Automated Design Optimization of Compressor Blades for Stationary, Large-Scale Turbomachinery

̈che, Dirk Bu; Guidati, Gianfranco; Stoll, Peter

doi:10.1115/gt2003-38421

Cited by 29 publications

(15 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Structured (mapped) hex meshes is used since the passages of the blade do not have complex geometry and can be considered as a "clean CAD" model. In order to connect the mesh regions, which are not matched, General Grid Interface (GGI) is applied as interface strategy [8], [9].…”

Section: Numerical Simulation Of Compressormentioning

confidence: 99%

A Practical Axial Compressor Design Optimization Approach Based on Gas Turbine Operation

Pakatchian¹,

Saeidi²

2019

Proceedings of the 6th International Conference on Fluid Flow, Heat and Mass Transfer (FFHMT'19)

View full text Add to dashboard Cite

In the current study, it is focused on blade optimization of compressor to achieve improved performance characteristics. Due to dependency of mass flow rate on the inlet temperature of the gas turbine, temperature changes influence on compressor performance and efficiency. In order to enhance the working conditions at design and off-design operation, an automated design process is applied. The process has three main steps including parametrization of the geometry, numerical simulation of flow and optimization design approach. Stochastic design approach is utilized for optimization. The objective of this improvement will push the airfoil geometry in a way that minimum loss value, extended acceptable off-design operation in constant exit flow angle can be achieved with being focused on hot day's operation. The considered case in the present study is a compressor of MGT-70 heavy-duty gas turbine and the optimization focuses on the first four stages. Based on numerical simulation of optimized compressor, 1% enhancement in efficiency in all operating conditions is achievable. Moreover, the mass flow rate can be enhanced roughly up to 0.8% and 1% for design and off-design conditions, respectively. After assembling the new developed parts, the first upgraded prototype of the gas turbine has been tested in sixth Unit of Parand power station. More than 600 signals of pressure and temperature in circumferential and radial directions were extracted from compressor section. The results show good agreement predicted in range inlet flow angle between measurements and theoretical targets.

show abstract

Section: Numerical Simulation Of Compressormentioning

confidence: 99%

A Practical Axial Compressor Design Optimization Approach Based on Gas Turbine Operation

Pakatchian¹,

Saeidi²

2019

Proceedings of the 6th International Conference on Fluid Flow, Heat and Mass Transfer (FFHMT'19)

View full text Add to dashboard Cite

show abstract

“…These blade design methods can be applied for improving the aerodynamic and heattransfer performance of turbomachinery cascades, leading to perform airfoils with in very little iteration. Many studies in the turbomachinery field (Sonoda et al [6], Shahpar and Radford [7], Kammerer et al [8], Buche et al [9], Benini and Tourlidakis [10], Bonaiuti and Pediroda [11]) have tried to exploit the time-saving potential of such a strategy and to integrate it into their design systems. Samad and Kim [12] performed a multi-objective optimization of an axial compressor rotor blade through genetic algorithm with total pressure and adiabatic efficiency as objective functions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Multi-Objective Multi-Point Optimization System for Axial Flow Compressor 2D Blade Design

Fathi

Shadaram

2013

Arab J Sci Eng

View full text Add to dashboard Cite

This paper introduces a multi-level framework to perform a multi-objective multi-point aerodynamic optimization of the axial compressor blade. This framework results in a considerable speed-up of the design process by reducing both the design parameters and the computational effort. To reduce the computational effort, optimization procedure is working on two levels of sophistication. Fast but approximate prediction methods has been used to find a near-optimum geometry at the firs-level, which is then further verified and refined by a more accurate but expensive Navier-Stokes solver. Surface curvature optimization was carried out in a first-level as a meta-function. Genetic algorithm and gradient-based optimization were used to optimize the parameters of first-level and second-level, respectively. This procedure considers both the aerodynamic and mechanical constraints. An initial blade has been optimized with three different design targets to highlight the ability of the design method and to develop design know-how. Leading-edge shape and curvature distributions of pressure and suction surface had major effects on the design philosophies of the blades. The result shows about −22.5 % reductions in pressure-loss coefficient at design condition and 23.6 % improvement in the allowable incidence-angle range at offdesign conditions compared to the initial blade.

show abstract

“…For aircraft external flows, examples range from airfoils [1][2][3][4][5], three-dimensional wings, and components [6][7][8][9], to an entire aircraft [10,11]. For internal flows, shape optimization has been applied to turbomachinery blade designs, such as the works reported in [12][13][14][15]. Also, various methods have been developed to improve the efficiency and accuracy of the optimization procedures, such as the genetic algorithm (e.g., [16]), surrogate models (such as response surface methods, e.g., [5]; Kriging, e.g., [9]), reduced-order models (e.g., [17]), etc.…”

mentioning

confidence: 98%

Combining Shaping and Flow Control for Aerodynamic Optimization

Zhang

2015

AIAA Journal

View full text Add to dashboard Cite

Shape optimization and flow control have been extensively studied in the past to improve the performance of wings and blades. However, they are commonly applied as two separate disciplines, with their interactions being rarely studied. The present work aims to examine and identify the potential to explore the interactions between the two disciplines and is chiefly concerned about two related issues: 1) how much more performance improvement can be obtained by combining the shape optimization with the flow control optimization, and 2) the effects of the sequencing of the disciplines when the optimizations are combined. To address these issues, five optimization approaches have been studied, representing the shaping only, the flow control optimization only, and three different sequencings of the disciplines in the combined optimizations. All the optimization approaches are applied consistently by using the same optimization system developed in this research. The system uses the Kriging surrogate method as the optimization algorithm, and it incorporates a standard computational fluid dynamics solver for which the validity and numerical sensitivities have been assessed. The developed methodology is applied to two optimization cases of practical interest: a compressor blade and a circulation controlled airfoil. The results of both cases show that, when shaping and flow control optimizations are combined, the performance enhancement is considerably higher than when the two disciplines are applied separately. Furthermore, in the combined optimizations for the airfoil, it is observed that the concurrent optimization leads to the highest performance, whereas the sequential optimizations tend to be "stuck" at less optimal solutions. Nomenclaturechord length in the x direction c 0 = chord length of the baseline airfoil L = lift Ma = Mach number _ m = mass flow rate N s = number of samples N v = number of design variables P j = power required by blade flow control p = static pressure p 0 = total pressure Re = Reynolds number t = blade cascade pitch length V = velocity magnitude x co;cc = chordwise position of the start of a Coanda surface sector x s;cc = chordwise position of the slot for circulation control x s1 = chordwise position of the blowing slot on the blade x s2 = chordwise position of the suction slot on the blade β = blade inlet flow angle β max = maximum operating inlet flow angle γ = blade stagger angle δ = direction normal to the blade chord η = efficiency ξ = direction along the blade chord Subscripts d = condition at the design inlet flow angle eq= equivalent quantity j = jet parameter s = slot parameter 0 = stagnation/baseline parameter 1 = condition of inlet-mass-averaged (blade), freestream (airfoil), or blowing (slot) 2 = condition of outlet-mass-averaged (blade) or suction (slot)

show abstract

Automated Design Optimization of Compressor Blades for Stationary, Large-Scale Turbomachinery

Cited by 29 publications

References 12 publications

A Practical Axial Compressor Design Optimization Approach Based on Gas Turbine Operation

A Practical Axial Compressor Design Optimization Approach Based on Gas Turbine Operation

Multi-Level Multi-Objective Multi-Point Optimization System for Axial Flow Compressor 2D Blade Design

Combining Shaping and Flow Control for Aerodynamic Optimization

Contact Info

Product

Resources

About