CFD Analysis of Fouling Effects on Aerodynamics Performance of Turbine Blades

Baheta, Aklilu Tesfamichael; Leong, K. P.; Sulaiman, Shaharin Anwar; Fentaye, Amare Desalegn

doi:10.1007/978-981-13-2357-7_5

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Ageing equipment suffers from unrecoverable performance loss, which cannot be corrected by maintenance. It is caused by operational or environmental factors such as wear of gears [14], fouling of the blades [15] or upper section peeling from high-velocity particle impacts [16,17]. It is estimated that wind turbines experience yearly unrecoverable performance loss [18], leading to capacity factor (CF) reductions of 21-28.5% after 19 years of operation, which may increase the levelized electricity cost up to 9% [12].…”

Section: Introductionmentioning

confidence: 99%

Higher Wind: Highlighted Expansion Opportunities to Repower Wind Energy

Haces-Fernandez

2021

Energies

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Decarbonizing the world economy, before the most damaging effects of climate change become irreversible, requires substantially increasing renewable energy generation in the near future. However, this may be challenging in mature wind energy markets, where many advantageous wind locations are already engaged by older wind farms, potentially generating suboptimal wind harvesting. This research developed a novel method to systematically analyze diverse factors to determine the level of maturity of wind markets and evaluate the adequacy of wind farm repowering at regional and individual levels. The approach was applied to wind markets in the United States (U.S.), in which several states were identified as having diverse levels of maturity. Results obtained from case studies in Texas indicated a consequential number of wind farms that have reached their twenty-year end-of-life term and earlier obsolescence levels. The proposed approach aided in determining wind farms that may benefit from total or partial repowering. The method indicated that total repowering of selected installations would significantly increase overall wind energy generation, considering that these older installations have access to some of the best wind speeds, infrastructure and areas to grow. The proposed method can be applied to different world wind markets.

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Section: Introductionmentioning

confidence: 99%

Higher Wind: Highlighted Expansion Opportunities to Repower Wind Energy

Haces-Fernandez

2021

Energies

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Turbine Blade Temperature Field Prediction Using the Numerical Methods

2021

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Nowadays, material science and stress characteristics are crucial in the field of jet engines. There are methods for fatigue life, stress, and temperature prediction; however, the conventional methods are ineffective and time-consuming. The article is devoted to the research in the field of application of the numerical methods in order to develop an innovative methodology for the temperature fields prediction based on the integration of the finite element methods and artificial neural networks, which leads to the creation of the novel methodology for the temperature field prediction. The proposed methodology was applied to the temperature field prediction on the surface blades of the experimental iSTC-21v jet engine turbine. The results confirmed the correctness of the new methodology, which is able to predict temperatures at the specific points on the surface of a turbine blade immediately. Moreover, the proposed methodology is able to predict temperatures at specific points on the turbine blade during the engine runs, even for the multiple operational regimes of the jet engine. Thanks to this new unique methodology, it is possible to increase the reliability and lifetime of turbines and hot parts of any jet engine and to reduce not only the maintenance but also the research and development costs due to the significantly lower time demands. The main advantage is to predict temperature fields much faster in comparison to the methods available today (computational fluid dynamics (CFD), etc.), and the major aim of the proposed article is to predict temperatures using a neural network. Apart from the above-mentioned advantages, the article's main purpose is devoted to the artificial neural networks, which have been until now used for many applications, but in our case, the neural network was for the first time applied for the temperature field prediction on the turbine blade.

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CFD Analysis of Fouling Effects on Aerodynamics Performance of Turbine Blades

Cited by 2 publications

References 9 publications

Higher Wind: Highlighted Expansion Opportunities to Repower Wind Energy

Higher Wind: Highlighted Expansion Opportunities to Repower Wind Energy

Turbine Blade Temperature Field Prediction Using the Numerical Methods

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