Application progress of artificial intelligence technology in new-generation wind power aerodynamics

Xu Bofeng; Chen Wangyinhao; Lyu Pin; Chen Peng; Han Xingxing; Wang Long; Wang Tongguang

doi:10.7638/kqdlxxb-2026.0057

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Application progress of artificial intelligence technology in new-generation wind power aerodynamics

更新时间：2026-07-11

- Application progress of artificial intelligence technology in new-generation wind power aerodynamics
- Vol. 44, Issue 6, Pages: 63-76(2026)
- 作者机构：
  
  1. 河海大学新能源学院,常州,213200
  2. 河海大学国家风力发电技术创新中心,常州,213200
  3. 金风科技股份有限公司,北京,100176
  4. 上海交通大学海洋工程全国重点实验室,上海,200240
  5. 南京航空航天大学航空学院,南京,210016
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2026.0057
  CLC： TK83
- Published：2026
- 稿件说明：
移动端阅览
许波峰, 陈王胤豪, 吕品, 等. 人工智能技术在新一代风电空气动力学领域的应用进展[J]. 空气动力学学报, 2026, 44(6): 63−76.

Xu Bofeng, Chen Wangyinhao, Lyu Pin, et al. Application progress of artificial intelligence technology in new-generation wind power aerodynamics[J]. 2026, 44(6): 63-76.
许波峰, 陈王胤豪, 吕品, 等. 人工智能技术在新一代风电空气动力学领域的应用进展[J]. 空气动力学学报, 2026, 44(6): 63−76. DOI： 10.7638/kqdlxxb-2026.0057

Xu Bofeng, Chen Wangyinhao, Lyu Pin, et al. Application progress of artificial intelligence technology in new-generation wind power aerodynamics[J]. 2026, 44(6): 63-76. DOI： 10.7638/kqdlxxb-2026.0057.

摘要

新一代风电技术正加速向大型化、柔性化、智能化与协同化方向演进，传统空气动力学研究方法在精度、效率与适应性方面面临严峻挑战。人工智能（artificial intelligence

AI）技术因其强大的高维非线性映射与数据驱动建模能力，正逐步重塑风电领域的研究路径。本文系统综述了AI在风力机叶片气动性能预测、气动外形优化设计、整机气弹建模、海上风电多物理场耦合、风电场流场建模与协同控制等方面的应用研究进展。结果表明，AI在提升计算效率、融合多源异构数据、构建高保真代理模型及实现自适应控制等方面展现出显著优势，正推动风电空气动力学研究从传统“经验/数值驱动”向“数据-物理协同驱动”转变。然而，当前AI应用仍面临高质量训练数据稀缺、模型跨工况泛化能力不足、实时性与计算复杂度难以兼顾，以及在复杂物理场景中缺乏物理一致性约束等问题。未来亟需突破纯数据驱动范式，向物理可解释、多场耦合方向发展，尤其需在海上风电与大型风电基地集群协同等复杂系统中，构建以“AI-物理协同”为特征的新一代风电多学科研究范式，为风电系统的高效、安全与智能化发展提供理论支撑与方法论基础。本综述可为AI技术在风电空气动力学中的深入研究或应用提供参考切入点。

Abstract

The new-generation wind power technology is rapidly evolving toward larger scale

greater flexibility

higher intelligence

and enhanced coordination. Traditional aerodynamic research methods are facing significant challenges in accuracy

efficiency

and adaptability. Artificial intelligence (AI)

with its powerful capability in high-dimensional nonlinear mapping and data-driven modeling

is progressively reshaping the research landscape of wind power aerodynamics. This paper provides a comprehensive review of AI applications in several key areas

including blade aerodynamic performance prediction

aerodynamic shape optimization

aeroelastic modeling of the entire turbine

multi-physics coupling in offshore wind power

and wind farm flow under field modeling with cooperative control. The results indicate that AI offers notable advantages in improving computational efficiency

integrating multi-source heterogeneous data

constructing high-fidelity surrogate models

and enabling adaptive control strategies. These advances are driving a paradigm shift in wind power aerodynamics from traditional experience-based or numerically driven approaches toward a data-physics collaborative framework. Nevertheless

current AI applications still face several common bottlenecks. These include the scarcity of high-quality training data

insufficient generalization capability across varying operational conditions

the inherent trade-off between real-time performance and model complexity

and a lack of physical consistency constraints in complex flow scenarios. Future research should move beyond purely data-driven paradigms and shift toward physically interpretable and multi-field coupled frameworks. In particular

for complex systems such as offshore wind power and large-scale wind farm cluster coordination

it is essential to establish a new-generation multidisciplinary research paradigm characterized by "AI-physics synergy". Such a paradigm would provide theoretical support and a methodological foundation for the efficient

safe

and intelligent development of wind power systems. This review aims to serve as a reference for further in-depth studies or practical applications of AI technologies in wind power aerodynamics.

关键词

Keywords

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