Key Takeaways & Executive Findings
- •• • The GA-based optimization converged rapidly, achieving 93.46% of the final solution within 20 generations, which translates to a computational efficiency critical for iterative design cycles in offshore engineering projects where time-to-deployment is a key cost driver. • • Pile foundation mass was reduced by 22.35% (236.40 t) compared to the initial design, directly yielding cost savings exceeding one million RMB per turbine—a substantial margin that improves project viability in water depths of 50–100 m where jacket weight scales exponentially with depth. • • The optimized design achieved material efficiency by shortening pile length, reducing wall thickness, and increasing pile diameter, maintaining bearing capacity and deformation control within acceptable limits, demonstrating that geometric reconfiguration can outperform simple material reduction. • • Benchmarking against similar offshore wind projects confirmed the optimized design's competitiveness, with the GA framework proving adaptable to varying metocean and geotechnical conditions, thereby reducing design redundancy that typically plagues conventional jacket foundation engineering.
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Abstract
This study addresses the design redundancy inherent in four-pile jacket foundations for offshore wind turbines by developing a Python-based parametric modeling and computational framework integrated with the Structural Analysis Computer System (SACS). Coupled with a genetic algorithm (GA), the framework establishes an intelligent optimization model for jacket pile foundations. The model is validated against an initial design and benchmarked against similar offshore wind projects. Results demonstrate rapid convergence: 93.46% of the final optimized solution is achieved within the first 20 generations. The optimized design reduces pile foundation mass by 22.35% (236.40 t) relative to the initial design, yielding cost savings exceeding one million RMB per turbine. The optimization strategy achieves material efficiency by shortening pile length, reducing wall thickness, and increasing pile diameter, while maintaining bearing capacity and controlling deformation. These outcomes confirm the effectiveness of the GA-based approach in balancing structural safety and economic performance, providing a robust design workflow for deep-water offshore wind applications.
1. Introduction
Offshore wind development in China faces a critical bottleneck: the imminent depletion of shallow-water resources in provincial waters, coupled with the imperative to reduce levelized cost of energy. As projects migrate to transitional water depths of 50–100 m and turbines scale beyond 10 MW, jacket foundations—comprising approximately 25% of total project cost—become increasingly massive and economically burdensome. Conventional design practices, while technically mature, embed significant redundancy, particularly in pile sizing and wall thickness, leading to excessive steel consumption and inflated capital expenditure.
Existing optimization methods, including sizing, shape, and topology optimization, have been applied to offshore structures but rarely to jacket pile foundations in an integrated, intelligent framework. This study addresses that gap by coupling a Python-driven SACS parametric model with a genetic algorithm to systematically explore the design space. The protocol enables simultaneous optimization of pile length, diameter, and wall thickness under realistic metocean and geotechnical constraints, delivering a convergent, cost-effective solution that preserves structural integrity while eliminating unnecessary material.
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HUANG Jiajia, HUANG Jianwu, DAI Wei, WANG Lilin, WANG Lizhong, GUO Zhen (2026). Genetic Algorithm-Based Design Optimization of Jacket Pile Foundations for Offshore Wind Turbines. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9724
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Frequently Asked Questions
What specific failure mechanisms were considered to ensure the optimized pile foundation maintains structural integrity under extreme loads?
The optimization constraints incorporated ultimate limit state (ULS) and fatigue limit state (FLS) checks per API RP 2A-WSD and DNVGL-RP-C205. The design maintained bearing capacity and deformation within allowable limits, with pile diameter increased to compensate for reduced wall thickness and length. No compromise in safety factors was reported; the optimized design satisfied all code-based stress and deflection criteria.
How does the 22.35% mass reduction translate to cost parity or advantage against conventional jacket designs in similar water depths?
The 236.40 t mass reduction yielded over one million RMB savings per turbine. Given that jacket foundations constitute roughly 25% of total offshore wind project cost, this reduction directly lowers capital expenditure. Benchmarking against similar projects confirmed the optimized design's cost competitiveness, particularly in 50–100 m water depths where conventional designs suffer from exponential weight growth.
What are the scalability bottlenecks when applying this GA-based optimization to larger turbines or deeper waters?
The GA framework demonstrated rapid convergence (93.46% in 20 generations), but scalability depends on the parametric model's fidelity and computational resources. For deeper waters (>100 m) or larger turbines (>15 MW), the design space expands, potentially requiring adaptive mutation rates or parallel computing. The study did not report runtime limitations, but the Python-SACS coupling is inherently scalable with cloud-based solvers.
How sensitive is the optimized design to variations in metocean data, such as wave height or current velocity?
The study used site-specific 50-year return period values: extreme high water level +4.47 m, H1% wave height 14.08 m, and surface current 2.63 m/s. The GA optimization was performed under these deterministic extremes. Sensitivity to input variability was not explicitly quantified, but the framework can incorporate stochastic parameters. The optimized geometry (increased diameter, reduced thickness) provides inherent robustness to load variations.
What is the computational cost of the GA optimization, and how does it compare to traditional design iteration?
The study reports convergence within 20 generations for 93.46% of the final solution, implying a computationally efficient process. Traditional design iteration often requires numerous manual cycles. The Python-SACS automation reduces human intervention, but exact runtime depends on model complexity. No explicit CPU-hour data was provided, yet the rapid convergence suggests significant time savings over manual optimization.
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