Insight into stall delay and computation of 3D sectional aerofoil characteristics of NREL phase VI wind turbine using inverse BEM and improvement in BEM analysis accounting for stall delay effect

Kabir, Ijaz Fazil Syed Ahmed; Ng, Eddie Yin-Kwee

doi:10.1016/j.energy.2016.11.102

Cited by 34 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the most commonly used methods in many blade design software packages, such as QBlade, WT_perf, etc. [2], is based on the Blade Element Momentum (BEM) method. This method offers a fast and simple way to design a blade based on a simplified aerodynamic model by assuming 2D inviscid flow using 2D airfoil data [3].…”

Section: Bem Methodsmentioning

confidence: 99%

“…One of the effects related to flows in the span-wise direction caused by the rotation of the wind turbine blade is 3D stall delay. This 3D rotating flow around wind turbine blades tends to delay the onset of flow separation [2], thus leading to potential high lift improvement because the local angle of attack can be increased to values greater than the corresponding 2D ones. Therefore, the consideration of the stall delay could improve blade designs.…”

Section: Stall Delaymentioning

confidence: 99%

See 1 more Smart Citation

3D Multidisciplinary Automated Design Optimization Toolbox for Wind Turbine Blades

et al. 2021

View full text Add to dashboard Cite

This paper presents two novel automated optimization approaches. The first one proposes a framework to optimize wind turbine blades by integrating multidisciplinary 3D parametric modeling, a physics-based optimization scheme, the Inverse Blade Element Momentum (IBEM) method, and 3D Reynolds-averaged Navier–Stokes (RANS) simulation; the second method introduces a framework combining 3D parametric modeling and an integrated goal-driven optimization together with a 4D Unsteady Reynolds-averaged Navier–Stokes (URANS) solver. In the first approach, the optimization toolbox operates concurrently with the other software packages through scripts. The automated optimization process modifies the parametric model of the blade by decreasing the twist angle and increasing the local angle of attack (AoA) across the blade at locations with lower than maximum 3D lift/drag ratio until a maximum mean lift/drag ratio for the whole blade is found. This process exploits the 3D stall delay, which is often ignored in the regular 2D BEM approach. The second approach focuses on the shape optimization of individual cross-sections where the shape near the trailing edge is adjusted to achieve high power output, using a goal-driven optimization toolbox verified by 4D URANS Computational Fluid Dynamics (CFD) simulation for the whole rotor. The results obtained from the case study indicate that (1) the 4D URANS whole rotor simulation in the second approach generates more accurate results than the 3D RANS single blade simulation with periodic boundary conditions; (2) the second approach of the framework can automatically produce the blade geometry that satisfies the optimization objective, while the first approach is less desirable as the 3D stall delay is not prominent enough to be fruitfully exploited for this particular case study.

show abstract

Section: Bem Methodsmentioning

confidence: 99%

Section: Stall Delaymentioning

confidence: 99%

3D Multidisciplinary Automated Design Optimization Toolbox for Wind Turbine Blades

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The fitting root-mean-square error reflects the deviation of the relation curve (between In Figure 12, the length of the line segment is used to describe the size of the fitting root-mean-square error. The fitting root-mean-square error reflects the deviation of the relation curve (between the hub angle and the pitch load) with respect to the sine function described in Equation (8). In other words, the larger the fitting root-mean-square error is, the greater the deviation is.…”

Section: Influence Of the Hub Angle On The Pitch Loadmentioning

confidence: 99%

“…Nevertheless, research of the pitch load has always been the focus of attention. Many methods, such as the blade element momentum theory [8][9][10], dynamic stall model [11,12], computational fluid dynamics [13][14][15] and wind tunnel experimental research [16,17], have been employed to investigate the aerodynamic characteristics of wind turbine blades, which is one of the main factors of the pitch load. For example, by combining the element momentum theory with the dynamic stalling model, a deep theoretical analysis of the pitch load characteristics of a megawatt-scale wind turbine was carried out.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Pitch Load of Large-Scale Wind Turbines Using Field SCADA Data

et al. 2019

View full text Add to dashboard Cite

Variable pitch technology is an indispensable key technology of large-scale wind turbines. The reliable pitch mechanism is the basic guarantee for achieving variable pitch. At present, the main problem with the design and maintenance of the variable pitch mechanism is that the pitch load is not clearly known. This paper focuses on obtaining pitch load characteristics through extracting SCADA (Supervisory Control and Data Acquisition) data. Here, the pitch load refers to the resistance moment to be overcome when the wind turbine blade is rotated on its own axis. From the data collected by the SCADA system, although the edgewise moment and the flapwise moment cannot be obtained, the pitch torque (load) can be extracted indirectly. This provides data support for the research. Specifically, the pitch moment is obtained by indirect calculation of the pitch motor current. Then, the effects of the wind speed, rotor speed, hub angle and pitch angle on the pitch load are theoretically analyzed. To obtain more reliable results, data preprocessing algorithms are presented to consider the data filtering range, the elimination of abnormal values and data dispersity. Subsequently, the influence mechanisms of wind speed, rotor speed, hub angle and pitch angle on the pitch load are investigated in detail based on the SCADA data.

show abstract

“…The accuracy of BEM method was improved by using the so-called three-dimensional (3-D) lift and drag coefficients extracted through the determined AoA. Such kinds of work were made by Yang et al [34,35] using SIS1, Schneider et al [36] using inverse BEM as well as AAT, and Syed Ahmed Kabir et al [37] using inverse BEM. There exists several other applications with AoA determination, such as improving the actuator line/Navier-Stokes (AL/NS) simulation (Wimshurst et al [38]), investigating stall delay as well as dynamic stall (Zhu et al [18]), discussing unsteady phenomena under yawed conditions (Elgammi et al [39] and Wen et al [40]), and analyzing the measurements of a wind turbine in the field (Wu et al [41]).…”

Section: Introductionmentioning

confidence: 99%

A New Method of Determination of the Angle of Attack on Rotating Wind Turbine Blades

et al. 2019

View full text Add to dashboard Cite

The angle of attack (AoA) is the key parameter when extracting the aerodynamic polar from the rotating blade sections of a wind turbine. However, the determination of AoA is not straightforward using computational fluid dynamics (CFD) or measurement. Since the incoming streamlines are bent because of the complex inductions of the rotor, discrepancies exist between various existing determination methods, especially in the tip region. In the present study, flow characteristics in the region near wind turbine blades are analyzed in detail using CFD results of flows past the NREL UAE Phase VI rotor. It is found that the local flow determining AOA changes rapidly in the vicinity of the blade. Based on this finding, the concepts of effective AoA as well as nominal AoA are introduced, leading to a new method of AOA determination. The new method has 5 steps: (1) Find the distributed vortices on the blade surface; (2) select two monitoring points per cross-section close to the aerodynamic center on both pressure and suction sides with an equal distance from the rotor plane; (3) subtract the blade self-induction from the velocity at each monitoring point; (4) average the velocity of the two monitoring points obtained in Step 3; (5) determine the AoA using the velocity obtained in Step 4. Since the monitoring points for the first time can be set very close to the aerodynamic center, leading to an excellent estimation of AoA. The aerodynamic polar extracted through determination of the effective AoA exhibits a consistent regularity for both the mid-board and tip sections, which has never been obtained by the existing determination methods.

show abstract

Insight into stall delay and computation of 3D sectional aerofoil characteristics of NREL phase VI wind turbine using inverse BEM and improvement in BEM analysis accounting for stall delay effect

Cited by 34 publications

References 10 publications

3D Multidisciplinary Automated Design Optimization Toolbox for Wind Turbine Blades

3D Multidisciplinary Automated Design Optimization Toolbox for Wind Turbine Blades

Investigation of the Pitch Load of Large-Scale Wind Turbines Using Field SCADA Data

A New Method of Determination of the Angle of Attack on Rotating Wind Turbine Blades

Contact Info

Product

Resources

About