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1. Wind load impact on tall building facades: damage observations during severe wind events and wind tunnel testing

   https://doi.org/10.3389/fbuil.2024.1514523  

   Metwally, Omar; Ibrahim, Haitham A; Elawady, Amal; Zisis, Ioannis; Chowdhury, Arindam Gan (February 2025, Frontiers in Built Environment)

   As global urbanization accelerates, the construction of tall buildings has surged, becoming a defining feature of modern cityscapes. Tall buildings, while contributing to economic growth and urban development, face substantial risks from extreme wind events, such as hurricanes and downbursts. This study provides a comprehensive evaluation of the performance of tall building facades under severe wind conditions, with a focus on recent events that impacted the Gulf Coast of the United States, specifically in Houston, during May to July 2024, including a powerful derecho and Hurricane Beryl. Through extensive damage assessments of various tall buildings, this research highlights the different damages observed from these wind events, revealing critical vulnerabilities in tall building façades, particularly in relation to wind channeling effects in densely built urban areas. The observed damage patterns, including extensive glass breakage and façade failures, underscore the need for a reassessment of wind effects on tall buildings to better reflect the complex interactions between wind forces and urban environments. Additionally, by integrating real-world damage observations with wind tunnel simulations carried out at the NSF NHERI Wall of Wind Experimental Facility, this research offers valuable insights into the factors that may have influenced the observed damage. In this wind tunnel testing campaign, a series of aerodynamic testing of a tall building model under both atmospheric boundary layer and downburst winds were conducted. Additionally, interference effects are tested for both types of events. The preliminary findings have shown that downburst winds can have higher negative pressures compared to atmospheric boundary layer (ABL) which needs to be further studied including several downburst events to characterize the difference between both types of winds. Also, the results indicated the need to conduct a detailed interference study to compare ABL and downburst to properly include these effects for dense urban areas. 

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2. Uncertainty Quantification and Simulation of Wind-Tunnel-Informed Stochastic Wind Loads

   https://doi.org/10.3390/wind3030022  

   Duarte, Thays G_A; Arunachalam, Srinivasan; Subgranon, Arthriya; Spence, Seymour M_J (September 2023, Wind)

   The simulation of stochastic wind loads is necessary for many applications in wind engineering. The proper-orthogonal-decomposition-(POD)-based spectral representation method is a popular approach used for this purpose, due to its computational efficiency. For general wind directions and building configurations, the data-informed POD-based stochastic model is an alternative that uses wind-tunnel-smoothed auto- and cross-spectral density as input, to calibrate the eigenvalues and eigenvectors of the target load process. Even though this method is straightforward and presents advantages, compared to using empirical target auto- and cross-spectral density, the limitations and errors associated with this model have not been investigated. To this end, an extensive experimental study on a rectangular building model considering multiple wind directions and configurations was conducted, to allow the quantification of uncertainty related to the use of short-duration wind tunnel records for calibration and validation of the data-informed POD-based stochastic model. The results demonstrate that the data-informed model can efficiently simulate stochastic wind loads with negligible model errors, while the errors associated with calibration to short-duration wind tunnel data can be important. 

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3. Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

   https://doi.org/10.1115/POWER2020-16557  

   Choe, Do-Eun; Talor, Gary; Kim, Changkyu (January 2020, ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering)

   null (Ed.)

   Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size. 

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4. Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning.

   Choe, Do-Eun; Talor, Gary; Kim, Changkyu (October 2022, roceedings of the ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering. ASME 2020 Power Conference)

   null (Ed.)

   Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size. 

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5. The near-Sun streamer belt solar wind: turbulence and solar wind acceleration

   https://doi.org/10.1051/0004-6361/202039872  

   Chen, C. H.; Chandran, B. D.; Woodham, L. D.; Jones, S. I.; Perez, J. C.; Bourouaine, S.; Bowen, T. A.; Klein, K. G.; Moncuquet, M.; Kasper, J. C.; et al (June 2021, Astronomy & Astrophysics)

   The fourth orbit of Parker Solar Probe (PSP) reached heliocentric distances down to 27.9 R ⊙ , allowing solar wind turbulence and acceleration mechanisms to be studied in situ closer to the Sun than previously possible. The turbulence properties were found to be significantly different in the inbound and outbound portions of PSP’s fourth solar encounter, which was likely due to the proximity to the heliospheric current sheet (HCS) in the outbound period. Near the HCS, in the streamer belt wind, the turbulence was found to have lower amplitudes, higher magnetic compressibility, a steeper magnetic field spectrum (with a spectral index close to –5/3 rather than –3/2), a lower Alfvénicity, and a ‘1∕ f ’ break at much lower frequencies. These are also features of slow wind at 1 au, suggesting the near-Sun streamer belt wind to be the prototypical slow solar wind. The transition in properties occurs at a predicted angular distance of ≈4° from the HCS, suggesting ≈8° as the full-width of the streamer belt wind at these distances. While the majority of the Alfvénic turbulence energy fluxes measured by PSP are consistent with those required for reflection-driven turbulence models of solar wind acceleration, the fluxes in the streamer belt are significantly lower than the model predictions, suggesting that additional mechanisms are necessary to explain the acceleration of the streamer belt solar wind. 

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