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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.

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A compressor is one of the key components of a gas turbine engine and its performance and characteristics significantly affect the overall performance of the engine. Axial flow compressors are one of the most conventional types of compressors and are widely used in turbine engines for large-scale power generation. Intelligent techniques are useful for numerical simulation, characterization of axial compressors, and predicting their performance. The present work reviews studies applying different intelligent methods for performance forecasting and modeling different aerodynamic aspects of axial compressors. Corresponding to the outcomes of the considered research works, it can be expressed that by using these methods, axial compressors can be characterized properly with acceptable exactness. In addition, these techniques are useful for performance prediction of the compressors. The accuracy and performance of these methods is impacted by several elements, specifically the employed method and applied input variables. Finally, some suggestions are made for future studies in the field.

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.