2D shape optimization of turbine blades

Montrone, Francesco; Wever, Utz; Zheng, Qing

doi:10.1002/zamm.200310087

Cited by 2 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Montrone et al . and Morris et al . presented 2D shape parameterization methods that can be used to optimize the turbine blade profile.…”

Section: Introductionmentioning

confidence: 89%

“…Starzmann [25] and Starzmann and Carolus [26] designed the Wells turbine blade using a code based on the classical blade element approach. Montrone et al [27] and Morris et al [28] presented 2D shape parameterization methods that can be used to optimize the turbine blade profile. Grasso [29] optimized the airfoil of a tidal turbine by coupling a gradient-based algorithm with the RFOIL solver and a composite Bezier geometrical parameterization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wells turbine blade profile optimization for better wave energy capture

Shaaban

2017

Int. J. Energy Res.

View full text Add to dashboard Cite

Summary Despite the fact that wave energy is available at no cost, it is always desired to harvest the maximum possible amount of this energy. The axial flow air turbines are commonly used with oscillating water column devices as a power take‐off system. The present work introduces a blade profile optimization technique that improves the air turbine performance while considering the complex 3D flow phenomena. This technique produces non‐standard blade profiles from the coordinates of the standard ones. It implements a multi‐objective optimization algorithm in order to define the optimum blade profile. The proposed optimization technique was successfully applied to a biplane Wells turbine in the present work. It produced an optimum blade profile that improves the turbine torque by up to 9.3%, reduces the turbine damping coefficient by 10%, and increases the turbine operating range by 5%. The optimized profile increases the annual average turbine power by up to 3.6% under typical sea conditions. Moreover, new blade profiles were produced from the wind turbine airfoil data and investigated for use with the biplane Wells turbine. The present work showed that two of these profiles could be used with low wave energy seas. Copyright © 2017 John Wiley & Sons, Ltd.

show abstract

“…Montrone et al . and Morris et al . presented 2D shape parameterization methods that can be used to optimize the turbine blade profile.…”

Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Wells turbine blade profile optimization for better wave energy capture

Shaaban

2017

Int. J. Energy Res.

View full text Add to dashboard Cite

show abstract

“…The most efficient method for providing gradient information for a large number of parameters is the adjoint method. It is discussed by Elliot [6], Giles et al [10], Iollo et al [17], and Jameson [19].The geometry optimization method in this paper, in its 2D aspects, is based upon [27] and [1]. Physical and technological aspects are discussed in [23,24].…”

mentioning

confidence: 99%

“…The geometry optimization method in this paper, in its 2D aspects, is based upon [27] and [1]. Physical and technological aspects are discussed in [23,24].…”

mentioning

confidence: 99%

3D shape optimization of turbine blades

Jung

Kämmerer

Paffrat

et al. 2005

Z. angew. Math. Mech.

Self Cite

View full text Add to dashboard Cite

879efficient 3D optimization requires, on the one hand, a stable grid generator and flow solver, and on the other hand, a blade parametrization that needs just a few parameters to describe the whole range of technical relevant 3D blade geometries.Many optimization concepts have already been introduced. Demeulenaere and Van der Braembussche [2] present fast inverse design methods for the Euler equations. These methods may be very powerful if one knows the optimal pressure distribution. Genetic algorithms are discussed in [15] and [34]. They are able to achieve the global minimum, but the convergence rate is very slow. Burns and Borggard [3,4] consider the sensitivity method. They differentiate the governing conservation law and then solve the sensitivity equations by a standard (Enquist-Osher) discretization [7]. The most efficient method for providing gradient information for a large number of parameters is the adjoint method. It is discussed by Elliot [6], Giles et al. [10], Iollo et al. [17], and Jameson [19].The geometry optimization method in this paper, in its 2D aspects, is based upon [27] and [1]. Physical and technological aspects are discussed in [23,24]. To our knowledge, we present one of the first complete 3D-optimization loops for the optimization of turbine blades.Sect. 2 starts with a short overview of the underlying fluid solver ITSM3D, see [22,26]. Then we discuss the components needed for a complete optimization loop. Due to the very expensive function evaluation, gradient based methods are preferred. A short flow chart of the SQP algorithm is given in the first part, see also [11]. The second part gives some hints on the parametrization of the turbine blade. The choice of parametrization of the blade is very sensitive with respect to performance of the optimization loop. Providing gradient information is the topic of the third part. Here, different methods for computing the gradients are discussed. Due to reasons of efficiency, the sensitivity analysis must be integrated into the solver ITSM3D and thus exploiting its infrastructure.The sensitivity analysis itself is discussed in Sect. 3. First, a theoretical result justifies the direct differentiation of the fluxes. The computation of these derivatives is discussed, with main focus on the treatment of boundary conditions. The requirement of changing blade geometries leads to an inhomogeneous wall condition for the velocity. Numerical compatibility conditions complete the condition for the wall. Furthermore the derivatives of the input and output boundary conditions are discussed.Sect. 4 considers the discretization of the flow solver and of the sensitivity equation. Here, the strong interaction of both discretizations are exposed.Sect. 5 deals with implementation aspects, namely the verification of fluxes and an alternative formulation of the sensitivity equations which is computationally more advantageous with respect to the inhomogeneous boundary conditions. Finally, in Sect. 6, the performance of the method is demonstrated on a complex stato...

show abstract

2D shape optimization of turbine blades

Cited by 2 publications

References 9 publications

Wells turbine blade profile optimization for better wave energy capture

Wells turbine blade profile optimization for better wave energy capture

3D shape optimization of turbine blades

Contact Info

Product

Resources

About