On-Board Real-Time Optimization Control for Turbo-Fan Engine Life Extending

A real-time optimization control method is proposed to extend turbo-fan engine service life. This real-time optimization control is based on an on-board engine mode, which is devised by a MRR-LSSVR (multi-input multi-output recursive reduced least squares support vector regression method). To solve...

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Bibliographic Details
Published in:International journal of turbo & jet-engines Vol. 34; no. 4; pp. 321 - 332
Main Authors: Zheng, Qiangang, Zhang, Haibo, Miao, Lizhen, Sun, Fengyong
Format: Journal Article
Language:English
Published: Berlin De Gruyter 01.12.2017
Walter de Gruyter GmbH
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ISSN:0334-0082, 2191-0332
Online Access:Get full text
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Summary:A real-time optimization control method is proposed to extend turbo-fan engine service life. This real-time optimization control is based on an on-board engine mode, which is devised by a MRR-LSSVR (multi-input multi-output recursive reduced least squares support vector regression method). To solve the optimization problem, a FSQP (feasible sequential quadratic programming) algorithm is utilized. The thermal mechanical fatigue is taken into account during the optimization process. Furthermore, to describe the engine life decaying, a thermal mechanical fatigue model of engine acceleration process is established. The optimization objective function not only contains the sub-item which can get fast response of the engine, but also concludes the sub-item of the total mechanical strain range which has positive relationship to engine fatigue life. Finally, the simulations of the conventional optimization control which just consider engine acceleration performance or the proposed optimization method have been conducted. The simulations demonstrate that the time of the two control methods from idle to 99.5 % of the maximum power are equal. However, the engine life using the proposed optimization method could be surprisingly increased by 36.17 % compared with that using conventional optimization control.
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ISSN:0334-0082
2191-0332
DOI:10.1515/tjj-2016-0015