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Design and optimization of a gas turbine regenerator with fixed pressure drop using GA and firefly algorithms

Bozorgan Navid, Ghafouri Ashkan, Assareh Ehsanolah, Safieddin Ardebili Seyed Mohammad

Journal of Theoretical and Applied Mechanics

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2020

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Vol. 58 nr 4

943--952

EN

The present study investigates eight design parameters such as seal coverage, core porosity, core volume ratio, core thickness, dimensionless core rotation rate, inner diameter of the core, air mass flow rate and exhaust mass flow rate to design and optimize a regenerator of a 20-MW power generation gas turbine with fixed pressure drop. The application of GA and Firefly algorithms to optimize the effectiveness of the regenerator is presented to demonstrate the efficiency and accuracy of the proposed algorithms. The effect of change in the seal coverage, core porosity, core volume ratio and dimensionless core rotation rate are evaluated as important design parameters having influence on the size and mass of the core of the regenerator. This could be done through fixing each of these parameters, while the other seven design parameters are selected as variables to optimize the effectiveness. The results show that the selection of all eight-design parameters proposed as operating variables is necessary to optimize the parameters to achieve the proper design of this regenerator.

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Multidisciplinary design and optimization of gas turbine engine low pressure turbine at preliminary design stage

Bar W., Orkisz M., Moustapha H.

Journal of KONES

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2014

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Vol. 21, No. 3

15--22

EN

The gas turbine engine has evolved rapidly during past decades to provide a reliable and efficient business solution for global transportation. The engine design process is clearly a large contributor to this evolution. This process is highly iterative, multidisciplinary and complex in nature. The success of an engine depends on a carefully balanced design that best exploits the interactions between numerous traditional engineering disciplines such as aerodynamics and structures as well as lifecycle analysis of cost, manufacturability, serviceability and supportability. To take into account all of these disciplines and optimization should be used. Currently most of present state-of-art numerical modelling methods, which are used mainly at detailed design stage, are unsuitable for this task due to very high computational time. The solution to this problem can be found in multidisciplinary design and optimization at preliminary design stage with use of simple 1-2D models. This paper presents current aero engine design process and indicates possibilities of future improvements by utilization of proposed methodology, which take into account aerodynamic, thermodynamic and structures (blade, fixing and disc) calculations, connected in one multidisciplinary model, which is suited for optimization. All disciplinary models are presented and described in this paper as well as connection between them, with study over design variable, goal function and constrains that should be used. Moreover, a strategy of optimization is proposed as well as methods for acceleration of optimization process by use of surrogate. The presentation of methodology is followed by example optimization of low-pressure aero engine turbine.

3

Computational electromagnetics for design optimization: the state of the art and conjectures for the future

Sykulski J.K.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2009

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Vol. 57, nr 2

123-131

EN

The paper reviews the state of the" art in modern field simulation techniques available to assist in the design and performance prediction of electromechanical and electromagnetic devices. Commercial software packages, usually exploiting finite element and/or related techniques, provide advanced and reliable tools for every-day use in the design office. At the same time Computational Electromagnetics continues to be a thriving area of research with emerging new techniques and methods, in particular for multi-physics applications and in the area of multi-objective optimization.