Developing a model for prediction of the combustion performance and emissions of a turboprop engine using the long short-term memory method

Kayaalp, Kıyas; Metlek, Sedat; Ekici, Selçuk; Şöhret, Yasin

doi:10.1016/j.fuel.2021.121202

Cited by 21 publications

(7 citation statements)

References 35 publications

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“…The Elman neural network reported the highest degree of agreement between predicted values and finite element calculations, followed by GA-BP, GRNN, and BP. The mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) [45] were used to evaluate the level of prediction error of these models as shown in Table 4. The total number of samples, predicted value, and actual value are denoted by n, y i , and y i , respectively.…”

Section: Analysis Of Prediction Resultsmentioning

confidence: 99%

Vehicle Interior Noise Prediction Based on Elman Neural Network

Zhou

Liu

et al. 2021

Applied Sciences

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Vehicle interior noise is an important factor affecting ride comfort. To reduce the noise inside the vehicle at the vehicle body design stage, a finite element model of the vehicle body must be established. While taking the first-order global modal of the body-in-white, the maximum sound pressure level of the target point in the vehicle, the body mass, and the side impact conditions into account, the thickness of the body panel as determined via sensitivity analysis is treated as the input variable, and the sample is determined by following the Hamersley experimental design. Specifically, the Elman neural network predicts the noise value in the vehicle, and a vehicle body structure optimization method that comprehensively considers NVH performance and side impact safety is established. The prediction errors of the Elman neural network algorithm were within 3%, which meets the prediction accuracy requirements. To achieve satisfactory restraint performance, the maximum sound pressure level of the target point in the vehicle is reduced by 5.92 dB, and the maximum intrusions of the two points on the B-pillar inner panel are reduced by 31.1 mm and 33.71 mm, respectively. The side impact performance is improved while the noise inside the vehicle is reduced. This study provides a reference method for multidisciplinary research aiming to optimize the design of vehicle body structures.

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Section: Analysis Of Prediction Resultsmentioning

confidence: 99%

Vehicle Interior Noise Prediction Based on Elman Neural Network

Zhou

Liu

et al. 2021

Applied Sciences

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“…W input weight used in equations (1)–(4), R recursive weight, and b bias is expressed. In addition, the term σ denotes the sigmoid function presented in equation (5) (Kayaalp et al , 2021). …”

Section: Methodsmentioning

confidence: 99%

“…By using LSTM and bi-directional long short term memory (BiLSTM) methods, which are machine learning algorithms, the estimation of the fault detection in the aircraft gearbox is performed (Mallikarjuna et al , 2020). In another study using the LSTM method, the combustion performance and emissions of a turboprop engine were estimated (Kayaalp et al , 2021). In a separate study, Huang and Cheng (2022) used classification and regression tree and neural networks models to estimate aircraft fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

Metlek

2023

AEAT

Self Cite

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Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

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“…Namely, coefficient of determination and least square error for energy efficiency modeling are gauged as 0.9998 and 5 × 10 −9 , respectively. Kayaalp et al 14 investigated turboprop engine named as T56‐A‐15 so as to predict its the exhaust emissions index and combustion efficiency using fuel flow, engine speed and air‐to‐fuel ratio as inputs. The authors stated that modeling of combustion efficiency of the engine is obtained with high accuracy, namely coefficient of determination is measured as 0.9507.…”

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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Developing a model for prediction of the combustion performance and emissions of a turboprop engine using the long short-term memory method

Cited by 21 publications

References 35 publications

Vehicle Interior Noise Prediction Based on Elman Neural Network

Vehicle Interior Noise Prediction Based on Elman Neural Network

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

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

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