Real Fuel Modeling for Gasoline Compression Ignition Engine

Wagh, Mayuri; Joo, Nahm Roh; Zoldak, Philip; Won, Hyejun; Ra, Youngchul; Shi, Yu

doi:10.4271/2020-01-0784

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…This development holds great promise for the research community, offering the potential for highly accurate simulations that demand significantly reduced execution times and computational resources compared to traditional methods. Neural networks have already demonstrated their utility in effectively simulating diverse combustion processes and equipment including but not limited to spark ignition engines, compression ignition engine, chemical kinetics, optical diagnostics, gas turbine, boilers, burners, rapid compression machines (RCMs), and shock tubes …”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

Molana,

Darougheh,

Biglar

et al. 2024

Ind. Eng. Chem. Res.

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This review explores machine learning approaches for predicting ignition delay in combustion processes. Ignition delay is a vital parameter in optimizing the engine design, fuel formulations, and combustion efficiency. The review examines the applications of artificial neural networks (ANNs) and convolutional neural networks (CNNs) in various combustion processes and equipment, such as engines, boilers, and rapid compression machines. The differences between ANNs and CNNs are discussed, highlighting their capabilities and limitations. Numerous studies are presented, demonstrating the successful application of neural networks in predicting ignition delay for different fuels and engines. Overall, machine learning approaches show great promise in accurately predicting the ignition delay and advancing energy utilization.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

Molana,

Darougheh,

Biglar

et al. 2024

Ind. Eng. Chem. Res.

Self Cite

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Development of a Multiple Injection Strategy for Heated Gasoline Compression Ignition (HGCI)

Zoldak¹,

Zyada²

2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">A multiple-injection combustion strategy has been developed for heated gasoline direct injection compression ignition (HGCI). Gasoline was injected into a 0.4L single cylinder engine at a fuel pressure of 300bar. Fuel temperature was increased from 25degC to a temperature of 280degC by means of electric injector heater. This approach has the potential of improving fuel efficiency, reducing harmful CO and UHC as well as particulate emissions, and reducing pressure rise rates. Moreover, the approach has the potential of reducing fuel system cost compared to high pressure (>500bar) gasoline direct injection fuel systems available in the market for GDI SI engines that are used to reduce particulate matter.</div><div class="htmlview paragraph">In this study, a multiple injection strategy was developed using electric heating of the fuel prior to direct fuel injection at engine speed of 1500rpm and load of 12.3bar IMEP. The effect of fuel temperature, fuel pressure and start of injection timing are reported for single injection strategy with impact on smoke and pressure rise rates. Double and Triple injection strategies were developed and utilized to maintain the indicated thermal efficiency and NOx emissions, while improving HC, CO, smoke emissions, as well as reducing pressure rise rates to less than 10bar/deg. The HGCI multiple injection strategy was attempted at higher load of 15bar IMEP to demonstrate the potential for low emissions, and high thermal efficiency, with reasonable pressure rise rates and suggest the strategy is scalable to higher speeds and loads.</div></div>

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Assessment of Fuel Consumption of a co-Optimized Gasoline Compression Ignition Engine in a Hybrid Electric Vehicle Platform

Zoldak¹,

Zhu²,

Ha³

2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Increasing regulatory demand to reduce CO<sub>2</sub> emissions has led to an industry focus on electrified vehicles while limiting the development of conventional internal combustion engine (ICE) and hybrid powertrains. Hybrid electric vehicle (HEV) powertrains rely on conventional SI mode IC engines that are optimized for a narrow operating range. Advanced combustion strategies such as Gasoline Compression Ignition (GCI) have been demonstrated by several others including the authors to improve brake thermal efficiency compared to both gasoline SI and Diesel CI modes. Soot and NO<sub>x</sub> emissions are also reduced significantly by using gasoline instead of diesel in GCI engines due to differences in composition, fuel properties, and reactivity. In this work, an HEV system was proposed utilizing a multi-mode GCI based ICE combined with a HEV components (e-motor, battery, and invertor).</div><div class="htmlview paragraph">To determine the total fuel consumption over the FTP75 drive cycle, a fueling map from a multi-mode GCI combustion presented in a previous study was used as the input data set for the 1D HEV drive cycle model (GT Drive) of a mid-sized SUV. An HEV operation strategy was proposed and followed by parametric studies of components including the e-motor sizing (20 kW to 80kW) and the battery capacity (2Ah to 8Ah), and evaluation on their impacts on fuel consumption.</div></div>

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Real Fuel Modeling for Gasoline Compression Ignition Engine

Cited by 3 publications

References 7 publications

Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

Development of a Multiple Injection Strategy for Heated Gasoline Compression Ignition (HGCI)

Assessment of Fuel Consumption of a co-Optimized Gasoline Compression Ignition Engine in a Hybrid Electric Vehicle Platform

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