Numerical Optimization of Turbomachinery Bladings

Burguburu, S.; Toussaint, Clément; Bonhomme, C.; Leroy, Gilles

doi:10.1115/gt2003-38310

Cited by 17 publications

(9 citation statements)

References 6 publications

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“…Several other studies on shape optimization of turbomachinery blades have been undertaken, with specific emphases on maximum camber [7], camber line [8][9], airfoil thickness [10], thickness location [11], trailing edge (TE) radius [12], and others.…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of axial fan blade through multi-objective optimization techniques

et al. 2010

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This paper presents an axial fan blade design optimization method incorporating a hybrid multi-objective evolutionary algorithm (hybrid MOEA). In flow analyses, Reynolds-averaged Navier-Stokes (RANS) equations were solved using the shear stress transport turbulence model. The numerical results for the axial and tangential velocities were validated by comparing them with experimental data. Six design variables relating to the blade lean angle and the blade profile were selected through Latin hypercube sampling of design of experiments (DOE) to generate design points within the selected design space. Two objective functions, namely, total efficiency and torque, were employed, and multi-objective optimization was carried out, to enhance the performance. A surrogate model, Response Surface Approximation (RSA), was constructed for each objective function based on the numerical solutions obtained at the specified design points. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with local search was used for multi-objective optimization. The Pareto-optimal solutions were obtained, and a trade-off analysis was performed between the two conflicting objectives in view of the design and flow constraints. It was observed that, by the process of multi-objective optimization, the total efficiency was enhanced and the torque reduced. The mechanisms of these performance improvements were elucidated by analysis of the Pareto-optimal solutions.

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Section: Introductionmentioning

confidence: 99%

Performance enhancement of axial fan blade through multi-objective optimization techniques

et al. 2010

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“…The optimization considering blade profile defined by Bazier curve is reported by Burguburu et al [10]. Multi-objective optimization of a compressor blade with efficiency and total pressure as objectives was performed by Benini [11], who considered variables of blade stacking line and thickness of blade profile.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of low-speed axial flow fan blade with three-dimensional RANS analysis

2008

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This work presents a numerical optimization procedure for a low-speed axial flow fan blade with polynomial response surface approximation model. Reynolds-averaged Navier-Stokes equations with SST turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. The blade profile as well as stacking line is modified to enhance blade total efficiency, i.e., the objective function. The design variables of blade lean, maximum thickness and location of maximum thickness are selected, and a design of experiments technique produces design points where flow analyses are performed to obtain values of the objective function. A gradient-based search algorithm is used to find the optimal design in the design space from the constructed response surface model for the objective function. As a main result, the efficiency is increased effectively by the present optimization procedure. And, it is also shown that the modification of blade lean is more effective to improve the efficiency rather than modifying blade profile.

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“…Burguburu et al. 10 also utilized a gradient-based approach with the motivation of minimizing losses by perturbing the turbine blade geometry. The study shows that significant improvements on objective functions are obtained, fulfilling the constraint requirements.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic optimization of through-flow design model of a high by-pass transonic aero-engine fan using genetic algorithm

Kor

Acarer

Özkol

2017

Proceedings of the Institution of Mechanical Engineers, Part A:

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This study deals with aerodynamic optimization of a high bypass transonic aero-engine fan module in a through-flow inverse design model at cruise condition. To the authors' best knowledge, although the literature contains through-flow optimization of the simplified cases of compressors and turbines, an optimization study targeting the more elaborate case of combined transonic fan and splitter through-flow model is not considered in the literature. Such a through-flow optimization of a transonic fan, combined with bypass and core streams separated by an aerodynamically shaped flow splitter, possesses significant challenges to any optimizer, due to highly non-linear nature of the problem and the high number of constraints, including the fulfillment of the targeted bypass ratio. It is the aim of this study to consider this previously untouched area in detail and therefore present a more sophisticated and accurate optimization environment for actual bypass fan systems. An in-house optimization code using genetic algorithm is coupled with a previously developed in-house through-flow solver which is using a streamline curvature technique and a set of in-house calibrated empirical models for incidence, deviation, loss and blockage. As the through-flow models are the backbone of turbomachinery design, and great majority of design decisions are taken in this phase, such a study is assessed to result in significant guidelines to the gas turbine community.

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Numerical Optimization of Turbomachinery Bladings

Cited by 17 publications

References 6 publications

Performance enhancement of axial fan blade through multi-objective optimization techniques

Performance enhancement of axial fan blade through multi-objective optimization techniques

Design optimization of low-speed axial flow fan blade with three-dimensional RANS analysis

Aerodynamic optimization of through-flow design model of a high by-pass transonic aero-engine fan using genetic algorithm

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