PPPS-2013: High pressure micro plasma discharge: Effect of conjugate heat transfer

Mobli, Mostafa; Mahamud, Rajib; Farouk, Tanvir

doi:10.1109/plasma.2013.6634987

Cited by 6 publications

(6 citation statements)

References 2 publications

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“…In contrast to engines that spark ignition or compression ignition, no mechanism directly initiates combustion in internal combustion engines. In engines that use spark ignition, the combustion process is started by lighting the spark plug, while in engines that use compression ignition, the combustion process is started by injecting gasoline [17,[23][24][25]HCCI engines, on the other hand, determine the fuel condition at the beginning of the compression process. This condition affects on the ignition time, which is determined by the volumetric compression ratio from the beginning of the compression process.…”

Section: Figure 1: Stages Of the Homogeneous Mixture Compression Igni...mentioning

confidence: 99%

Modeling of Average Pressure of Homogeneous Mixture Compression Ignition Engine using Neural Network

2024

International Journal of Science and Engineering Applications

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Homogeneous mixture compression ignition (HCCI) internal combustion engines are considered to be a potentially new kind of combustion due to the combustion mechanism that occurs inside these engines. This is because it improves the efficiency of the engine and efficiently lowers the levels of nitrogen oxides. Furthermore, the improvement and control of internal combustion engines, which are amazingly homogeneous mixture compression ignition engines, have always been something that has captured my interest. The cylinder pressure is used as a form of feedback by controllers for internal combustion engines. A costly method that requires the use of dependable computer systems is required to get an exact measurement of the pressure inside the cylinder via the utilization of pressure sensors. This research has developed a model that uses a dynamic multilayer perceptron neural network to determine the ideal average pressure for a homogeneous mixture compression ignition engine. A dynamic model of a homogeneous mixture compression ignition engine was utilized using the MATLAB program in order to get the required data for neural network training. This was done prior to the training of the neural network.

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Section: Figure 1: Stages Of the Homogeneous Mixture Compression Igni...mentioning

confidence: 99%

Modeling of Average Pressure of Homogeneous Mixture Compression Ignition Engine using Neural Network

2024

International Journal of Science and Engineering Applications

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“…On the other hand, there are two different aspects of each software model: the accuracy of the model and the speed of simulation and calculation, so each one must be ignored slightly to reach the other one. Considering all of the mentioned issues, neural networks are an attractive option for modeling internal combustion engines [15,23,[29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the mean effective pressure of a spark ignition internal combustion engine using a neural network, considering the wall-wetting dynamics

Mahnaz Zameni,

Mahdi Ahmadi,

Arash Talebi

2024

Global J. Eng. Technol. Adv.

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The management and development of internal combustion engines stand as critical pursuits within the automotive and related industries. Utilizing cylinder pressure as feedback, engine controllers rely on intricate systems to regulate performance. However, due to the inherent complexity and nonlinearity of engines, direct measurement of cylinder pressure through pressure sensors is costly and computationally demanding. Consequently, the need for accurate and detailed engine models becomes paramount. Neural networks offer a promising avenue for simulating internal combustion engines, combining speed and precision. By treating the engine as an enigmatic entity, neural networks can construct detailed models. This study aims to employ two types of neural networks—multilayer perceptron and radial basis functions—to train and build a model of an internal combustion engine. These networks will simulate and estimate the engine's mean suitable pressure, allowing for a comparison of their effectiveness. Prior to implementing the neural network architecture, an engine model was constructed in MATLAB to gather necessary training data. This preliminary step ensured a robust foundation for subsequent network design and implementation. In summary, this research focuses on leveraging neural networks to model internal combustion engines, utilizing both multilayer perceptron and radial basis functions to simulate engine behavior and estimate mean suitable pressure.

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“…However, the accuracy of the model and the speed with which it can simulate and do computations are two separate characteristics of any software model; hence, one must overlook the other to attain any of these qualities [21][22][23]. Neural networks, owing to their precision and speed of reaction, are an appealing choice for simulating internal combustion engines in all of the aforementioned circumstances [6,[24][25][26][27][28][29][30]. In order to train and construct a model, this inquiry included the utilization of a multilayer perceptron neural network.…”

Section: Introductionmentioning

confidence: 99%

Creating a neural network-based model to predict the exhaust gas temperature of the internal combustion engine

Mahnaz Zameni,

Mahdi Ahmadi,

Arash Talebi

2024

GSC Adv. Res. Rev.

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The regulation and improvement of the performance of internal combustion engines is a continual primary focus of research and development activities conducted within the automobile industry and other relevant sectors. To succeed in reaching this objective, it is necessary to have an accurate and complete model of these engines. However, due to internal combustion engines' complex and nonlinear nature, accurately replicating their behavior may be challenging and time-consuming. Neural networks are a potentially useful strategy for simulating these engines successfully since they offer a solution that strikes a healthy balance between speed and precision. This research investigates the process of building a model of an internal combustion engine by using not one but two separate kinds of neural networks: multilayer perceptrons and radial basis functions. These neural networks aim to simulate and make predictions about the temperature of the engine's exhaust gas. They are especially useful for modeling nonlinear systems because of their incredible convergence speed and excellent accuracy levels.

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PPPS-2013: High pressure micro plasma discharge: Effect of conjugate heat transfer

Cited by 6 publications

References 2 publications

Modeling of Average Pressure of Homogeneous Mixture Compression Ignition Engine using Neural Network

Modeling of Average Pressure of Homogeneous Mixture Compression Ignition Engine using Neural Network

Estimation of the mean effective pressure of a spark ignition internal combustion engine using a neural network, considering the wall-wetting dynamics

Creating a neural network-based model to predict the exhaust gas temperature of the internal combustion engine

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