Multi-dimensional energetic performance modeling of an aircraft engine with the aid of enhanced least–squares estimation based genetic algorithm method

Kaba, Aziz; Aygün, Hakan; Turan, Önder

doi:10.1007/s10973-021-10922-z

Cited by 10 publications

(6 citation statements)

References 39 publications

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“…In this regard, many studies are worked on the aero system based on intelligent systems. Kaba et al [22] dramatically enhanced both civil and military aircraft engines with the assistance of an improved least-squares estimation-based genetic algorithm (LSEGA) in fight phases. The parametric studies such as Thrust, specific fuel consumption (SFC), overall and exergy efficiencies are considered.…”

Section: Introductionmentioning

confidence: 99%

Predicting the energy and exergy performance of F135 PW100 turbofan engine via deep learning approach

Sabzehali

Rabiee

Alibeigi

et al. 2022

Energy Conversion and Management

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Section: Introductionmentioning

confidence: 99%

Predicting the energy and exergy performance of F135 PW100 turbofan engine via deep learning approach

Sabzehali

Rabiee

Alibeigi

et al. 2022

Energy Conversion and Management

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“…GA is based on the evolution theory, and is used to find a solution to a problem called an objective function using a numerical algorithm. In physics, engineering, industry, economics, and finance, GAs have been used as a computational algorithm in various fields including aerospace [21][22][23][24][25][26]. In the genetic algorithm, the steps and processes are all mathematical operations.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Metering Pin Diameter Optimization of an Aircraft Landing Gear Shock Absorber

Dinç

2021

Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi B - Teorik Bilimler

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One of the most important components of an aircraft is the landing gear. In today`s modern landing gears, mostly oleopneumatic shock struts are used. Analytically, landing gear can be modeled as a mass-spring-damper system. The model used in the study included landing gear components such as the oleo-pneumatic shock strut, tire and wheel. Furthermore, a tapered metering pin was added to model in order to control the area of the orifice by which hydraulic oil flows during the act of landing impact force. Landing gear design usually aims to minimize two elements which are vertical acceleration and displacement of aircraft mass. The impact force during landing is indicated by vertical acceleration. Displacement of shock absorber should be minimum to decrease the size, weight and space needed for the landing gear system. An optimization problem was defined to minimize those two parameters within the range of given inputs. For this purpose, a composite objective function was created to include and optimize the two output parameters simultaneously with equal weight. Among many inputs, metering pin hub and tip external diameters were selected as variables for the optimization and other inputs were kept constant. For the optimization study, genetic algorithm method was coupled with the Matlab/Simulink model of the landing gear model. After some iterations, solution was converged to determine the two diameters of the metering pin where the vertical acceleration and displacement of aircraft mass are minimized as an objective. At the end of optimization process, vertical acceleration of aircraft mass was reduced from 2.433 g to 1.7828 g (-36.47%) within the given constraint of 2 g maximum. Displacement of aircraft mass X1 was increased from 0.2982 m to 0.3682 m (+23.47%) which is in an acceptable limit of 0.4 m.

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“…The authors stated that thrust of the engine was determined as 516 daN whereas its SFC was measured as 19.706 g/kNs. Kaba et al 17 tried to modeling of energy and exergy efficiency pertinent to variable cycle engine (VCE) using least square method (LSM) based on genetic algorithm (LSEGA). The findings showed that R 2 of energy efficiency is achieved as 0.9999 with high accuracy.…”

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

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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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Multi-dimensional energetic performance modeling of an aircraft engine with the aid of enhanced least–squares estimation based genetic algorithm method

Cited by 10 publications

References 39 publications

Predicting the energy and exergy performance of F135 PW100 turbofan engine via deep learning approach

Predicting the energy and exergy performance of F135 PW100 turbofan engine via deep learning approach

Metering Pin Diameter Optimization of an Aircraft Landing Gear Shock Absorber

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

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