Modelling and Control of Combustion Phasing of an Rcci Engine

Sadabadi, Kaveh Khodadadi

doi:10.37099/mtu.dc.etds/966

Cited by 4 publications

(3 citation statements)

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“…The nonlinear RCCI COM implemented in this work consists of submodels presented in this section. The main structure and majority of the submodels are based on [12,13] with an exception of calculating in-cylinder temperature, pressure and subsequently the IMEP [29][30][31]. The flowchart of the developed RCCI COM is presented in Figure 1.…”

Section: Nonlinear Rcci Control-oriented Modelmentioning

confidence: 99%

“…Many works have concentrated on light-and heavy-duty engines. Sadabadi et al [12,13] developed a physics-based control-oriented model (COM) for RCCI combustion capable of predicting main combustion indicators such as start of combustion (𝜃 SOC ), burn duration (𝜃 BD ), combustion phasing (𝜃 50 ) and indicated mean effective pressure (IMEP). This model has been widely applied in works covering model-based control of RCCI process.…”

Section: Introductionmentioning

confidence: 99%

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Low Temperature Combustion Modeling and Predictive Control of Marine Engines

Modabberian,

Storm,

Shamekhi

et al. 2024

Applied Sciences

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The increase of popularity of reactivity-controlled compression ignition (RCCI) is attributed to its capability of achieving ultra-low nitrogen oxides (NOx) and soot emissions with high brake thermal efficiency (BTE). The complex and nonlinear nature of the RCCI combustion makes it challenging for model-based control design. In this work, a model-based control system is developed to control the combustion phasing and the indicated mean effective pressure (IMEP) of RCCI combustion through the adjustments of total fuel energy and blend ratio (BR) in fuel injection. A physics-based nonlinear control-oriented model (COM) is developed to predict the main combustion performance indicators of an RCCI marine engine. The model is validated against a detailed thermo-kinetic multizone model. A novel linear parameter-varying (LPV) model coupled with a model predictive controller (MPC) is utilized to control the aforementioned parameters of the developed COM. The developed system is able to control combustion phasing and IMEP with a tracking error that is within a 5% error margin for nominal and transient engine operating conditions. The developed control system promotes the adoption of RCCI combustion in commercial marine engines.

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Section: Nonlinear Rcci Control-oriented Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low Temperature Combustion Modeling and Predictive Control of Marine Engines

Modabberian,

Storm,

Shamekhi

et al. 2024

Applied Sciences

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“…Hence, the RCCI has received widespread attention. Due to the higher compression ratios [8], leaner charges [9], and lower throttle losses [10], spark ignition engines and compression ignition engines are typically more efcient. However, the difculty of CIE is their increased emissions of particulate matter (PM) and NOx.…”

Section: Introductionmentioning

confidence: 99%

Response Analysis of an Experimental Study on the Effect of Speed and Premixed Fuel Ratio on Performance and Emissions in RCCI Engine

Disassa,

Ancha,

Nallamothu

et al. 2024

International Journal of Chemical Engineering

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Even though CI engines are more efficient than SI engines due to their ability to operate at a greater compression ratio, a leaner charge, and lower throttle losses, they have higher PM and nitrogen oxide emissions. The induction system modification with fuel port injection is used as a parameter in RCCI engine operations for controlling emission through in-cylinder charge reactivity and combustion phasing. By varying the amount of hydrous bioethanol in the premixed fuel injection ratio, the engine’s performance and emissions are greatly affected. In this study, an experimental investigation of a triple-fuel RCCI engine running on port-injected gasoline-bioethanol blend and direct-injected diesel fuel was conducted. Taguchi’s experimental design method was employed to assess the impact of various independent variables utilizing three set levels and two factors with the L9 orthogonal array. From the findings, the delta value shows the highest average response for each factor. Engine speed has the largest effect on the signal-to-noise ratio (SNR) with the (delta value of: 10.7446, rank = 1), and the delta value of 38.96, rank = 1, has the largest effect on the response of means at engine speeds of 3000 rpm. The premixed fuel ratio of G25BE75 (delta: 87.30, rank = 1) has the largest effect on the standard deviation. The lines are not parallel in all emission and performance cases except for Tb and CO2, which are close to parallel. The best means in engine speed and premixed blended fuel ratio were NOx, CO, HC, and brake power. At 3000 rpm, the speed had the larger main effect plots of SNR. The premixed fuel ratio of G25BE75 had higher main effect plots for means and standard deviations. The residues appear to have been dispersed normally based on a straight line by using a normal probability plot. The data are normally distributed, as demonstrated by the normal probability plot, and the factors had an impact on the response. Conferring to the experiment result, a high engine speed and higher ethanol content in the RCCI premixed fuel are preferred for reducing nitrogen oxides (NOx) and carbon dioxide (CO2), while unburned hydrocarbons (UHCs) and carbon monoxide (CO) showed a slight increase.

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