Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches
In modern wind turbine design, two significant challenges arise: achieving optimal aerodynamic performance while minimizing acoustic noise emissions. However, the extensive numerical computations required for accurate evaluation often hinder the implementation of multi-objective optimization strateg...
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Sharif University of Technology
2024-12-01
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| Series: | مهندسی مکانیک شریف |
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| Online Access: | https://sjme.journals.sharif.edu/article_23603_2a92273aed1ff9044099ea9d921c7c0a.pdf |
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| author | M. Sadeghi malekabadi A.R. Davari |
| author_facet | M. Sadeghi malekabadi A.R. Davari |
| author_sort | M. Sadeghi malekabadi |
| collection | DOAJ |
| description | In modern wind turbine design, two significant challenges arise: achieving optimal aerodynamic performance while minimizing acoustic noise emissions. However, the extensive numerical computations required for accurate evaluation often hinder the implementation of multi-objective optimization strategies. This paper introduces an innovative approach to address this issue, leveraging a combination of neural network-based reduced order modeling and a multi-objective genetic algorithm. This methodology aims to optimize the aerodynamic and aero-acoustic characteristics of an S8xx-series airfoil, including the trailing edge serration geometry. Utilizing Class-Shape Transformation to parameterize various serrated airfoil geometries, the method minimizes the need for costly computational fluid dynamics (CFD) simulations. Instead, a feed-forward neural network (NN) is trained with a minimal dataset to predict airfoil behavior within a specified range. Comparisons between CFD results and NN predictions validate the accuracy of the neural network. Significantly, this approach substantially reduces optimization time by approximately 95%, maintaining high levels of accuracy. In conducting multi-objective optimization for both the airfoil and serration shapes, the study demonstrates notable improvements: a 5 to 7% enhancement in aerodynamic performance alongside a simultaneous 1-4% reduction in noise compared to benchmark airfoils. Then, in the second step, experimental methodology is employed to investigate the aeroacoustic attributes of a small horizontal-axis wind turbine with optimized blades. Conducted within a semi-anechoic chamber, this investigation meticulously positions both original and optimized geometry models to measure sound pressure levels (SPL) across various rotational speeds and positions. The results reveal subtle enhancements in aerodynamic performance with the optimized serrated blade configuration, accompanied by a remarkable reduction in noise levels across the frequency spectrum, culminating in an impressive overall sound pressure reduction of approximately 10 dB. Additionally, intriguing observations highlight the impact of turbine rotational speed on noise production, particularly in the downstream domain. Notably, the noise emission reduction for the serrated optimized blade is more dispersed in the plane of rotation compared to the original blade, which exhibited nearly uniform noise distribution. Overall, these findings offer valuable insights into the intricate interplay between aerodynamics and aeroacoustics in the context of small wind turbines with optimized blades. |
| format | Article |
| id | doaj-art-3ec88fd91fd5440eaee6276af46e2d86 |
| institution | DOAJ |
| issn | 2676-4725 2676-4733 |
| language | fas |
| publishDate | 2024-12-01 |
| publisher | Sharif University of Technology |
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| series | مهندسی مکانیک شریف |
| spelling | doaj-art-3ec88fd91fd5440eaee6276af46e2d862025-08-20T03:12:40ZfasSharif University of Technologyمهندسی مکانیک شریف2676-47252676-47332024-12-01402526310.24200/j40.2024.64121.170423603Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approachesM. Sadeghi malekabadi0A.R. Davari1Department of Aerospace Engineering Science and Research Branch Islamic Azad UniversityDepartment of Aerospace Engineering Science and Research Branch Islamic Azad UniversityIn modern wind turbine design, two significant challenges arise: achieving optimal aerodynamic performance while minimizing acoustic noise emissions. However, the extensive numerical computations required for accurate evaluation often hinder the implementation of multi-objective optimization strategies. This paper introduces an innovative approach to address this issue, leveraging a combination of neural network-based reduced order modeling and a multi-objective genetic algorithm. This methodology aims to optimize the aerodynamic and aero-acoustic characteristics of an S8xx-series airfoil, including the trailing edge serration geometry. Utilizing Class-Shape Transformation to parameterize various serrated airfoil geometries, the method minimizes the need for costly computational fluid dynamics (CFD) simulations. Instead, a feed-forward neural network (NN) is trained with a minimal dataset to predict airfoil behavior within a specified range. Comparisons between CFD results and NN predictions validate the accuracy of the neural network. Significantly, this approach substantially reduces optimization time by approximately 95%, maintaining high levels of accuracy. In conducting multi-objective optimization for both the airfoil and serration shapes, the study demonstrates notable improvements: a 5 to 7% enhancement in aerodynamic performance alongside a simultaneous 1-4% reduction in noise compared to benchmark airfoils. Then, in the second step, experimental methodology is employed to investigate the aeroacoustic attributes of a small horizontal-axis wind turbine with optimized blades. Conducted within a semi-anechoic chamber, this investigation meticulously positions both original and optimized geometry models to measure sound pressure levels (SPL) across various rotational speeds and positions. The results reveal subtle enhancements in aerodynamic performance with the optimized serrated blade configuration, accompanied by a remarkable reduction in noise levels across the frequency spectrum, culminating in an impressive overall sound pressure reduction of approximately 10 dB. Additionally, intriguing observations highlight the impact of turbine rotational speed on noise production, particularly in the downstream domain. Notably, the noise emission reduction for the serrated optimized blade is more dispersed in the plane of rotation compared to the original blade, which exhibited nearly uniform noise distribution. Overall, these findings offer valuable insights into the intricate interplay between aerodynamics and aeroacoustics in the context of small wind turbines with optimized blades.https://sjme.journals.sharif.edu/article_23603_2a92273aed1ff9044099ea9d921c7c0a.pdfwind turbineaeroacousticsoptimizationcomputational fluid dynamicslarge eddies simulation |
| spellingShingle | M. Sadeghi malekabadi A.R. Davari Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches مهندسی مکانیک شریف wind turbine aeroacoustics optimization computational fluid dynamics large eddies simulation |
| title | Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches |
| title_full | Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches |
| title_fullStr | Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches |
| title_full_unstemmed | Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches |
| title_short | Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches |
| title_sort | experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches |
| topic | wind turbine aeroacoustics optimization computational fluid dynamics large eddies simulation |
| url | https://sjme.journals.sharif.edu/article_23603_2a92273aed1ff9044099ea9d921c7c0a.pdf |
| work_keys_str_mv | AT msadeghimalekabadi experimentalacousticstudyofsmallhorizontalaxiswindturbinesbasedoncomputationalfluiddynamicsandartificialintelligenceapproaches AT ardavari experimentalacousticstudyofsmallhorizontalaxiswindturbinesbasedoncomputationalfluiddynamicsandartificialintelligenceapproaches |