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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 experime...
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Context 1
... this experimental study, the values of the flight phases in terms of RPM are given in Table 2. In the evaluation of engine emissions, the changes of CO, CO 2 , unburned hydrocarbons (UHC) and NO 2 emissions of the turbofan engine according to four different engine operating loads (idle, approach, climb, take-off) are performed on the basis of the International Civil Aviation Organization (ICAO) emission measurement methodology. ...Context 2
... shown in Figure 4, LTO can be summarized as the aircraft leaving the hangar to the runway, taking off from the runway, landing at the target airport after flying at a certain altitude in the air and entering back into the hangar. The LTO cycle used in the study is also detailed in Table 2. ...Context 3
... addition, the combustion efficiency was calculated with the EI CO , EI UHC data and H f value presented in Table 3 and presented in Table 4. MLFF-NN models are designed separately for each exhaust emission data and combustion efficiency. The error values of the designed models were calculated using the functions given in the results and discussion section and presented in Table 12. ...Context 4
... shown in Table 4, the combustion efficiency of the engine used in the study varies between 97.8 and 100%. In the tested MLFF-NN model, using UHC and CO as inputs, real values could be estimated with 7.6165e-10 error, as shown in Table 12. ...Similar publications
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Citations
... The main theme is to explore the fitness of several machine learning (ML) techniques including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), general regression neural network (GRNN), radial basis function (RBFN), and support vector regression (SVR) for predicting performance and exhaust emissions of the diesel engine fueled with biodiesel blends [43]. The aim is to approximate diverse airfuel ratio motor shaft speed and fuel flow rates under the performance limits contingent on the indices of ignition efficiency and exhaust emission of the engine, a turboprop multilayer feed forward artificial neural network model [44]. This study applies machine learning tools for constrained multi-objective optimization of an HCCI (Homogeneous Charge Compression Ignition) engine. ...
In this work, a study was conducted to investigate the effects of different biodiesel blends with hydrogen peroxide additive on the performance and emissions of an internal combustion engine under various operating parameters. A CI engine was operated with diesel, four dissimilar biodiesels, and H2O2 at various proportions. The biodiesel blends used were Jatropha (D60JB30A10, D60JB34A6, D60JB38A2, D60JB40), Honge (D60HB30A10, D60HB34A6, D60HB38A2, D60HB40), Simarouba (D60SB30A10, D60SB34A6, D60SB38A2, D60SB40), and Neem (D60NB30A10, D60NB34A6, D60NB38A2, D60NB40). The engine was tested at different injection operating pressures (200, 205, and 210 bar), a speed of 1500 rpm, and a CR of 17.5:1. From the experiments conducted, it was highlighted that, under specific conditions, i.e., with an injection pressure of 205 bar, 80% load, a compression ratio of 17.5, an injection timing set at 230 before top dead center, and an engine speed of 1500 rpm, the biodiesel blends D60JB30A10, D60HB30A10, D60SB30A10, and D60NB30A10 achieved the highest brake thermal efficiencies of 24%, 23.9675%, 23.935%, and 23.9025%, respectively. Notably, the blend D60JB30A10 stood out with the highest brake thermal efficiency of 24% among these tested blends. Similarly, when evaluating emissions under the same operational conditions, the D60JB30A10 blend exhibited the lowest emissions levels: CO (0.16% Vol), CO2 (7.8% Vol), HC (59 PPM), and Smoke (60 HSU), while NOx (720 PPM) emissions showed a relative increase with higher concentrations of the hydrogen-based additive. The D60HB30A10, D60SB30A10, and D60NB30A10 blends showed higher emissions in comparison. Additionally, the study suggests that machine learning techniques can be employed to predict engine performance and emission characteristics, thereby cutting down on time and costs associated with traditional engine trials. Specifically, machine learning methods, like XG Boost, random forest regressor, decision tree regressor, and linear regression, were utilized for prediction purposes. Among these techniques, the XG Boost model demonstrated highly accurate predictions, followed by the random forest regressor, decision tree regressor, and linear regression models. The accuracy of the predictions for XG Boost model was assessed through evaluation metrics such as R2-Score (0.999), Root Mean Squared Error (0.540), Mean Squared Error (0.248), and Mean Absolute Error (0.292), which allowed for a thorough analysis of the algorithm’s performance compared to actual values.
... kg/y, respectively (Akdeniz, 2022). The literature includes various studies conducted based on the aircraft emissions analysis (Akyüz, 2022a(Akyüz, , 2022bAtasoy et al., 2021;Kafali and Altuntas, 2020;Kayaalp and Metlek, 2021;Orhan, 2021;Yildiz et al., 2022;Zhu et al., 2021). ...
Purpose- This study aims to examine turboprop and turbofan-powered aircraft, with the same seating capacity flying on the same route and trajectory and investigate their environmental effects.
Design/methodology/approach- The Integrated Aircraft Noise and Emissions Modelling Platform (IMPACT) developed by EUROCONTROL is employed for the calculation of fuel burn, CO2, H2O, and other gas emissions (NOx, SOx, CO, HC, soot, and other trace compounds) for the per phase of flight.
Findings- The striking findings are that turboprop-powered aircraft offer lower required thrust, fuel consumption, and total emissions for a short-haul flight, but turbofan-powered aircraft have lower PM, CO, and HC emissions than turboprop-powered aircraft. This study suggests that turboprop-powered aircraft are superior to turbofan-powered aircraft in terms of environmental impact for a short-haul flight.
Practical implications- The current research conducts comprehensively fuel consumption and amounts of emissions aspects of turboprop and turbofan powered aircraft for sustainable development of airlines by a versatile simulation approach and sheds light on airlines intending to create fleets.
Originality/value- The research offers a systematic aircraft selection for investigators, scientists, airline operators, policy analysts, and legislators, by a comprehensive computer simulation method that acknowledges consistently the fuel consumption and detailed emissions analysis of turboprop and turbofan powered aircraft.
... As a consequence, the CCOA developed the basic GT performance meaningfully, and specific consumption (TSFC) was reduced up to 3.6%. Kayaalp and Metlek [37] predicted successfully the pollutants such as CO, CO2, UHC, and NO2 that the absolute mean error values for each one were 0.1473, 0.0442, CO2, 0.0369, and 0.0028, respectively. ...
In this integrated study, a turboshaft engine was evaluated by adding inlet air cooling and regenerative cooling based on energy-environment analysis. First, impacts of flight-Mach number, flight altitude, the compression ratio of compressor-1 in the main cycle, the turbine inlet temperature of turbine-1 in the main cycle, temperature fraction of turbine-2, the compression ratio of the accessory cycle, and inlet air temperature variation in inlet air cooling system on some functional performance parameters of Regenerative turboshaft engine cycle equipped with inlet air cooling system such as power-specific fuel consumption, Power output, thermal efficiency, and mass flow rate of Nitride oxides (NOx) including NO and NO2 has been investigated via using hydrogen as fuel working. Consequently, based on the analysis, a model was developed to predict the energy-environment performance of the Regenerative turboshaft engine cycle equipped with a cooling air cooling system based on a deep neural network (DNN) with 2 hidden layers with 625 neurons for each hidden layer. The model proposed to predict the amount of thermal efficiency and the mass flow rate of nitride oxide (NOx) containing NO and NO2. The results demonstrated the accuracy of the integrated DNN model with the proper amount of the MSE, MAE, and RMSD cost function for both predicted outputs to validate both testing and training data. Also, R and R^2 are noticeably calculated very close to 1 for both thermal Efficiency and NOx emission mass flow rate for both validations of thermal efficiency and NOx emission mass flow rate prediction values with its training and its testing data.
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.
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 (R2) 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.
Internet of Things, Edge Computing and 5G networks are rapidly becoming key enablers for a wide range of new services. This new infrastructure opens more possibilities for devices with embedded systems and microcontrollers to perform data processing and analysis. The need for real-time decisions now defeats the purpose of sending sensor data to the Cloud for further processing. Storing and analyzing data near its source introduce new constraints on resource-constrained and battery-powered devices. Scalable, Efficient, and Fast classifieR (SEFR) is one such algorithm that brings machine learning to low-power microcontrollers. The main contribution of this work is the definition of a new decision boundary, also known as the bias, to improve the accuracy of the SEFR binary classifier. Experiments performed on an 8-bit Arduino microcontroller using 5-fold cross-validation demonstrate that the improved algorithm (iSEFR) increases Precision, Recall and Accuracy by an average of 9%, 14% and 11% respectively. F1-score increases on average from 80% to 92%, which represents an increase of 12% in the model accuracy for uneven class distribution. MCC also increases from 64% to 85% by an average of 21%, and this represents a better binary classification quality. Training time and space complexity remain constant but the testing time increases marginally by an average of 0.0013 seconds for low-capacity processors. Moreover, iSEFR performs better on a 64-bit intel i5 processor with thousands of data points and features. Experiments also demonstrate that the accuracy of iSEFR is comparable to Support Vector Machine with a linear kernel.
