针对海上风电机组四桩导管架基础,依托某海上风电工程项目,开发基于Python的导管架基础SACS参数化建模计算程序,结合遗传算法优化策略,构建海上风电机组导管架桩基础智能优化模型,并与初步设计方案、其他类似海上风电工程项目进行对比分析以评估其优化性能。研究结果表明：1）基于遗传算法的导管架桩基础优化模型收敛速度较快,前20代即完成最终方案的93.46%;2）与初步设计方案相比,最终优化方案降低桩基础质量22.35%,约236.40 t,单台风电机组导管架基础节约造价超百万元;3）优化方案通过缩短桩长、减小壁厚并增大桩径,以提高材料利用效率,确保在优化桩基础质量的同时,不降低其承载性能,并有效控制变形幅度,体现出优化策略在平衡结构安全性与经济性方面的有效性。
Abstract
For the four-pile jacket foundation of offshore wind turbines, a Python-based parametric modeling program for SACS was developed, relying on data from a practical offshore wind power project. By integrating a genetic algorithm optimization strategy with the program, an intelligent optimization model for the piles in a jacket foundation is developed. Comparative analyses were conducted with the preliminary design and other similar offshore wind power projects to evaluate the optimization performance. The results indicate that： （1） the genetic algorithm-based optimization model exhibits a rapid convergence rate, achieving 93.46% of the final optimal scheme within the first 20 generations; （2） compared with the preliminary design, the optimized scheme reduces the pile foundation mass by 22.35% （approximately 236.40 t）, saving over one million RMB per jacket foundation. Compared with other similar projects, the average mass reduction reaches 18.18%; （3） the optimization scheme improves material utilization efficiency by shortening the pile length, reducing the wall thickness, and increasing the pile diameter, thereby reducing mass without compromising bearing capacity and effectively controlling deformation amplitude, which highlights the effectiveness of the proposed optimization strategy in balancing structural safety and economic efficiency. These results demonstrate that the genetic algorithm effectively balances global exploration and local convergence in multi-constraint structural optimization problems, providing a feasible and efficient technical pathway for intelligent optimization design of complex offshore structures.
关键词
海上风电 /
设计优化 /
遗传算法 /
桩基础 /
导管架 /
参数化建模
Key words
offshore wind power /
design optimization /
genetic algorithms /
pile foundations /
jacket structure /
parametric modeling.
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