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- ArticleYalnız künye2024Deep learning-based forecasting modeling of micro gas turbine performance projection: An experimental approach
Kilic, Ugur; Villareal-Valderrama, Francisco; Ayar, Murat; Ekici, Selcuk; Amezquita-Brooks, Luis; Karakoc, T. Hikmet
Performance forecasting of aeroengines is crucial for achieving better operational efficiency, ensuring safety, reducing costs, and minimizing environmental impact in the aviation industry. It enables engineers and researchers to make informed decisions, leading to advancements in technology and the overall evolution of aviation. This study is focused on the effects of flight conditions on the performance of a turbojet, which in consequence affects the environmental aspect of operation. By investigating the relationships between aeroengine efficiency and performance indicators such as thrust, shaft speed, and exhaust gas temperature (EGT), and flight characteristics expressed in terms of environmental and operational conditions, the study seeks to elucidate these connections. The article's significance lies in its successful application of Long Short-Term Memory (LSTM) networks to predict thrust, shaft speed, and EGT variations in turbojet engines under varying flight conditions. Experimental data from a turbojet test bench is processed with deep learning, specifically LSTM recurrent neural networks that are developed based on Matrix Laboratory (MATLAB). The model inputs are free stream air speed, compressor inlet pressure, combustor inlet temperature, combustor inlet pressure, turbine inlet temperature, turbine inlet pressure, nozzle inlet pressure and fuel flow, and the outputs are thrust, shaft speed and EGT. Predicted thrust closely aligns with actual thrust values, though with minor discrepancies. Shaft speed predictions exhibit a similar trend, while EGT predictions showcase a comparable pattern with slight variations. Despite the prediction errors, a thorough evaluation of median values, box plots, and probability density functions confirms that the models effectively capture available information, though discrepancies may arise from measurement inaccuracies and initial engine conditions. These results show that it is possible to accurately predict turbojet performance using LSTM recurrent neural network. This research paves the way for enhanced aeroengine performance prediction, particularly in scenarios requiring off-board or high-performance applications.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Performance based analysis for the Ankara-London route in terms of emissions and fuel consumption of different combinations of aircraft/engine: An IMPACT application
Ekici, Selcuk; Ayar, Murat; Kilic, Ugur; Karakoc, T. Hikmet
This study analyzes two types of aircraft offered to the industry by two different manufacturers on an identical route and trajectory for commercial aviation and the noxious emissions resulting from equipping these aircraft with the most frequently used engine combinations today. The research covers the most recent Base of Aircraft Data (BADA v4.2_1), the Aircraft Noise and Performance (ANP v2.3), and the ICAO Engine Emission Database (254-ICAO EDDB25/FOCA) based integrated aircraft noise and emissions modelling platform (IMPACT) developed by EUROCONTROL. The striking findings in fuel consumption and emissions of four different scenarios during identical flight operations are detailed. The research presents the application of a systematic aircraft-engine pair decision mechanism for investigators, scientists, airline operators, policy analysts and legislators, in accordance with a comprehensive computer simulation method that consistently acknowledges the fuel consumption and emissions analysis of aircraft-engine parings.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2022A Comparative Analysis of Exhaust Gas Temperature Based on Machine Learning Models for Aviation Applications
Atasoy, Vehbi Emrah; Suzer, Ahmet Esat; Ekici, Selcuk
The main objective of this study is to investigate elaborately the relationship between exhaust gas temperature (EGT) and various operational parameters specific to aero-engine for the cruise phase. EGT prediction is performed based on different models, including deep learning (DL) and support vector machine (SVM), using a set of historical flight data, more than 1300. In order to achieve this goal, the EGT is taken as the output parameter while the most key variables for the EGT prediction are taken as the input parameters to the models. Several statistical goodness tests, namely root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), are conducted to make a fair comparison between the efficiency and performance of each model that is developed based on Matrix Laboratory (matlab) and R code. The relative importance for the altitude (ALT) parameter of 11.89% has the highest value while the lowest relatively importance parameter is vibration (VIB) of 5.00%. EGT variation for the actual data is in the range of 459.05 and 607.32 °C. It is observed that the EGT variation of DL and SVM ranges from 457.09 to 604.52 and from 454.64 to 603.23 °C, respectively. Furthermore, the prediction error for DL and SVM fluctuates between a minimum of -21.61 to a maximum of 22.50 °C and a minimum of -13.34 to a maximum of 12.44 °C, respectively. In the light of the statistical test results, it is concluded that the DL model with RMSE of 4.3922, MAE of 3.3981, and R2 of 0.9834 shows more excellent ability in predicting EGT than the SVM model with RMSE of 5.5212, MAE of 4.0527, and R2 of 0.9712. This study may effectively be applied to different aircraft types as a useful roadmap for academic and industrial researchers in this sort of application and it shed the light on optimizing performance for a specific aircraft by thermodynamic methods.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2021Architecture design and performance analysis of a hybrid hydrogen fuel cell system for unmanned aerial vehicle
Ozbek, Emre; Yalin, Gorkem; Karaoglan, Mustafa Umut; Ekici, Selcuk; Colpan, C. Ozgur; Karakoc, T. Hikmet
The flight endurance of UAV systems is an important issue that restricts the operational capabilities. Thus, different energy systems and alternative onboard energy generation systems have been tested for the UAVs. Within these systems, fuel cells provide high energy density that can increase flight endurance greatly. In this study, a PEM fuel cell – Li-Po battery hybrid system has been developed by evaluating three architecture models. In the guide of the experimental power demand data of a fixed-wing UAV, modeling and testing procedures were performed. Battery voltage and fuel cell current variations observed during the ground tests were evaluated. It has been observed that approximately 160–170 W of the 250 W power is met by the fuel cell since no preconditioning has been applied and the temperature values at which the fuel cell exhibits its optimum performance. In the case where the fuel cell could provide 7.8 An under conditions where the humidification effects were not included in the model, the required current was over 7.8 A between approximately 400-1200 s. The fuel cell and battery behavior in response to the sudden power changes and to the uncertainties corresponding to the changes in the motor power during the flight are also detailed.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2020Evaluation of design methodology, limitations, and iterations of a hydrogen fuelled hybrid fuel cell mini UAV
Özbek, Emre; Yalin, Gorkem; Ekici, Selcuk; Karakoc, T. Hikmet
Fossil fuels are being depleted day by day. The dependency on fossil fuels must be eliminated in the following years. Also, in the matter of emissions, the cleaner energy generation and usage techniques must be evaluated. The aviation industry is a big energy consumer nowadays. More electric aircraft and All-electric aircraft concepts are being worked on globally to achieve a sustainable aviation industry. Though the electric motors can produce enough thrust force for airliners, due to energy storage technology limitations, the thrust force can't be sustained for a long duration flight. Battery technologies are being developed to overcome these limitations. Moreover, hybrid energy systems and sustainable onboard energy generation systems are trending to achieve sustainability. Fuel Cell and Hybrid Fuel Cell systems are two of the feasible onboard energy systems. The applications of these systems on UAVs (Unmanned Aerial Vehicle) produce data for later studies. The system's behavior during flight phases must be examined on platforms like mini UAVs to understand the system capabilities. In this work, the development phases of a mini UAV designed for Hybrid Fuel Cell flight are introduced. Design philosophy and methodology for designing a UAV with a hybrid propulsion system, limitations of such designs and iterations required during the flight tests are explained. Also, the detailed design estimations of power consumption and final power consumption data collected from flight tests of the aircraft were compared to provide valuable data for following power consumption limited aircraft design studies.
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