Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks

Baklacıoğlu, Tolga; Turan, Önder; Aydın, Hakan

doi:10.1016/j.energy.2015.04.025

Cited by 74 publications

(25 citation statements)

References 56 publications

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“…In Table 2, an essential equation set for this purpose is summarized. More information may also be found in a number of texts [14,[20][21][22][23][24][25][26]29]. …”

Section: Developing Exergy-based Sustainability Indicatorsmentioning

confidence: 99%

“…Exergy and exergy related methods are practicable for this small scale turbojet engine. Similar engines, including aircraft type gas turbines, have been evaluated by many researchers using exergy methods [20][21][22][23][24][25][26]. Even though sustainability assessment studies are fewer in number than performance evaluation studies, they can be accessed in the literature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sustainability Metrics of a Small Scale Turbojet Engine

Ekici

Şöhret

Coban

et al. 2018

International Journal of Turbo &Amp; Jet-Engines

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Over the last decade, sustainable energy consumption has attracted the attention of scientists and researchers. The current paper presents sustainability indicators of a small scale turbojet engine, operated on micro-aerial vehicles, for discussion of the sustainable development of the aviation industry from a different perspective. Experimental data was obtained from an engine at full power load and utilized to conduct an exergy-based sustainability analysis. Exergy efficiency, waste exergy ratio, recoverable exergy ratio, environmental effect factor, exergy destruction factor and exergetic sustainability index are evaluated as exergetic sustainability indicators of the turbojet engine under investigation in the current study. The exergy efficiency of the small scale turbojet engine is calculated as 27.25 % whereas the waste exergy ratio, the exergy destruction factor and the sustainability index of the engine are found to be 0.9756, 0.5466 and 0.2793, respectively.

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“…In Table 2, an essential equation set for this purpose is summarized. More information may also be found in a number of texts [14,[20][21][22][23][24][25][26]29]. …”

Section: Developing Exergy-based Sustainability Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sustainability Metrics of a Small Scale Turbojet Engine

Ekici

Şöhret

Coban

et al. 2018

International Journal of Turbo &Amp; Jet-Engines

View full text Add to dashboard Cite

show abstract

“…The S function and the algebraic ring problem in the model needed to be solved with Newton-Raphson iteration method, which complicates the modeling process. Foreign literatures on aeroengine mainly focus on the research of turboprop engine model [3,4] and performance simulation [5,6]. Due to the structural differences of turboprop engines and the confidentiality of design departments, the relevant literatures about the basic and more accurate aerothermal simulation calculation of turboprop engines have not been consulted [7], so the simulation research on fault data is even less.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of aeroengine fault data simulation generator

Song

et al. 2019

SN Appl. Sci.

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As a core component of an aircraft, once the aeroengine occurs faults, its safety and reliability reduce during the flight. These faults cause unpredictable consequences. Therefore, it is necessary to study how to model the faults of the engine. Based on Simulink software, aeroengine simulation model is established from aeroengine mathematical model which can be derived from aerothermodynamics equations. This paper Simulates various typical faults to aeroengine simulation model, the simulation results show that the designed model can correctly obtain the engine aerothermodynamics export parameters and common faults data, verifying the rationality and effectiveness of the model. The simulation generator provides a visual tool for engine modeling, and supplies data support for aeroengine fault diagnosis and prediction.

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“…Therefore, a few scholars proposed to use the evolutionary algorithm with global optimization characteristics to improve neural network models and apply it to mechanical fault diagnosis. For example, Baklacioglu [7] applied genetic algorithm to neural networks and proposed the improved neural network model, which well solved the problem of local extremum and efficiently improved diagnosis rate. However, genetic algorithm has complex operations such as coding, decoding, crossover, mutation, large population size and long training cycle.…”

Section: Introductionmentioning

confidence: 99%

Research on intelligent fault diagnosis of mechanical equipment based on sparse deep neural networks

Qin¹,

Bai²,

Yuan³

2017

J. vibroeng.

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In the big data background, the accuracy of fault diagnosis and recognition has been difficult to be improved. The deep neural network was used to recognize the diagnosis rate of the bearing with four kinds of conditions and compared with traditional BP neural network, genetic neural network and particle swarm neural network. Results showed that the diagnosis accuracy and convergence rate of the deep neural network were obviously higher than those of other models. Fault diagnosis rates with different sample sizes and training sample proportions were then studied to compare with the latest reported methods. Results showed that fault diagnosis had a good stability using deep neural networks. Vibration accelerations of the bearing with different fault diameters and excitation loads were extracted. The deep neural network was used to recognize these faults. Diagnosis accuracy was very high. In particular, the fault diagnosis rate was 98 % when signal features of vibration accelerations were very obvious, which indicated that using deep neural network was effective in diagnosing and recognizing different types of faults. Finally, the deep neural network was used to conduct fault diagnosis for the gearbox of wind turbines and compared with the other models to present that it would work well in the industrial environment.

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Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks

Cited by 74 publications

References 56 publications

Sustainability Metrics of a Small Scale Turbojet Engine

Sustainability Metrics of a Small Scale Turbojet Engine

Design and implementation of aeroengine fault data simulation generator

Research on intelligent fault diagnosis of mechanical equipment based on sparse deep neural networks

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