A First-Principles Based Methodology for Design of Axial Compressor Configurations

Iyengar, Vishwas; Sankar, Lakshmi N.; Denney, Russ

doi:10.1115/gt2007-27513

Cited by 4 publications

(1 citation statement)

References 89 publications

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“…The efficiency and pressure ratio are plotted as function of the nondimensional mass flow rate. It is defined as the ratio of flow rate over choking flow rate, and the same notation was used by Iyengar 18 for the purpose of convenient comparison of performance curves. In comparison with the experimental curves, similar variation trends are predicted for both efficiency and pressure ratio; the predicted curves lie below the experimental, and the overall performances are underestimated by the numerical method, but the deviations are quite small.…”

Section: Flow Simulation Validationsmentioning

confidence: 99%

Axial flow compressor blade optimization through flexible shape tuning by means of cooperative co-evolution algorithm and adaptive surrogate model

Song

Sun

Wang

2014

Proceedings of the Institution of Mechanical Engineers, Part A:

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The present study is to explore potential benefits of axial flow compressor blade optimization through a flexible tuning. Modifications of blade sectional profiles and their stacking line can control spanwise blade loading distribution, reduce shock losses, and extend operating flow range. Most previous studies focused on tuning either sectional profiles or stacking line, but little work was conducted by collaboratively varying both, which may be due to abrupt rise of optimization variables and complexity. An efficient optimization method is developed to handle highly nonlinear high-dimension blade optimization problem with simultaneous variation of both sectional profiles and stacking line. It incorporates an improved cooperative co-evolution algorithm optimizer and one-stage expected improvement based adaptive surrogate model. The former decomposes the high-dimension problem into low-dimension subproblems and they can be readily solved; the latter enables the optimizer to jump out of the local minima and conduct the aim-oriented optimal search toward global optimum. A coarse surrogate model is firstly constructed with some DOE samples but it is refined during optimization process with newly identified and evaluated samples. The model prediction accuracy is gradually improved, thus it captures the distinct features (especially global optimum) of optimization problem. Both blade sectional profiles and their spatial positions are simultaneously varied. Four sectional profiles of hub, 33% span, 67% span, and shroud are parameterized, and each is defined by a mean camber line and thickness distribution. Both of them are represented, respectively, by a third-order B-Spline curve. Spatial position of each profile varies in term of sweep and lean. Blade design optimization is conducted for Rotor67 at design flow on a single workstation of Dell 7500. Performance gains are significant: at design flow, overall efficiency and pressure ratio are increased, respectively, by 1.44 and 7.24%; off-design performances are also improved over the entire flow range.

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Section: Flow Simulation Validationsmentioning

confidence: 99%

Axial flow compressor blade optimization through flexible shape tuning by means of cooperative co-evolution algorithm and adaptive surrogate model

Song

Sun

Wang

2014

Proceedings of the Institution of Mechanical Engineers, Part A:

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Comprehensive Application of a First Principles Based Methodology for Design of Axial Compressor Configurations

Iyengar

Sankar

2012

Journal of Turbomachinery

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Axial compressors are widely used in many aerodynamic applications. The design of an axial compressor configuration presents many challenges. It is necessary to retool the design methodologies to take advantage of the improved accuracy and physical fidelity of these advanced methods. Here, a first-principles based multiobjective technique for designing single stage compressors is described. The study accounts for stage aerodynamic characteristics and rotor-stator interactions. The proposed methodology provides a way to systematically screen through the plethora of design variables. This method has been applied to a rotor-stator stage similar to NASA Stage 35. By selecting the most influential design parameters and by optimizing the blade leading edge and trailing edge mean camber line angles, phenomena such as tip blockages, blade-to-blade shock structures and other loss mechanisms can be weakened or alleviated. It is found that these changes to the configuration can have a beneficial effect on total pressure ratio and stage adiabatic efficiency, thereby improving the performance of the axial compression system.

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Blade Geometry Optimization for Axial Flow Compressor

Zheng

Xiang

Sun

2010

Volume 7: Turbomachinery, Parts A, B, and C

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Blade geometry design is crucial for turbomachinery performance and stability. A blade optimization method is developed and presented in this paper. The method consists of several elements: geometry parameterization, numerical simulation of flow and performance, optimization algorithm. Sweep and lean are defined by centroid coordinates of sectional blades while the blade geometry is represented by B-Splines approach, through which deformation of blade shape in terms of sweep and lean can then be readily realized. Flow simulation is carried out with a commercial CFD package-NUMECA. In addition, response surface is incorporated to approximate the objective function during the optimization process for the purpose of time-saving, and two different models (polynomial model and basic-function model) are applied respectively to construct the response surface and their differences highlighted. Genetic Algorithms is incorporated to achieve a global optimal geometry of blade. For case study, the developed method was used to optimize the transonic NASA Rotor67 blading. It is demonstrated that an optimal tuning of blade sweep and lean results in evident improvement of both isentropic efficiency and pressure ratio at design and off-design flow.

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A First-Principles Based Methodology for Design of Axial Compressor Configurations

Cited by 4 publications

References 89 publications

Axial flow compressor blade optimization through flexible shape tuning by means of cooperative co-evolution algorithm and adaptive surrogate model

Axial flow compressor blade optimization through flexible shape tuning by means of cooperative co-evolution algorithm and adaptive surrogate model

Comprehensive Application of a First Principles Based Methodology for Design of Axial Compressor Configurations

Blade Geometry Optimization for Axial Flow Compressor

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