Application of genetic algorithm in exergy and sustainability: A case of aero-gas turbine engine at cruise phase

Aygün, Hakan; Turan, Önder

doi:10.1016/j.energy.2021.121644

Cited by 34 publications

(8 citation statements)

References 31 publications

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“…Therefore, minimum absolute error (MAE), minimum square error (MSE), RMSE, and coefficient of determination (R 2 ) parameters were measured to evaluate the proposed ANN and LSTM model. The equations of the parameters are given as following 18

MAE = \frac{1}{n} {false\sum}_{i = 1}^{n} |Y_{italicactual} - Y_{italicpredicted}|

MSE = \frac{1}{n} {false\sum}_{i = 1}^{n} {((), Y_{actual} goodbreak- Y_{predicted})}^{2}

RMSE = \sqrt{\frac{1}{n} {false\sum}_{i = 1}^{n} {((), Y_{actual} goodbreak- Y_{predicted})}^{2}}

R goodbreak= \frac{n \sum_{i = 1}^{n} Y_{actual} Y_{predicted} - \sum_{i = 1}^{n} Y_{actual} \sum_{i = 1}^{n} Y_{predicted}}{\sqrt{n \sum_{i = 1}^{n} {Y_{italicactual}}^{2} - {(\sum_{i = 1}^{n} Y_{italicactual})}^{2}} \sqrt{n \sum_{i = 1}^{n} {Y_{italicpredicted}}^{2} - {(\sum_{i = 1}^{n} Y_{italicpredicted})}^{2}}} .

…”

Section: Methodsmentioning

confidence: 99%

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Modeling of performance and thermodynamic metrics of a conceptual turboprop engine by comparing different machine learning approaches

Dursun

Toraman

Aygün

2022

Intl J of Energy Research

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In this study, several performance and thermodynamics metrics of a conceptual turboprop engine (C-TPE) were computed at 50 (fifty) power settings. Based on these computations, these parameters were predicted by employing artificial neural networks (ANN) and long-short term memory (LSTM) approaches. The obtained parametrically data were subjected to preprocessing for normalization. After determining model inputs to estimate the engine outputs, these data were introduced to ANN and LSTM. Then, the findings by two methods were compared with each other. In this context, fuel flow, air mass flow, exhaust velocity, compressor pressure ratio, turbine outlet temperature, and revolutions per minute (RPM) were selected as model inputs to estimate several metrics such as net thrust, specific fuel consumption (SFC), overall efficiency, exergy efficiency, and environmental effect factor (EEF) regarding the C-TPE. For modeling of the mentioned metrics, 80% and 20% of the data were used for training and testing, respectively. According to the findings of energetic and exergetic computations, SFC value of the C-TPE increases from 16.205 to 17.737 g/ kNs whereas its overall efficiency varies from 23.63% to 22.31% owing to decrement in RPM. Moreover, exergy efficiency of C-TPE remains between 23.93% and 26.2% whilst its EFF value resides between 2.66 and 3.05 throughout power settings. Finally, the modeling results indicate that coefficient of determination (R 2 ) regarding overall efficiency of C-TPE was found as 0.951867 with respect to the ANN model whereas it was calculated as 0.999906 with the proposed LSTM. It could be deduced that since LSTM is capable of providing lower the model error than the ANN for the same set of data, this method could be implemented to predict thermodynamics metrics of different kinds of gas turbine engine where obtaining data is limited.

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MAE = \frac{1}{n} {false\sum}_{i = 1}^{n} |Y_{italicactual} - Y_{italicpredicted}|

MSE = \frac{1}{n} {false\sum}_{i = 1}^{n} {((), Y_{actual} goodbreak- Y_{predicted})}^{2}

RMSE = \sqrt{\frac{1}{n} {false\sum}_{i = 1}^{n} {((), Y_{actual} goodbreak- Y_{predicted})}^{2}}

R goodbreak= \frac{n \sum_{i = 1}^{n} Y_{actual} Y_{predicted} - \sum_{i = 1}^{n} Y_{actual} \sum_{i = 1}^{n} Y_{predicted}}{\sqrt{n \sum_{i = 1}^{n} {Y_{italicactual}}^{2} - {(\sum_{i = 1}^{n} Y_{italicactual})}^{2}} \sqrt{n \sum_{i = 1}^{n} {Y_{italicpredicted}}^{2} - {(\sum_{i = 1}^{n} Y_{italicpredicted})}^{2}}} .

…”

Section: Methodsmentioning

confidence: 99%

“…The findings showed that R 2 of energy efficiency is achieved as 0.9999 with high accuracy. Aygun and Turan 18 modeled exergetic metrics for PW4000 engine by comparing conventional LSM and genetic algorithm (GA). They expressed that the exergy efficiency of the engine is predicted as R 2 = 0.9974 with LSM and R 2 = 0.9999 with GA‐LSM.…”

Section: Introductionmentioning

confidence: 99%

Modeling of performance and thermodynamic metrics of a conceptual turboprop engine by comparing different machine learning approaches

Dursun

Toraman

Aygün

2022

Intl J of Energy Research

Self Cite

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“…It has the characteristics of local estimation and has a good t for problems with a high degree of nonlinearity. The following is a reference to the derivation process of Kriging method theory [16,17]. In the analysis of regression model, the model assumes that…”

Section: Basic Kriging Modelmentioning

confidence: 99%

Optimization of aero-engine combustion chambers with the assistance of Hierarchical-Kriging surrogate model based on POD downscaling method

Tong

Guo

et al. 2023

Preprint

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In view of the long calculation cycle, high processing test and cost of the traditional aero-engine combustion chamber design process, which restricts the engine optimization design cycle, this paper innovatively proposes a surrogate model for the performance of aero-engine combustion chamber based on the POD-Hierarchical-Kriging method. Through experiments, the predicted results of the POD-Hierarchical-Kriging model are compared and analyzed with the calculated results of the one-dimensional program, and the root mean square error of the predicted values of combustion efficiency and total pressure loss are 0.0064% and 0.1995%, respectively. The accuracy of the POD-Hierarchical-Kriging model is compared with the cubic polynomials model, the basic Kriging model and the Hierarchical-Kriging model. It verifies the feasibility and accuracy of the POD-Hierarchical-Kriging model for the prediction of performance of aero-engine combustion chambers. The global sensitivity analysis method is applied to obtain the influence effect of the design variables on the performance. Then, a multi-objective optimization method based on NSGA-II algorithm is studied, and finally the optimal set of Pareto solutions is obtained and analyzed, which can be used to guide the optimal design of aero-engine combustion chamber and accelerate the progress of aero-engine development.

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“…Some discovered that commercial turbofan engines with high bypass and overall pressure ratio showed prominent advantages considering energy efficiency and environmental costs [22]. Taking the exergetic parameters as the target, the cycle parameters of the PW-4000 engine could be optimized to improve the efficiency of energy utilization [23]. The conventional exergy method identifies the locations and magnitudes of thermodynamic irreversibility.…”

Section: Introductionmentioning

confidence: 99%

Exergetic Effects of Cooled Cooling Air Technology on the Turbofan Engine during a Typical Mission

Zhuang

Dong

et al. 2022

Energies

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The cooled cooling air technology (CCA technology) shows expected performance in solving the growing thermal challenge for advanced aero engines by reducing the temperature of cooling air. The effect of CCA technology on the overall propelling performance with or without adjusting cycle parameters is controversial. Based on this, both the energy and exergy methods have been adopted to elaborate the specific mechanisms of the above energy utilization discrepancy. As a result, the scheme of CCA technology without optimizing cycle parameters has lower propelling work and efficiency with the total exergy destruction increasing 0.5~2%. Oppositely, as for the scheme of CCA with meliorated cycle parameters, the propelling efficiency improved by around 2~4% with total exergy destruction reduced by 1~3.5%. By analyzing the distribution of exergy destruction, the avoidable and unavoidable exergy destruction caused by the combustion chamber, compressors, and turbines accounts for the largest proportion, which indicates that more attention needs to be paid in the future. During the whole flight mission, the percentage of exergy destruction is much higher in supersonic, subsonic cruise, combat, and escape conditions. In conclusion, the improvement of cycle parameters to reduce the exergy destruction should be considered when introducing CCA technology.

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Application of genetic algorithm in exergy and sustainability: A case of aero-gas turbine engine at cruise phase

Cited by 34 publications

References 31 publications

Modeling of performance and thermodynamic metrics of a conceptual turboprop engine by comparing different machine learning approaches

Modeling of performance and thermodynamic metrics of a conceptual turboprop engine by comparing different machine learning approaches

Optimization of aero-engine combustion chambers with the assistance of Hierarchical-Kriging surrogate model based on POD downscaling method

Exergetic Effects of Cooled Cooling Air Technology on the Turbofan Engine during a Typical Mission

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