A two-zone reaction-based combustion model for a spark-ignition engine

Li, Ruixue C.; Zhu, Guoming; Men, Yifan

doi:10.1177/1468087419841746

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…Hence, the uncorrected pressure gradient term in Equation () can be replaced by

λ {\partial \overset{true¯}{p} / \partial x_{k}|}_{corrected}

as follows:

\frac{ρ}{{((), λ \overset{ε}{true})}^{2}} {\overset{true¯}{u}}_{l} \frac{\partial {\overset{true¯}{u}}_{k}}{\partial x_{l}} goodbreak= goodbreak- \frac{\partial \overset{p}{true}}{\partial x_{k}} goodbreak+ \frac{μ}{λ \overset{ε}{true}} \frac{\partial}{\partial x_{l}} \frac{\partial {\overset{true¯}{u}}_{k}}{\partial x_{l}} goodbreak- \frac{μ}{λ \overset{κ}{true}} {\overset{true¯}{u}}_{k} in Ω_{pr} .

In addition to the incorporation of the λ parameter, we also investigated other approaches. In particular, we explored the two zone modeling approach inspired by References 37 and 38 for combustion simulations. In the two zone approach, two separate depth‐averaged models for the channels and electrode are developed and coupled by mass source/sink terms that depend on the pressure difference between two zones.…”

Section: Physical Modelmentioning

confidence: 99%

A generalized reduced fluid dynamic model for flow fields and electrodes in redox flow batteries

et al. 2022

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High‐dimensional models typically require a large computational overhead for multiphysics applications, which hamper their use for broad‐sweeping domain interrogation. Herein, we develop a modeling framework to capture the through‐plane fluid dynamic response of electrodes and flow fields in a redox flow cell, generating a computationally inexpensive two‐dimensional (2D) model. We leverage a depth‐averaging approach that also accounts for variations in out‐of‐plane fluid motion and departures from Darcy's law that arise from averaging across three‐dimensions (3D). Our resulting depth‐averaged 2D model successfully predicts the fluid dynamic response of arbitrary in‐plane flow field geometries, with discrepancies of <5% for both maximum velocity and pressure drop. This corresponds to reduced computational expense, as compared to 3D representations (<1% of duration and 10% of RAM usage), providing a platform to screen and optimize a diverse set of cell geometries.

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“…Hence, the uncorrected pressure gradient term in Equation () can be replaced by

λ {\partial \overset{true¯}{p} / \partial x_{k}|}_{corrected}

as follows:

\frac{ρ}{{((), λ \overset{ε}{true})}^{2}} {\overset{true¯}{u}}_{l} \frac{\partial {\overset{true¯}{u}}_{k}}{\partial x_{l}} goodbreak= goodbreak- \frac{\partial \overset{p}{true}}{\partial x_{k}} goodbreak+ \frac{μ}{λ \overset{ε}{true}} \frac{\partial}{\partial x_{l}} \frac{\partial {\overset{true¯}{u}}_{k}}{\partial x_{l}} goodbreak- \frac{μ}{λ \overset{κ}{true}} {\overset{true¯}{u}}_{k} in Ω_{pr} .

Section: Physical Modelmentioning

confidence: 99%

A generalized reduced fluid dynamic model for flow fields and electrodes in redox flow batteries

et al. 2022

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“…NOx emission [26,28,30,137] Exhaust aftertreatment system [145] Combustion mode transition [53] Airpath of turbocharged engines [32,34,52,141,144] Non-Convex Cyclic Variability [54,152] Soot and uHC emissions [153,154] Knock and MPRR [155][156][157] Engine dynamics have different time scales as shown in Figure 8. The response time of the controlled dynamics is a critical factor in design of the appropriate MPC framework.…”

Section: Convex or Convexifiablementioning

confidence: 99%

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

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An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

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“…Beside the application of reaction-based combustion model to CI diesel engines, a two-zone two-steps reaction-based combustion model is also used for an SI (spark-ignited) engine, 124,125 where the in-cylinder thermal-dynamic parameters such as the mass-fractionburned, in cylinder pressure and temperature, and individual chemical species concentrations in the combustion process were predicted well. Validation of the model using experimental data shows less than 6:2% peak relative pressure error.…”

Section: Applications Of Control-oriented Reaction-based Combustion M...mentioning

confidence: 99%

Review of engine control-oriented combustion models

Tang

Zhu

Men

2021

International Journal of Engine Research

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As requirements for continuously improving internal combustion engine fuel economy with satisfactory emissions, model-based control strategies are often used to optimize the combustion process. To apply advanced control techniques for closed-loop engine combustion control, control-oriented engine combustion models are necessary and they are physics-based, accurate enough for model-based control, computationally low cost, and capable of real-time simulations. In addition, control-oriented combustion model with adequate fidelity may need to adapt to physical system and environment changes over time to maintain model-based control performance, such as model-reference (guided) control, where the control-oriented combustion model runs in real-time to generate an error signal between physical system and reference-model output for feedback control. This paper provides a review of existing control-oriented engine combustion models, along with their associated applications. Three main groups of control-oriented combustion models are reviewed from simple to sophisticated physics-based dynamic models, including mean-value, Wiebe function-based, and reaction-based models. The fundamental principle of each model group is reviewed briefly and its applications are also addressed. At the macro level, a control-oriented engine model can be used for crank angle-based and/or cycle-based control. As the engine control hardware performance continuous improving with reduced cost, model-reference (guided) combustion control shall become reality since now it is feasible to run a physics-based control-oriented engine combustion model inside an engine control module. On the other hand, each model group, even for the simple mean-value model, has its own applicable scenarios.

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A two-zone reaction-based combustion model for a spark-ignition engine

Cited by 12 publications

References 22 publications

A generalized reduced fluid dynamic model for flow fields and electrodes in redox flow batteries

A generalized reduced fluid dynamic model for flow fields and electrodes in redox flow batteries

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Review of engine control-oriented combustion models

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