Numerical investigation of the effect of spanwise length and mesh density on flow around cylinder at Re = 3900 using LES model

Ali, Haider; Khan, Niaz Bahadur; Jameel, Muhammad; Khan, Azam; Sajid, Muhammad; Munir, Adnan; Ahmed, Awais; Alharbi, Khalid Abdulkhaliq M.; Galal, Ahmed M.

doi:10.1371/journal.pone.0266065

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…However, it has its limitations. One of the primary constraints is computational expense, as LES demands significant computational resources and time, especially for high Reynolds number flows [33,34]. Additionally, the accuracy of LES is highly dependent on the quality of subgrid-scale models employed to represent unresolved scales, and inadequate modeling can lead to erroneous results.…”

Section: Wind Turbine Wake Aerodynamicsmentioning

confidence: 99%

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

Gajendran,

Kabir,

Vadivelu

et al. 2023

JMSE

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As wind energy continues to be a crucial part of sustainable power generation, the need for precise and efficient modeling of wind turbines, especially under yawed conditions, becomes increasingly significant. Addressing this, the current study introduces a machine learning-based symbolic regression approach for elucidating wake dynamics. Utilizing WindSE’s actuator line method (ALM) and Large Eddy Simulation (LES), we model an NREL 5-MW wind turbine under yaw conditions ranging from no yaw to 40 degrees. Leveraging a hold-out validation strategy, the model achieves robust hyper-parameter optimization, resulting in high predictive accuracy. While the model demonstrates remarkable precision in predicting wake deflection and velocity deficit at both the wake center and hub height, it shows a slight deviation at low downstream distances, which is less critical to our focus on large wind farm design. Nonetheless, our approach sets the stage for advancements in academic research and practical applications in the wind energy sector by providing an accurate and computationally efficient tool for wind farm optimization. This study establishes a new standard, filling a significant gap in the literature on the application of machine learning-based wake models for wind turbine yaw wake prediction.

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Section: Wind Turbine Wake Aerodynamicsmentioning

confidence: 99%

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

Gajendran,

Kabir,

Vadivelu

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…The simulation domain is initially based on a two-dimensional mesh of quadrilaterals, as shown in Figure 3, which is then extruded along the spanwise direction in a way that allows for the desired waviness to be incorporated. The dimensions of the domain, i.e., the distances upstream and downstream of the body, the height and the width were based on dimensions previously established as sufficient to avoid blockage, with a blockage ratio (diameter/height) less than 3.4%, and allow the proper development of the wake [47,48].…”

Section: Computational Domain and Baseline Meshmentioning

confidence: 99%

Numerical Investigation of Flow Past Bio-Inspired Wavy Leading-Edge Cylinders

et al. 2022

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A numerical investigation is proposed to explore the flow past a novel wavy circular cylinder as a passive flow control, whose shape is determined by a sinusoidal function applied to its leading edge line, similar to studies with wavy leading-edge airfoils. The latter are motivated by the wavy-shaped tubercles found in the flippers of humpback whales, which are believed to improve their maneuverability. Our attempt is, therefore, to assess the effects of leading-edge waviness now on a simpler and canonical geometry: circular cylinders. The present work relies on iLES simulations conducted with Nektar++ at a Reynolds number of 3900. Besides the straight cylinder, two wavy geometries are assessed, which are determined by a single wavelength of 37.5% for two amplitudes, 3% and 11%, based on the mean diameter of the wavy cylinder. Our results showed that, contrary to what is usually the case with traditional wavy cylinders at similar Reynolds numbers, waviness caused a reduction in the near-wake recirculation length and an increase in the mean near-wake turbulent kinetic energy compared to the straight cylinder. This was followed by a reduction in base pressure (up to about 36%) leading to a rise in lift oscillations and also to a significant increase in the mean drag coefficient of up to about 28%. An attempt to detail the flow phenomena is provided, evidencing the emergence of counter-rotating pairs of streamwise vortices between peaks. It is argued that the differences observed in recirculation length, turbulent kinetic energy, and force coefficients start even prior to the formation of these coherent structures and end up with interactions with the near wake.

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Effect of wall stress models and subgrid-scale models for flow past a cylinder at Reynolds number 3900

Fan,

Liu,

Zhao

et al. 2024

Physics of Fluids

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The wall-modeled large eddy simulation has achieved some success in simulating wall-bounded flows. However, their predictive accuracy in separated flow still requires further validation. In this study, we employ the open-source computational fluid dynamics toolbox OpenFOAM to numerically investigate the flow past a cylinder at subcritical Reynolds numbers ReD=3900. At two different sampling heights, h = 2nd and h = 4th, three wall stress models: the algebraic model, the equilibrium wall model (EQWM), and the non-EQWM (NEQWM), and five subgrid-scale (SGS) models: the Smagorinsky (SMAG) model, the k-equation SGS (KSGS) model, the wall-adapting local eddy viscosity (WALE) model, the dynamic SMAG (DSMAG) model, and the dynamic KSGS (DKSGS) model, are selected for comparative study. Various physical quantities, including statistical flow quantities, wall pressures, time-averaged wake velocity profiles, and Reynolds stresses, are extracted and compared with the experimental data. Power spectral analyses for wake velocity are conducted, and the three-dimensional vortex structures are illustrated. The results indicate that for small sampling height, all wall models yield favorable numerical simulation results. However, for larger sampling height, the NEQWM is preferred over the other two wall models. In terms of SGS models, the DKSGS model and WALE model perform better than other SGS models. The SMAG and KSGS models, due to inherent model limitations, struggle to accurately predict the flow separation angle and the Reynolds stresses in the free shear layer.

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Numerical investigation of the effect of spanwise length and mesh density on flow around cylinder at Re = 3900 using LES model

Cited by 5 publications

References 46 publications

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

Numerical Investigation of Flow Past Bio-Inspired Wavy Leading-Edge Cylinders

Effect of wall stress models and subgrid-scale models for flow past a cylinder at Reynolds number 3900

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