scholarly journals Improving the Thermal Efficiency of the Homogeneous Charge Compression Ignition Engine by Using Various Combustion Patterns

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3002 ◽  
Author(s):  
Yuh-Yih Wu ◽  
James H. Wang ◽  
Faizan Mushtaq Mir
Keyword(s):  
Thermal Efficiency ◽  
High Efficiency ◽  
Homogeneous Charge Compression Ignition ◽  
Combustion Engine ◽  
Combustion Characteristics ◽  
Compression Ignition ◽  
Compression Ignition Engine ◽  
Cycle Simulation ◽  
Homogeneous Charge ◽  
Wiebe Function

The efficiency of an internal combustion engine (ICE) is essential for automobiles and motorcycles. Several studies have demonstrated that the homogeneous charge compression ignition (HCCI) is a promising technology for realizing engines with high efficiency and low emissions. This study investigated the combustion characteristics of the HCCI using a 125 cc motorcycle engine with n-heptane fuel. The engine performance, combustion characteristics, and thermal efficiency were analyzed from experimental data. The results revealed that a leaner air–fuel mixture led to higher engine efficiency and output. The improvement of engine output is contradictory to the general trend. Energy balance analysis revealed that lower heat loss, due to the low cylinder gas temperature of lean combustion, contributed to higher efficiency. A double-Wiebe function provided excellent simulation of the mass fraction burned (MFB) of the HCCI. Air cycle simulation with the MFB, provided by the double-Wiebe function, was executed to investigate this phenomenon. The results indicated that a better combustion pattern led to higher thermal efficiency, and thus the engine output and thermal efficiency do not require a fast combustion rate in an HCCI engine. A better combustion pattern can be achieved by adjusting air–fuel ratio (AFR) and the rates of dual fuel and exhaust gas recirculation (EGR).

Parametric study of combustion characteristics in a direct-injection diesel homogeneous charge compression ignition engine with a low-pressurefuel injector

2005 ◽  
Vol 6 (3) ◽  
pp. 215-230 ◽  
Author(s):  
Y Ra ◽  
E J Hruby ◽  
R D Reitz
Keyword(s):  
Numerical Simulations ◽  
Diesel Fuel ◽  
Homogeneous Charge Compression Ignition ◽  
Direct Injection ◽  
Low Pressure ◽  
Combustion Characteristics ◽  
Compression Ignition ◽  
Fuel Injector ◽  
Compression Ignition Engine ◽  
Homogeneous Charge

Homogeneous charge compression ignition (HCCI) combustion is an alternative to current engine combustion systems and is used as a method to reduce emissions. It has the potential nearly to eliminate engine-out NOx emissions while producing diesel-like engine efficiencies, when a premixture of gas-phase fuel and air is burned spontaneously and entirely by an autoignition process. However, when direct injection is used for diesel fuel mixture preparation in engines, the complex in-cylinder flow field and limited mixing times may result in inhomogeneity of the charge. Thus, in order to minimize non-uniformity of the charge, early injection of the fuel is desirable. However, when fuel is injected during the intake or early compression stroke, the use of high-pressure injection is limited by the relatively low in-cylinder gas pressure because of spray impingement on the cylinder walls. Thus, it is also of interest to consider low-pressure injectors as an alternative. In the present paper, the parametric behaviour of the combustion characteristics in an HCCI engine operated with a low-pressure fuel injector were investigated through numerical simulations and engine experiments. Parameters including the start-of-injection (SOI) timing and exhaust gas recirculation were considered, and diesel and n-heptane fuels were used. The results show good agreement of behaviour trends between the experiments and the numerical simulations. With its lower vaporization rates, significant effects of the SOI timing and intake gas temperature were seen for diesel fuel due to the formation of wall films. The modelling results also explained the origin of high-temperature NO x-producing regions due to the effect of the gas density on the spray.

scholarly journals Computationally efficient evaluation of optimum homogeneous charge compression ignition operating range with accelerated multizone engine cycle simulation

2020 ◽  
pp. 146808742092948
Author(s):  
Juan Manuel Garcia-Guendulain ◽  
Alejandro Ramirez-Barron ◽  
José Manuel Riesco-Avila ◽  
Russell Whitesides ◽  
Salvador M Aceves
Keyword(s):  
Thermal Stratification ◽  
High Efficiency ◽  
Homogeneous Charge Compression Ignition ◽  
Operating Conditions ◽  
Compression Ignition ◽  
Cycle Simulation ◽  
Performance Map ◽  
Engine Cycle ◽  
Homogeneous Charge ◽  
A Performance

The very intensive calculations necessary to define a performance map requiring evaluation of over a hundred individual operating points can be efficiently conducted with accelerated multizone for engine cycle simulation, leading to a definition of regions of acceptable and optimum homogeneous charge compression ignition operation. Accelerated multizone for engine cycle simulation has the virtue of enabling accurate evaluation of many operating conditions based on thermal stratification data from a single fluid mechanics run at motored conditions. This is possible because thermal stratification is more sensitive to engine geometry than to operating conditions. In this article, accuracy of accelerated multizone for engine cycle simulation is demonstrated by comparison with experimental data for iso-octane homogeneous charge compression ignition operation over a broad range of lean equivalence ratios (0.14–0.28). The validated accelerated multizone for engine cycle simulation model is then applied to generating a performance map for an engine controlled by appropriately adjusting equivalence ratio and internal exhaust gas recirculation. Regions of acceptable and optimum combustion are identified. It is finally demonstrated that while indicated mean effective pressure remains low for optimum homogeneous charge compression ignition operation (1–4 bar), this is sufficient for a large fraction of typical driving in light-duty vehicles. Much driving including idle can therefore be done in homogeneous charge compression ignition mode at high efficiency and low (essentially zero) NO x and particulate matter emissions.

scholarly journals Support vector machine based emissions modeling using particle swarm optimization for homogeneous charge compression ignition engine

2021 ◽  
pp. 146808742110555
Author(s):  
David Gordon ◽  
Armin Norouzi ◽  
Gero Blomeyer ◽  
Julian Bedei ◽  
Masoud Aliramezani ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Homogeneous Charge Compression Ignition ◽  
Combustion Engine ◽  
Training Data ◽  
Support Vector ◽  
Compression Ignition ◽  
Ann Model ◽  
Compression Ignition Engine ◽  
Homogeneous Charge

The internal combustion engine faces increasing societal and governmental pressure to improve both efficiency and engine out emissions. Currently, research has moved from traditional combustion methods to new highly efficient combustion strategies such as Homogeneous Charge Compression Ignition (HCCI). However, predicting the exact value of engine out emissions using conventional physics-based or data-driven models is still a challenge for engine researchers due to the complexity the of combustion and emission formation. Research has focused on using Artificial Neural Networks (ANN) for this problem but ANN’s require large training datasets for acceptable accuracy. This work addresses this problem by presenting the development of a simple model for predicting the steady-state emissions of a single cylinder HCCI engine which is created using an metaheuristic optimization based Support Vector Machine (SVM). The selection of input variables to the SVM model is explored using five different feature sets, considering up to seven engine inputs. The best results are achieved with a model combining linear and squared inputs as well as cross correlations and their squares totaling 26 features. In this case the model fit represented by R2 values were between 0.72 and 0.95. The best model fits were achieved for CO and CO2, while HC and NOx models have reduced model performance. Linear and non-linear SVM models were then compared to an ANN model. This comparison showed that SVM based models were more robust to changes in feature selection and better able to avoid local minimums compared to the ANN models leading to a more consistent model prediction when limited training data is available. The proposed machine learning based HCCI emission models and the feature selection approach provide insight into optimizing the model accuracy while minimizing the computational costs.

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