Sürdürülebilir Kalkınma Amaçları

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Alt birimler

• Uçak Bakım ve Onarım Anabilim Dalı

Araştırmacılar(5)

• Atasoy, Vehbi Emrah
• Aydın, Emre
• Ercümen, K.M.
• Yilmaz, Harun
• Yüca, Emre

• ArticleAçık Erişim2025/r/ebyu/19556
  Yurtiçi Uçuşlarda Farklı Uçak Modellerinin Emisyon Etkilerinin Analizi İçin Gelişmiş Modelleme Araçlarının Kullanılması

  Atasoy, Vehbi Emrah

  Havacılık sektörü, uçuş operasyonları sırasında oluşan gaz ve partikül madde emisyonlarının hassas bir şekilde ölçülmesi ve değerlendirilmesi yoluyla çevresel sürdürülebilirlik hedeflerine ulaşabilir. Araştırmada, aynı sayıda yolcu talebini karşılayacak geniş ve dar gövdeli uçaklar kullanarak İstanbul Sabiha Gökçen Havalimanı (LTFJ) ile Antalya Havalimanı (LTAI) arasında gerçekleştirilen farklı senaryolar üzerinden operasyonel ve çevresel etkileri analiz edilmektedir. Çalışma, EUROCONTROL'ün IMPACT web tabanlı platformu ve analiz için gerekli bilgileri sağlayan BADA veritabanı gibi araçlar kullanılarak gerçekleştirilmektedir. Çalışma kapsamında elde edilen sonuçlar, geniş gövdeli uçağın LTAI-LTFJ ve LTFJ-LTAI uçuşlarında ortalama 17796 kg CO₂ emisyona neden olduğunu göstermektedir. Buna karşılık, ilgili rotalarda iki dar gövdeli uçak kullanılarak aynı sayıda yolcu taşındığında ise toplam 12597 kg CO₂ emisyonu oluşmaktadır. Bu sonuçlar, uçak tipinin toplam emisyon seviyelerinde önemli bir rol oynadığının bir örneğidir ve dar gövdeli uçakların kısa mesafeli uçuşlarda karbon salınımını azalttığını vurgulamaktadır. Bu çalışma, operasyonel ve çevresel sürdürülebilirliğe ulaşmada uçak seçiminin öneminin altını çizerek, yakıt tüketimini ve emisyonları düşürmeyi planlayan bir havayolunun, web tabanlı modelleme teknolojisini kullanarak filo planlama stratejilerini daha etkili hale getirebileceğini göstermektedir.

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• ArticleAçık Erişim2022/r/ebyu/20474
  Fuel estimation of commercial aircraft for the climb-out phase using gaussian process regression model

  Atasoy, Vehbi Emrah

  In this study, Gaussian Process Regression (GPR) is utilised to accurately estimate fuel consumption. For this purpose, ten randomly determined flights performed by Boeing B737-800 twin-engine medium-haul narrow-bodied commercial aircraft are selected. In this context, actual flight data obtained from the Flight Data Recorder (FDR) is used to estimate fuel consumption during the climb-out phase. Different statistical tests, namely Root Mean Square Error (RMSE), coefficient of determination (R2), and Mean Absolute Error (MAE), are applied to evaluate the performance of the GPR in this paper. RMSE, R2, and MAE values for GPR is calculated to be 209.41, 0.99, and 111.38, respectively. As can be seen from the results of all statistical tests, the GPR model indicates successful performance.

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• ArticleYalnız künye2022/r/ebyu/13927
  A 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.

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• ArticleAçık Erişim2019/r/ebyu/22247
  Numerical Investigation of Combustion and Emission Behavior of Shale Gas Mixtures in a Laboratory Scale Combustor

  Yilmaz, Harun

  In this study, effects of gas composition and swirl number on combustion and emission behavior of shale gas mixtures were numerically investigated in a laboratory scale combustor. For this purpose, a two-dimensional model of an experimentally tested premixed combustor was formed. In this model, methane/air combustion was simulated, and achieved results were compared with experimental data to validate applicability of the model. Numerical studies were performed using computational fluid dynamics software, ANSYS/Fluent. Tested shale gas mixtures are; 86% CH4 - 14% C2H6, 81% CH4 - 10% C2H6 - 9% N2 and 58% CH4 - 20% C2H6 - 12% C3H8 - 10% CO2. Combustion and emission behavior of the respective mixtures were determined by examining temperature and species (CO2 and NOx) distributions throughout the combustion chamber. Results of this study showed that %86 CH4- %14 C2H6 mixture is favorable in terms of combustion and emission performance; emissions of NOx are highly dependent on gas composition and swirl number, and axial temperature values and reaction rate are directly proportional to swirl number.

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