Method for optimizing underwater deployment position of reclaimed water buoy

Abstract

The invention discloses a method for optimizing the underwater deployment position of a reclaimed water buoy, and relates to the field of design and optimization of ocean engineering equipment. The optimization variables comprise the linear horizontal distance between the reclaimed water buoy and a vertical pipe suspension point, the vertical distance between the reclaimed water buoy and the vertical pipe suspension point, the length of a vertical pipe at the top of the reclaimed water buoy and the length of a vertical pipe at the bottom of the reclaimed water buoy; setting constraint conditions of optimization variables; constructing an optimization objective function according to the optimization variables; based on the optimization objective function and the optimization variable, multi-objective optimization and optimal scheme output are carried out through an attention enhanced GDMPA algorithm; relative position variables of the reclaimed water buoy and the riser are systematically defined and optimized, constraint conditions and a multi-objective function are combined, and an attention enhanced GDMPA algorithm is introduced for intelligent optimization, so that comprehensive minimization of riser suspension point tension, anchoring point tension and grounding point bending moment is realized, and optimization efficiency and engineering applicability are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of marine engineering equipment design and optimization, and in particular to a method for optimizing the underwater deployment location of mid-water pontoons. Background Technology

[0002] As a key support structure for Plaintwave flexible risers, greywater pontoons can optimize riser configuration, reduce riser dynamic response, and avoid riser interference or even collision. Since the relative position of greywater pontoons and risers significantly affects riser tension and bending moment distribution, they need to be arranged reasonably to suppress dynamic response.

[0003] In existing technologies, traditional methods rely on design experience combined with physical model experiments to determine position parameters. However, this approach has several significant technical and economic limitations in practical applications, restricting the optimization efficiency and engineering reliability of system design. First, the design operating conditions are not fully covered. Empirical methods cannot systematically cover all possible combinations of position parameters, especially in systems with multivariable and nonlinear responses. This can easily lead to the omission of certain critical or special operating conditions, resulting in unforeseen dynamic instability, fatigue damage, or interference risks in actual operation. Second, to verify the dynamic response characteristics of risers under different position parameters, it is usually necessary to create a high-precision scaled physical model for each set of parameters to be measured and conduct repeatable tests in a large marine engineering pool. This process requires significant investment in model preparation, test implementation, and data analysis, which seriously affects the economic efficiency of scheme optimization.

[0004] Therefore, a method for optimizing the underwater deployment location of medium-water pontoons is provided to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing the underwater deployment location of medium-water pontoons, breaking through the limitations of traditional relative position design between medium-water pontoons and risers, improving design efficiency, and providing core technical support for the rapid and safe deployment of deep-sea risers and medium-water pontoons.

[0006] To achieve the above objectives, the present invention provides a method for optimizing the underwater deployment location of medium-water pontoons, comprising the following steps: S1: Define optimization variables, including the straight-line horizontal distance between the greywater pontoon and the suspension point of the riser. Vertical distance between the float and the suspension point of the riser Length of the top riser of the middle-water float Length of bottom riser of the pontoon ; S2: Set optimization variables , , and Constraints; S3: Based on optimization variables , , and Construct the optimization objective function ; S4: Based on optimizing the objective function and optimization variables , , , The attention-enhanced GDMPA algorithm is used for multi-objective optimization and output of the optimal solution.

[0007] Preferably, in step S1, two mooring cables are symmetrically arranged at the bottom of the mid-water buoy, a riser is arranged at the top of the mid-water buoy, and cables are arranged on both sides of the riser. The riser and cables extend to both sides of the mid-water buoy, and both the riser and cables are connected to the floating production storage and offloading vessel (FPSO). The riser is a Plaintwave type riser.

[0008] Preferably, step S2 specifically includes the following steps: S21: Set the straight-line horizontal distance between the middle-water float and the suspension point of the riser. The constraints are specifically set as follows: ; ; ; in, This indicates the straight-line horizontal distance between the greywater pontoon and the suspension point of the riser. The minimum value, This indicates the straight-line horizontal distance between the greywater pontoon and the suspension point of the riser. The maximum value, Indicates the total length of the riser. This indicates the horizontal straight-line distance between the riser suspension point and the riser anchor point; S22: Set the vertical distance between the pontoon and the suspension point of the riser. The constraints are specifically set as follows: ; ; ; in, Indicates the vertical distance between the greywater pontoon and the suspension point of the riser. The minimum value, Indicates the vertical distance between the greywater pontoon and the suspension point of the riser. The maximum value, Indicates the water depth of the operating area; S23: Set the length of the top riser of the middle-water pontoon The constraints are specifically set as follows: ; ; ; ; in, Indicates the length of the riser at the top of the greywater pontoon. The minimum value, Indicates the length of the riser at the top of the greywater pontoon. The maximum value; S24: Set the length of the bottom riser of the middle-water float The constraints are specifically set as follows: ; ; ; ; in, Indicates the length of the bottom riser of the greywater pontoon. The minimum value, Indicates the length of the bottom riser of the greywater pontoon. The maximum value.

[0009] Preferably, in step S3, the objective function is optimized. Specifically set as follows: ; + + =1; in, This indicates the effective tension at the riser suspension point. Indicates the effective tension at the anchor point of the riser. This indicates the bending moment at the point of contact with the riser. , and All of these represent weighting coefficients.

[0010] Preferably, step S4 specifically includes the following steps: S41: Perform population initialization and set the population size. and maximum number of iterations Generate the initial population Positions ( ); S42: Based on the effective tension at the riser suspension point The maximum value, the effective tension of the riser anchor point Maximum value and riser contact point bending moment Find the maximum value and construct the target value matrix. objective ( ), and based on the target value matrix objective ( Constructing the fitness function fitness ; S43: Calculate the fitness function fitness Based on the calculation results, the weight coefficients are updated through an attention mechanism. ; S44: Update population position based on Lévy flight and Brownian motion, and introduce adaptive transition probability. Balancing global exploration with local development; S45: Calculate the fitness function after population position update fitness Based on the calculation results, the current population is screened to obtain the optimal individuals; S46: If the current iteration count reaches the maximum iteration count. If all individuals in the current population are optimal, output the Pareto solution set; otherwise, repeat steps S42-S45. S47: Output the final Pareto solution set.

[0011] Preferably, in step S41, the population size The value range is [200, 400], and the maximum number of iterations is... The value range is [10, 30].

[0012] Preferably, step S43 specifically includes the following steps: Step 1: Adjust the fitness function fitness The historical minimum fitness is normalized, and the performance score of the current solution is calculated. The performance score of the current solution Specifically set as follows: ; in, This indicates the fitness of the current solution. The smallest individual representing the fitness of the current solution; Step 2: Score the current solution based on its performance. Calculate the attention score of the next generation solution Attention score of the next generation solution Specifically set as follows: ; in, Indicates the current number The attention score for each solution. This represents the learning rate. The value range is [0,1]; Step 3: Attention score for the next generation solution Normalization is performed to obtain the updated weight coefficients. Updated weight coefficients Specifically set as follows: ; in, Indicates the current number The attention required for each solution express The value of the natural exponential function, express The value of the natural exponential function.

[0013] Therefore, the present invention employs the above-mentioned method for optimizing the underwater deployment location of mid-water pontoons, which has the following beneficial effects: (1) By optimizing the spatial position of the mid-water pontoon, this scheme can significantly reduce the effective tension and bending moment of the riser, greatly improve the structural stability of the entire operating system, and avoid fatigue damage caused by stress concentration under complex sea conditions. (2) This scheme introduces the attention-enhanced GDMPA algorithm to construct a multi-objective optimization framework with dynamic weight allocation capability. This algorithm can automatically adjust the weight distribution according to the response characteristics of the riser system during the optimization process, thereby improving the global search capability and convergence stability of multi-objective optimization. (3) This scheme focuses on the matching of the value range with the actual engineering situation, takes into account parameters such as riser configuration and marine environment, improves the quality of the initial population, and further improves the convergence efficiency.

[0014] The method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method for optimizing the underwater deployment location of a mid-water pontoon according to the present invention; Figure 2 This is a flowchart illustrating the optimization of the attention-enhanced GDMPA algorithm of this invention; Figure 3 This is a schematic diagram of the underwater deployment of the buoys in this invention; Figure 4 This is a comparison diagram of the position of the water pontoon before and after optimization in an embodiment of the present invention, wherein (a) is the position of the water pontoon before optimization, and (b) is the position of the water pontoon after optimization. Figure 5The graphs are time-domain curves of riser response before and after the water float position optimization in the embodiments of the present invention, wherein (a) is the effective tension of the riser suspension point before and after optimization, (b) is the effective tension of the riser anchor point before and after optimization, and (c) is the bending moment of the riser contact point before and after optimization.

[0016] Among them, 1. Floating Production Storage and Offloading (FPSO); 2. Medium-water buoy; 3. Riser; 4. Cable; 5. Mooring cable. Detailed Implementation

[0017] The method of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] Unless otherwise defined, the methodological or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0019] The terms "comprising" or "including" as used in this invention mean that the element preceding the term encompasses the element listed after the term, and do not exclude the possibility of encompassing other elements. Terms such as "inner," "outer," "upper," and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In this invention, unless otherwise explicitly specified and limited, the term "attached" and similar terms should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication of two elements or the interaction relationship between two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0020] Example like Figures 1-3 As shown, this invention provides a method for optimizing the underwater deployment location of medium-water pontoons, comprising the following steps: S1: Define optimization variables, including the straight-line horizontal distance between the greywater pontoon and the suspension point of the riser. Vertical distance between the float and the suspension point of the riser Length of the top riser of the middle-water float Length of bottom riser of the pontoon Among them, the length of the top riser of the middle-water float The length of the riser between the riser suspension point and the contact point between the riser and the greywater float, and the length of the riser at the bottom of the greywater float. The length of the riser between the contact point between the riser and the greywater float and the riser anchoring point; In step S1, two mooring cables 5 are symmetrically arranged at the bottom of the middle-water buoy 2, a riser 3 is arranged at the top of the middle-water buoy 2, and cables 4 are arranged on both sides of the riser 3. The riser 3 and cables 4 extend to both sides of the middle-water buoy 2. Both the riser 3 and cables 4 are connected to the floating production storage and offloading vessel FPSO 1. The riser 3 is a Plaintwave type riser.

[0021] S2: Set optimization variables , , and Constraints; Step S2 specifically includes the following steps: S21: Set the straight-line horizontal distance between the middle-water float and the suspension point of the riser. The constraints are: the straight-line horizontal distance between the float and the suspension point of the riser. The distance between the riser suspension point and the riser anchor point is less than the horizontal straight-line distance to ensure that the top and bottom risers have sufficient radii of curvature. The specific constraint conditions are set as follows: ; ; ; in, This indicates the straight-line horizontal distance between the greywater pontoon and the suspension point of the riser. The minimum value, This indicates the straight-line horizontal distance between the greywater pontoon and the suspension point of the riser. The maximum value, This indicates the total length of the riser. In this embodiment, the total length of the riser... It is 360m. This represents the horizontal straight-line distance between the riser suspension point and the riser anchor point. Therefore, the specific constraint conditions are set as follows: ; ; S22: Set the vertical distance between the pontoon and the suspension point of the riser. The constraints are specifically set as follows: ; ; ; in, Indicates the vertical distance between the greywater pontoon and the suspension point of the riser. The minimum value, Indicates the vertical distance between the greywater pontoon and the suspension point of the riser. The maximum value, This indicates the water depth of the operating area; in this embodiment, the water depth of the operating area is... The value is 124.5m, therefore, the specific constraint conditions are set as follows: ; S23: Set the length of the top riser of the middle-water pontoon The constraints are specifically set as follows: ; ; ; ; in, Indicates the length of the riser at the top of the greywater pontoon. The minimum value, Indicates the length of the riser at the top of the greywater pontoon. The maximum value of , therefore, the constraint condition is specifically set as follows: ; ; S24: Set the length of the bottom riser of the middle-water float The constraints are specifically set as follows: ; ; ; ; in, Indicates the length of the bottom riser of the greywater pontoon. The minimum value, Indicates the length of the bottom riser of the greywater pontoon. The maximum value of , therefore, the constraint condition is specifically set as follows: ; .

[0022] S3: Based on optimization variables , , and Construct the optimization objective function ; In step S3, the optimization objectives are the effective tension at the riser suspension point, the effective tension at the riser anchor point, and the bending moment at the riser contact point. The optimization objective function is... Specifically set as follows: ; + + =1; in, This indicates the effective tension at the riser suspension point. Indicates the effective tension at the anchor point of the riser. This indicates the bending moment at the point of contact with the riser. , and All represent weighting coefficients. In this embodiment, the weighting coefficients are... , and The initial values ​​were set to 0.3, 0.3 and 0.4 respectively.

[0023] S4: Based on optimizing the objective function and optimization variables , , , The attention-enhanced GDMPA algorithm is used for multi-objective optimization and output of the optimal solution.

[0024] Step S4 specifically includes the following steps: S41: Perform population initialization and set the population size. and maximum number of iterations Generate the initial population Positions ( In this embodiment, the population size is... Set to 200, maximum number of iterations Set to 20; In step S41, population size The value range is [200, 400], and the maximum number of iterations is... The value range is [10, 30].

[0025] S42: Based on the effective tension at the riser suspension point The maximum value, the effective tension of the riser anchor point Maximum value and riser contact point bending moment Find the maximum value and construct the target value matrix. objective ( ), and based on the target value matrix objective ( Constructing the fitness function fitness Target value matrix objective ( )for A 4×4 matrix, where each row stores the optimization variable value of an individual. In this embodiment, the target value matrix... objective ( () is a 200×4 matrix; S43: Calculate the fitness function fitness Based on the calculation results, the weight coefficients are updated through an attention mechanism. ; Step S43 specifically includes the following steps: Step 1: Adjust the fitness function fitness The historical minimum fitness is normalized, and the performance score of the current solution is calculated. The performance score of the current solution Specifically set as follows: ; in, This indicates the fitness of the current solution. The smallest individual representing the fitness of the current solution. These correspond to three optimization objective functions; Step 2: Score the current solution based on its performance. Calculate the attention score of the next generation solution Attention score of the next generation solution Specifically set as follows: ; in, Indicates the current number The attention score for each solution. This represents the learning rate. The value range of is [0,1]. In this embodiment, the learning rate is... Take 0.5; Step 3: Attention score for the next generation solution Normalization is performed to obtain the updated weight coefficients. Updated weight coefficients Specifically set as follows: ; in, Indicates the current number The attention required for each solution express The value of the natural exponential function, express The value of the natural exponential function.

[0026] S44: Update population position based on Lévy flight and Brownian motion, and introduce adaptive transition probability. The optimization process is dynamically guided based on the predator's fitness in each iteration, balancing the ability to explore globally and exploit locally. S45: Calculate the fitness function after population position update fitness Based on the calculation results, the current population is screened to obtain the optimal individuals; S46: If the current iteration count reaches the maximum iteration count. If all individuals in the current population are optimal, output the Pareto solution set; otherwise, repeat steps S42-S45. S47: Output the final Pareto solution set.

[0027] like Figure 4 As shown, the straight-line horizontal distance between the pontoon and the suspension point of the riser in the initial design is... Vertical distance between the float and the suspension point of the riser Length of the top riser of the middle-water float Length of bottom riser of the pontoon After optimization, the straight-line horizontal distance between the greywater pontoon and the riser suspension point is... Vertical distance between the float and the suspension point of the riser Length of the top riser of the middle-water float Length of bottom riser of the pontoon .

[0028] like Figure 5 As shown, after optimization, the maximum effective tension at the riser suspension point decreased by 10.6%; the maximum tension at the riser anchor point decreased by 7.1%; and the maximum bending moment at the riser contact point decreased by 5.7%.

[0029] Therefore, the present invention adopts the above-mentioned method for optimizing the underwater deployment position of the mid-water pontoon. By dynamically optimizing the position of the mid-water pontoon through the attention-enhanced GDMPA algorithm, the effective tension and bending moment of the riser are significantly reduced, thereby improving the structural stability and design efficiency of the deep-sea riser system.

[0030] Finally, it should be noted that the above embodiments are only used to illustrate the method of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the method of the present invention, and these modifications or equivalent substitutions should not cause the modified method to deviate from the spirit and scope of the method of the present invention.

Claims

1. A method for optimizing the underwater deployment location of a medium-water pontoon, characterized in that, Includes the following steps: S1: Define optimization variables, including the straight-line horizontal distance between the greywater pontoon and the suspension point of the riser. Vertical distance between the float and the suspension point of the riser Length of the top riser of the middle-water float Length of bottom riser of the pontoon ; S2: Set optimization variables , , and Constraints; S3: Based on optimization variables , , and Construct the optimization objective function ; S4: Based on optimizing the objective function and optimization variables , , , The attention-enhanced GDMPA algorithm is used for multi-objective optimization and output of the optimal solution.

2. The method for optimizing the underwater deployment location of a medium-water pontoon according to claim 1, characterized in that, In step S1, two mooring cables are symmetrically arranged at the bottom of the middle-water buoy, a riser is arranged at the top of the middle-water buoy, and cables are arranged on both sides of the riser. The riser and cables extend to both sides of the middle-water buoy, and both the riser and cables are connected to the floating production storage and offloading vessel (FPSO). The riser is a Plaintwave type riser.

3. The method for optimizing the underwater deployment location of a medium-water pontoon according to claim 1, characterized in that, Step S2 specifically includes the following steps: S21: Set the straight-line horizontal distance between the middle-water float and the suspension point of the riser. The constraints are specifically set as follows: ； ； ； in, This indicates the straight-line horizontal distance between the greywater pontoon and the suspension point of the riser. The minimum value, This indicates the straight-line horizontal distance between the greywater pontoon and the suspension point of the riser. The maximum value, Indicates the total length of the riser. This indicates the horizontal straight-line distance between the riser suspension point and the riser anchor point; S22: Set the vertical distance between the pontoon and the suspension point of the riser. The constraints are specifically set as follows: ； ； ； in, Indicates the vertical distance between the greywater pontoon and the suspension point of the riser. The minimum value, Indicates the vertical distance between the greywater pontoon and the suspension point of the riser. The maximum value, Indicates the water depth of the operating area; S23: Set the length of the top riser of the middle-water pontoon The constraints are specifically set as follows: ； ； ； ； in, Indicates the length of the riser at the top of the greywater pontoon. The minimum value, Indicates the length of the riser at the top of the greywater pontoon. The maximum value; S24: Set the length of the bottom riser of the middle-water float The constraints are specifically set as follows: ； ； ； ； in, Indicates the length of the bottom riser of the greywater pontoon. The minimum value, Indicates the length of the bottom riser of the greywater pontoon. The maximum value.

4. The method for optimizing the underwater deployment location of a medium-water pontoon according to claim 3, characterized in that, In step S3, the objective function is optimized. Specifically set as follows: ； + + =1； in, This indicates the effective tension at the riser suspension point. Indicates the effective tension at the anchor point of the riser. This indicates the bending moment at the point of contact with the riser. , and All of these represent weighting coefficients.

5. The method for optimizing the underwater deployment location of a medium-water pontoon according to claim 4, characterized in that, Step S4 specifically includes the following steps: S41: Perform population initialization and set the population size. and maximum number of iterations Generate the initial population Positions ( ); S42: Based on the effective tension at the riser suspension point The maximum value, the effective tension of the riser anchor point Maximum value and riser contact point bending moment Find the maximum value and construct the target value matrix. objective ( ), and based on the target value matrix objective ( Constructing the fitness function fitness ; S43: Calculate the fitness function fitness Based on the calculation results, the weight coefficients are updated through an attention mechanism. ; S44: Update population position based on Lévy flight and Brownian motion, and introduce adaptive transition probability. Balancing global exploration with local development; S45: Calculate the fitness function after population position update fitness Based on the calculation results, the current population is screened to obtain the optimal individuals; S46: If the current iteration count reaches the maximum iteration count. If all individuals in the current population are optimal, output the Pareto solution set; otherwise, repeat steps S42-S45. S47: Output the final Pareto solution set.

6. The method for optimizing the underwater deployment location of a medium-water pontoon according to claim 5, characterized in that, In step S41, population size The value range is [200, 400], and the maximum number of iterations is... The value range is [10, 30].

7. The method for optimizing the underwater deployment location of a medium-water pontoon according to claim 5, characterized in that, Step S43 specifically includes the following steps: Step 1: Adjust the fitness function fitness The historical minimum fitness is normalized, and the performance score of the current solution is calculated. The performance score of the current solution Specifically set as follows: ； in, This indicates the fitness of the current solution. The smallest individual representing the fitness of the current solution; Step 2: Score the current solution based on its performance. Calculate the attention score of the next generation solution Attention score of the next generation solution Specifically set as follows: ； in, Indicates the current number The attention score for each solution. This represents the learning rate. The value range is [0,1]; Step 3: Attention score for the next generation solution Normalization is performed to obtain the updated weight coefficients. Updated weight coefficients Specifically set as follows: ； in, Indicates the current number The attention required for each solution express The value of the natural exponential function, express The value of the natural exponential function.

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