Submarine pipeline NES parameter optimization method based on particle swarm-genetic algorithm

Abstract

The invention provides a submarine pipeline NES parameter optimization method based on a particle swarm-genetic algorithm, and the method comprises the steps: constructing a submarine pipeline-nonlinear energy trap coupled vibration model which integrates Van der pol wake flow vibrator excitation, a pipeline transverse flow direction vibration equation and a nonlinear energy trap kinetic equation; the amplitude reduction rate of the midspan position of the pipeline is defined as a vibration reduction efficiency objective function; solving an optimal parameter combination of nonlinear energy trap parameters by adopting a fusion optimization strategy of a particle swarm algorithm and a genetic algorithm, wherein a local optimal solution is quickly converged through the particle swarm algorithm; inputting the global optimal solution of the particle swarm into the genetic algorithm population in each iteration, and executing crossover mutation operation; regulating and controlling the solution space diversity by using a cross distribution index and a variation distribution index; and outputting a nonlinear energy sink mass, rigidity and damping parameter combination which maximizes the vibration reduction efficiency.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of submarine pipeline vibration control, and particularly relates to a submarine pipeline NES parameter optimization method based on a particle swarm-genetic algorithm. BACKGROUND

[0002] The vibration control of submarine water pipelines in complex marine environments faces severe challenges. These pipelines are subjected to the multiple coupling effects of ocean current excitation, internal fluid motion, and transient water hammer effects for a long time, and this complex dynamic load can easily cause structural fatigue damage, resonance instability, and even overall failure. In traditional vibration control methods, linear control technology is difficult to effectively cope with the unique strong nonlinearity, wide frequency band characteristics, and complex working conditions of multiple source excitations in the marine environment due to its inherent narrow frequency characteristics and linear assumption limitations.

[0003] As a passive control device, the non-linear energy sink (NES) has shown unique potential in the field of pipeline vibration suppression. Through its unique target energy transfer mechanism, the device can redistribute low-frequency large-amplitude vibration energy to high-frequency small-amplitude modes and dissipate energy using the high-damping characteristics of the structure. This wide-frequency energy absorption characteristic overcomes the frequency limitation problem of traditional linear controllers while maintaining the lightweight structural characteristics. However, in practical applications, the vibration reduction effect of NES shows high sensitivity to external excitation conditions. Due to the significant dynamic characteristics of the internal and external flow environment of submarine pipelines, the vibration characteristics under different sea conditions differ, resulting in significant fluctuations in the optimal parameter configuration.

[0004] In terms of parameter optimization, the tuning of NES for submarine pipelines faces three challenges: first, multiple physical parameters such as mass ratio, stiffness coefficient, and damping ratio need to be optimized simultaneously, forming a complex multi-dimensional search space; second, the value range of a single parameter is large, making the solution space highly discrete; in addition, the solution process of the pipeline-NES coupled vibration model has high computational cost, further increasing the complexity of the optimization process. Traditional optimization algorithms often fall into local optimal solutions due to insufficient global search capability, leading to premature convergence, making it difficult to obtain a truly effective parameter configuration scheme. This limitation directly restricts the practical application effect of NES devices in submarine pipeline engineering, and a more reliable optimization method is urgently needed to solve the parameter sensitivity and environmental adaptability problem. SUMMARY

[0005] In view of the defects and deficiencies in the prior art, the present application provides a non-linear energy sink parameter optimization method and system for submarine pipeline vibration control, which solves the core problems of traditional methods in complex marine environments, such as early convergence and low parameter optimization efficiency, by innovatively integrating field customized modeling and intelligent optimization algorithms. The core innovative design includes:

[0006] 1. High fidelity coupled vibration model

[0007] A dedicated model is constructed to integrate the Van der Pol wake oscillator, the pipe cross-flow dynamics and the nonlinear energy sink nonlinear response, and to accurately characterize the three-way dynamic coupling mechanism of ocean current excitation-pipe vibration-NES energy transfer. By introducing two simply supported boundary constraints and a dimensionless parameter system, the adaptability of the model to actual sea conditions is significantly improved.

[0008] 2. Intelligent fusion optimization strategy

[0009] An particle swarm-genetic collaborative optimization framework is proposed, and a bidirectional information exchange mechanism is designed:

[0010] The particle swarm algorithm is used to quickly converge to the optimal solution domain;

[0011] The genetic algorithm is used to expand the global search capability through crossover and mutation operations;

[0012] The distribution index control technology (fixed value 10) is used to balance the solution space exploration and development process, and effectively suppress the premature convergence phenomenon.

[0013] 3. Engineering-driven parameter optimization

[0014] Based on experimental verification, the key parameter range is set:

[0015] The mass ratio range covers 0.001 to 0.999, which adapts to different pipe diameter requirements;

[0016] The stiffness coefficient is limited to the range of 4500 to 12000, ensuring that the nonlinear stiffness and pipe stiffness are of the same order of magnitude;

[0017] The damping ratio range is 0.001 to 4, taking into account the critical damping theory and the variability of the marine environment.

[0018] The technical scheme adopted by the present application to solve the technical problem is:

[0019] A submarine pipeline NES parameter optimization method based on particle swarm-genetic algorithm:

[0020] A submarine pipeline-nonlinear energy sink coupled vibration model is constructed, which integrates the Van der Pol wake oscillator excitation, the pipe cross-flow vibration equation and the nonlinear energy sink dynamics equation, and defines the amplitude reduction rate of the pipe across the middle position as the vibration reduction efficiency objective function;

[0021] The fusion optimization strategy of particle swarm and genetic algorithm is used to solve the optimal parameter combination of the nonlinear energy sink parameters, wherein:

[0022] The particle swarm algorithm quickly converges to the local optimal solution;

[0023] The global optimal solution of the particle swarm is input into the population of the genetic algorithm in each iteration, and a crossover and mutation operation is performed;

[0024] The solution space diversity is regulated by using a crossover distribution index and a mutation distribution index;

[0025] The non-linear energy sink mass, stiffness, and damping parameter combination that maximizes the vibration reduction efficiency is output.

[0026] Further, the coupled vibration model comprises a fluid-excited wake oscillator term, a pipe structure vibration term, and a non-linear energy sink nonlinear response term, wherein:

[0027] The wake oscillator term uses a Van der Pol equation to describe the flow-induced vibration excitation;

[0028] The non-linear energy sink term comprises a cubic stiffness characteristic and a velocity-dependent damping characteristic.

[0029] Further, the vibration reduction efficiency objective function is defined as the maximum amplitude change rate of the pipe mid-span position vibration response before and after the non-linear energy sink is applied.

[0030] Further, the crossover and mutation operation in the fusion optimization strategy comprises:

[0031] The distribution index is used to control the degree of solution space diversity;

[0032] The global optimal solution of the particle swarm and the population information of the genetic algorithm are fused through the crossover operation;

[0033] Parameter perturbation is performed in the neighborhood of the global optimal solution through the mutation operation.

[0034] Further, the distribution index includes a crossover distribution index and a mutation distribution index, and the values of both are 10.

[0035] Further, the non-linear energy sink parameters include:

[0036] A mass ratio parameter with a value range of 0.001 to 0.999;

[0037] A stiffness coefficient parameter with a value range of 4500 to 12000;

[0038] A damping ratio parameter with a value range of 0.001 to 4.

[0039] Further, the pipe vibration model satisfies the simply supported constraint condition at both ends, and the pipe displacement and curvature are constrained at the boundaries.

[0040] Further, the updating process of the particle swarm algorithm comprises:

[0041] An inertia guidance mechanism based on the historical optimal solution;

[0042] A synergistic guiding mechanism of individual optimal and global optimal;

[0043] An adjustable exploration and development balance mechanism.

[0044] And a subsea pipeline NES parameter optimization system based on a particle swarm-genetic algorithm, comprising:

[0045] A model construction module: used for constructing a subsea pipeline-nonlinear energy sink coupled vibration model, the model integrating a van der pol wake oscillator excitation, a pipeline cross-flow vibration equation and a nonlinear energy sink dynamics equation;

[0046] A target function definition module: defining a pipeline mid-span position amplitude reduction rate as a vibration reduction efficiency target function;

[0047] A fusion optimization module: executing a fusion optimization strategy of a particle swarm and a genetic algorithm, comprising:

[0048] A particle swarm algorithm unit: quickly converging to a local optimal solution;

[0049] An information exchange unit: inputting a particle swarm global optimal solution into a genetic algorithm population;

[0050] A diversity control unit: regulating solution space diversity by using a crossover distribution index and a mutation distribution index;

[0051] A parameter output module: outputting a nonlinear energy sink mass, stiffness and damping parameter combination maximizing the vibration reduction efficiency.

[0052] Further, the fusion optimization module further comprises:

[0053] A crossover and mutation unit: executing a crossover operation and a mutation operation, wherein the crossover distribution index is 10 and the mutation distribution index is 10;

[0054] A boundary condition processing unit: ensuring that the pipeline vibration model satisfies a simply supported constraint condition at both ends.

[0055] And a computer device comprising a memory, a processor and a computer program stored on the memory, the processor implementing the method as described above when executing the computer program.

[0056] A non-transitory computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method as described above.

[0057] Compared with the prior art, the present application and the preferred schemes thereof at least have the following beneficial effects:

[0058] 1. Solving the problem of premature convergence

[0059] The depth synergy mechanism of particle swarm and genetic algorithm combines the fast local convergence ability and global solution space exploration ability, effectively overcoming the premature convergence defect of traditional optimization algorithm in multi-parameter optimization of submarine pipeline.

[0060] 2. High-precision vibration control modeling

[0061] Based on the three-way coupling model of Van der Pol wake oscillator-pipeline structure-NES dynamics, the nonlinear characteristics of ocean current excitation and pipeline response are fully reproduced. The strict constraint of simply supported boundary conditions ensures that the model is highly consistent with the submarine pipeline engineering scenario, greatly improving the applicability of the optimization results in actual working conditions.

[0062] 3. Breakthrough in engineering parameter adaptability

[0063] The collaborative optimization range of mass ratio (0.001-0.999), stiffness coefficient (4500-12000), and damping ratio (0.001-4) covers the dual boundaries of pipeline lightweight and high energy consumption requirements. This parameter system has been experimentally verified to be adaptable to different pipe diameters and flow rates in complex marine environments, breaking through the parameter sensitivity dilemma of traditional empirical design.

[0064] 4. Double improvement of calculation efficiency and accuracy

[0065] The fusion optimization strategy can converge to a stable optimal solution within 10 iterations, significantly reducing the calculation time compared to single algorithm. Through the information exchange mechanism, high-quality solutions are dynamically selected to avoid repeated calculation of invalid parameter combinations, achieving simultaneous leap of optimization efficiency and solution quality. BRIEF DESCRIPTION OF DRAWINGS

[0066] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0067] Figure 1 is a flowchart of the method of the embodiment of the present application;

[0068] Figure 2 is a schematic diagram of the submarine flow pipeline-NES coupling system of the embodiment of the present application;

[0069] Figure 3 is a comparison chart of the submarine pipeline-NES vibration efficiency of the embodiment of the present application, wherein (a) is the PSO optimization algorithm, and (b) is the PSO-GA optimization algorithm;

[0070] Figure 4 is a comparison chart of the vibration mode of the submarine pipeline after algorithm optimization of the embodiment of the present application, wherein (a) is the PSO optimization algorithm, and (b) is the PSO-GA optimization algorithm. DETAILED DESCRIPTION

[0071] In order to make the features and advantages of the present application more apparent, the following detailed examples are provided, described in detail as follows:

[0072] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. 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 application belongs.

[0073] It is to be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments consistent with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0074] In order to solve the problem that the traditional optimization algorithm is prone to premature convergence and thus falls into a local optimal solution, affecting the optimization quality, in the embodiments of the present application, a genetic algorithm (GA) is introduced on the basis of a particle swarm optimization (PSO), the genetic population is integrated into the excellent solution of the particle swarm through an information exchange mechanism, the particles are processed by a crossover and mutation operation, a PSO-GA is formed, the fast convergence characteristic of the PSO is combined with the global search capability of the GA, the collaborative optimization of the multi-physical parameters of the NES quality, stiffness and damping is realized, and the optimization effect is improved and the vibration reduction efficiency of the submarine pipeline NES is improved. The method comprises the following steps: setting the NES parameter range and initializing the particle swarm parameters; constructing a submarine pipeline-NES coupled vibration model and defining the vibration reduction efficiency as an objective function; iteratively solving, updating the optimal solution of the NES parameters by the particle swarm algorithm; in each iteration, introducing the crossover and mutation operation of the genetic algorithm, reselecting and eliminating the candidate solution and updating the optimal solution of the NES parameters; until the iteration number is met, and the optimal solution of the NES parameters is output. The method can widely search for a new optimal parameter solution in the search space and finely search near the known better solution, thereby improving the search precision. The method is especially suitable for multi-dimensional engineering optimization problems with strong nonlinear characteristics, can be used to improve the NES parameter optimization effect of the submarine pipeline, and can be used to assist the optimization design of the NES in actual engineering and improve the vibration control level of the submarine pipeline in a complex marine environment.

[0075] The implementation of the scheme aims to improve the optimization effect of the submarine pipeline NES parameters, to assist the optimization design of NES in actual engineering, and to improve the vibration control level of submarine pipelines in complex marine environment. The genetic algorithm is introduced on the basis of the particle swarm algorithm, the high-quality solutions of the genetic population and the particle swarm are fused through the information exchange mechanism, and the crossover and mutation operations are introduced to disturb the particles in diversity, so as to enhance the global search ability, realize the collaborative optimization of NES quality, stiffness and damping multi-physical parameters, improve the optimization effect, and improve the vibration reduction efficiency.

[0076] As shown in Figure 1 , the implementation of the scheme includes the following steps:

[0077] S1: setting the NES parameter range, the control parameters of the particle swarm and the genetic algorithm, and the maximum iteration number;

[0078] S2: generating Np NES parameter combinations according to the range of NES parameters and the population size Np, defining the initial position, initial speed and initial population parameters of particles;

[0079] S3: introducing the submarine pipeline-NES coupled vibration model, defining the vibration reduction efficiency, and calculating the vibration reduction efficiency corresponding to the Np NES parameter combinations;

[0080] S4: iterative solution, constantly updating the optimal solution of NES parameters through the particle swarm algorithm;

[0081] S5: in each iteration solution, introducing the crossover and mutation of genetic algorithm, screening out the better NES parameter combination and the corresponding vibration reduction efficiency, and storing the results;

[0082] S6: repeat S4 and S5 until the maximum iteration number is reached, and output the optimal solution of NES parameters.

[0083] As a preferred scheme of the embodiment, step S1 sets the NES parameters: the value range of mass ratio m is [0.001, 0.999], the value range of stiffness coefficient k is [4500, 12000], and the value range of damping ratio c is [0.001, 4]; the total number of particles is set to 20, the dimension of each particle is 3, the population size Np is set to 10, the crossover probability P c is set to 0.8, the crossover distribution index I c is set to 10, which is used to control the diversity in the crossover process, the mutation probability P m is set to 0.8, the mutation distribution index I m is set to 10, which is used to control the amplitude of mutation, and the maximum iteration number is 10.

[0084] As a preferred scheme of the embodiment, step S2 generates Np combinations of NES parameters according to the range of NES parameters and the population size Np, initializes the individual optimal position y0 and the initial velocity V0 of each particle, randomly generates Np combinations of initialized NES parameters according to the population size Np, and generates the current iteration velocity V i and the current position y i may be expressed as:

[0085] V i = zV i-1 + a1r1(Gbest i-1 -y i-1 ) + a2r2(Pbest i-1 -y i-1 )(1)

[0086] y i = y i-1 + V i-1 (2)

[0087] In the formula, i is the current iteration number, r1 and r2 are random numbers in the interval [0, 1], a1 and a2 are learning factors, which are respectively set to 0.8 and 0.9. z is an inertia weight, reflecting the influence weight of the previous iteration result on the current velocity, and is set to 0.5 to balance the global exploration and local development ability of the particle.

[0088] As a preferred scheme of the embodiment, step S3 introduces a submarine pipeline-NES coupling vibration model, and the submarine pipeline-NES coupling vibration equation set can be obtained by simultaneously solving the Van der Pol wake oscillator model, the NES dynamic equation and the pipeline vibration equation:

[0089]

[0090] In the formula: W is the transverse displacement of the submarine water pipeline at time t, E is the elastic modulus of the pipeline; I is the moment of inertia of the pipeline section; EI is the bending stiffness; m = m i +m p +m a , m i is the internal flow fluid mass per unit length, the additional mass per unit length of the pipeline m a = πC a ρ e D 2 / 4, C a is the additional mass coefficient, which is usually taken as 1 for a cylindrical structure, ρ e is the density of the ocean current fluid, and D is the diameter of the pipeline; the pipeline structural damping C s = 2ζ(m p +mi +m a )ω n , ω n is the natural frequency of the pipeline, ζ is the structural damping coefficient; the ocean current additional damping C f =C D ρ e D p U e / 2, U e is the ocean current velocity, the dimensionless normalized flow velocity is U r =2πU e / ω1D, wherein ω1 is the first order natural frequency of the pipeline; C D is the drag coefficient, the water depth is 0-150m, and 1.2 is usually taken, and the water depth is greater than 150m, and 0.7 is usually taken; U i is the internal flow velocity; P is the static water pressure at both ends of the pipeline, A i is the internal flow cross-sectional area; T e is the axial tension of the pipeline; L is the pipeline length; K NES is the nonlinear stiffness of the NES; C NES is the damping coefficient of the NES; W NES is the vibration displacement of the NES; C L0 is the lift coefficient; q is the dimensionless wake oscillator.m NES is the mass of the NES device, and satisfies m NES <<m; ε z , A z are empirical coefficients which can be adjusted according to experimental results, and are taken as 0.3 and 12; ω s is the vortex shedding angular frequency, and its expression is ω s =2πS t U e / D, and St is the Strouhal number.

[0091] In order to make the application result more applicable, dimensionless parameters are introduced:

[0092]

[0093] The dimensionless NES-seabed pipeline vibration control model is:

[0094]

[0095] The dimensionless boundary conditions of the seabed water pipeline simply supported at both ends are:

[0096] W(0,t)=W(1,τ)=W″(0,t)=W″(1,t)=0(6)

[0097] q(0,t)=q(1,t)=q″(0,t)=q″(1,t)=0(7)

[0098] The NES vibration reduction efficiency is defined for the NES vibration reduction of the submarine pipeline under the internal-external flow coupling excitation. Since the maximum displacement of the pipeline appears at the mid-span position, the target function for screening the optimal NES parameter combination can be set as the maximum amplitude reduction rate maxη:

[0099]

[0100] In the formula, η is the NES vibration reduction efficiency, w 初 is the pipeline amplitude before the NES vibration reduction, and w 末 is the pipeline amplitude after the NES vibration reduction.

[0101] The NES combination under the excitation condition is optimized, and the vibration reduction efficiencies corresponding to Np NES parameter combinations are calculated. The position of each particle represents a mass ratio ε, a stiffness coefficient k NES , and a damping ratio c NES . The fitness value of each particle represents the vibration reduction efficiency corresponding to the NES parameter combination.

[0102] As a preferred scheme of the embodiment, step S4 performs iterative solving, and the velocity and position of each particle are updated through the velocity formula (1) and the position formula (2). The fitness value of the new position is calculated, and if it is better than the historical individual optimal, the individual optimal NES parameter solution is updated. The individual optimal of all particles is checked, and if there is a better solution, the global optimal NES parameter solution is updated.

[0103] As a preferred scheme of the embodiment, step S5 introduces the crossover and mutation operations of the genetic algorithm in each iteration. According to the crossover rule, a random number r is generated. If r < P c , the crossover operation is performed on each pair of parent individuals s1 and s2 to generate two new child individuals t1 and t2; if r ≥ P c , the parent individuals are directly retained. The crossover process can be represented as:

[0104]

[0105] t 1,i = 0.5[(1+β i )s 1,i +(1-β i )s 2,i ](10)

[0106] t 2,i = 0.5[(1-β i )s 1,i +(1+β i )s 2,i ](11)

[0107] In the formula, β is a crossover parameter, i represents the starting index of the parent individual pair currently processed, and i takes the values 1, 3, 5, …, Np-1 (Np is an even number) since the parent individuals are processed in groups of two.

[0108] According to the mutation rule, a random number r is generated, if r m , the individual is mutated; if r m , the parent individual is directly retained. The mutation process can be expressed as:

[0109]

[0110] t(i,j) = t(i,j) + (u b (j)-l b (j))·δ (13)

[0111] In the formula, δ is a mutation parameter, i represents the index of the individual, i takes the values 1, 2, 3, …, Np; j is the number of NES parameters, u b , and l b are the upper limit and the lower limit of the NES parameter values, respectively.

[0112] The optimal solution in S4 is integrated into the genetic algorithm population through information exchange, the population is arranged in ascending order or descending order according to the damping efficiency, and the optimal NES parameter combination and the damping efficiency of each iteration are screened and stored.

[0113] As a preferred scheme of the embodiment, step S6 repeats S4 and S5 until the maximum number of iterations is reached, and the optimal solution of the NES parameters is output.

[0114] The present application combines the particle swarm algorithm and the genetic algorithm, introduces the crossover and mutation operations of the genetic algorithm on the basis of the particle swarm optimization algorithm, improves the global search efficiency of the algorithm and the quality of the optimal parameter combination solution of the NES, and can provide a reliable parameter configuration scheme for the damping design of the submarine pipeline to assist the design of the NES in engineering practice.

[0115] Based on the above method, the problem of premature convergence leading to local optimization existing in the traditional optimization algorithm for multi-dimensional parameter optimization of the NES of the submarine pipeline is solved by using the hybrid optimization strategy of fusing the particle swarm algorithm and the genetic algorithm, combining the fast convergence characteristics of the particle swarm algorithm with the global search ability of the genetic algorithm, defining the damping efficiency objective function based on the submarine pipeline-NES coupled vibration model, performing diversity screening on the candidate parameter solution through the crossover and mutation operations, and finding the combination of the submarine pipeline NES mass, stiffness and damping multi-physical parameters with the optimal vibration control effect. The accuracy and effectiveness of the method proposed in the present application are verified by comparing the traditional method and the method of the present application.

[0116] The above provided solutions of the present application are further demonstrated and introduced through a specific test example as follows:

[0117] A seabed flow pipeline-NES coupling system as shown in Figure 2 is constructed, and the effect of NES on the vibration behavior of the seabed water pipeline is studied by taking a 100 m long steel pipeline as an example. The design parameters of the NES-seabed pipeline vibration control model used are shown in Table 1.

[0118] Table 1 Basic parameters of the pipeline numerical model

[0119]

[0120] The excitation condition is set as a dimensionless fluctuation frequency w0=13.4157, a dimensionless vortex shedding frequency w q =13.4157, and an initial disturbance v0=0.005. There are three NES parameters, the mass ratio m ranges from 0.001 to 0.999, the stiffness coefficient k ranges from 4500 to 12000, and the damping ratio c ranges from 0.001 to 4.

[0121] It can be seen from Figure 3 that after 10 iterations, the maximum amplitude reduction rate obtained by the PSO algorithm is 20.95%, and the corresponding NES parameter combination is m=0.128, k=9764.42, and c=1.698. The PSO-GA algorithm proposed in the present application improves the reduction rate to 31.87%, with a relative increase of 10.92%, and the corresponding NES parameter combination is m=0.627, k=9695.22, and c=3.561. From Figure 4 the comparison of the pipeline vibration modes, it can be seen that the NES optimized by the PSO-GA algorithm proposed in the present application has a greater degree of pipeline amplitude suppression, which also reflects the superiority of the algorithm in the NES optimization process compared to the traditional algorithm, improves the optimization effect, better realizes the collaborative optimization of the NES mass, stiffness, and damping parameters, and more efficiently improves the NES vibration reduction efficiency. The NES parameter combination obtained by the PSO-GA algorithm can significantly improve the vibration control effect of the seabed pipeline under complex marine environment, and can provide a reliable parameter configuration scheme for the NES design in actual engineering.

[0122] Based on the same inventive concept, the present application further provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the program comprises program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are configured to implement one or more instructions, and are specifically configured to load and execute one or more instructions in the computer storage medium to implement the above method.

[0123] It needs to be further explained that, based on the same inventive concept, the present application further provides a computer storage medium, which stores a computer program, and the computer program is executed by the processor to perform the above method. The storage medium can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, but is not limited to, an electrical, magnetic, optical, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: electrical connections with one or more conductive wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, device or component.

[0124] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0125] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

[0126] The present invention is not limited to the above-mentioned best embodiment. Anyone can derive various other forms of NES parameter optimization methods for submarine pipelines based on particle swarm-genetic algorithm based on the enlightenment of the present invention. All equivalent changes and modifications made within the scope of the patent application of the present invention shall fall within the scope of the present invention.

Claims

1. A method for optimizing NES parameters of submarine pipelines based on particle swarm-genetic algorithm, characterized by: A submarine pipeline-nonlinear energy sink coupled vibration model is constructed. The model integrates the van der Pol wake oscillator excitation, the pipeline transverse flow vibration equation, and the nonlinear energy sink dynamic equation. The amplitude reduction rate at the pipeline mid-span position is defined as the vibration reduction efficiency objective function. The fusion optimization strategy of particle swarm and genetic algorithm is used to solve the optimal parameter combination of nonlinear energy sink parameters, where: Rapidly converge to the local optimal solution through particle swarm optimization; In each iteration, the global optimal solution of the particle swarm is input into the genetic algorithm population, and crossover and mutation operations are performed; Use the cross-distribution index and variation distribution index to regulate the diversity of solution space; Output the nonlinear energy sink mass, stiffness, and damping parameter combination that maximizes the vibration reduction efficiency.

2. The method for optimizing NES parameters of submarine pipelines based on particle swarm-genetic algorithm according to claim 1, characterized in that: The coupled vibration model includes the fluid-excited wake oscillator term, the pipeline structure vibration term, and the nonlinear energy sink nonlinear response term, where: The wake oscillator term uses the van der Pol equation to describe the flow-induced vibration excitation; The nonlinear energy sink term includes cubic stiffness characteristics and velocity-dependent damping characteristics.

3. The method for optimizing NES parameters of submarine pipelines based on particle swarm-genetic algorithm according to claim 2, characterized in that: The vibration reduction efficiency objective function is defined as the maximum amplitude change rate of the vibration response at the mid-span position of the pipeline before and after the nonlinear energy sink is applied.

4. The method for optimizing NES parameters of submarine pipelines based on particle swarm-genetic algorithm according to claim 1, characterized in that: The crossover and mutation operations in the fusion optimization strategy include: The distribution index is used to control the diversity of the solution space; The particle swarm global optimal solution and genetic algorithm population information are integrated through crossover operation; The parameters are perturbed in the neighborhood of the global optimal solution through mutation operation.

5. The method for optimizing NES parameters of submarine pipelines based on particle swarm-genetic algorithm according to claim 1, characterized in that: The distribution index includes a cross-distribution index and a variation distribution index, both of which have a value of 10.

6. The method for optimizing NES parameters of submarine pipelines based on particle swarm-genetic algorithm according to claim 1, characterized in that: The nonlinear energy well parameters include: The mass ratio parameter has a value range of 0.001 to 0.999; Stiffness coefficient parameter, its value range is 4500 to 12000; Damping ratio parameter, its value range is 0.001 to 4.

7. The method for optimizing NES parameters of submarine pipelines based on particle swarm-genetic algorithm according to claim 1, characterized in that: The pipeline vibration model satisfies the simply supported constraint conditions at both ends, and the pipeline displacement and curvature are constrained at the boundaries.

8. The method for optimizing NES parameters of submarine pipelines based on particle swarm-genetic algorithm according to claim 1, characterized in that: The update process of the particle swarm optimization algorithm includes: Inertial guidance mechanism based on historical optimal solutions; A collaborative guidance mechanism for individual and global optimization; Adjustable exploration and development balance mechanism.

9. A NES parameter optimization system for submarine pipelines based on particle swarm-genetic algorithm, characterized in that: include: Model construction module: used to construct a submarine pipeline-nonlinear energy sink coupled vibration model, which integrates van der Pol wake oscillator excitation, pipeline transverse flow vibration equations, and nonlinear energy sink dynamics equations; Objective function definition module: defines the amplitude reduction rate at the mid-span position of the pipeline as the vibration reduction efficiency objective function; Fusion optimization module: Executes the fusion optimization strategy of particle swarm and genetic algorithm, including: Particle swarm optimization unit: quickly converges to the local optimal solution; Information exchange unit: inputs the global optimal solution of the particle swarm into the genetic algorithm population; Diversity control unit: uses the crossover distribution index and the variation distribution index to regulate the diversity of the solution space; Parameter output module: outputs the nonlinear energy sink mass, stiffness, and damping parameter combination that maximizes the vibration reduction efficiency.

10. The NES parameter optimization system for submarine pipelines based on particle swarm-genetic algorithm according to claim 9, characterized in that: The fusion optimization module also includes: Crossover and mutation unit: performs crossover and mutation operations, where the crossover distribution index is 10 and the mutation distribution index is 10; Boundary condition processing unit: ensures that the pipeline vibration model meets the simply supported constraints at both ends.

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