Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Badra, Jihad; Khaled, Fethi; Tang, Meng; Pei, Yuanjiang; Kodavasal, Janardhan; Pal, Pinaki; Owoyele, Opeoluwa; Fuetterer, Carsten; Brenner, Mattia; Farooq, Aamir

doi:10.1115/1.4047978

Cited by 52 publications

(16 citation statements)

References 42 publications

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“…The merit function in equation ( 1) is similar to the one used in Badra et al 36 and Moiz et al 37 but neglected soot emissions, peak cylinder pressure, or pressure rise rate. Equation (2) shows the normalization way for the parameters considered here (i.e.…”

Section: Machine Learning Applications For Engine Combustion Modelingmentioning

confidence: 99%

On the use of artificial neural networks to model the performance and emissions of a heavy-duty natural gas spark ignition engine

Huang

Liu

Ulishney

et al. 2021

International Journal of Engine Research

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The use of computational models for internal combustion engine development is ubiquitous. Numerical simulations using simpler to complex physical models can predict engine’s performance and emissions, but they require large computational capabilities. By comparison, statistical methodologies are more economical tools in terms of time and resources. This paper investigated the use of an artificial neural network algorithm to simulate the nonlinear combustion process inside the cylinder. Three engine control variables (i.e. spark timing, mixture equivalence ratio, and engine speed) were set as the model inputs. Outputs included peak cylinder pressure and its location, maximum pressure rise rate, indicated mean effective pressure, ignition lag, combustion phasing, burn duration, exhaust temperature, and engine-out emissions (i.e. nitrogen oxides, carbon monoxide, and unburned hydrocarbons). Eighty percent of the experimental data from a heavy-duty natural gas spark ignition engine were utilized to train the model. The perceptions accurately learned the combustion characteristics and predicted engine responses with acceptable errors, evidenced by close-to-unity coefficient of determination and close-to-zero root-mean-square error. Moreover, the regressors captured the effect of key operating variables on the engine response, suggesting the well-trained models successfully identified the complex relationships and can help assist engine analysis. Overall, the neural network algorithm was appropriate for the application investigated in this study.

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Section: Machine Learning Applications For Engine Combustion Modelingmentioning

confidence: 99%

On the use of artificial neural networks to model the performance and emissions of a heavy-duty natural gas spark ignition engine

Huang

Liu

Ulishney

et al. 2021

International Journal of Engine Research

View full text Add to dashboard Cite

show abstract

“…Compared with experimental measurement and physical calculation models, machine learning has advantages such as fast response, high accuracy, and strong generalization ability [11,12]. In recent years, because of its powerful induction and reasoning capabilities, machine learning has been gradually applied to pattern recognition and performance optimization of engines [13][14][15][16][17]. Jihad A. Badra et al developed a Machine Learning Grid Gradient Ascent (ML-GGA) approach to optimize the performance of internal combustion engines and demonstrated the potential of ML-GGA to significantly reduce the time needed for optimization problems, without a loss of accuracy compared with traditional approaches [18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm

Sun

Lee

et al. 2022

Applied Sciences

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In-cylinder pressure is one of the most important references in the process of diesel engine performance optimization. In order to acquire effective in-cylinder pressure value, many physical tests are required. The cost of physical testing is high; various uncertain factors will bring errors to test results, and the time of an engine test is so long that the test results cannot meet the real-time requirement. Therefore, it is necessary to develop technology with high accuracy and a fast response to predict the in-cylinder pressure of diesel engines. In this paper, the in-cylinder pressure values of a high-speed diesel engine under different conditions are used to train the extreme gradient boosting model, and the sparrow search algorithm—which belongs to the swarm intelligence optimization algorithm—is introduced to optimize the hyper parameters of the model. The research results show that the extreme gradient boosting model combined with the sparrow search algorithm can predict the in-cylinder pressure under each verification condition with high accuracy, and the proportion of the samples which prediction error is less than 10% in the validation set is 94%. In the process of model optimization, it is found that compared with the grid search method, the sparrow search algorithm has stronger hyper parameter optimization ability, which reduces the mean square error of the prediction model by 27.99%.

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“…[2] Additionally, within the goals of the Paris Agreement, the transportation sector is viewed as a critical target for decarbonization. [2,3] This is because fossil fuels still account for over 92% of transportation energy, [2,4] which is the greatest among all other sectors. EVs and alternative fuel vehicles make up only a small share.…”

Section: Introductionmentioning

confidence: 99%

Thermal Modeling and Analysis of an Electric Vehicle Charging System

Chea¹,

Agelin‐Chaab²,

Mastali³

et al. 2022

Energy Tech

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In this article, computational fluid dynamics is used to analyze the thermal performance of an electric vehicle charging system with lithium‐ion battery packs. In the analysis, the locations of an air conditioner, circulation fans, and safety barrier are varied to parametrically study the effectiveness of the thermal management system of the charging system. Temperature iso‐surfaces are generated and used to identify the regions within the charging system that are within normal working temperature ranges. The circulation fans greatly impact the effectiveness of the cooling system. The orientation of the fans also plays an important role in the distribution of temperatures in the battery packs. Furthermore, the individual battery improvement is quantified using the rack cooling index (RCI), where an RCI increase of up to 92.4% is reported when the circulation fans are activated. The results from this study demonstrate that a combination of circulation fans and an air conditioner can result in a simple and cost‐effective thermal management approach for a charging system, whereas an air conditioner alone cannot achieve the same cooling effectiveness.

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Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Cited by 52 publications

References 42 publications

On the use of artificial neural networks to model the performance and emissions of a heavy-duty natural gas spark ignition engine

On the use of artificial neural networks to model the performance and emissions of a heavy-duty natural gas spark ignition engine

Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm

Thermal Modeling and Analysis of an Electric Vehicle Charging System

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