Combustion Phasing Modelling of Dual Fuel Engines

Sui, Wenbo; Gonzalez, Jorge Pulpeiro; Hall, Carrie M.

doi:10.1016/j.ifacol.2018.10.067

Cited by 10 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To model important combustion metrics, e.g., IMEP g or CA50, basic physics-based models have been proposed [11][12][13][14][15][16]. These models provide a deterministic and dynamic view of the relationship between actuation and combustion metrics without determining the full in-cylinder pressure.…”

Section: Physics-based Combustion Modelsmentioning

confidence: 99%

Data-Based In-Cylinder Pressure Model with Cyclic Variations for Combustion Control: An RCCI Engine Application

Vlaswinkel,

Willems

2024

Energies

View full text Add to dashboard Cite

Cylinder-pressure-based control is a key enabler for advanced pre-mixed combustion concepts. In addition to guaranteeing robust and safe operation, it allows for cylinder pressure and heat release shaping. This requires fast control-oriented combustion models. Over the years, mean-value models have been proposed that can predict combustion metrics (e.g., gross indicated mean effective pressure (IMEPg), or the crank angle where 50% of the total heat is released (CA50)) or models that predict the full in-cylinder pressure. However, these models are not able to capture cycle-to-cycle variations. The inclusion of the cycle-to-cycle variations is important in the control design for combustion concepts, like reactivity-controlled compression ignition, that can suffer from large cycle-to-cycle variations. In this study, the in-cylinder pressure and cycle-to-cycle variations are modelled using a data-based approach. The in-cylinder conditions and fuel settings are the inputs to the model. The model combines principal component decomposition and Gaussian process regression. A detailed study is performed on the effects of the different hyperparameters and kernel choices. The approach is applicable to any combustion concept, but is most valuable for advance combustion concepts with large cycle-to-cycle variation. The potential of the proposed approach is successfully demonstrated for a reactivity-controlled compression ignition engine running on diesel and E85. The average prediction error of the mean in-cylinder pressure over a complete combustion cycle is 0.051 bar and of the corresponding mean cycle-to-cycle variation is 0.24 bar2. This principal-component-decomposition-based approach is an important step towards in-cylinder pressure shaping. The use of Gaussian process regression provides important information on cycle-to-cycle variation and provides next-cycle control information on safety and performance criteria.

show abstract

Section: Physics-based Combustion Modelsmentioning

confidence: 99%

Data-Based In-Cylinder Pressure Model with Cyclic Variations for Combustion Control: An RCCI Engine Application

Vlaswinkel,

Willems

2024

Energies

View full text Add to dashboard Cite

show abstract

“…A physics-based model that utilizes parameterized physical SOC and BD submodels as the main model inputs to an adaptive simple Wiebe function for CA50 prediction. The Wiebe function is given by Equation ( 1), in which the SOC and BD are the main input parameters [4]:…”

Section: Model Developmentmentioning

confidence: 99%

“…The physics-based SOC model used in this work was modified from that of [27], and is shown in (4). The SOC was taken to be the algebraic sum of the start of injection command (SOI), delay due to the physical injector electrodynamics (τ elec ), and the ignition delay (τ ID ).…”

Section: Physics-based Soc Modelmentioning

confidence: 99%

“…Some of the complex subsystems introduced in modern diesel engines include the turbocharger and high-and low-pressure exhaust gas recirculation loops [1]. High-fidelity models or combustion phasing feedback are essential to ensuring proper combustion management on these devices [3,4]. While some proposed modeling and control improvements require additional sensors [5,6], these changes would complicate the engine econometrics, and may not be economically viable [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Physics-Based and Stochastic Neural Network Model Structure for Diesel Engine Combustion Events

Ankobea-Ansah

Hall

2022

Vehicles

View full text Add to dashboard Cite

Estimation of combustion phasing and power production is essential to ensuring proper combustion and load control. However, archetypal control-oriented physics-based combustion models can become computationally expensive if highly accurate predictive capabilities are achieved. Artificial neural network (ANN) models, on the other hand, may provide superior predictive and computational capabilities. However, using classical ANNs for model-based prediction and control can be challenging, since their heuristic and deterministic black-box nature may make them intractable or create instabilities. In this paper, a hybridized modeling framework that leverages the advantages of both physics-based and stochastic neural network modeling approaches is utilized to capture CA50 (the timing when 50% of the fuel energy has been released) along with indicated mean effective pressure (IMEP). The performance of the hybridized framework is compared to a classical ANN and a physics-based-only framework in a stochastic environment. To ensure high robustness and low computational burden in the hybrid framework, the CA50 input parameters along with IMEP are captured with a Bayesian regularized ANN (BRANN) and then integrated into an overall physics-based 0D Wiebe model. The outputs of the hybridized CA50 and IMEP models are then successively fine-tuned with BRANN transfer learning models (TLMs). The study shows that in the presence of a Gaussian-distributed model uncertainty, the proposed hybridized model framework can achieve an RMSE of 1.3 × 10−5 CAD and 4.37 kPa with a 45.4 and 3.6 s total model runtime for CA50 and IMEP, respectively, for over 200 steady-state engine operating conditions. As such, this model framework may be a useful tool for real-time combustion control where in-cylinder feedback is limited.

show abstract

“…As stated earlier, the goal is to deliver improved engine power and reduce pollutant emissions. Several effective methods can be applied, such as: changing the intake manifold shape [1], using an alternative fuel [2,3], using a fuel injection system instead of a carburetor [4], determining optimal ignition timing [5], recovering lost heat [6], using a supercharger [7], and changing cam profiles [8]. Because valve mechanisms have a significant effect on engine performance and engine emission characteristics, a new valve mechanism design has the potential to improve the engine performance.…”

Section: Introductionmentioning

confidence: 99%

A Study to Investigate the Effect of Valve Mechanisms on Exhaust Residual Gas and Effective Release Energy of a Motorcycle Engine

Khoa

2021

Energies

View full text Add to dashboard Cite

The purpose of this study was to investigate the effect of valve mechanisms on the exhaust residual gas (ERG) and effective release energy (ERE) of a motorcycle engine. Here, a simulation model and the estimation a new valve mechanism design is presented. An AVL-Boost simulation model and an experiment system were established. The classical spline approximation method was used to design a new cam profile for various valve lifts. The simulation model was used to estimate the effect of the new valve mechanism designs on engine performance. A new camshaft was produced based on the research data. The results show that the engine obtained a maximum engine brake torque of 21.53 Nm at 7000 rpm, which is an increase of 3.2% compared to the engine using the original valve mechanism. In addition, the residual gas was improved, the maximum engine effective release energy was 0.83 kJ, the maximum engine power was 18.1 kW, representing an improvement of 7.2%, and the air mass flow was improved by 4.97%.

show abstract

Combustion Phasing Modelling of Dual Fuel Engines

Cited by 10 publications

References 15 publications

Data-Based In-Cylinder Pressure Model with Cyclic Variations for Combustion Control: An RCCI Engine Application

Data-Based In-Cylinder Pressure Model with Cyclic Variations for Combustion Control: An RCCI Engine Application

A Hybrid Physics-Based and Stochastic Neural Network Model Structure for Diesel Engine Combustion Events

A Study to Investigate the Effect of Valve Mechanisms on Exhaust Residual Gas and Effective Release Energy of a Motorcycle Engine

Contact Info

Product

Resources

About