Exergy analysis of a three-spool turboprop engine during the flight of a cargo aircraft

Dinç, Ali; Şöhret, Yasin; Ekici, Selçuk

doi:10.1108/aeat-05-2020-0087

Cited by 31 publications

(10 citation statements)

References 36 publications

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“…To comprehend the mechanism regarding the conversion of energy forms to each other necessitates the first law of thermodynamics, whereas whose second law determines how much quality energy forms convert to each other. When implementing both laws of thermodynamics known as exergy analysis, possible improvements about the system or components could be detected, enabling the engineers to increase system efficiency in designing process (Dinc et al 2020 ). Since exergetic method considers both quantity and quality of energy, the important knowledge such as exergy destruction and improvement potential rate for thermal systems could be obtained (Balli and Caliskan 2021 ).…”

Section: Methodology and Thermodynamics Backgroundmentioning

confidence: 99%

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Deep learning approach for prediction of exergy and emission parameters of commercial high by-pass turbofan engines

Dursun

Toraman

Aygün

2022

Environ Sci Pollut Res

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Aviation emissions originated from the fuel burn have been hot topics by engineers and policy-makers due to their harmful effects on the environment and thereby human health as well as sustainability. In this study, it is tried that several emission indexes (EIs) involving CO, HC and NOx as well as fuel flow of several commercial aircraft engines (CAEs) are predicted using support vector regression (SVR) and long short-term memory (LSTM) approaches for take-off phase. Moreover, exergo-environmental parameters involving exergy efficiency (ExEFF), wasted exergy ratio (WExR) and environmental effect factor (EEF) pertinent to CAEs are computed employing thermodynamics laws. While establishing the models, rated thrust, by-pass ratio, overall pressure ratio and combustion type of the CAEs are utilized as the model inputs. According to the findings of emission modelling, the coefficient of determination ( R 2 ) of EI NOx and EI CO of the CAEs is found as 0.929074 and 0.960277 with SVR, whereas their R 2 values are elevated to 0.954878 and 0.989283 with LSTM approach, respectively. However, R 2 of EI HC is determined lower with 0.632280 (by SVR) and 0.651749 (by LSTM). On the other hand, exergo-environmental parameters for the CAEs are estimated with high correctness at both models. Namely, R 2 of ExEFF and EEF regarding the CAEs are computed as 0.991748 and 0.989067 by SVR; however, these are calculated as 0.994785 and 0.992797 by LSTM method. To model these parameters with low error by using significant design variables as model inputs could help in predicting emission and environmental metrics for new engine designs.

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Section: Methodology and Thermodynamics Backgroundmentioning

confidence: 99%

“…In terms of thermodynamics, to alleviate fuel consumption, thereby environmental damage requires removing irreversibility from the system as much as possible (Dinc et al 2020 ). In this context, energy, emission, exergy and economy issues are related with each other employing thermodynamics laws to thermal systems.…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach for prediction of exergy and emission parameters of commercial high by-pass turbofan engines

Dursun

Toraman

Aygün

2022

Environ Sci Pollut Res

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“…In general, performance evaluations, as systems that operate depending on power generation, for different altitude conditions, it depends on different fuel consumption and examples of this can be seen in the literature. For example Dinc et al (2020) investigated performance analysis of the cargo aircraft three-spool engine is performed for each flight path point with the aid of exergy and calculated major exergy parameters of the engine during the complete flight. For this, especially in flight planning, engine fuel consumption estimates are made and managed based on these predictions.…”

Section: Environmental Effects In Flight Processmentioning

confidence: 99%

Environmental assessment of entropy control in flight process

Söğüt

2021

AEAT

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Purpose This study aims to examine exergy efficiency of engines and entropy performances at the flight process. In addition, the improvements that can be achieved in the system with the effective parametric controls of the engine have been evaluated in terms of both efficiency and entropy in the system. Design/methodology/approach According to the flight characteristics of the engine, the altitude-dependent irreversibilities and their environmental effects were discussed with two developed indicators, energy performance indicator (EPI) and sustainability indicator (SI). Findings According to the results of both indicators, the energy efficiency potential of the engine during the flight process was found to be 15.02%, while the fuel-based efficiency potential was 18.84%. Research limitations/implications It is limited by the flight process of a Turboprop engine. Practical implications The management tools and criteria of entropy are very difficult model studies. The study offers an evaluable approach based on two basic criteria developed for engines. Social implications In monitoring and review of entropy management related to fossil fuel technologies, key indicators developed can be used as benchmarks for managing emission sources Originality/value The two basic indicators developed can be used as monitoring measurement tools of sustainable energy and environmental performances for engines and applications.

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“…Dinç et al have conducted the exergy analysis of a threespool turboprop engine during the complete flight. They found that the main reason for the improvement in exergy efficiency was directly related to the decrease in ambient temperature and pressure due to altitude change (Dinc et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Prediction of burning performance and emissions indexes of a turboprop motor with artificial neural network

Kayaalp

Metlek

2021

AEAT

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Purpose The purpose of this paper is to estimate different air–fuel ratio motor shaft speed and fuel flow rates under the performance parameters depending on the indices of combustion efficiency and exhaust emission of the engine, a turboprop multilayer feed forward artificial neural network model. For this purpose, emissions data obtained experimentally from a T56-A-15 turboprop engine under various loads were used. Design/methodology/approach The designed multilayer feed forward neural network models consist of two hidden layers. 75% of the experimental data used was allocated as training, 25% as test data and cross-referenced by the k-fold four value. Fuel flow, rotate per minute and air–fuel ratio data were used for the training of emission index input values on the designed models and EICO, EICO2, EINO2 and EIUHC data were used on the output. In the system trained for combustion efficiency, EICO and EIUHC data were used at the input and fuel combustion efficiency data at the output. Findings Mean square error, normalized mean square error, absolute mean error functions were used to evaluate the error obtained from the system as a result of the test. As a result of modeling the system, absolute mean error values were 0.1473 for CO, 0.0442 for CO2, 0.0369 for UHC, 0.0028 for NO2, success for all exhaust emission data was 0.0266 and 7.6165e-10 for combustion efficiency, respectively. Originality/value This study has been added to the literature T56-A-15 turboprop engine for the current machine learning methods to multilayer feed forward neural network methods, exhaust emission and combustion efficiency index value calculation.

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Exergy analysis of a three-spool turboprop engine during the flight of a cargo aircraft

Cited by 31 publications

References 36 publications

Deep learning approach for prediction of exergy and emission parameters of commercial high by-pass turbofan engines

Deep learning approach for prediction of exergy and emission parameters of commercial high by-pass turbofan engines

Environmental assessment of entropy control in flight process

Prediction of burning performance and emissions indexes of a turboprop motor with artificial neural network

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