A large porous wind fence enclosure has been built and tested to optimize wind noise reduction at infrasonic frequencies between 0.01 and 10 Hz to develop a technology that is simple and cost effective and improves upon the limitations of spatial filter arrays for detecting nuclear explosions, wind turbine infrasound, and other sources of infrasound. Wind noise is reduced by minimizing the sum of the wind noise generated by the turbulence and velocity gradients inside the fence and by the area-averaging the decorrelated pressure fluctuations generated at the surface of the fence. The effects of varying the enclosure porosity, top condition, bottom gap, height, and diameter and adding a secondary windscreen were investigated. The wind fence enclosure achieved best reductions when the surface porosity was between 40% and 55% and was supplemented by a secondary windscreen. The most effective wind fence enclosure tested in this study achieved wind noise reductions of 20-27 dB over the 2-4 Hz frequency band, a minimum of 5 dB noise reduction for frequencies from 0.1 to 20 Hz, constant 3-6 dB noise reduction for frequencies with turbulence wavelengths larger than the fence, and sufficient wind noise reduction at high wind speeds (3-6 m/s) to detect microbaroms.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1121/1.4908568 | DOI Listing |
Sci Rep
January 2025
Department of Electrical Engineering, College of Engineering, Taif University, Taif, 21944, Saudi Arabia.
This paper presents a novel approach to modeling and controlling a solar photovoltaic conversion system(SPCS) that operates under real-time weather conditions. The primary contribution is the introduction of an uncertain model, which has not been published before, simulating the SPCS's actual functioning. The proposed robust control strategy involves two stages: first, modifying the standard Perturb and Observe (P&O) algorithm to generate an optimal reference voltage using real-time measurements of temperature, solar irradiance, and wind speed.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Competence Center for Renewable Energies and Energy Efficiency, Hamburg University of Applied Sciences, Hamburg, Germany.
With the increasing height and rotor diameter of wind turbines, bat activity monitoring within the risk area becomes more challenging. This study investigates the impact of Unmanned Aerial Systems (UAS) on bat activity and explores acoustic bat detection via UAS as a new data collection method in the vicinity of wind turbines. We tested two types of UAS, a multicopter and a Lighter Than Air (LTA) UAS, to understand how they may affect acoustically recorded and analyzed bat activity level for three echolocation groups: Pipistrelloid, Myotini, and Nyctaloid.
View Article and Find Full Text PDFIntegr Zool
January 2025
Animal Behaviour Group, Department of Environment and Genetics, La Trobe University, Melbourne, Victoria, Australia.
Animal signals are complex, comprising multiple components influenced by ecological factors and viewing perspectives that together impact their overall effectiveness. Our study explores how these factors affect the efficacy of multi-component signals in the Qinghai toad-headed agama, Phrynocephalus vlangalii. Using 3D animations, we simulated natural environments to evaluate how tail coiling and tail lashing-two primary tail displays-vary in effectiveness from both conspecific and predator perspectives under different ecological conditions.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Health Canada, Consumer and Clinical Radiation Protection Bureau, Non-Ionizing Radiation Health Sciences Division, Ottawa, Ontario K1A 1C1, Canada.
The World Health Organization Environmental Noise Guidelines provide source-based nighttime sound level (Lnight) recommendations. For non-aircraft sources, the recommended Lnight is where the absolute prevalence of high sleep disturbance (HSD) equals 3%. The Guideline Development Group did not provide an Lnight for wind turbines due to inadequate data.
View Article and Find Full Text PDFThis study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!