A Real-Time Model for Spark Ignition Engine Combustion Phasing Prediction

Wang, Shu; Prucka, Robert; Zhu, Qilun; Prucka, Michael; Dourra, Hussein

doi:10.4271/2016-01-0819

Cited by 17 publications

(8 citation statements)

References 35 publications

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“…Liu et al 14 demonstrated a simplified turbulent combustion model on a carbureted 4-cylinder spark ignited engine. Chen et al 15 and Wang et al 16 extend this work to fuel injected SI engines, also with successful results. Yang and Zhu 17 applied these techniques to a two-zone SACI engine model.…”

Section: Introductionmentioning

confidence: 74%

“…The turbulence model relates u' to mean piston speed, equation (3). Wang et al 16 demonstrate in their application of this relationship between mean piston speed (MPS) and turbulence intensity (u'). This relationship was based on prior work by Liu et al, 14 where the typical value of coefficient c 1 can be from 0.5 to 1.5.…”

Section: Model Structure and Descriptionmentioning

confidence: 99%

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Evaluation of control-oriented flame propagation models for production control of a spark-assisted compression ignition engine

Robertson

Prucka

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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The drive to improve internal combustion engines has led to efficiency objectives that exceed the capability of conventional combustion strategies. As a result, advanced combustion modes are more attractive for production. These advanced combustion strategies typically add sensors, actuators, and degrees of freedom to the combustion process. Spark-assisted compression ignition (SACI) is an efficient production-viable advanced combustion strategy characterized by spark-ignited flame propagation that triggers autoignition in the remaining unburned gas. Modeling this complex combustion process for control demands a careful selection of model structure to maximize predictive accuracy within computational constraints. This work comprehensively evaluates a physics-based and a data-driven model. The physics-based model produces a burn duration by computing laminar flame speed as a function of test point conditions. The crank-angle domain is intentionally excluded to reduce computational expense. The data-driven model is an artificial neural network (ANN). The candidate models are compared to a one-dimensional engine model validated to experimental SACI engine data. Though both models capture the trends in burn rates, the ANN model has a root-mean square error (RMSE) of 1.4 CAD, significantly lower than the 10.4 CAD RMSE of the physics-based model. The exclusion of the crank-angle domain results in insufficient detail for the physics-based model, while the ANN can tolerate this exclusion.

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Section: Introductionmentioning

confidence: 74%

Section: Model Structure and Descriptionmentioning

confidence: 99%

Evaluation of control-oriented flame propagation models for production control of a spark-assisted compression ignition engine

Robertson

Prucka

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

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show abstract

“…Such approaches feature a good compromise between accuracy and computational effort [7]. They are not as fast as ANN based models [8] but can ensure acceptable fastrunning characteristics [9] and several levels of detail can be implemented for augmenting predictive capabilities [10,11]. Data recorded on optically accessible units can ensure a solid starting point for model development and validation, as they feature high spatial resolution [12,13].…”

Section: Introductionmentioning

confidence: 99%

Towards better correlation between optical and commercial spark ignition engines through quasi-dimensional modeling of cycle-to-cycle variability

2022

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Internal combustion engines are still the main choice when considering propulsion technology in the transport sector. Spark ignition (SI) units offer the advantage of good efficiency with simpler after-treatment systems. Lean operation is a promising strategy that would further improve efficiency, but requires mitigation of cycle-to-cycle variability (CCV). Within this context, and given the increasing trend of using simulation based evaluations during engine development, the current work investigated combustion in an optical SI engine through measurements and quasi-dimensional simulation. The possibility of visualizing in-cylinder processes provides unique insight, but also introduces complications with respect to commercial engines. For this reason, quasi-dimensional simulation was applied so as to better understand the factors that induce CCV. For the specific case of the investigated engine, cycle-to-cycle measured exhaust air-fuel ratio was found to be directly correlated to variations of engine output. Several routes of incorporating these effects into simulations were evaluated. Introducing a random component in the period of laminar-turbulent flame transition was found to ensure good grounds for simulating peak pressure variability. Indicated mean effective pressure (IMEP) on the other hand was found to depend less on the initial stages of combustion and was strongly correlated to aforementioned variability of exhaust air-fuel ratio.

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“…For application of the spark ignition (SI) engine model for control purposes, the SI combustion model should strike a balance between model fidelity and computational cost [ 27 ]. There have been various types of combustion modeling, which can be grouped into non-predictive models, such as the Wiebe function, and predictive models [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Minimization of Torque Deviation of Cylinder Deactivation Engine through 48V Mild-Hybrid Starter-Generator Control

Shin¹,

Jung

Han

et al. 2021

Sensors

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Cylinder deactivation (CDA) is an effective technique to improve fuel economy in spark ignition (SI) engines. This technique enhances volumetric efficiency and reduces throttling loss. However, practical implementation is restricted due to torque fluctuations between individual cylinders that cause noise, vibration, and harshness (NVH) issues. To ease torque deviation of the CDA, we propose an in-cylinder pressure based 48V mild-hybrid starter-generator (MHSG) control strategy. The target engine realizes CDA with a specialized engine configuration of separated intake manifolds to independently control the airflow into the cylinders. To handle the complexity of the combined CDA and mild-hybrid system, GT-POWER simulation environment was integrated with a SI turbulent combustion model and 48V MHSG model with actual part specifications. The combustion model is essential for in-cylinder pressure-based control; thus, it is calibrated with actual engine experimental data. The modeling results demonstrate the precise accuracy of the engine cylinder pressures and of quantities such as MAF, MAP, BMEP, and IMEP. The proposed control algorithm also showed remarkable control performance, achieved by instantaneous torque calculation and dynamic compensation, with a 99% maximum reduction rate of engine torque deviation under target CDA operations.

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A Real-Time Model for Spark Ignition Engine Combustion Phasing Prediction

Cited by 17 publications

References 35 publications

Evaluation of control-oriented flame propagation models for production control of a spark-assisted compression ignition engine

Evaluation of control-oriented flame propagation models for production control of a spark-assisted compression ignition engine

Towards better correlation between optical and commercial spark ignition engines through quasi-dimensional modeling of cycle-to-cycle variability

Minimization of Torque Deviation of Cylinder Deactivation Engine through 48V Mild-Hybrid Starter-Generator Control

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