Advances in Wind Energy in the Era of Artificial Intelligence (CISM International Centre for Mechanical Sciences) / Baniotopoulos, Charalampos (EDT)/ Marino, Enzo (EDT)

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Advances in Wind Energy in the Era of Artificial Intelligence (CISM International Centre for Mechanical Sciences)

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Advances in Wind Energy in the Era of Artificial Intelligence (CISM International Centre for Mechanical Sciences)

Baniotopoulos, Charalampos (EDT)/ Marino, Enzo (EDT)

ウェブストア価格 ¥50,983（本体¥46,349）
Springer, Berlin; Springer（2026/08発売）
外貨定価 EUR 181.89
ポイント 463pt

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製本 Hardcover:ハードカバー版
商品コード 9783032288141

Full Description

This book explores current trends in the use of artificial intelligence to advance wind energy. Alongside fundamental concepts of wind dynamics and energy generation, it presents emerging technologies such as LiDAR for assessing aeolian potential, as well as Machine Learning and Digital Twin approaches applied to the operation and maintenance of wind energy systems. Recent advances in improving the accuracy of wind resource assessment and wind flow characterization are also discussed.

This book further examines how knowledge of offshore wind structure dynamics supports the development of data‑driven predictive models. In particular, it highlights advances in the design and optimized maintenance of wind energy converters enabled by Machine Learning. Today, timely prediction of system response and performance—based on high‑quality monitoring and inspection data—is a key game changer. Digital Twin concepts are therefore employed to bridge the gap between numerical models and physical assets, integrating measurements that are difficult to obtain using traditional tools. From this perspective, AI‑based Digital Twin prototypes offer a promising solution to optimize and control wind energy systems by integrating monitoring, inspection, and Machine Learning data, providing new insights into the condition of wind energy infrastructure.

Current Trends on the Use of AI to Advance Wind Energy Infrastructures.- Integrating Machine Learning with Computational Fluid Dynamics for Wind Energy Applications From Turbulence Fundamentals to Intelligent Simulation.- Dynamics of offshore wind energy systems fundamentals and data driven methods.- Digital Twins and Data Driven Methods for Intelligent Operation and Interceptive Maintenance of Wind Energy Structures.- Evolution and State of the art of Intelligent Vibration Control for Wind Turbine Structures.- Utilization of measurement data for wind energy structures.- Knowledge Enhanced Machine Learning in Wind Engineering.