Estimating bubble interfacial heat transfer coefficient in pool boiling

Mobli, Mostafa; Bayat, Mahmoud; Li, Chen

doi:10.1016/j.molliq.2022.118541

Cited by 12 publications

(4 citation statements)

References 45 publications

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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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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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show abstract

“…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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“…The interpretation of zinc-ion battery behavior using Electrochemical Impedance Spectroscopy (EIS) emerges as a cornerstone in identifying crucial performance indicators [33][34][35][36]. EIS not only enables the quantification of key parameters but also serves as a diagnostic tool, shedding light on impedance changes, resistance buildup, ion movement constraints, and capacity fade over cycling [37][38][39]. The elucidation of these phenomena through EIS facilitates the identification of underlying causes, such as electrode degradation, electrolyte interface evolution, and other factors contributing to impedance growth and performance degradation [25,40,41].…”

Section: Introductionmentioning

confidence: 99%

Modeling and applications of Electrochemical Impedance Spectroscopy (EIS) for Zinc-ion batteries

Mahnaz Zameni

2024

Magna Sci. Adv. Res. Rev.

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Electrochemical Impedance Spectroscopy (EIS) has emerged as a valuable tool for evaluating secondary batteries due to their efficiency and accuracy. However, interpreting the multifaceted impedance spectra derived from EIS demands a comprehensive grasp of the electrochemical context. This study focuses on devising and applying an Electrochemical Impedance Spectroscopy (EIS) model customized specifically for deciphering the intricacies of zinc-ion batteries. The model construction is tailored to the battery's complexities, enabling extraction of circuit elements that faithfully depict the system's dynamics. Moreover, this research investigates the relationship between parameters of the governing equations. Through experimental validation employing real-time EIS measurements from a zinc-ion battery prototype, the reliability and efficacy of the proposed model are confirmed. This validated model not only enhances our understanding of zinc-ion battery behavior but also propels advancements in utilizing this technology across various energy storage applications.

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Estimating bubble interfacial heat transfer coefficient in pool boiling

Cited by 12 publications

References 45 publications

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

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

Modeling and applications of Electrochemical Impedance Spectroscopy (EIS) for Zinc-ion batteries

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