Numerical Prediction of Cyclic Variability in a Spark Ignition Engine Using a Parallel Large Eddy Simulation Approach

Ameen, Mohammed; Mirzaeian, Mohsen; Millo, Federico; Som, Sibendu

doi:10.1115/1.4039549

Cited by 24 publications

(6 citation statements)

References 28 publications

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“…More importantly, the simple physics-based model does not capture all the complexity of the combustion process typically recreated by computational fluid dynamics simulations. 55 The Q-learning algorithm introduced by Watkins 56 was used to adaptively learn the Q-table without knowledge of the transition probability (model-free learning). The Q-factor update law used a constant learning rate k as follows:…”

Section: Reinforcement Learning Algorithmmentioning

confidence: 99%

Reinforcement learning applied to dilute combustion control for increased fuel efficiency

Maldonado,

Kaul,

Schuman

et al. 2024

International Journal of Engine Research

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To reduce the modeling burden for control of spark-ignition engines, reinforcement learning (RL) has been applied to solve the dilute combustion limit problem. Q-learning was used to identify an optimal control policy to adjust the fuel injection quantity in each combustion cycle. A physics-based model was used to determine the relevant states of the system used for training the control policy in a data-efficient manner. The cost function was chosen such that high cycle-to-cycle variability (CCV) at the dilute limit was minimized while maintaining stoichiometric combustion as much as possible. Experimental results demonstrated a reduction of CCV after the training period with slightly lean combustion, contributing to a net increase in fuel conversion efficiency of 1.33%. To ensure stoichiometric combustion for three-way catalyst compatibility, a second feedback loop based on an exhaust oxygen sensor was incorporated into the fuel quantity controller using a slow proportional-integral (PI) controller. The closed-loop experiments showed that both feedback loops can cooperate effectively, maintaining stoichiometric combustion while reducing combustion CCV and increasing fuel conversion efficiency by 1.09%. Finally, a modified cost function was proposed to ensure stoichiometric combustion with a single controller. In addition, the learning period was shortened by half to evaluate the RL algorithm performance on limited training time. Experimental results showed that the modified cost function could achieve the desired CCV targets, however, the learning time was reduced by half and the fuel conversion efficiency increased only by 0.30%.

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Section: Reinforcement Learning Algorithmmentioning

confidence: 99%

Reinforcement learning applied to dilute combustion control for increased fuel efficiency

Maldonado,

Kaul,

Schuman

et al. 2024

International Journal of Engine Research

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“…The total number of computational cells during the simulation ranged from 1 . 8 to 18 M. This grid resolution is comparable to the previous LES of similar SI engines using CONVERGE. 18,61 The effect of AMR on the predicted CCVs is not studied but needs further investigation. The simulation was performed using 140 cores from −130 to 370 CAD and 280 cores from 370 to 590 CAD.…”

Section: Engine Configuration and Simulation Set-upmentioning

confidence: 99%

Laminar-to-turbulent flame transition and cycle-to-cycle variations in large eddy simulation of spark-ignition engines

Splitter

Kim

2020

International Journal of Engine Research

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This paper investigates the effect of laminar-to-turbulent flame transition modeling on the prediction of cycle-to-cycle variations (CCVs) in large eddy simulation (LES) of spark-ignition (SI) engines. A laminar-to-turbulent flame transition model that describes the non-equilibrium sub-filter flame speed evolution during an early stage of flame kernel growth is developed. In the present model, the flame transition is characterized by the flame kernel size at which the flame transition ends, defined here as the flame transition scale. The proposed model captures the effects that variations in a turbulent flow field have on the evolution of early-stage burning rates, through variations in the flame transition scale. The proposed flame transition model is combined with the front propagation formulation (FPF) method and a spark-ignition model to predict CCVs in a gasoline direct injection SI engine. It is found that multi-cycle LES with the proposed flame transition model reproduces experimentally-observed CCVs satisfactorily. When the transition model is not considered or when variations in the transition process are neglected, CCVs are significantly under-predicted for the case considered here. These results indicate the importance of modeling the laminar-to-turbulent flame transition and the effect of turbulence on the transition process, when predicting CCVs, under certain engine conditions. The LES results are also used to analyze sources for variations in the flame transition. It is found, for the present engine case, that the most important source is the cycle-to-cycle variation in the turbulence dissipation rate, which is used to measure the strength of turbulence in the proposed model, near a spark plug. The large-scale velocity field and the variations of the laminar flame speed due to the mixture composition and thermal stratification are also found to be important factors to contribute to the variations in the flame transition.

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“…This motivates the lower number of simulated cycles for Engine 2 (23) with respect to Engine 1 (60); for both engines the first 2 complete LES cycles were discarded to cancel the effect of flow initialization on the analyzed solution. An efficient strategy to reduce the overall time needed for these analyses and bridge LES simulations to the standard industrial development is the Parallel Perturbation Model (PPM) proposed by Ameen et al in [61], which proved able to separate long time-scale CCV into several short time-scale simulations, effectively reproducing the same degree of cyclic variability.…”

Section: Figure 2 Section View Of Grid At Tdc For Engine 1 (Left) And...mentioning

confidence: 99%

Understanding the Origin of Cycle-to-Cycle Variation Using Large-Eddy Simulation: Similarities and Differences between a Homogeneous Low-Revving Speed Research Engine and a Production DI Turbocharged Engine

D'Adamo

Breda

Berni

et al. 2018

SAE Int. J. Engines

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A numerical study using Large-Eddy Simulations to reproduce and understand sources of cycle-to-cycle variation (CCV) in spark-initiated internal combustion engines (ICEs) is presented. Two relevantly different spark-ignition (S.I.) units, i.e. a homogeneous-charge slow-speed single-cylinder research unit (the TCC-III, Engine 1) and a stratified-charge high-revving speed GDI (Engine 2) one are analyzed in fired operations. Multiple-cycle simulations are carried out for both engines and LES results well reproduce the experimentally measured combustion CCV.A correlation study is carried out, emphasizing the decisive influence of the early flame period variability (MFB1) on the entire combustion event in both ICEs. The focus is moved onto the early flame characteristics and the crucial task to determine the dominant causes of its variability (if any) is undertaken. A two-level analysis is carried out: the influence of global parameters is assessed at first; secondly, local details in the ignition region are analyzed. A comparison of conditions at combustion onset is carried out and case-specific leading factors for combustion CCV are identified and ranked.Finally, comparative simulations are presented using a simpler flame deposition ignition model: the simulation flaws are evident due to modelling assumptions in the flame/flow interaction at ignition. The relevance of this study is the knowledge extension of turbulence-driven phenomena in ICEs allowed by advanced CFD simulations. The application to different engine types proves the soundness of the used models and it confirms that CCV is based on engine-specific factors. Simulations show how CCV originates from the interplay of small-and large-scale factors in Engine 1, due to the lack of coherent flows, whereas in Engine 2 the dominant CCV promoters are local AFR and flow velocity at ignition. This confirms the absence of a generally valid ranking and it demonstrates the use of LES as a development and designorienting tool for next-generation engines.

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Numerical Prediction of Cyclic Variability in a Spark Ignition Engine Using a Parallel Large Eddy Simulation Approach

Cited by 24 publications

References 28 publications

Reinforcement learning applied to dilute combustion control for increased fuel efficiency

Reinforcement learning applied to dilute combustion control for increased fuel efficiency

Laminar-to-turbulent flame transition and cycle-to-cycle variations in large eddy simulation of spark-ignition engines

Understanding the Origin of Cycle-to-Cycle Variation Using Large-Eddy Simulation: Similarities and Differences between a Homogeneous Low-Revving Speed Research Engine and a Production DI Turbocharged Engine

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