CN-121479979-B - Multi-parameter automatic collaborative optimization design method for tension leg type floating fan

CN121479979BCN 121479979 BCN121479979 BCN 121479979BCN-121479979-B

Abstract

The invention belongs to the field of ocean renewable energy and floating structure engineering optimization design, and discloses a multi-parameter automatic collaborative optimization design method and system for a tension leg type floating fan. The invention realizes high-efficiency automatic design, avoids the problem of unstable interfaces, has reproducible and expandable flow, can optimize in a larger design space to obtain a comprehensive design scheme with better matching of floating bodies and anchoring performance and lower total system cost, effectively balances efficiency and reliability, and can stably process complex constraint by a genetic algorithm self-adaptive optimizing mechanism based on multi-constraint punishment.

Inventors

GUO JIANING
LIU MINGYUE
ZHU SHENGLONG
WU WENCHENG
Gao Linghao
XU JUNLONG

Assignees

上海交通大学三亚崖州湾深海科技研究院

Dates

Publication Date: 20260512
Application Date: 20260112

Claims (9)

1. A tension leg type floating fan platform collaborative design optimization method based on full-flow automation is characterized by comprising the following steps: S1, determining an optimized variable and a design space, and establishing a value range of an independent design variable and an automatic association relationship between a dependent variable and the independent design variable; S2, generating an initial population in the design space; s3, carrying out parameterized geometric modeling on population individuals and automatically generating a face element grid; S4, automatically calculating hydrostatic force and mass characteristics, and determining ballast in a linkage manner according to a static balance relation so as to match given tendon pretension; s5, calculating a frequency domain hydrodynamic coefficient to obtain additional mass, potential flow damping, still water recovery characteristics and wave excitation; S6, rapidly evaluating the average drift displacement based on the wind-wave combined action; S7, a motion model of the platform and tendon coupling is built based on the platform quality characteristics, the additional quality, the still water recovery characteristics, the wave excitation and tendon linearization rigidity, and the inherent period is solved; s8, constructing an optimized mathematical model, taking the estimated cost as a target, and taking periodic avoidance, displacement limitation, tendon tension, section strength and minimum drainage as constraints; s9, constructing a composite punishment fitness function, and converting the cost minimization problem into a fitness maximization problem; S10, executing genetic algorithm evolution iteration, namely repeatedly executing S3 to S9 on each generation of individuals, updating the population, and terminating when the convergence condition is met; s11, outputting a globally optimal individual iterated for the past time, and generating a corresponding evaluation report and a three-dimensional model file of optimal design; The step S5 comprises solving the radiation and diffraction problems under a linear potential flow theory framework to obtain additional mass, potential flow damping and wave excitation which change along with frequency, and calculating the still water recovery characteristic, wherein the still water recovery characteristic only provides recovery terms for heave, roll and pitch in six degrees of freedom, the heave recovery terms are proportional to the area of the water plane, the roll recovery terms are proportional to the moment of inertia of the water plane around the longitudinal axis, the pitch recovery terms are proportional to the moment of inertia of the water plane around the transverse axis, and the still water recovery terms of the rest degrees of freedom are zero.
2. The method of claim 1, wherein S1 comprises the steps of setting upper and lower limits for each independent design variable to form an initial design space, wherein the independent design variables comprise draft, lower column height, lower column diameter, upper column diameter, pontoon length and pontoon width, and the rest of the geometric parameters are automatically associated with the independent design variables by a predefined geometric relationship.
3. The method of claim 1, wherein S3 comprises automatically generating a three-dimensional geometric model of the platform based on the independent design variables, the three-dimensional geometric model comprising a column and pontoon assembly, and automatically performing surface meshing on the three-dimensional geometric model to generate a bin mesh file suitable for linear potential flow theoretical boundary bin hydrodynamic calculation.
4. The method of claim 1, wherein S4 comprises automatically calculating the water drainage volume, the water plane area, the floating center position and the drifting position of the platform based on the three-dimensional geometric model, automatically calculating the structural mass according to the geometric dimension of the assembly and the preset material density, and determining the ballast weight according to the static balance condition in a linkage way, so that the balance between the buoyancy force born by the platform, the total weight of the platform and the total tendon pretension is met, and the initial tendon pretension reaches a set value.
5. An automated performance assessment method for collaborative design optimization of a tension leg type floating fan platform is characterized in that an assessment result for constraint judgment and cost calculation is automatically output for any given design scheme, and the assessment method comprises the following steps: e1, automatically generating a platform geometric model and generating a surface element grid; e2, automatically calculating hydrostatic force and mass characteristics and determining ballast in parallel, so that static force balance is established and the set tendon pretension is met; E3, automatically calculating a frequency domain hydrodynamic coefficient to obtain additional mass, potential flow damping, still water recovery characteristics and wave excitation; e4, rapidly evaluating average drift displacement; e5, establishing a motion model of platform and tendon coupling based on platform quality characteristics, additional quality, still water recovery characteristics, wave excitation and tendon linearization rigidity, and solving a natural period; e6, outputting an evaluation result vector containing average drift displacement, inherent period, tendon tension and strength criteria, drainage amount and cost items for the optimization module to call; in E4, solving the radiation and diffraction problems under a linear potential flow theory framework to obtain additional mass, potential flow damping and wave excitation which change along with frequency, and calculating the still water recovery characteristic, wherein the still water recovery characteristic only provides recovery terms for heave, roll and pitch in six degrees of freedom, the heave recovery terms are in direct proportion to the area of the water plane, the roll recovery terms are in direct proportion to the moment of inertia of the water plane around the longitudinal axis, the pitch recovery terms are in direct proportion to the moment of inertia of the water plane around the transverse axis, and the still water recovery terms of the rest degrees of freedom are zero.
6. The method of claim 5, wherein the average drift displacement in E4 is estimated as the sum of average wind thrust and average wave drift force divided by the total stiffness in the heave direction, wherein the average wind thrust is obtained by table look-up interpolation from a rated wind speed and fan thrust coefficient curve, and the total stiffness in the heave direction is determined by the platform still water recovery contribution and tendon system contribution.
7. The method of claim 6, wherein the average wave drift force in E4 is calculated using an empirical estimation relationship that satisfies the average wave drift force proportional to sea water density, gravitational acceleration, square of design amplitude, empirical drift force coefficient, and platform characteristic diameter, wherein the design amplitude is determined by the design wave height.
8. A tension leg type floating wind turbine platform co-design optimization system for implementing the method of any one of claims 1 to 4, comprising: The design space definition and variable mapping module is used for establishing automatic mapping of independent design variables and dependent variables and generating candidate designs; the parameterized modeling and grid generating module is used for generating a three-dimensional geometric model and a face element grid; The hydrostatic-mass-ballast matching module is used for calculating hydrostatic force and mass characteristics and connecting with a ballast to match the tendon pretension; The frequency domain hydrodynamic force calculation module is used for calculating additional mass, potential flow damping, still water recovery characteristics and wave excitation; the fast response evaluation module is used for evaluating the average drift displacement and solving the inherent period; The compound punishment fitness genetic optimization module is used for constructing fitness based on the objective function and the constraint and driving population iteration; and the result output and post-processing module is used for outputting the global optimal design and generating an evaluation report and a three-dimensional model file.
9. The system of claim 8 wherein the complex penalty fitness genetic optimization module uses real numbers for coding and crossover, mutation and selection mechanisms for global optimization, wherein the fitness is constructed as the inverse of the sum of the estimated cost and the total penalty, wherein the total penalty is calculated from the violations of the periodic constraint, the displacement constraint and the tendon-related constraint, respectively, and wherein the penalties of each constraint are summed up as the square of the relative violations exceeding a target or tolerance, and wherein the rate of change of the population optimal fitness is monitored, and convergence is determined and iteration is terminated in advance when successive generations are below a preset threshold.

Description

Multi-parameter automatic collaborative optimization design method for tension leg type floating fan Technical Field The invention belongs to the technical field of ocean renewable energy and floating structure engineering optimization design, and particularly relates to a multi-parameter automatic collaborative optimization design method and system for a tension leg type floating fan. Background As offshore wind power develops into deep sea, floating wind power becomes a core technical direction. The tension leg platform is particularly suitable for supporting a high-power fan due to excellent motion stability and small horizontal displacement amplitude. However, the design of tension leg type floating fans is a typical complex engineering optimization problem of multiple variables, multiple constraints and strong coupling, and involves the coordination of multiple dimensions of floating body main dimensions, ballast configuration, anchoring rigidity and the like. The design optimization of the existing tension leg type floating fan still has the following technical bottlenecks: (1) The design flow is split, the degree of automation is low, the traditional design depends on manual iteration, links such as floating body design, anchoring design, hydrodynamic force analysis, cost estimation and the like are mutually independent, and the design is long in period and low in efficiency due to the fact that commercial software interfaces are relied on, and systematic exploration of a large-scale parameter space is difficult to realize. (2) The optimization dimension is limited, and the synergy is insufficient, the existing research is focused on optimizing the local size of the floating body (such as the radius of a heave plate, the diameter of a column, and the like, usually about 3), the anchoring system (tendon length, diameter and pretension) cannot be used as an endogenous variable for integrated collaborative optimization, the floating body-anchoring coupling effect is ignored, and the globally optimal solution is difficult to obtain. (3) The computing fidelity and efficiency are difficult to balance, namely in the conceptual design stage, if high-fidelity time domain coupling simulation is directly adopted for evaluation, the result is accurate, but the computing cost is too high to support large-scale parameter screening, and if constraint conditions are not considered sufficiently, the design scheme is possibly invalid in the detailed design stage. In the process of engineering design and industrial application, a tension leg type floating fan platform in the prior art generally adopts a staged and serial design flow taking manual experience as a leading part, each discipline analysis model is mutually fractured, and a uniform cooperative mechanism is lacked among geometric design, hydrodynamic force analysis, motion response check and cost evaluation. When the platform periodically avoids in the subsequent checking stage, average displacement or tendon stress does not meet the requirement, a designer needs to rely on manual repeated rollback and adjust structural parameters, so that the design efficiency is low, the iteration cost is high, and an optimal scheme in the global sense is difficult to obtain under the complex constraint condition. Especially under the background that the scale of the large-capacity fan platform is continuously increased, the design mode is difficult to simultaneously consider dynamic safety and engineering economy, and has become a core technical bottleneck for restricting the large-scale application of the tension leg type floating fan platform. Disclosure of Invention The invention provides a collaborative design optimization method capable of realizing multi-disciplinary performance automatic linkage in a design stage and directly guiding a cost optimal solution on the premise of meeting key dynamics and structural constraints, so as to solve the problems of dependence on manual experience, low design efficiency and unstable optimization result in the prior art. In order to solve the technical problems, the invention provides a multi-parameter automatic collaborative optimization design method of a tension leg type floating fan, which integrates geometric modeling, hydrostatic and mass calculation, frequency domain hydrodynamic analysis, motion response evaluation and cost estimation uniformly by constructing an automatic design-evaluation-optimization closed loop taking a parameterized design vector as a core. The method comprises the steps of systematically introducing platform structural parameters serving as independent design variables into a design space, automatically generating a geometric model through parameterized modeling in any design scheme, completing ballasting and tendon pretension matching under a static balance condition, then obtaining key dynamic indexes such as average drift displacement and inherent period of a platform based on frequency domain hy