Effects of engine parameters on ionization current and modeling of excess air coefficient by artificial neural network

Şahin, Fatih

doi:10.1016/j.applthermaleng.2015.06.100

Cited by 26 publications

(2 citation statements)

References 24 publications

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“…However, these sensors are very costly. Sahin [ 57 ] suggested the estimation of this coefficient with ionization current data using the secondary spark plug and different engine variables. The developed ANN model involves the excess air coefficient as an output and ignition angle, engine speed, and the peak ionization current with the location were the input parameters.…”

Section: Modeling Of Internal Combustion Enginesmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.

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Section: Modeling Of Internal Combustion Enginesmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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“…Their results showed that the proposed adaptive learning method performs with higher learning speed, reduced computational resources and lower network complexities. Şahin [21] used ANN model to predict the in-cylinder air-fuel ratio by using data of the ionization current. The ANN model predicted the air-fuel ratio with a prediction accuracy of 0.99508.…”

Section: Introductionmentioning

confidence: 99%

Cylinder Pressure Prediction of An HCCI Engine Using Deep Learning

Yaşar

Çağıl

Torkul

et al. 2021

Chin. J. Mech. Eng.

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Engine tests are both costly and time consuming in developing a new internal combustion engine. Therefore, it is of great importance to predict engine characteristics with high accuracy using artificial intelligence. Thus, it is possible to reduce engine testing costs and speed up the engine development process. Deep Learning is an effective artificial intelligence method that shows high performance in many research areas through its ability to learn high-level hidden features in data samples. The present paper describes a method to predict the cylinder pressure of a Homogeneous Charge Compression Ignition (HCCI) engine for various excess air coefficients by using Deep Neural Network, which is one of the Deep Learning methods and is based on the Artificial Neural Network (ANN). The Deep Learning results were compared with the ANN and experimental results. The results show that the difference between experimental and the Deep Neural Network (DNN) results were less than 1%. The best results were obtained by Deep Learning method. The cylinder pressure was predicted with a maximum accuracy of 97.83% of the experimental value by using ANN. On the other hand, the accuracy value was increased up to 99.84% using DNN. These results show that the DNN method can be used effectively to predict cylinder pressures of internal combustion engines.

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Data-driven nonlinear modeling studies on removal of Acid Yellow 59 using Si-doped multi-walled carbon nanotubes

Büyükada

2017

Int. J. Environ. Sci. Technol.

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Effects of engine parameters on ionization current and modeling of excess air coefficient by artificial neural network

Cited by 26 publications

References 24 publications

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Cylinder Pressure Prediction of An HCCI Engine Using Deep Learning

Data-driven nonlinear modeling studies on removal of Acid Yellow 59 using Si-doped multi-walled carbon nanotubes

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