Analysis of the high Reynolds number 2D tests on a wind turbine airfoil performed at two different wind tunnels

Pires, Oscar; Munduate, Xabier; Ceyhan, O.; Jacobs, Markus; Madsen, Jesper; Schepers, J.G.

doi:10.1088/1742-6596/749/1/012014

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…As the focus of this work is on the prediction and training behavior of the proposed CNN-DCNN model, rather than accuracy of the CFD method, we assume that results from the square grid with a size of 40 c × 40 c are acceptable for further training and testing of the CNN-DCNN model. Additionally, the flow still has laminar to turbulent transition at Reynolds number of millions of orders of magnitude [49]. Nevertheless, it should be emphasized that the final collected flow field data is under steady flow status.…”

Section: Train and Evaluation Methodsmentioning

confidence: 99%

Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network

Yuan

et al. 2022

Applied Sciences

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We propose a steady-state aerodynamic data-driven method to predict the incompressible flow around airfoils of NACA (National Advisory Committee for Aeronautics) 0012-series. Using the Signed Distance Function (SDF) to parameterize the geometric and flow condition setups, the prediction core of the method is constructed essentially by a consecutive framework of a convolutional neural network (CNN) and a deconvolutional neural network (DCNN). Impact of training parameters on the behavior of the proposed CNN-DCNN model is studied, so that appropriate learning rate, mini-batch size, and random deactivation rate are specified. Tested by “unseen” airfoil geometries and far-field velocities, it is found that the prediction process is three orders of magnitudes faster than a corresponding Computational Fluid Dynamics (CFD) simulation, while relative errors are maintained lower than 1% on most of the sample points. The proposed model manages to capture the essential dynamics of the flow field, as its predictions correspond reasonably with the reconstructed field by proper orthogonal decomposition (POD). The performance and accuracy of the proposed model indicate that the deep learning-based approach has great potential as a robust predictive tool for aerodynamic design and optimization.

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Section: Train and Evaluation Methodsmentioning

confidence: 99%

Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network

Yuan

et al. 2022

Applied Sciences

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“…Within the EU FP7 AVATAR project (AdVanced Aerodynamic Tools of lArge Rotors), high Reynolds number (but low Mach number) wind tunnel tests on 2D airfoil sections have been performed with the aim to gather reliable validation data within a representative operational range. The experimental campaigns were performed at the LM Wind Power inhouse wind tunnel for chord-based Reynolds numbers up to 6 × 10 6 and at the DNW High Pressure Wind Tunnel in Göttingen (HDG) for chord-based Reynolds numbers up to 15 × 10 6 on the DU00-W-212 airfoil [3,4]. At Princeton University, a compressed-air wind tunnel is used to experimentally investigate the aerodynamic performance of 2D airfoil sections [5], but also the power and thrust coefficient of a horizontal-axis wind turbine as a function of the Reynolds number [6].…”

Section: Introductionmentioning

confidence: 99%

The emergence of supersonic flow on wind turbines

Tavernier

Terzi

2022

J. Phys.: Conf. Ser.

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The future generation of wind turbines will be characterised by longer and more flexible blades. These large wind turbines are facing higher Reynolds numbers, as a consequence of longer chord lengths and increased relative wind speeds. Higher tip speeds, however, also result in an increased Mach number. Although the maximum tip speed in steady design conditions may remain (well) below the critical value, the presence of turbulence, wind gusts, blade deflections, etc. in combination with the flow acceleration over the airfoil surface, may cause a significant increase in the velocity perceived over the blade surface. We have evaluated the operational conditions of the IEA 15MW reference turbine using OpenFAST in normal design and off-design conditions to demonstrate that, if unabated, near-future wind turbines will be at risk of suffering from local supersonic flow. The driving factor is identified to be inflow turbulence, however, the tip airfoil is also of major importance. Local supersonic flow conditions may lead to severe lifetime degradation.

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Examples of Wind Tunnels for Testing Wind Turbine Airfoils

Yilmaz¹

2021

Handbook of Wind Energy Aerodynamics

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Analysis of the high Reynolds number 2D tests on a wind turbine airfoil performed at two different wind tunnels

Cited by 5 publications

References 2 publications

Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network

Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network

The emergence of supersonic flow on wind turbines

Examples of Wind Tunnels for Testing Wind Turbine Airfoils

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