Abstract
This study presents a data-driven model to predict mean turbulence intensities at desired generic locations, for all wind directions. The model, a multilayer perceptron, requires only information about the local topography and a historical dataset of wind measurements and topography at other locations. Five years of data from six different wind measurement mast locations were used. A k-fold cross-validation evaluated the model at each location, where four locations were used for the training data, another location was used for validation, and the remaining one to test the model. The model outperformed the approach given in the European standard, for both performance metrics used. The results of different hyperparameter optimizations are presented, allowing for uncertainty estimates of the model performances.
Highlights
Wind turbulence, in the atmospheric boundary layer, is an important phenomenon in the design of civil structures for both static and dynamic wind loads, and for the safe operation of transport vehicles
Cheynet et al [5] showed a high heterogeneity of wind turbulence in a fjord with the wind direction, which can significantly impact the design of wind-sensitive bridges and other man-made structures
If there is enough diversity in the topography of the available measurement locations and sufficient wind data is available, it is in principle possible to use machine learning to learn the complex effects that the upstream topography has on the wind turbulence
Summary
In the atmospheric boundary layer, is an important phenomenon in the design of civil structures for both static and dynamic wind loads, and for the safe operation of transport vehicles. It arises from both mechanical and thermal sources. Cheynet et al [5] showed a high heterogeneity of wind turbulence in a fjord with the wind direction, which can significantly impact the design of wind-sensitive bridges and other man-made structures In these situations, wind measurements, when available, are often only found at nearby locations. If there is enough diversity in the topography of the available measurement locations and sufficient wind data is available, it is in principle possible to use machine learning to learn the complex effects that the upstream topography has on the wind turbulence
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
