Multi-objective optimization design method for steel catenary riser system

Abstract

The invention discloses a multi-objective optimization design method for a steel catenary riser system, which comprises the following steps of: defining a system design variable set which covers the length, the wall thickness, the outer diameter, the suspension angle and the plane coordinate of each riser; a multi-objective function with the lowest total cost, the highest structural strength (based on the yield strength ratio) and the longest system fatigue life is constructed, and cost calculation comprehensive material cost, a manufacturing coefficient and the unit price of the pipe laying ship are carried out; establishing a strength constraint, a fatigue life constraint, a riser anti-collision constraint and a suspension angle process constraint; establishing a single riser dynamic model according to the platform position, and integrating wave, ocean current and seabed load; a vortex-induced and wave-induced dual fatigue evaluation model is combined to establish an agent model for strength analysis and fatigue evaluation of the riser; the design space is solved through an NSGA-III multi-objective optimization algorithm, and a comprehensive optimization scheme meeting the requirements of cost economy, structural reliability and long service life is generated.

Description

Technical Field

[0001] This invention relates to the field of steel catenary riser system design technology, and in particular to a multi-objective optimization design method for steel catenary riser systems. Background Technology

[0002] As a core transportation device for deep-sea oil and gas development, the research and application of steel catenary risers are directly related to energy security and the autonomy of marine engineering. With the expansion of deep-sea oil and gas development into ultra-deep water and harsh sea conditions, steel catenary risers have become an important solution for deep-sea risers due to their advantages such as simple structure, low cost, resistance to high temperature and pressure, and adaptability to large-scale floating body drift. However, their service environment is extremely harsh: they need to operate stably for more than 20 years under the coupled loads of wind, waves, currents, and platform motion, especially in the contact point and suspension point areas, where they are subjected to repeated bending stress and vortex-induced vibration, leading to a significantly increased risk of fatigue failure. These technical characteristics and service challenges impose a rigid requirement for multi-dimensional collaborative optimization of steel catenary riser design methods.

[0003] Due to the high risks and long service life requirements of steel catenary riser systems, traditional single-objective design methods struggle to coordinate and optimize economic efficiency, structural reliability, and fatigue life, often leading to soaring costs or premature failure. Parallel arrangement of multiple risers easily results in collisions and interference, and existing isolated analysis models cannot effectively assess system-level risks. Simultaneously, the computational load of high-precision dynamics and fatigue analysis is enormous, limiting the efficiency of multi-scheme optimization, while engineering constraints such as suspension angle limitations and installation cost control lack systematic integration. These bottlenecks severely restrict the safety and economic benefits of deep-water riser systems. Therefore, there is an urgent need to develop a design method that integrates multi-objective optimization, system coupling analysis, and an efficient computational framework to comprehensively improve the performance of steel catenary risers throughout their entire life cycle. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a multi-objective optimization design method for steel catenary riser systems, aiming to reduce the cost of steel catenary riser systems while ensuring their strength and lifespan.

[0005] The present invention solves its problems through the following technical solution: A multi-objective optimization design method for a steel catenary riser system includes the following steps: S1. Define the set of multi-objective optimization variables for the steel catenary riser system: In the formula, n is the serial number of the steel catenary riser, and L i t i D i With θ iThe length, wall thickness, outer diameter, and suspension angle of the i-th steel catenary riser are respectively, [x i ,y i [ ] represents the plane coordinates of the i-th steel catenary riser.

[0006] S2. Define the objective function for multi-objective optimization of the steel catenary riser system as achieving optimal total cost, maximum strength, and maximum fatigue life: In the formula, C total R σ With N sys These represent total cost, structural strength (characterized by maximum yield strength ratio), and fatigue life, respectively, where ρ is the material density of the steel catenary riser, and P... steel Let P be the unit volume price of the steel catenary riser, k1 and k2 be the manufacturing cost fitting coefficients, β be the installation difficulty coefficient, and P be the unit volume price of the steel catenary riser. vessel This refers to the unit price for pipelaying vessel operations.

[0007] S3. Establish the constraints for multi-objective optimization of the steel catenary riser system, which include strength constraint g1(X), fatigue life constraint g2(X), riser interference constraint g3(X), and suspension angle process constraint g4(X): In the formula, σ y and These represent the yield strength and maximum equivalent stress of the steel catenary riser material, respectively, in N. fatigue,i Let be the fatigue life of the i-th steel catenary riser. The distance between the risers of the steel catenary is the Euclidean distance, where H is the water depth and L is the distance between the risers. horiz,i The horizontal distance from wellhead i to the platform.

[0008] S4. Based on the location of the wellhead and the platform, establish a dynamic model for each catenary riser, which includes a riser model, an ocean current wave model, and a seabed model.

[0009] S5. Establish a fatigue analysis model for each steel catenary riser, including vortex-induced fatigue and wave-induced fatigue assessment functions.

[0010] S6. Based on the NSGA-III algorithm, conduct multi-objective optimization of the steel catenary riser system to determine the optimal design scheme. The optimization model is as follows: Optionally, in step S1, the outer diameter of the steel catenary riser ranges from 0.1m to 0.3m.

[0011] Optionally, in step S4, a dynamic model of the steel catenary riser system is established based on the catenary equation and Euler-Bernoulli beam theory; the time history of random wave loads is generated using the JONSWAP spectrum, and the combined wave and current loads on the riser are calculated using the Morison equation; the nonlinear pipe-soil contact behavior in the contact point zone (TDZ) is characterized using the ABY seabed model; and the six-degree-of-freedom motion boundary conditions of the floating platform are coupled, wherein the platform motion includes wave frequency motion caused by first-order wave force and low-frequency motion caused by second-order drift force.

[0012] Optionally, in step S5, an eddy-induced fatigue analysis model for the steel catenary riser is established based on the wake strobe model. The damage is calculated using the rainflow counting method and the Palmgren-Miner criterion, taking into account the combined effects of eddy-induced vibration and wave-induced vibration on the fatigue life of the steel catenary riser.

[0013] Optionally, in steps S4 and S5, by parametrically scanning design variables (pipe diameter, wall thickness, suspension angle) and environmental conditions, a high-precision dynamic model is called to calculate the stress response and fatigue damage of the riser in batches, and a training database is constructed; key physical features (geometric parameters, dynamic response indices, vortex-induced characteristics) are extracted from the dynamic response; a dual-channel deep learning architecture is constructed to simultaneously learn the mapping relationship between local stress distribution and global damage; based on the pre-trained model on the public dataset, adaptive migration to the target sea area is achieved through parameter fine-tuning; the prediction accuracy is verified using an independent test set, and finally a proxy model for dynamic response analysis and fatigue assessment of the steel catenary riser system is established.

[0014] Optionally, in step S6, the population is first initialized within the design space constrained by the API specification. The stress, fatigue life, and cost of each scheme are quickly evaluated using a data-driven surrogate model. Then, non-dominated sorting is performed and uniform reference points are associated. Feasible solutions are screened using a constraint violation penalty mechanism. Genetic operations are performed using simulated binary crossover and polynomial mutation to dynamically adjust the distribution of reference points to maintain the diversity of the target space. The population is iteratively updated using an elite retention strategy until the hypervolume index converges (improvement rate <0.5% or up to 200 generations). Finally, the design scheme with the best overall performance is selected from the Pareto front based on entropy weight-TOPSIS decision, achieving synergistic optimization of cost, strength, and life, which significantly improves economy and reliability compared to the initial design.

[0015] In summary, the technical effects and advantages of this invention are as follows: 1. Global collaborative optimization: Simultaneously balancing cost, strength, and lifespan, breaking through the limitations of traditional single-objective design; 2. Efficient decision-making mechanism: Combining proxy models to accelerate evaluation and multi-attribute decision-making, significantly reducing computation time; 3. Improved reliability: Pre-controlling operational risks through interference constraints and dual fatigue models; 4. Strong engineering applicability: Process constraints ensure the feasibility of the solution, and the cost model accurately reflects actual expenditures. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a multi-objective optimization design method for a steel catenary riser system according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0020] This embodiment provides a multi-objective optimization design method for a steel catenary riser system. Figure 1 This is a flowchart of a multi-objective optimization method.

[0021] like Figure 1 As shown, the method includes: S1. Define the set of multi-objective optimization variables for the steel catenary riser system: In the formula, n is the serial number of the steel catenary riser, and L i t i D i With θ i Let x represent the length, wall thickness, outer diameter, and suspension angle of the i-th steel catenary riser, where the outer diameter of the steel catenary riser ranges from 0.1 m to 0.3 m; [x] i ,y i [ ] represents the plane coordinates of the i-th steel catenary riser.

[0022] S2. Define the objective function for multi-objective optimization of the steel catenary riser system as achieving optimal total cost, maximum strength, and maximum fatigue life: In the formula, C total R σ With N sys These represent total cost, structural strength (characterized by maximum yield strength ratio), and fatigue life, respectively, where ρ is the material density of the steel catenary riser, and P... steel Let P be the unit volume price of the steel catenary riser, k1 and k2 be the manufacturing cost fitting coefficients, β be the installation difficulty coefficient, and P be the unit volume price of the steel catenary riser. vessel This refers to the unit price for pipelaying vessel operations.

[0023] S3. Establish the constraints for multi-objective optimization of the steel catenary riser system, which include strength constraint g1(X), fatigue life constraint g2(X), riser interference constraint g3(X), and suspension angle process constraint g4(X): In the formula, σ y and These represent the yield strength and maximum equivalent stress of the steel catenary riser material, respectively, in N. fatigue,i Let be the fatigue life of the i-th steel catenary riser. The distance between the risers of the steel catenary is the Euclidean distance, where H is the water depth and L is the distance between the risers. horiz,i The horizontal distance from wellhead i to the platform.

[0024] S4. Based on the location of the wellhead and the platform, establish a dynamic model for each steel catenary riser, which includes a riser model, an ocean current wave model, and a seabed model; establish a dynamic model of the steel catenary riser system based on the catenary equation and Euler-Bernoulli beam theory; generate random wave load time histories using the JONSWAP spectrum, and calculate the combined wave and ocean current loads on the steel catenary riser using the Morison equation; characterize the nonlinear pipe-soil contact behavior in the contact point zone (TDZ) using the ABY seabed model; and couple the six-degree-of-freedom motion boundary conditions of the floating platform, where the platform motion includes wave frequency motion caused by first-order wave forces and low-frequency motion caused by second-order drift forces.

[0025] S5. Establish a fatigue analysis model for each steel catenary riser, including vortex-induced fatigue and wave-induced fatigue assessment functions; establish a vortex-induced fatigue analysis model for the steel catenary riser based on the wake strobe model, apply the rainflow counting method and Palmgren-Miner criterion to calculate damage, and consider the comprehensive influence of vortex-induced vibration and wave-induced vibration on the fatigue life of the steel catenary riser.

[0026] Furthermore, in steps S4 and S5, the design variables (pipe diameter, wall thickness, suspension angle) and environmental conditions are parametrically scanned, and a high-precision dynamic model is called to calculate the stress response and fatigue damage of the riser in batches, thus constructing a training database. Key physical features (geometric parameters, dynamic response indices, vortex-induced characteristics) are extracted from the dynamic response. A dual-channel deep learning architecture is constructed to simultaneously learn the mapping relationship between local stress distribution and global damage. Based on a pre-trained model using a public dataset, adaptive migration to the target sea area is achieved through parameter fine-tuning. The prediction accuracy is verified using an independent test set, and finally, a proxy model for dynamic response analysis and fatigue assessment of the steel catenary riser system is established.

[0027] S6. Based on the NSGA-III algorithm, conduct multi-objective optimization of the steel catenary riser system to determine the optimal design scheme. The optimization model is as follows: The specific optimization process for the above optimization model is as follows: First, the population is initialized within the design space constrained by the API specification. A data-driven surrogate model is used to quickly evaluate the stress, fatigue life, and cost of each scheme. Then, non-dominated sorting is performed and uniform reference points are associated. A constraint violation penalty mechanism is used to screen feasible solutions. Simulated binary crossover and polynomial mutation are used for genetic operations to dynamically adjust the distribution of reference points to maintain diversity in the target space. The population is iteratively updated using an elite retention strategy until the hypervolume index converges (improvement rate <0.5% or reaches 200 generations). Finally, based on entropy-weighted TOPSIS decision-making, the design scheme with the optimal overall performance is selected from the Pareto front, achieving coordinated optimization of cost, strength, and lifespan, significantly improving economy and reliability compared to the initial design. The platform's heading angle and offset distance parameters under long-term environmental operating conditions are considered to evaluate the strength and fatigue life of the steel catenary riser system.

[0028] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A multi-objective optimization design method for a steel catenary riser system, characterized in that, Includes the following steps: S1. Define the set of multi-objective optimization variables for the steel catenary riser system: In the formula, n is the serial number of the steel catenary riser, and L i t i D i With θ i The length, wall thickness, outer diameter, and suspension angle of the i-th steel catenary riser are respectively, [x i ,y i [ ] represents the plane coordinates of the i-th steel catenary riser; S2. Define the objective function for multi-objective optimization of the steel catenary riser system as achieving optimal total cost, maximum strength, and maximum fatigue life: In the formula, C total R σ With N sys These represent total cost, structural strength (characterized by maximum yield strength ratio), and fatigue life, respectively, where ρ is the material density of the steel catenary riser, and P... steel Let P be the unit volume price of the steel catenary riser, k1 and k2 be the manufacturing cost fitting coefficients, β be the installation difficulty coefficient, and P be the unit volume price of the steel catenary riser. vessel The unit price for pipelaying vessel operations; S3. Establish the constraints for multi-objective optimization of the steel catenary riser system, which include strength constraint g1(X), fatigue life constraint g2(X), riser interference constraint g3(X), and suspension angle process constraint g4(X): In the formula, σ y and These represent the yield strength and maximum equivalent stress of the steel catenary riser material, respectively, in N. fatigue,i Let be the fatigue life of the i-th steel catenary riser. The distance between the risers of the steel catenary is the Euclidean distance, where H is the water depth and L is the distance between the risers. horiz,i The horizontal distance from wellhead i to the platform; S4. Based on the location of the wellhead and the platform, establish a dynamic model for each steel catenary riser, which includes a riser model, an ocean current wave model, and a seabed model. S5. Establish a fatigue analysis model for each steel catenary riser, including vortex-induced fatigue and wave-induced fatigue assessment functions. S6. Based on the NSGA-III algorithm, conduct multi-objective optimization of the steel catenary riser system to determine the optimal design scheme. The optimization model is as follows: 。 2. The multi-objective optimization design method for a steel catenary riser system according to claim 1, characterized in that, In step S1, the outer diameter of the steel catenary riser ranges from 0.1m to 0.3m.

3. The multi-objective optimization design method for a steel catenary riser system according to claim 1, characterized in that, In step S4, a dynamic model of the steel catenary riser system is established based on the catenary equation and Euler-Bernoulli beam theory; the time history of random wave load is generated using the JONSWAP spectrum, and the combined wave and current load on the steel catenary riser is calculated using the Morison equation; the nonlinear pipe-soil contact behavior in the contact point area is characterized by the ABY seabed model. It also couples the six-degree-of-freedom motion boundary conditions of the floating platform, where the platform motion includes wave frequency motion caused by first-order wave force and low-frequency motion caused by second-order drift force.

4. The multi-objective optimization design method for a steel catenary riser system according to claim 1, characterized in that, In step S5, an eddy-induced fatigue analysis model for the steel catenary riser is established based on the wake strobe model. The damage is calculated using the rainflow counting method and the Palmgren-Miner criterion, taking into account the combined effects of eddy-induced vibration and wave-induced vibration on the fatigue life of the steel catenary riser.

5. The multi-objective optimization design method for a steel catenary riser system according to claim 1, characterized in that, In steps S4 and S5, the design variables and environmental conditions are parametrically scanned, and the dynamic model is called to calculate the stress response and fatigue damage of the riser in batches to build a training database. Key physical features are extracted from the dynamic response, including geometric parameters, dynamic response indices, and vortex-induced characteristics. A dual-channel deep learning architecture is constructed to simultaneously learn the mapping relationship between local stress distribution and global damage. Based on a pre-trained model using a public dataset, adaptive migration to the target sea area is achieved through parameter adjustment. During the training process, the platform's heading angle and offset distance parameters are considered. The prediction accuracy is verified using an independent test set, and finally, a proxy model for dynamic response analysis and fatigue assessment of the steel catenary riser system is established.

6. The multi-objective optimization design method for a steel catenary riser system according to claim 5, characterized in that, In step S6, the population is first initialized within the design space constrained by the API specification. The stress, fatigue life, and cost of each scheme are evaluated using a data-driven surrogate model. Then, non-dominated sorting is performed and uniform reference points are associated. Feasible solutions are screened using a constraint violation penalty mechanism. Genetic operations are performed using simulated binary crossover and polynomial mutation to dynamically adjust the distribution of reference points to maintain the diversity of the target space. The population is iteratively updated using an elite retention strategy until the hypervolume index converges. Finally, the design scheme with the best overall performance is selected from the Pareto front based on entropy weight-TOPSIS decision.

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