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The purpose of this study was to investigate wear behaviours of brake pads produced from carbon–carbon (C/C) composites in both wet and dry friction sliding conditions. Carbon is probably the most remarkable element in science and also C/C composites are a family of advanced composite materials. They are the most advanced form of carbon and consist of fibre based on carbon precursors embedded in a carbon matrix. In the present work, wear test specimens were prepared according to the related standards and they were exposed to pin-on-disc wear testing in wet and dry sliding conditions with different loads as 10, 20, 30 and 40 N with 1 m/s constant sliding speed. Wet friction process was conducted on all specimens by means of rain water collected from the nature.

Pin-on-disc wear test tribology lubrication was used.

Mechanical and physical property measurements of C/C composite brake pad materials: hardness, modulus of elasticity, density and water absorption capacity. Wear performance of materials were measured as coefficient of friction, volumetric loss and specific wear rate.

C/C composite brake pads are used in railway vehicles. Wear performances of them are very important for safety. In this study, wear behaviours of these materials were investigated not only in dry sliding friction condition but also in wet sliding one. Because safety braking is important in all weather conditions for trains, and we used natural rain water to observe the wet sliding friction behaviour of brake pads. “Water lubrication” is an important aspect mentioned in tribology handbooks.

The purpose of this study is to determine and compare the total and per passenger HC, CO, NO_x and CO₂ emissions from aircraft landing and takeoff (LTO) cycle before and during the COVID-19 pandemic. In addition, it is aimed to determine the global warming potential (GWP), environmental impacts (EIs) and enviroeconomic cost (eco-cost) of these emissions in total and per passenger.

Analyses were carried out with the help of the International Civil Aviation Organization’s Engine Emission Databank, using real flight data recorded by the airport authority.

During the COVID-19 pandemic, total pollutant emissions (HC, CO, NO_x and CO₂) decreased between 23.7% and 30.8% compared with the pre-pandemic period. In addition, per passenger pollutant emissions increased during the pandemic. Compared with the pre-pandemic period, GWP, EI and eco-cost values decreased by 24.1%, 23.89% and 23.93%, respectively, in the pandemic. However, the per passenger GWP, EI and eco-cost values increased by about 10% compared with the pre-pandemic period.

This study reveals the effects of COVID-19 in terms of EIs and environmental costs caused by aircraft in the LTO cycle.

The originality of this study is to calculate the pollutant emissions caused by aircraft in the LTO cycle with real flight data and to reveal the effects of the COVID-19 pandemic. The novelty of this study is the determination and comparison of total and per passenger pollutant emissions, GWP, EI and eco-cost before and during the pandemic.

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.

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).

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%.

It is limited by the flight process of a Turboprop engine.

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.

In monitoring and review of entropy management related to fossil fuel technologies, key indicators developed can be used as benchmarks for managing emission sources

The two basic indicators developed can be used as monitoring measurement tools of sustainable energy and environmental performances for engines and applications.

The purpose of this paper is to calculate the fuel consumption and emissions of carbon monoxide (CO), nitrogen oxide (NO_x) and hydrocarbons (HC) in the taxi-out period of aircraft at the International Diyarbakir Airport in 2018 and 2019.

Calculations were performed by determining the engine operating times in the taxi-out period with the flight data obtained from the airport authority. In the analyses, aircraft series and aircraft engine types were determined, and the Engine Exhaust Emission Databank of the International Civil Aviation Authority (ICAO) were used for the calculation.

Total fuel consumption in the taxi-out period in 2018 and 2019 was calculated as 525.64 and 463.69 tons, respectively. In 2018, HC, CO and NO_x emissions caused by fuel consumption were found to be 1,109, 10,668 and 2,339 kg, respectively. In 2019, the total HC, CO and NO_x emissions released to the atmosphere during the taxi-out phase are 966, 9,391 and 2,126 kg, respectively. B737 Series aircraft have the largest share in total fuel consumption and pollutant emissions.

This study explains the importance of determining fuel consumption and pollutant emissions by considering engine operating times in the taxi-out period. The study provides aviation authorities with scientific methods to follow in calculating fuel consumption and emissions from aircraft operations.

The originality of this study is the calculation of fuel consumption and pollutant emissions by determining real-time engine running times in the taxi-out period. In addition, calculations were made with real engine operating times determined in the taxi-out period using real flight data.

Temperature anomalies in the upper troposphere have become a reality as a result of global warming, which has a noticeable impact on aircraft performance. The purpose of this study is to investigate the total air temperature (TAT) anomaly observed during the cruise level and its impact on engine parameter variations.

Empirical methodology is used in this study, and it is based on measurements and observations of anomalous phenomena on the tropopause. The primary data were taken from the Boeing 747-8F's enhanced flight data recorder, which refers to the quantitative method, while the qualitative method is based on a literature review and interviews. The GEnx Integrated Vehicle Health Management system was used for the study's evaluation of engine performance to support the complete range of operational priorities throughout the entire engine lifecycle.

The study's findings indicate that TAT and SAT anomalies, which occur between 270- and 320-feet flight level, have a substantial impact on aircraft performance at cruise altitude and, as a result, on engine parameters, specifically an increase in fuel consumption and engine exhaust gas temperature values. The TAT and Ram Rise anomalies were the focus of the atmospheric deviations, which were assessed as major departures from the International Civil Aviation Organizations–defined International Standard Atmosphere, which is obvious on a positive tendency and so goes against the norms.

Necessary fixed flight parameters gathered from the aircraft's enhanced airborne flight recorder (EAFR) via Aeronautical Radio Incorporated (ARINC) 664 Part 7 at a certain velocity and altitude interfacing with the diagnostic program direct parameter display (DPD), allow for analysis of aircraft performance in a real-time frame. Thus, processed data transmits to the ground maintenance infrastructure for future evaluation and for proper maintenance solutions.

A real-time analysis of aircraft performance is possible using the diagnostic program DPD in conjunction with necessary fixed flight parameters obtained from the aircraft's EAFR via ARINC 664 Part 7 at a specific speed and altitude. Thus, processed data is transmitted to the ground infrastructure for maintenance to be evaluated in the future and to find the best maintenance fixes.

The purpose of this study is to develop and test a new deep learning model to predict aircraft fuel consumption. For this purpose, real data obtained from different landings and take-offs were used. As a result, a new hybrid convolutional neural network (CNN)-bi-directional long short term memory (BiLSTM) model was developed as intended.

The data used are divided into training and testing according to the k-fold 5 value. In this study, 13 different parameters were used together as input parameters. Fuel consumption was used as the output parameter. Thus, the effect of many input parameters on fuel flow was modeled simultaneously using the deep learning method in this study. In addition, the developed hybrid model was compared with the existing deep learning models long short term memory (LSTM) and BiLSTM.

In this study, when tested with LSTM, one of the existing deep learning models, values of 0.9162, 6.476, and 5.76 were obtained for R², root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. For the BiLSTM model when tested, values of 0.9471, 5.847 and 4.62 were obtained for R², RMSE and MAPE, respectively. In the proposed hybrid model when tested, values of 0.9743, 2.539 and 1.62 were obtained for R², RMSE and MAPE, respectively. The results obtained according to the LSTM and BiLSTM models are much closer to the actual fuel consumption values. The error of the models used was verified against the actual fuel flow reports, and an average absolute percent error value of less than 2% was obtained.

In this study, a new hybrid CNN-BiLSTM model is proposed. The proposed model is trained and tested with real flight data for fuel consumption estimation. As a result of the test, it is seen that it gives much better results than the LSTM and BiLSTM methods found in the literature. For this reason, it can be used in many different engine types and applications in different fields, especially the turboprop engine used in the study. Because it can be applied to different engines than the engine type used in the study, it can be easily integrated into many simulation models.

This study aims to obtain the lowest surface roughness (Ra) and drill bit adhesion values (AV) depending on the change in control factors (cutting speed-Vc, feed rate-f and drill bit-D) during drilling of the Al 5083 H116 alloy. Low roughness values increase the fatigue strength of the final part and affect tribological properties such as lubrication and friction. In the machining of ductile materials, the AV increases the Ra value and negatively affects the tool life.

Drilling tests were conducted using Taguchi L16 orthogonal array. The experimental measurement findings for Ra and AV were adjusted utilizing the Grey Relational Analysis (GRA), the Response Surface Method (RSM) and Artificial Neural Networks (ANN) to generate prediction values. SEM detected drill-tip adhesions and chip morphology and they were analyzed by EDX.

Ra and AV increased as the f increased. Vc affects AV; 86.04% f on Ra and 54.71% Vc on AV were the most effective control parameters. After optimizing Ra and AV using GRA, the f is the most effective control factor. Vc: 120 m/min, f: 0.025 mm/rev and D2 were optimal. ANN predicted with Ra 99.6% and AV 99.8% accurately. Mathematical models are obtained with RSM. The increase in f increased AV, which had a negative effect on Ra, whereas the increase in Vc decreased the adhesion tendency. With the D1 drill bit with the highest flute length, a relatively lower Ra was measured, as it facilitates chip evacuation. In addition, the high correlations of the mathematical models obtained indicate that the models can be used safely.

The novelty of this study is to determine the optimum drilling parameters with GRA and ANN for drilling the necessary holes for the assembly of ammunition wing propulsion systems, especially those produced with Al 5083 H116 alloy, with rivets and bolts.

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.

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 EI_CO, EI_CO2, EI_NO2 and EI_UHC data were used on the output. In the system trained for combustion efficiency, EI_CO and EI_UHC data were used at the input and fuel combustion efficiency data at the output.

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.

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.

The purpose of this paper is to investigate the preschool teachers’ cultural intelligence, their attitudes towards multicultural education and the correlation between these two variables.

The sample consisted of 203 preschool teachers working in preschool classes within primary schools administrated by the Ministry of Education in Turkish Republic of Northern Cyprus (TRNC). Data were collected from the teachers by cultural intelligence scale (CQS), teacher multicultural attitude survey (TMAS) and demographic information form.

The analysis revealed that cultural intelligence and attitudes towards multicultural education did not differ according to teaching experience in a foreign country. Significant differences were found in metacognition, motivation and behaviour sub-dimension scores, cultural intelligence total scores and multicultural education attitude scores because of having a friend in foreign country, cognition sub-dimension scores because of having a non-Turkish-speaking student (NTSS) in class and cultural intelligence total scores because of knowing a foreign language. Significant relation was found between cultural intelligence scores and multicultural education attitude scores of the participants.

Despite the increasing cultural diversity in the country, there are no official regulations or efforts to establish arrangements for multicultural education in TRNC. Thus, this research is valuable for clarifying the situation in TRNC, determining the requirements and providing insights for future scientific work and implications.