The present work employs a statistical-numerical method to
predict and optimize the shape of the serrations for maximum aerodynamic
improvement. A long-term objective for the rotor is to achieve relatively high
aerodynamic performance in particular wind conditions. Inspired by the
remarkable flight characteristics of owls, an optimal trailing edge serration
design is investigated and proposed for a wind turbine rotor blade. Fluid flow
interaction with the proposed serrations is explored for different wind conditions.
A potential solution to improve existing wind turbines is the addition of
flow-control devices to the rotor blades. Flow control devices can effectively
prevent or delay flow separation and suppress turbulence resulting in improved
aerodynamic and aeroacoustics performance, load reduction, fluctuation
suppression, and ultimately increased wind turbine power output. The result is
supported by subsequent validation with three-dimensional numerical tools. The
optimal combination is found using the Taguchi design of experiment with three
factors: Amplitude, wavelength, and serration thickness. The viability of the
solution on an application is assessed using the Weibull distribution of wind
in three selected regions. Results show that the presence of serration is
capable of improving the annual power generation in all the investigated cities
by up to 12%. The rated speed is also shifted from 10 m/s to 8m/s for most
configurations. Additionally, all configurations show similar trends for the
instantaneous torque where an increase is observed in pre-rated speed whereas a
decrease is noticed in the post-rated speed region. A look at the flow field
pattern for the optimal design in comparison with the clean blade shows that
the modified blade is able to generate more lift in the pre-stall region, while
for the post-stall region, early separation and increased wake dominate the
flow. Further studies in this area can cover the mechanical aspect and
aero-acoustic impact of the trailing edge serrations as well as the correlation
between acoustic emission, power generation, and aerodynamic forces in the
improvement of an overall wind turbine performance.
Author(s) Details:
Khaoula Qaissi,
LERMA Lab, School of Aerospace and Automotive Engineering, Faculty
Engineering and Architecture, Université I Rabat, Campus UIR Parc Technopolis
Rocade, Rabat-Sale, Sala Al Jadida 11100, Morocco.
Omer Elsayed,
LERMA Lab,
School of Aerospace and Automotive Engineering, Faculty Engineering and
Architecture, Université I Rabat, Campus UIR Parc Technopolis Rocade,
Rabat-Sale, Sala Al Jadida 11100, Morocco.
Mustapha Faqir,
LERMA Lab, School of Aerospace and Automotive Engineering, Faculty
Engineering and Architecture, Université I Rabat, Campus UIR Parc Technopolis
Rocade, Rabat-Sale, Sala Al Jadida 11100, Morocco.
Elhachmi Essadiqi,
LERMA Lab, School of Aerospace and Automotive Engineering, Faculty
Engineering and Architecture, Université I Rabat, Campus UIR Parc Technopolis
Rocade, Rabat-Sale, Sala Al Jadida 11100, Morocco.
Please see the link here: https://stm.bookpi.org/CPSTR-V5/article/view/13279
Tuesday, 13 February 2024
Taguchi Modified Additive Model for Aerodynamic Optimization of Wind Turbine Blade Trailing-Edge Serrations | Chapter 5 | Contemporary Perspective on Science, Technology and Research Vol. 5
Tuesday, 9 February 2021
A Statistical Analysis and Artificial Neural Network Behavior on Wind Speed Prediction: Case Study | Chapter 4 | Theory and Practices of Mathematics and Computer Science Vol. 6
The quest for renewable and emission-free energy sources has been encouraged by the increased usage of energy and the decline of fossil fuel supplies, along with an increase in environmental pollution. Wind energy is one of these. In the last few years, the wind power industry has seen exponential growth. The increase in wind turbine orders has resulted in a manufacturer's market. This disparity in the market, the relative immaturity of the wind industry and the rapid evolution of data processing technologies have provided opportunities to enhance the efficiency of wind farms and to change the myths surrounding their operations. This study provides data-driven modeling, a new paradigm for the wind power industry. For several parameters, each wind mast produces extensive data, recorded as frequently as every minute. Since the predictive performance approach is new to the wind industry, it is important to build a viable road map for study. This paper proposes a long-term wind forecasting (ANN) predictive analysis and data-mining approach, which is ideal for dealing with broad real-world datasets. The paper provides a case study focused on a real database of five years of wind speed data for a location and addresses wind power density results calculated using the probability density functions of Weibull and Rayleigh. Wind speed predicted using wind speed data using intelligent technologies such as Artificial Neural Networks with Datamining methodology (ANN). The MATLAB R2008a Neural Network Toolbox is designed to measure the monthly and annual mean wind speed for the training of the ANN back propagation algorithm and the PROLOG software. The statistical analysis of wind speed prediction shows that the distribution of Weibull is more acceptable than the distribution of Rayleigh and we can infer that higher values of k mean a sharper limit in the frequency distribution curve and thus a lower density of wind power by seeing the values of k.
Aurthor(s) Details:
K. Mahesh
Department of
Electrical and Electronics Engineering Sir M Visvesvaraya Institute of
Technology, Bengaluru, India.