首页|基于非线性气弹模型的风力机长柔性叶片优化方法

基于非线性气弹模型的风力机长柔性叶片优化方法

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当前大型风力机叶片朝着更长更柔的方向发展,其气动、结构综合性能优化对机组降本增效尤为关键.针对长柔性叶片的非线性特点,采用变步长变形差分法对线性欧拉-伯努利梁模型进行了非线性修正,基于修正的非线性结构模型与叶素动量理论耦合建立了一种兼顾仿真精度与效率的非线性气弹模型.以单机年发电量最大和叶根挥舞弯矩最小为优化目标,采用快速非支配排序遗传算法第二版对DTU 10 MW风力机叶片进行气动-结构一体化优化.通过分析得出非线性气弹模型作为评价函数比线性气弹模型作为评价函数的优化结果综合性能更优,在提升年发电量的基础上,叶根挥舞弯矩均有所降低.
Optimization Method of Wind Turbine Long Flexible Blades Based on Nonlinear Aeroelastic Model
Currently,large wind turbine blades are developing towards greater length and flexibility,making the optimization of their comprehensive aerodynamic and structural performance particularly critical for reducing costs and enhancing efficiency.In this paper,the Euler-Bernoulli beam theory is modified using the variable step deformation difference method for the nonlinear characteristics of long flexible blades.A nonlinear aeroelastic model that considers the simulation accuracy and efficiency is established based on the coupling of the corrected nonlinear structural model and the blade element momentum theory.With the optimization objectives of maximizing the annual power generation of a single machine and minimizing the blade root waving moment,the aerodynamic-structural integration optimization of the DTU 10 MW wind turbine blade was carried out using the non-dominated sorting genetic algorithm-II.The optimization objectives are to maximize annual energy production while minimizing the blade root flapping bending moment.The results indicate that the nonlinear aeroelastic model used as the evaluation function has better overall performance than the linear aeroelastic model used as the evaluation function.The former optimization results can increase the annual energy production and reduce the blade root flapping bending moment.

long flexible bladeoptimized designnonlinearityaeroelastic modelgenetic algorithm

张祯、许波峰、李振、范星星、李奎、汪亚洲

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河海大学 新能源学院,江苏 常州 213200

河海大学苏州研究院,江苏 苏州 215100

浙江省新能源投资集团股份有限公司,杭州 310020

德力佳传动科技(江苏)股份有限公司,江苏 无锡 214105

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长柔性叶片 优化设计 非线性 气弹模型 遗传算法

2024

新能源进展

新能源进展

CSTPCD北大核心
影响因子:0.796
ISSN:
年,卷(期):2024.12(6)