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Influence of rotor solidity on trailing edge noise from wind turbine blades

Advances in Aerodynamics 2020年第1期2卷 301-320页

作者： asishta bhargava nukala Satya Prasad Maddula Department of Mechanical Engineering Sreyas Institute of Engineering and TechnologyNagoleHyderabadTelanganaIndia Department of Aerospace Engineering GITAM School of TechnologyPatancheruTelanganaIndia

Noise prediction from streamlined bodies such as wind turbine blades can be predicted accurately using CFD computations that use spatio-temporal turbulence models at the expense of high computational *** this work,empirical methods proposed from BPM,Grosveld and Lowson are used to compute numerically to analyse the influence of rotor solidity factor on broadband trailing edge noise from a 2 MW horizontal axis wind turbine with a blade length of 37 *** to acoustic solver are the velocity vector field and boundary layer data which are obtained using blade element momentum and *** outputs from acoustic solver are directivity and far field sound pressure on a receiver located at distance of 120m from tower *** results have shown that for a wind speed of 10 m/s measured at 10m above ground,sound power level was found to increase between mid-bands to high frequencies for all three *** solidity effect was illustrated at constant rotational speed of 17 RPM and receiver height of 0.5m located in downwind position.A minimum difference of 1.5dBA was found at f~100 Hz for Lowson method and maximum of~2.8dBA at 1 kHz between two and three blade *** BPM and Grosveld methods however,the sound levels were~5dBA lower for two blade rotor than three blade rotor between f~100 Hz and f~1 *** study also demonstrated that as number of blades increase by integral multiples,the effect on noise radiation from trailing edge of blades increase by 2-5dBA due to amplitude modulation.

关键词： Sound power Boundary layer Wind turbine Blade Trailing edge

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Computational Verification of Low-Frequency Broadband Noise from Wind Turbine Blades Using Semi-Empirical Methods

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Sound & Vibration 2024年第1期58卷 133-150页

作者： Vasishta bhargava nukala Chinmaya Prasad Padhy Department of Mechanical Engineering School of TechnologyGITAM(Deemed to be University)Hyderabad502319India

A significant aerodynamic noise from wind turbines arises when the rotating blades interact with turbulent *** the trailing edge of the blade is an important source of noise at high frequencies,the present work deals with the influence of turbulence distortion on leading edge noise from wind turbine blades which becomes significant in low-frequency *** quasi-empirical methods are studied to verify the accuracy of turbulent inflow noise predicted at low frequencies for a 2 MW horizontal axis wind *** have shown that all methods exhibited a downward linear trend in noise spectra for a given mean wind speed except at very low *** an increase in turbulence intensity from 6%to 14%,the sound power was found to increase almost linearly,and the standard error for sound power was reduced for all methods *** computed results were validated and agreed well with experiment noise data from Siemens SWT-2.3MW 93 wind turbine.

关键词： Noise radiation sound pressure turbulence intensity wind turbine blade

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An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms

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Open Journal of Applied Biosensor 2014年第4期3卷 29-39页

作者： bhargava Teja nukala Naohiro Shibuya Amanda Rodriguez Jerry Tsay Jerry Lopez Tam Nguyen Steven Zupancic Donald Yu-Chun Lie Texas Tech University (TTU) Lubbock USA (On Leave) The Department Electrical & Electronic Engineering Tokushima University Tokushima Japan Texas Tech University Health Sciences Center (TTUHSC) Lubbock USA

In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.

关键词： Artificial Neural Network (ANN) Back Propagation Fall Detection Fall Prevention Gait Analysis Sensor Support Vector Machine (SVM) Wireless Sensor

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