Towards simulation of NASA35 axial compressor

Xiang, Junting; Schlüter, Jörg; Duan, Fei

doi:10.1108/aeat-03-2015-0073

Cited by 3 publications

(6 citation statements)

References 7 publications

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“…Our previous study of the NASA stage35 axial compressor has laid good reference for the current study. 34 All basic flow features from previous study are relevant for centrifugal compressor as well. Flow is accelerated and redirected towards the radial direction when it reaches the diffusor.…”

Section: Design Details Of Kj-66 Mgt Compressormentioning

confidence: 99%

“…A similar approach for model generation with a NASA stage35 compressor 34 is utilized in this study. For the KJ-66 MGT compressor, due to concerns of computational time and resources, only one-fifth of the compressor is simulated with 72 azimuthally, consisting of two rotor blades and three stator blades.…”

Section: Numerical Approach Setupmentioning

confidence: 99%

“…Referring to our previous case study of NASA stage35 compressor, 34 the numerical setup of current study follow similar technique. Adjustment of specific operational conditions for the KJ-66 compressor is also addressed, according to its design parameters.…”

Section: Introductionmentioning

confidence: 97%

“…In this study, numerical investigation of the KJ-66 MGT compressor is carried out, with steady and transient simulations, to deepen our understanding of the MGT compressor performance. Referring to our previous case study of NASA stage35 compressor, 34 the numerical setup of current study follow similar technique. Adjustment of specific operational conditions for the KJ-66 compressor is also addressed, according to its design parameters.…”

Section: Introductionmentioning

confidence: 99%

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Study of KJ-66 micro gas turbine compressor: Steady and unsteady Reynolds-averaged Navier–Stokes approach

Xiang

Schlüter

Duan

2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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Numerical study on the compressor stage of a KJ-66 micro gas turbine was conducted in this paper through both steady and unsteady Reynolds-averaged Navier-Stokes. The study was conducted for the numerical prediction of micro gas turbine compressor performance at various operation conditions, with special attention given to the transient flow behaviors during compressor operation. The numerical results showed reasonable agreements with experimental data while providing predictions for the charting of compressor performance map at various operation speeds. The simulation results indicated that the increase of operation speed from 80 k r/min to 117 k r/min would leads to an increased peak total pressure ratio from 1.54 to 1.96, while decreasing the peak adiabatic efficiency from 0.73 to 0.55. This paper also provided discussion on details of transient flow field within the compressor stage as well as demonstrated the smooth flow transition through rotor-stator interactions.

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Section: Design Details Of Kj-66 Mgt Compressormentioning

confidence: 99%

Section: Numerical Approach Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Study of KJ-66 micro gas turbine compressor: Steady and unsteady Reynolds-averaged Navier–Stokes approach

Xiang

Schlüter

Duan

2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Among the various methods of electricity generation, turbomachines have the significant share. Several studies have been conducted on various components of turbomachinery systems to enhance their performance and achieve more reliable operation (Mazzei et al , 2017; Neupert et al , 2016; Xiang et al , 2015). Performance of axial flow compressors plays a key role in the efficiency and stability of heavy-duty gas turbines.…”

Section: Introductionmentioning

confidence: 99%

CFD-based blade shape optimization of MGT-70(3)axial flow compressor

Pakatchian

Saeidi

Ziamolki

2019

HFF

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Purpose This study aims at enhancing the performance of a 16-stage axial compressor and improving the operating stability. The adopted approaches for upgrading the compressor are artificial neural network, optimization algorithms and computational fluid dynamics. Design/methodology/approach The process starts with developing several data sets for certain 2D sections by means of training several artificial neural networks (ANNs) as surrogate models. Afterward, the trained ANNs are applied to the 3D shape optimization along with parametrization of the blade stacking line. Specifying the significant design parameters, a wide range of geometrical variations are considered by implementation of appropriate number of design variables. The optimized shapes are analyzed by applying computational fluid dynamic to obtain the best geometry. Findings 3D optimal results show improvements, especially in the case of decreasing or elimination of near walls corner separations. In addition, in comparison with the base geometry, numerical optimization shows an increase of 1.15 per cent in total isentropic efficiency in the first four stages, which results in 0.6 per cent improvement for the whole compressor, even while keeping the rest of the stages unchanged. To evaluate the numerical results, experimental data are compared with obtained data from simulation. Based on the results, the highest absolute relative deviation between experimental and numerical static pressure is approximately 7.5 per cent. Originality/value The blades geometry of an axial compressor used in a heavy-duty gas turbine is optimized by applying artificial neural network, and the results are compared with the base geometry numerically and experimentally.