A framework of data assimilation for wind flow fields by physics-informed neural networks-Reference-Cited by-同舟云学术

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A framework of data assimilation for wind flow fields by physics-informed neural networks

Published:2024-10 Issue: Volume:371 Page:123719
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Yan Chang^ORCID,Xu Shengfeng^ORCID,Sun Zhenxu^ORCID,Lutz Thorsten,Guo Dilong,Yang Guowei

Publisher

Elsevier BV

Reference46 articles.

1. Concurrent deficit and surplus situations in the future renewable Swiss and European electricity system;Lienhard;Energy Strategy Rev,2023

2. Load response of a floating wind turbine to turbulent atmospheric flow;Doubrawa;Appl Energy,2019

3. Integration of atmospheric stability in wind resource assessment through multi-scale coupling method;Jin;Appl Energy,2023

4. Lidar for turbine control;Harris,2006

5. Investigation of wind turbine performance coupling wake and topography effects based on LiDAR measurements and SCADA data;Gao;Appl Energy,2019

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