时变腐蚀损伤作用下海上风机结构响应预测方法研究

Prediction method for structural response of offshore wind turbines under time-varying corrosion damage

  • 摘要:
    目的 针对海上风机时变腐蚀效应,提出一种考虑时变腐蚀损伤作用下的海上风机结构响应预测方法。
    方法 构建考虑结构几何损伤与材料性能劣化特性的时变腐蚀等效模型,动态更新结构力学性能状态,结合CFD方法构建涵盖海上风机全服役期的高保真结构响应数据库并对结构时变劣化规律开展研究,阐明海上风机结构响应分布状态随腐蚀时变损伤的非线性关系,并基于插值推演算法增补未计算数据,实现关键结构响应数据的快速准确预测。
    结果 结果表明,所建时变腐蚀等效模型能够反映结构性能劣化,实现海上风机结构腐蚀快速建模;结合CFD技术搭建的结构响应数据库,能够揭示结构响应演化特征;采样策略误差分析显示,密集采样策略应力与变形误差(平均绝对百分比误差MAPE)为0.31%和2.23%,中度采样策略仍具良好精度,而稀疏采样策略误差增至3.31%和8.71%,但结构整体响应演化趋势仍能准确反映。
    结论 所提预测方法能够快速、有效地反映海上风机结构响应特征与腐蚀损伤演化之间的非线性关系,可为海上风电结构设计优化、长期性能评估及运维决策提供技术支撑。

     

    Abstract:
    Objective To address the effects of time-varying corrosion on offshore wind turbines (OWTs), a structural response prediction method incorporating corrosion-induced deterioration is proposed.
    Method A time-varying corrosion equivalent model is developed to account for structural geometric damage and material property degradation, enabling dynamic updating of the structural mechanical performance. Combined with CFD simulations, a high-fidelity structural response database covering the entire service life of OWTs is constructed to investigate the time-varying structural deterioration mechanism. The nonlinear relationship between structural response distributions and time-varying corrosion damage is characterized. Furthermore, an interpolation and deduction algorithm is introduced to supplement uncalculated data, enabling rapid and accurate prediction of key structural response parameters.
    Results The results show that the proposed time-varying corrosion equivalent model can effectively capture the degradation of structural performance and enable rapid corrosion modeling of OWT structures. The structural response database established using CFD simulations can reveal the evolution characteristics of structural responses. Error analysis of different sampling strategies indicates that the dense sampling strategy achieves stress and deformation prediction errors of 0.31% and 2.23%, respectively, while the moderate sampling strategy also maintains satisfactory predictive accuracy. Although the errors associated with the sparse sampling strategy increase to 3.31% and 8.71%, the overall evolution trends of the structural responses are still accurately captured.
    Conclusion The proposed prediction method can quickly and effectively characterized the nonlinear relationship between structural response characteristics and corrosion damage evolution in OWTs. It provides technical support for structural design optimization, long-term performance assessment, and operation and maintenance decision-making of OWT structures.

     

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