Stand pipe configuration method based on improved particle swarm optimization algorithm

Abstract

The invention discloses a riser construction method based on an improved particle swarm optimization algorithm, and the method comprises the following steps: S1, building a mechanical model of a steel catenary riser system according to the material characteristics, geometric dimensions and marine environment conditions of a riser; s2, determining constraint conditions of the steel catenary riser system; s3, performing hybrid initialization on particle swarms; s4, constructing a nonlinear dynamic weight fitness function to carry out multi-objective optimization; and S5, iteratively solving a globally optimal solution through the improved particle swarm optimization. According to the method, rapid and accurate configuration of the steel catenary riser is realized by inputting different riser parameters, environmental parameters, population quantity and iteration times and combining nonlinear dynamic weight distribution, a hybrid initialization strategy and cooperative constraint processing.

Description

Technical Field

[0001] The present invention relates to the technical field of ocean engineering, and particularly to a riser configuration method based on an improved particle swarm optimization algorithm. Background Technique

[0002] At present, the configuration design of ocean risers is mainly based on empirical formulas and traditional numerical simulation methods. Empirical formulas are usually obtained by summarizing and generalizing limited experimental data and actual engineering cases under specific working conditions.

[0003] However, this method lacks comprehensive consideration and adaptability to complex ocean environments. The ocean environment has a high degree of uncertainty and complexity, and the sea conditions and geological conditions vary greatly in different sea areas. Empirical formulas are difficult to accurately reflect the influence of these complex factors on the riser configuration, resulting in risers designed may not meet the actual engineering requirements. Although traditional numerical simulation methods can consider more factors, this method has the problem of low computational efficiency. Especially for large-scale and complex riser systems, the simulation calculation may consume a large amount of time and computational resources.

[0004] Therefore, there is an urgent need for a new optimization method to improve the riser configuration process. Summary of the Invention

[0005] The purpose of the present invention is to provide a riser configuration method based on an improved particle swarm optimization algorithm to optimize the riser configuration. By optimizing the riser configuration, the riser can better withstand various loads in a complex ocean environment, reduce stress concentration and deformation, improve the reliability of its structure, and provide more reliable technical support for the development and utilization of ocean resources.

[0006] The present invention is implemented by the following technical solutions: A riser configuration method based on an improved particle swarm optimization algorithm, comprising the following steps: S1: Establish a mechanical model of the steel catenary riser system according to the material properties, geometric dimensions and ocean environmental conditions of the riser; S2: Determine the constraint conditions of the steel catenary riser system; S3: Hybridly initialize the particle swarm; S4: Construct a non-linear dynamic weight fitness function for multi-objective optimization; S5: Iteratively solve the global optimal solution through the improved particle swarm algorithm.

[0007] Further, the mechanical model of the steel catenary riser system is: ; In the formula, is the element mass matrix, is the acting force of the external load, is the element damping matrix, is the element stiffness matrix, is the axial load acting force, is the riser acceleration, is the riser velocity, is the riser displacement.

[0008] Furthermore, the constraint conditions include the strength constraint conditions of the steel catenary riser system: ; Wherein, is the equivalent stress, is the allowable stress.

[0009] Furthermore, the constraint conditions also include the displacement constraint conditions of the steel catenary riser system: ; Wherein, is the top platform offset, is the seawater depth.

[0010] Furthermore, the constraint conditions also include the vibration constraint conditions of the steel catenary riser system: ; Wherein, is the vortex shedding frequency, is the natural frequency.

[0011] Furthermore, the hybrid initialization particle swarm is carried out by the following method: ; Wherein, xi,j represents the value of the j-th dimension of the i-th group of particles, uj and dj respectively represent the upper and lower boundaries of the j-th problem variable, rand represents a random number between the interval [0,1], N represents the number of particles in the particle swarm, and m represents the number of problems to be solved.

[0012] Furthermore, the non-linear dynamic weight fitness function is: ; Wherein, is the weight, is the objective function.

[0013] Furthermore, the dynamic adjustment method of the weight is: Calculate the relative importance of each objective function at the current iteration; Normalize the relative importance to obtain the dynamic weight.

[0014] Further, step S5 is specifically as follows: continuously update the positions and velocities of the particles, and the particle swarm continuously searches for a better riser configuration scheme in the search space.

[0015] Further, the velocity update formula for the particles is: ; where is the velocity of particle i at the (k + 1)-th iteration, is the inertia weight; is the guiding particle; c1 and c2 are learning factors; is the control guiding particle influence coefficient; r1 and r2 are random numbers in the interval [0, 1]; is the historical optimal position of particle i; is the historical optimal position of the population; is the position of particle i at the k-th iteration; The position update formula for the particles is: ; where is the position of particle i at time t + 1; is the position of particle i at time t; is the velocity of particle i at time t + 1.

[0016] The beneficial effects of the present invention are as follows: by inputting different riser parameters, environmental parameters, population quantity, and iteration times, and combining non-linear dynamic weight allocation, hybrid initialization strategy, and collaborative constraint processing, the present invention realizes the fast and accurate configuration of the steel catenary riser. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the structures shown in these drawings.

[0018] Figure 1 is a schematic diagram of a steel catenary riser unit; Figure 2 is a calculation flow chart of the configuration method of the particle swarm optimization algorithm; Figure 3 is a flow chart of updating the velocities and positions of the population particles; Figure 4 is a comparison chart of the configuration errors of the riser hanging section; Figure 5 is a comparison chart of the configuration errors of the riser touchdown section. Detailed implementation mode

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Usually, the components of the embodiments of the present invention described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0020] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0021] The following will describe in detail some implementation modes of the present invention with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0022] As Figure 2 shown, a riser configuration method based on an improved particle swarm optimization algorithm includes the following steps: S1: Establish a mechanical model of the steel catenary riser system according to the material properties, geometric dimensions, and marine environmental conditions of the riser. S2: Determine the constraint conditions of the steel catenary riser system. S3: Initialize the particle swarm hybridly. S4: Construct a non-linear dynamic weight fitness function for multi-objective optimization. S5: Iteratively solve the global optimal solution through the improved particle swarm algorithm.

[0023] In this embodiment, step S1 is specifically as follows: According to the continuum theory, the arbitrary position coordinates on the axis of the riser element can be expressed as a cubic polynomial of the material coordinate : ; (1) The riser element model with a length of L is as Figure 1 shown. For the node element with a length of L, its three-dimensional node coordinates can be expressed as: ; (2) The displacement field function of the element can be obtained by cubic Hermite interpolation of the element node coordinates: ; (3) where is the element shape function: ; (4) In the formula, It is a third-order unit matrix.

[0024] The shape functions and nodal coordinates in Equation (2) are only related to the material coordinates and time variables. Therefore, the material derivatives, velocities, and accelerations of the element nodes are expressed as follows: ; (5) Assume that the cross-section of the riser is , and the density is . Then its kinetic energy is: ; (6) Among them, is the element mass matrix and can be expressed as: ; (7) The element considers various mechanical behaviors such as the bending and axial deformation of the steel catenary riser. The axial strain and curvature can be used to characterize its deformation degree. The specific expressions are as follows: ; (8) ; (9) According to the Euler-Bernoulli theory, the strain energy of the element can be expressed as: ; (10) Among them, and represent the axial strain energy and bending strain energy respectively, and are the elastic modulus and moment of inertia of the cross-section respectively.

[0025] Taking the partial derivative of the element strain energy with respect to the local coordinates, the elastic force of the element can be obtained: ; (11) The acting force of the external load can be expressed as: ; (12) The acting force of the gravity load can be expressed as: ; (13) According to the principle of virtual work, the sum of the virtual works of each item in the steel catenary riser system is zero. Therefore, it can be obtained: ; (14) Among them, is the virtual work of the inertial force, is the virtual work of the elastic force, is the virtual work of the external load.

[0026] For a constrained steel catenary riser system, its mechanical equations can be specifically expressed as: .(15) In this embodiment, step S2 is specifically as follows: Take the maximum equivalent stress, top displacement, and natural frequency of the steel catenary riser as the optimization objectives and constraint conditions. The maximum stress is an important indicator to measure the structural strength of the riser. If the stress exceeds the allowable stress of the material, the riser may be damaged. The top displacement reflects the deformation degree of the steel catenary riser under the action of ocean current loads. Excessive displacement may affect the connection between the riser and other equipment, and even lead to the instability of the riser. The natural frequency is closely related to the resonance risk of the riser. If the set natural frequency is close to the vibration frequency under the current sea conditions, resonance will be triggered, causing the vibration amplitude of the riser to increase sharply. Therefore, it is necessary to constrain the strength, displacement, and vibration conditions of the riser system model.

[0027] The strength constraint condition can be expressed as: ;(16) The displacement constraint condition is: ;(17) The vibration constraint condition is: ;(18) Where is the allowable stress, MPa; is the equivalent stress, MPa; is the top platform displacement, m; is the seawater depth, m; is the vortex shedding frequency, Hz; is the natural frequency, Hz.

[0028] In this embodiment, step S3 is specifically as follows: Randomly generate a group of particles, and each particle represents a riser configuration scheme. Its position vector represents the geometric parameters of the riser, such as pipe diameter, wall thickness, curvature, etc. The position vector of each particle can be expressed as a multi-dimensional vector. Initially, these parameters are randomly generated within a certain value range to ensure that the particle swarm can cover a wider search space. The random initialization of the particle swarm can be carried out in the following way: ;(19) In the formula, x i,j represents the value of the j-th dimension of the i-th group of particles; rand represents a random number in the interval [0,1]; N represents the number of particles in the particle swarm; m represents the number of problems to be solved; u j , d jrespectively represent the upper and lower boundaries of the j-th problem variable.

[0029] In this embodiment, step S4 is specifically as follows: In the riser configuration optimization problem, there are usually multiple conflicting optimization objectives, such as the maximum stress, maximum displacement, and vibration frequency of the riser. To comprehensively consider these objectives, a dynamic weight fitness function is constructed.

[0030] Assume there are optimization objectives, which are respectively , where x represents the riser configuration parameter vector (i.e., the particle position vector). Each objective function has its corresponding weight , and these weights change dynamically with the iteration number t.

[0031] The dynamic weight fitness function is defined as: ; (20) The dynamic adjustment method of the weight is as follows. First, calculate the relative importance of each objective function at the current iteration number. Let and be the minimum and maximum values corresponding to all particles of the i-th objective function at the current iteration number, respectively. Then the relative importance of the i-th objective function can be expressed as: ; (21) Then, normalize the relative importance to obtain the dynamic weight : ; (22) By this way of dynamically adjusting the weight, during the iteration process, the weight of each objective function in the fitness function can be automatically adjusted according to the change situation of each objective function, so that the algorithm can optimize each objective more pertinently at different stages.

[0032] When the traditional particle swarm algorithm is initialized, the starting points of the particles are randomly generated, which may lead to a slow convergence speed of the algorithm or even falling into a local optimal solution. The improved algorithm of the present invention pre-determines some possible optimal solution regions by analyzing the mechanical characteristics and optimization objectives of the riser, and then initializes the particle swarm within these regions, enabling the particle swarm to converge to the global optimal solution faster.

[0033] In this embodiment, step S5 is specifically as follows: First, readjust the dynamic inertia weight. Nonlinearize the adaptive inertia weight: ; (23) where and are the initial maximum weight and the mid - term weight reference value respectively; and are control coefficients, which can be determined by fitting historical data; is the end threshold of the exploration stage. This strategy can maintain a high global search ability in the initial stage of iteration through non - linear adjustment of the inertia weight, and can also achieve local fine search in the later stage.

[0034] An adaptive learning factor is proposed to control the learning intensity of the particle towards its own optimal position ( ) and the global optimal position ( ) respectively. By dynamically adjusting and , the algorithm mainly conducts global search in the early stage and focuses on local optimization in the later stage to avoid premature convergence. The adaptive learning factor is expressed by the following formula: ;(24) where, is the maximum number of iterations.

[0035] According to the iteration formula of the particle swarm optimization algorithm, update the position and velocity of the particle. The processes of particle velocity update and position update are as Figure 3 shown. The velocity update formula of the particle is: ;(25) where, is the velocity of particle i at the (k + 1)-th iteration, is the inertia weight, which is used to control the influence degree of the particle's previous velocity on the current velocity; is the guiding particle; c1 and c2 are learning factors, which respectively represent the particle's ability to learn from its own historical optimal position and the group's historical optimal position; is the control guiding particle influence coefficient, 0.8 - 1.2; r1 and r2 are random numbers in the interval [0, 1]; is the historical optimal position of particle i, is the historical optimal position of the group, is the position of particle i at the k - th iteration.

[0036] The position update formula of the particle is: ;(26) Furthermore, continuously update the position and velocity of the particle through step S5, and the particle swarm continuously searches for a better riser configuration scheme in the search space.

[0037] Take a certain well as an example for illustration It is known that the fixed-end distance of the riser is 743 m, the water depth is 1100 m, the outer diameter of the riser is 0.3556 m, the inner diameter of the riser is 0.3206 m, the density of steel is 8085.9 kg / m³, and the density of seawater is 1025 kg / m³. The configuration results of the steel catenary riser are as Figure 4 and Figure 5 shown. Compared with commercial software, the configuration error of the catenary section of the steel catenary riser system is only 2.29%.

[0038] Based on the above embodiments, it can be seen that the present invention realizes the rapid and accurate configuration of the steel catenary riser by inputting different riser parameters, environmental parameters, population quantity and iteration times, and combining non-linear dynamic weight allocation, hybrid initialization strategy and collaborative constraint processing.

[0039] For the foregoing embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this application.

[0040] In the above embodiments, the basic principles, main features and advantages of the present invention are described. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principle of the present invention. Without departing from the spirit and scope of the present invention, any changes and modifications made by those skilled in the art that do not depart from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A riser configuration method based on an improved particle swarm optimization algorithm, characterized in that, It includes the following steps: S1: Establish a mechanical model of the steel catenary riser system according to the material properties, geometric dimensions and marine environmental conditions of the riser; S2: Determine the constraint conditions of the steel catenary riser system; S3: Hybrid initialize the particle swarm; S4: Construct a non-linear dynamic weight fitness function for multi-objective optimization; S5: Iteratively solve the global optimal solution by the improved particle swarm algorithm.

2. The riser configuration method based on the improved particle swarm optimization algorithm according to claim 1, characterized in that, The mechanical model of the steel catenary riser system is: ； In the formula, is the element mass matrix, is the acting force of the external load, is the element damping matrix, is the element stiffness matrix, is the acting force of the axial load, is the riser acceleration, is the riser velocity, is the riser displacement.

3. The riser configuration method based on the improved particle swarm optimization algorithm according to claim 1, characterized in that The constraint conditions include the strength constraint conditions of the steel catenary riser system: ； Among them, is the equivalent stress, is the allowable stress.

4. The riser configuration method based on the improved particle swarm optimization algorithm according to claim 3, characterized in that, The constraint conditions also include the displacement constraint conditions of the steel catenary riser system: ； Among them, is the offset of the top platform, is the seawater depth.

5. The riser configuration method based on the improved particle swarm optimization algorithm according to claim 4, characterized in that, The constraint conditions also include the vibration constraint conditions of the steel catenary riser system: ； Among them, is the vortex shedding frequency, is the natural frequency.

6. The riser configuration method based on the improved particle swarm optimization algorithm according to claim 1, characterized in that Hybrid initialize the particle swarm and adopt the following method: ； Among them, xi,j represents the value of the j-th dimension of the i-th group of particles, uj and dj respectively represent the upper and lower boundaries of the j-th problem variable, rand represents a random number between the interval [0,1], N represents the number of particles in the particle swarm, and m represents the number of problems to be solved.

7. The riser configuration method based on the improved particle swarm optimization algorithm according to claim 1, characterized in that, The non-linear dynamic weight fitness function is: ； Among them, is the weight, is the objective function.

8. The riser configuration method based on the improved particle swarm optimization algorithm according to claim 7, characterized in that, Weight The dynamic adjustment method is as follows: Calculate the relative importance of each objective function at the current iteration; Normalize the relative importance to obtain the dynamic weight.

9. The riser configuration method based on the improved particle swarm optimization algorithm according to claim 1, characterized in that Step S5 is specifically: Continuously update the position and velocity of the particles, and the particle swarm continuously searches for a better riser configuration scheme in the search space.

10. The riser configuration method based on the improved particle swarm optimization algorithm according to claim 9, wherein, The velocity update formula of the particle is: ； Among them, is the velocity of particle i at the (k + 1)-th iteration, is the inertia weight; is the guiding particle; c1 and c2 are learning factors; is the influence coefficient for controlling the guiding particle; r1 and r2 are random numbers within the interval [0, 1]; is the historical optimal position of particle i; is the historical optimal position of the population; is the position of particle i at the k-th iteration; The position update formula of the particle is: ； Among them, is the position of particle i at time t + 1; is the position of particle i at time t; is the velocity of particle i at time t + 1.

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