimension as the vertical axis. Map the rate of change of dimension of each wave at every two adjacent moments during its test period to the two-dimensional space to obtain corresponding scatter points. Fit the scatter points using the least squares method, and record the resulting fitted curve as the dimension curve. The moment corresponding to the first maximum point of the dimension curve symbolizes that the wave has entered the rolling stage, at which point the interface begins to bend but has not yet broken; the moment corresponding to the peak point represents that the wave has entered the breaking stage, at which point the interface violently rolls and breaks into a gas-liquid mixture.

[0067] The energy accumulation value represents the energy accumulated by the wave from its initial rolling. A larger energy accumulation value indicates that no breaking or energy loss event has occurred since the wave began rolling, meaning the wave has accumulated more energy. Simultaneously, when high-energy waves impact a structure, their kinetic and potential energy are transformed into more complex fluid motion. Energy diffuses in three-dimensional space through turbulent eddies and wave breaking, and boundary forces are no longer concentrated locally, resulting in a more dispersed spatial distribution of boundary forces on the structure's surface. A larger complexity potential energy index indicates a greater complexity of the interface between each wave and the air, and a shorter time for the wave to reach the structure's boundary after breaking, meaning a higher wave potential energy. This high interface complexity and wave potential energy, coupled with the generation of more micro-scale eddies during wave breaking, disperse boundary forces over a wider surface area, further contributing to a more dispersed spatial distribution of boundary forces on the structure's surface. A higher probability of distorted waves indicates a greater amplification of the impact boundary force during breaking. Larger wave heights are usually accompanied by longer wavelengths, enhancing diffraction effects and further dispersing the force field, thus resulting in a more dispersed spatial distribution of boundary forces on the structure's surface. Therefore, the cumulative energy value, complex potential energy index, and probability of distorted waves are all positively correlated with the divergence index. In this embodiment of the invention, the product of the cumulative energy value, complex potential energy index, and probability of distorted waves for each wave is used as the divergence index.

[0068] It is important to note that the amplitude of the peak point of the dimension curve is the maximum amplitude among all data on the dimension curve; the arrival time after each wave breaks is the ratio of the distance from the wave's position at the moment of breakage to the structure boundary to the wave's propagation speed. Specifically, a 3D laser scanner is used to acquire the 3D coordinates of the structure's surface, and the shortest distance from the wave boundary to the structure boundary is taken as the distance from the wave to the structure boundary.

[0069] Preferably, in some possible implementations of the embodiments of the present invention, the clustering method includes: calculating the mean signal of the pressure signal and the mean signal of the strain signal of all test points on the surface of the structure under each wave impact, and recording them as the overall pressure signal and the overall strain signal of the corresponding wave; performing wavelet packet decomposition on the overall pressure signal to obtain the wavelet packet decomposition coefficients corresponding to each decomposition frequency band; calculating the sum of squares of all wavelet packet decomposition coefficients in each decomposition frequency band as the energy characterization value; recording the ratio of the energy characterization value of each decomposition frequency band to the sum of the energy characterization values ​​of all decomposition frequency bands as the energy proportion of each decomposition frequency band; and obtaining information on the energy proportion of all decomposition frequency bands corresponding to the overall pressure signal. Entropy, denoted as modal energy entropy; the times corresponding to the maximum points on the overall pressure signal and overall strain signal of the same wave are respectively denoted as pressure analysis time and deformation analysis time; the time interval between each pressure analysis time and its next adjacent deformation analysis time is obtained, and the ratio of the time intervals corresponding to all pressure analysis times is taken as the deformation lag time; the mean of the time intervals between all adjacent maximum points on the overall pressure signal is calculated as the wave period; the ratio of the deformation lag time to the wave period is taken as the lag coefficient; the modal energy entropy and the lag coefficient constitute the characteristic binary of each wave; based on the Euclidean distance of the characteristic binary of different waves, all waves are clustered to obtain several clusters.

[0070] It should be noted that small-scale waves are easily affected by disturbances such as wind and turbulence, resulting in complex waves with multiple frequency bands superimposed, and energy dispersed over a wide frequency band. Large-scale waves are mainly dominated by gravity, with energy concentrated in the dominant frequency band. Mesoscale waves contain both high- and low-frequency components, with energy concentrated in several significant frequency bands. Therefore, when the modal energy entropy is large, the wave is small-scale; when the modal energy entropy is small, the wave is large-scale; and when the modal energy entropy is moderate, the wave is a multimodal mixed state. Small-scale waves have a short duration, and the local deformation of the structure can follow immediately, meaning that the boundary force of small-scale waves and the deformation of the structure are almost synchronous. However, the long-term load of large-scale waves induces overall structural vibration or inertial effects, requiring a longer time to accumulate displacement, causing the deformation of the structure caused by large-scale waves to lag behind the boundary force. Modal energy entropy reflects the frequency domain characteristics of the wave itself, while the hysteresis coefficient reflects the temporal coupling relationship between the wave and the structure; both together cover the dynamic characteristics of the wave and the structural response behavior.

[0071] In one implementation of this invention, the K-means clustering algorithm is used to cluster the waves, where K is 3.

[0072] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the optimized critical duration includes: obtaining the critical time step; using the ratio of modal energy entropy to hysteresis coefficient as the force-structure coupling factor of each wave; obtaining the coefficient of variation of the force-structure coupling factor of all waves in the cluster to which each wave is located; negatively correlating and normalizing the product of the coefficient of variation of each wave and the divergence index when each wave reaches the structure to obtain the time adjustment coefficient of each wave; and using the time adjustment coefficient to weight the critical time step to obtain the optimized critical duration of each wave.

[0073] It should be noted that a larger force-structure coupling factor for each wave indicates a strong, instantaneous coupling between the wave and the structure, suggesting the wave may be small-scale. Conversely, a smaller factor indicates a weak, progressive coupling, suggesting the wave may be large-scale. A larger coefficient of variation in the force-structure coupling factor across all waves within a given cluster indicates less stable force-structure coupling at the wave scale, with significant temporal differences in force transmission between waves. To avoid smoothing out transient impact peaks or missing short-term resonance effects, the time step should be smaller. A larger divergence index indicates non-uniform and dynamically changing boundary forces on the structural surface. When the distribution changes rapidly, a coarse timescale cannot capture the instantaneous transfer of spatial loads; therefore, the time step should be smaller. Thus, both the coefficient of variation and the divergence index are negatively correlated with the critical optimization time. In this embodiment of the invention, the product of the coefficient of variation of the force structure coupling factor of all waves in the cluster to which each wave belongs and the divergence index when each wave arrives at the structure is negatively correlated and normalized to obtain the time adjustment coefficient; the time adjustment coefficient is weighted with the critical time step to obtain the optimized critical duration of each wave.

[0074] In this embodiment, the test data is used as the exponent of an exponential function with the natural constant as the base to achieve negative correlation and normalization of the test data. Alternatively, negative correlation mapping and normalization can be achieved by taking the reciprocal and using the Sigmoid function. This is not limited here.

[0075] It should be noted that in the SPH algorithm, the critical time step is calculated first based on the CFL condition to ensure that the particle motion does not exceed the safe proportion of the smooth length, thus maintaining numerical stability. Then, the accuracy is calibrated by combining key physical characteristics of typical wave impacts, such as the pressure peak and period. Finally, the minimum value of the two constraints is taken, which is the critical value to ensure both numerical stability and physical accuracy. Specifically, the CFL time step determined based on the CFL condition is calculated as follows: the ratio of the smooth length of the wave particles as the numerator and the sum of the sound velocity and propagation speed as the denominator is calculated, and the product of this ratio and the Coulomb number is taken as the CFL time step, where the Coulomb number is taken as 0.2. The calibration time step determined based on the key physical characteristics of typical wave impacts is calculated as follows: the ratio of the wave period to 50 and the ratio of the time for the pressure signal to rise from static pressure to the peak value to 10 are calculated, and the minimum of the two ratios is recorded as the calibration time step. The minimum of the CFL time step and the calibration time step is recorded as the critical time step for each wave.

[0076] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the interference-sensitive area includes: interpolating and fusing the pressure signals of all test points on the surface of the structure under each wave impact to generate a spatial pressure field; performing numerical differentiation on the spatial pressure field to obtain a pressure gradient field; calculating the Poincaré exponent of the singularities in the pressure gradient field, and recording the singularities with non-zero exponents as force field singularities; dividing all force field singularities into several clusters based on the Euclidean distance between force field singularities, and selecting the region enclosed by the convex hull boundary of the cluster with a number of singularities greater than a preset threshold as the interference-sensitive area for each wave.

[0077] It should be noted that this embodiment uses radial basis functions for interpolation fusion and performs numerical differentiation of the spatial pressure field using the central difference method. The Poincaré exponent of the pressure gradient field describes the rotational characteristics of the gradient field and serves as the accumulation center for disturbance energy, with pressure fluctuations being higher than the surrounding areas. The disturbance-sensitive region provides precise spatial targets for disturbance correction. This scheme uses the K-means clustering algorithm for clustering, with the K value determined using the elbow method.

[0078] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the modal coupling coefficient includes: decomposing the coupling pressure signal of the interference-sensitive area of ​​each wave into different component signals; calculating the mutual information entropy between each component signal and all preset single-scale wave reference signals and performing normalization processing; selecting the component signals whose mutual information entropy normalization result is greater than or equal to a preset threshold as analysis signals; taking the ratio of the integral of the square of the amplitude of each analysis signal to the sum of the integrals of the squares of the amplitudes of all analysis signals as the energy contribution value of each analysis signal; decomposing the coupling pressure signal of the interference-sensitive area to obtain component signals of different frequencies, and selecting the component signals with the largest frequency difference. The two largest component signals are denoted as target signals, and the two target signals are converted to the time-frequency domain to obtain wavelet coefficients. Based on the wavelet coefficients, the cross-wavelet spectrum between the two target signals and the wavelet power spectrum of each target signal are calculated. The wave coherence coefficient is obtained by multiplying the square of the magnitude of the cross-wavelet spectrum with the product of the wavelet power spectra of the two target signals. The two analysis signals are linearly superimposed in the time domain to generate a synthetic signal. The ratio obtained by dividing the integral of the square of the amplitude of the synthetic signal by the sum of the energy contributions of the two target signals as the numerator and the sum of the energy contributions of the two target signals as the denominator is used as the coupling ratio. The modal coupling coefficient of each wave is obtained according to the coupling ratio and the wave correlation coefficient.

[0079] It should be noted that, in this embodiment of the invention, the mean signal of the overall pressure signal of all waves within each cluster is denoted as a preset single-scale wave reference signal, and three clusters correspond to three preset single-scale wave reference signals. This embodiment uses an empirical mode decomposition algorithm to decompose the coupled pressure signal and employs a wavelet transform algorithm to convert the target signal to the time-frequency domain. Each component signal obtained after signal decomposition of the coupled pressure signal corresponds to a single-scale wave contribution. Low mutual information entropy indicates that the component signal is almost independent of the reference signal, suggesting that the component signal may be noise or interference and does not require analysis. A larger wave correlation coefficient indicates the existence of energy cascading, i.e., nonlinear energy transfer from large-scale waves to mesoscale waves, and from mesoscale waves to small-scale waves. The cascading efficiency decreases as the scale difference increases.

[0080] When the difference between the integral of the square of the amplitude of the synthesized signal and the sum of the energy contributions of the two target signals is greater than 0, it indicates that the coupling has amplified the energy, such as a large wave pushing a small wave, resulting in a stronger impact; conversely, it indicates that the coupling has canceled out the energy, such as a small wave breaking and consuming the energy of a large wave. The larger the wave coherence coefficient, the stronger the nonlinear energy transfer between the two scale waves. The modal coupling coefficient reflects the strength of the nonlinear interference caused by the active coupling of the two scale waves. If the modal coupling coefficient of each wave is larger, the wave is a strong coupling interference. At this time, the energy is nonlinearly superimposed, and the SPH model needs to specifically strengthen the nonlinear interaction between the two scale particles to accurately reproduce the complex impact of the large wave carrying the small wave; conversely, the wave is a weak coupling interference, which can be approximated as linear superposition, and the model can be simplified to linear superposition correction to avoid over-adjustment and the introduction of new errors. Therefore, the coupling ratio and the wave correlation coefficient are both positively correlated with the modal coupling coefficient. In this embodiment of the invention, the product of the coupling ratio and the wave correlation coefficient of each wave is used as the modal coupling coefficient.

[0081] In one implementation of this invention, the preset threshold is set to 0.3.

[0082] In this embodiment of the invention, time-frequency analysis is performed on the collected mixed wave pressure signals. The dominant frequency and corresponding wave height with the highest energy proportion are determined through Hilbert transform or wave height meter calibration, and these are denoted as dominant parameters. Waves closest to the dominant parameters are selected from single-scale waves, with the matching criterion being the minimum Euclidean norm. Successfully matched single-scale waves are denoted as reference waves. SPH numerical simulation is performed on the reference waves, initializing the artificial viscosity coefficient to a range of 0.01 to 1. The artificial viscosity coefficient is iteratively adjusted using a bisection method until the following convergence condition is met: the relative error of the pressure peak is less than 5%. The artificial viscosity coefficient of the optimal solution is then used as the reference artificial viscosity coefficient for that scale of wave. It should be noted that single-scale waves can be waves of all scales collected in step S1, or waves of each scale collected from impacts. The scale division follows the same criteria as in step S1.

[0083] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the optimized artificial viscosity coefficient includes: multiplying the ratio of the optimized critical duration to the critical time step of each wave and the modal coupling coefficient as the duration correction coefficient of each wave; recording the average of the duration correction coefficients of waves of the same scale as the final correction coefficient; and using the sum of the constant 1 and the final correction coefficient to weight the benchmark artificial viscosity coefficient of each scale of wave to obtain the optimized artificial viscosity coefficient of the corresponding scale of wave.

[0084] It should be noted that the duration correction coefficient binds the precision of the time scale to the intensity of coupling interference, making the adjustment of the viscosity coefficient adaptable to different scenarios. Small-scale waves require a fine time step, i.e., a small critical time step, to capture high-frequency impact details. The optimized critical duration is small. If the viscosity is too high, it will smooth out the instantaneous force peak. Therefore, only a small increase in viscosity is needed to match the weak nonlinearity and avoid destroying details. Large-scale waves use a coarse time step to simulate the cumulative effect. The optimized critical duration is large. In strong coupling, energy cascading is active. For example, large waves push small waves. The viscosity needs to be greatly increased to simulate nonlinear energy transfer and compensate for the loss of coupling details caused by the coarse time step.

[0085] If the scale of the new wave collected falls within the scale range of a certain wave scale, then the optimized artificial viscosity coefficient of that wave scale will be used for the wave impact boundary force simulation analysis.

[0086] When simulating wave impact boundaries using the SPH algorithm, a three-dimensional space containing wave generation, impact, and wave-dissipation zones is first defined, with the ocean scene and structural dimensions input to determine the range. Next, seawater particles with mass, density, and initial velocity are filled into the fluid zone, initialized according to wave type and distribution rules, with particle resolution determining the level of detail. Then, a structural model is constructed using fixed particles, with denser distribution at key locations, and the structural shape and surface features are input. Next, particle interaction rules are set, and motion is calculated using kernel functions, state equations, and force balance, with fluid characteristics input to obtain the computational model. Finally, the simulation is initiated, allowing wave particles to impact the structure and generate boundary forces. After processing in the wave-dissipation zone, the force output time history curves and distribution cloud maps are statistically analyzed to provide data for engineering projects.

[0087] This invention is now complete.

[0088] Example 2:

[0089] This invention also presents a schematic diagram of a computer device for simulating and analyzing wave impact boundary forces based on SPH. Please refer to [link / reference]. Figure 2 The computer device includes a memory 501, a processor 502, and a computer program 503 stored in the memory 501 and running on the processor 502. When the processor 502 executes the computer program 503, the computer device can execute any of the wave impact boundary force simulation analysis methods based on SPH described above.

[0090] Furthermore, this application also protects an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform a wave impact boundary force simulation analysis method based on SPH provided in this application.

[0091] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0092] When each module is divided according to its function, the device may also include a communication module, a signal analysis module, a complexity analysis module, and a positioning module. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0093] It should be understood that the apparatus provided in this embodiment is used to perform the above-described wave impact boundary force simulation analysis method based on SPH, and therefore can achieve the same effect as the above-described implementation method.

[0094] When using integrated units, the device may include a processing module and a storage module. When applied to a workpiece, the processing module can be used to control and manage the workpiece's operations. The storage module can be used to support the execution of program code by the workpiece.

[0095] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits contained in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0096] Example 3:

[0097] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement the wave impact boundary force simulation and analysis method based on SPH provided in the above embodiment.

[0098] Example 4:

[0099] This embodiment also provides a computer program product. When the computer program product is run on a computer, the computer performs the above-mentioned related steps to realize the wave impact boundary force simulation and analysis method based on SPH provided in the above embodiment.

[0100] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0101] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0102] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0103] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A wave impact boundary force simulation and analysis method based on SPH, characterized in that, The method includes: The pressure and strain signals of test points on the surface of the structure were acquired under each wave impact, and the waves had different scales. Based on the energy accumulation efficiency of each wave from its initiation to its breaking, the spatiotemporal distribution characteristics of energy release during breaking, and the wave height, the divergence index of each wave is obtained; all waves are divided into different clusters; based on the degree of disorder in the frequency domain energy distribution of the pressure signal of each wave within its cluster and the degree of dispersion of the structural deformation relative to the pressure delay, and the aforementioned divergence index, the critical time step is adjusted to obtain the optimized critical duration of each wave. Based on the fluctuation of the pressure signal at each test point on the structure surface under each wave impact, the interference sensitive area of ​​each wave is obtained; based on the correlation strength and energy distribution of the pressure signal in the time-frequency domain of each wave's interference sensitive area, the modal coupling coefficient of each wave is obtained. Obtain the baseline artificial viscosity coefficient for each wave scale; adjust the baseline artificial viscosity coefficient according to the optimized critical duration and the modal coupling coefficient to obtain the optimized artificial viscosity coefficient for each wave scale, and use the SPH algorithm to perform wave impact boundary force simulation analysis.

2. The wave impact boundary force simulation and analysis method based on SPH according to claim 1, characterized in that, The process of obtaining the divergence index for each wave includes: Acquire wave images for each wave at each moment during its test period, perform edge detection on the wave images to extract wave boundary lines, and calculate the fractal dimension of the wave images based on the spatial distribution of the wave boundary lines. For each wave during its test period, the ratio of the difference in fractal dimension between the next moment and the previous moment to the time interval is taken as the dimension change rate. Curve fitting is performed on all dimension change rates to obtain the dimension curve. The peak point and the first maximum point of the dimension curve are recorded as the breaking moment and the starting moment of each wave, respectively. Obtain the wave height values ​​of each wave at the moment of initiation and the moment of breakup, as well as the arrival time of each wave after breakup; The ratio of the wave height at the moment of initiation to the moment of breaking is recorded as the cumulative energy value. The ratio of the fractal dimension of the wave image at the moment of breakup to the arrival time after breakup is denoted as the complex potential energy index. The ratio of the wave height of each wave at the breaking moment to the average wave height of all waves at the corresponding breaking moment is denoted as the abnormal wave probability. Based on the energy accumulation value, the complex potential energy index, and the probability of the distorted wave, the divergence index of each wave is obtained.

3. The wave impact boundary force simulation and analysis method based on SPH according to claim 1, characterized in that, The process of dividing all waves into different clusters includes: Calculate the mean pressure signal and mean strain signal of all test points on the surface of the structure under each wave impact, and record them as the overall pressure signal and overall strain signal of the corresponding wave, respectively. Wavelet packet decomposition is performed on the overall pressure signal to obtain the wavelet packet decomposition coefficients corresponding to each decomposition frequency band; the sum of squares of all wavelet packet decomposition coefficients in each decomposition frequency band is calculated as the energy characterization value; the ratio of the energy characterization value of each decomposition frequency band to the sum of the energy characterization values ​​of all decomposition frequency bands is denoted as the energy proportion of each decomposition frequency band; the information entropy of the energy proportion of all decomposition frequency bands corresponding to the overall pressure signal is obtained and denoted as the modal energy entropy. The times corresponding to the maximum points of the overall pressure signal and the overall strain signal of the same wave are respectively recorded as the pressure analysis time and the deformation analysis time. The time interval between each pressure analysis time and the next adjacent deformation analysis time is obtained, and the ratio of the time intervals corresponding to all pressure analysis times is taken as the deformation hysteresis time. The mean time interval between all adjacent maxima on the overall pressure signal is calculated as the wave period; the ratio of the deformation lag time to the wave period is used as the lag coefficient; the modal energy entropy and the lag coefficient constitute the feature tuple for each wave; based on the Euclidean distance of the feature tuples of different waves, all waves are clustered to obtain several clusters.

4. The wave impact boundary force simulation and analysis method based on SPH according to claim 3, characterized in that, The process of obtaining the optimal critical duration for each wave includes: Obtain the critical time step; use the ratio of the modal energy entropy to the hysteresis coefficient as the force structure coupling factor for each wave; obtain the coefficient of variation of the force structure coupling factors of all waves within the cluster of each wave; The time adjustment coefficient of each wave is obtained by negatively correlating the product of the coefficient of variation of each wave and the divergence index when each wave reaches the structure and normalizing it. The critical time step is weighted using the aforementioned time adjustment coefficient to obtain the optimized critical duration for each wave.

5. The wave impact boundary force simulation and analysis method based on SPH according to claim 1, characterized in that, The acquisition of the interference-sensitive area for each wave includes: The pressure signals of all test points on the surface of the structure under each wave impact are interpolated and fused to generate a spatial pressure field; the spatial pressure field is numerically differentiated to obtain the pressure gradient field; the Poincaré exponent of the singularities in the pressure gradient field is calculated, and the singularities with non-zero exponents are denoted as force field singularities. Based on the Euclidean distance between force field singularities, all force field singularities are divided into several clusters. The region enclosed by the convex hull boundary of the cluster corresponding to the maximum number of singularities within the cluster is selected as the disturbance sensitive area of ​​each wave.

6. The wave impact boundary force simulation and analysis method based on SPH according to claim 1, characterized in that, The process of obtaining the modal coupling coefficient for each wave includes: The coupled pressure signal of the interference sensitive area of ​​each wave is decomposed into different component signals. The mutual information entropy between each component signal and all preset single-scale wave reference signals is calculated and normalized. The component signal with a mutual information entropy normalization result greater than or equal to the preset threshold is selected as the analysis signal. The energy contribution value of each analytical signal is the ratio of the integral of the square of the amplitude of each analytical signal to the sum of the integrals of the squares of the amplitudes of all analytical signals. The coupled pressure signal in the interference-sensitive area is decomposed to obtain component signals of different frequencies. The two component signals with the largest frequency difference are selected as target signals, and the two target signals are converted to the time-frequency domain to obtain wavelet coefficients. Based on the wavelet coefficients, the cross-wavelet spectrum between the two target signals and the wavelet power spectrum of each target signal are calculated. The wave coherence coefficient is obtained by taking the ratio of the square of the modulus of the cross-wavelet spectrum to the product of the wavelet power spectra of the two target signals. The two analytical signals are linearly superimposed in the time domain to generate a composite signal; the ratio obtained by taking the integral of the square of the amplitude of the composite signal and the sum of the energy contribution values ​​of the two target signals as the numerator and the sum of the energy contribution values ​​of the two target signals as the denominator, is used as the coupling ratio. Based on the coupling ratio and the wave correlation coefficient, the modal coupling coefficient of each wave is obtained.

7. The wave impact boundary force simulation and analysis method based on SPH according to claim 4, characterized in that, The process of obtaining the optimized artificial viscosity coefficient for each wave scale includes: The product of the ratio of the optimized critical duration to the critical time step and the modal coupling coefficient for each wave is used as the duration correction coefficient for each wave; the average of the duration correction coefficients for waves of the same scale is recorded as the final correction coefficient. By using the sum of constant 1 and the final correction coefficient, the baseline artificial viscosity coefficient of waves at each scale is weighted to obtain the optimized artificial viscosity coefficient of waves at the corresponding scale.

8. The wave impact boundary force simulation and analysis method based on SPH according to claim 6, characterized in that, The method for decomposing the coupled pressure signal of each wave's interference-sensitive area into different component signals is the variational mode decomposition algorithm.

9. The wave impact boundary force simulation and analysis method based on SPH according to claim 6, characterized in that, The coupled pressure signal of the interference sensitive area of ​​each wave is the average pressure signal of all test points within the interference sensitive area of ​​each wave under the corresponding wave impact.

10. The wave impact boundary force simulation and analysis method based on SPH according to claim 6, characterized in that, The preset threshold is 0.3.

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