Upscaling is central to offshore wind's cost-reduction strategy, with increasingly large rotors and nacelles requiring taller and stronger towers. In Floating Offshore Wind Turbines (FOWTs), this trend amplifies fatigue loads due to coupled wind-wave dynamics and platform motion. Conventional fatigue evaluation requires millions of high-fidelity simulations, creating prohibitive computational costs and slowing design innovation. This paper presents FLOAT (Fatigue-aware Lightweight Optimization and Analysis for Towers), a framework that accelerates fatigue-aware tower design. It integrates three key contributions: a lightweight fatigue estimation method that enables efficient optimization, a Monte Carlo-based probabilistic wind-wave sampling approach that reduces required simulations, and enhanced high-fidelity modeling through pitch/heave-platform calibration and High-Performance Computing execution. The framework is applied to the IEA 22 MW FOWT tower, delivering, to the authors' knowledge, the first fatigue-oriented redesign of this benchmark model: FLOAT 22 MW FOWT tower. Validation against 6,468 simulations shows that the optimized tower extends the estimated fatigue life from 9 months to 25 years while avoiding resonance, and that the lightweight fatigue estimator provides conservative predictions with a mean relative error of -8.6%. Achieving this lifetime requires increased tower mass, yielding the lowest-mass fatigue-compliant design. All results and the reported lifetime extension are obtained within the considered fatigue scope (DLC 1.2, aligned wind-wave conditions). By reducing simulation requirements by orders of magnitude, FLOAT enables reliable and scalable tower design for next-generation FOWTs, bridging industrial needs and academic research while generating high-fidelity datasets that can support data-driven and AI-assisted design methodologies.

翻译：规模化是海上风电降本策略的核心，日益增大的转子与机舱要求塔架更高更强。对于漂浮式海上风力发电机（FOWT），这一趋势因风浪耦合动力学及平台运动而加剧了疲劳载荷。传统疲劳评估需进行数百万次高保真仿真，导致计算成本过高并阻碍设计创新。本文提出FLOAT（面向塔架的疲劳感知轻量化优化与分析框架），该框架可加速疲劳感知塔架设计。它整合了三个关键贡献：支持高效优化的轻量化疲劳估计方法、通过蒙特卡洛概率风浪采样减少所需仿真的策略，以及通过俯仰/垂荡平台校准与高性能计算执行增强的高保真建模。该框架应用于IEA 22 MW FOWT塔架，据作者所知，首次实现了该基准模型的疲劳导向重新设计：FLOAT 22 MW FOWT塔架。基于6,468次仿真的验证表明，优化后塔架将预估疲劳寿命从9个月延长至25年且避免共振，同时轻量化疲劳估计器提供了保守预测（平均相对误差为-8.6%）。实现此寿命需增加塔架质量，从而得到满足疲劳要求的最低质量设计方案。所有结果及报告的寿命延长均在既定疲劳范畴内获得（DLC 1.2，对齐的风浪条件）。通过将仿真需求降低数个数量级，FLOAT为下一代FOWT实现了可靠且可扩展的塔架设计，弥合了工业需求与学术研究，并生成可支持数据驱动与AI辅助设计方法的高保真数据集。