Biofuels and its spray interactions under pilot-main injection strategy

From the different power plants, the compression ignition diesel engines are considered the best alternative to be used in the transport sector due to its high efficiency. However, the current emission standards impose drastic reductions for the main pollutants, that is, NO x and soot, emitted by this type of engines. To accomplish with these restrictions, alternative combustion concepts as the premixed charge compression ignition are being investigated nowadays. The objective of this work is to evaluate the impact of different fuel injection strategies on the combustion performance and engine-out emissions of the premixed charge compression ignition combustion regime. For that, experimental measurements were carried out in a single-cylinder medium-duty compression ignition diesel engine at low-load operation. Different engine parameters as the injection pattern timing, main injection timing and main injection fuel quantity were sweep. The best injection strategy was determined by means of a methodology based on the evaluation of a merit function. The results suggest that the best injection strategy for the low-load premixed charge compression ignition operating condition investigated implies using a high injection pressure and a triple-injection event with a delayed main injection with almost 15% of the total fuel mass injected.

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Development of optimal diesel injection strategy based on the prediction of performance and emissions using deep neural network

International Journal of Engine Research ◽

10.1177/14680874211057756 ◽

2021 ◽

pp. 146808742110577

Author(s):

Erdoğan Güner ◽

Aliriza Kaleli ◽

Kadir Bakirci ◽

Mehmet Akif Ceviz

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Operating Conditions ◽

Exhaust Emission ◽

Pilot Injection ◽

Data Set ◽

Injection Strategy ◽

Wide Range ◽

Performance And Emissions ◽

Main Injection

This study aims to determine the optimal injection strategy by predicting the performance and exhaust emission parameters of a four-cylinder CRDI engine under several operating conditions. The experimental determination procedure is challenging and expensive calibration task since it requires a high number of tests. Many studies have focused on a limited level of parameters. In this study, design of experiments technique and deep neural network (DNN) modeling are used together. The experimental data set for the model is created using Taguchi L16 and L32 orthogonal arrays. The DNN model is developed to predict [Formula: see text], [Formula: see text], HC, and CO emissions with speed, torque, injection timings and fuel quantities of each injection called as pilot1, pilot2, main, and post. In this way, it has become possible to evaluate the effects of a larger number of operating parameters in a wide range than the literature. The developed DNN model predicts the [Formula: see text], [Formula: see text], HC, and CO with R2 0.939, 0.943, 0.963, and 0.966, respectively. Additionally, RMSE and MAE values for the model are between 0.024 and 0.048. The proposed method compared with the conventional look-up table method performs better in reducing the complexity and cost of experiments and exploration of the effects of injection parameters on engine emission and performance characteristics in a wide engine operating range. In conclusion, until 2300 rpm at specified torque (90 Nm), it is found that 70% of fuel quantity should inject in main injection to minimize [Formula: see text] and [Formula: see text] emissions. The post injection quantity should be increased by reducing the amount of main injection from this operating condition on. Furthermore, it is observed that the ratios of pilot injection durations do not change with increasing engine speed, but quantity of first pilot injection is more than that of second pilot injection.

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Analysis of Pilot Injection Effects on Combustion Noise in PPCI Diesel Engines

ASME 2016 Internal Combustion Engine Fall Technical Conference ◽

10.1115/icef2016-9356 ◽

2016 ◽

Author(s):

Long Liu ◽

Hongzi Fei ◽

Jingtao Du

Keyword(s):

Diesel Engines ◽

High Frequency ◽

Combustion Process ◽

High Load ◽

Operating Conditions ◽

Combustion Noise ◽

Pilot Injection ◽

Injection Strategy ◽

Injection Quantity ◽

Main Injection

With the common-rail fuel injection systems widely used in diesel engines, the pilot injection strategy has been paid more attention for suppressing pollutants emissions and combustion noise. Using pilot injection strategies, leaner and more homogenous mixture formed in pilot spray results in the combustion process partially fulfill Premixed Charge Compression Ignition (PCCI). Therefore the combustion process of diesel engines with pilot injection strategy can be considered as partial PCCI (PPCI). Pilot injection causes the in-cylinder temperature increase before main injection, which shortens the ignition delay of main spray and consequently reduces the combustion noise, so that the pilot injection has potential to extend PPCI combustion model to high load operation. However, the mechanism of pilot injection effects on the combustion noise has not been fully understood, consequently it is difficult to estimate the lower combustion noise among different pilot injection conditions, that results in difficult selection of the pilot injection parameters in proper way. Thus, in this study, experiments were performed on a single-cylinder DI-diesel engine with pilot and main injection under high load operating conditions. The synthesized in-cylinder pressure levels (CPLs) in different frequency ranges as a novel method were proposed to analyze the pilot injection effects on combustion noise. The results reveal that pilot spray combustion mainly influences the high frequency combustion noise, and the later pilot injection timing causes the higher combustion noise. In the case of the short dwell between pilot and main injection, the increasing pilot injection quantity enhances the high frequency combustion noise. Meanwhile because of the pilot injection quantity increase, decrease of main injection quantity leads to lower combustion noise in middle frequency range.

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Fuel Injection Strategy for Utilization of Mineral Diesel-Methanol Blend in a Common Rail Direct Injection Engine

Journal of Energy Resources Technology ◽

10.1115/1.4046225 ◽

2020 ◽

Vol 142 (8) ◽

Cited By ~ 10

Author(s):

Akhilendra Pratap Singh ◽

Nikhil Sharma ◽

Vikram Kumar ◽

Dev Prakash Satsangi ◽

Avinash Kumar Agarwal

Keyword(s):

Fuel Injection ◽

Direct Injection ◽

Injection Pressure ◽

Pilot Injection ◽

Oxides Of Nitrogen ◽

Gas Temperature ◽

Injection Strategy ◽

Main Injection ◽

Pilot Fuel ◽

Fuel Injection Pressure

Abstract Methanol fueled internal combustion (IC) engines have attracted significant attention due to their contributions in reducing environmental pollution and fossil fuel consumption. In this study, a single-cylinder research engine was operated on MD10 (10% (v/v) methanol blended with mineral diesel) and baseline mineral diesel to explore an optimized fuel injection strategy for efficient combustion and reduced emissions. The experiments were conducted at constant engine speed (1500 rpm) and load (3 kW) using two different fuel injection strategies, namely, single pilot injection (SPI) and double pilot injection (DPI) strategy. For each pilot fuel injection strategy, the start of main injection (SoMI) timing was varied from −3 to 6° crank angle (CA) before top dead center (bTDC). To examine the effect of fuel injection pressure (FIP), experiments were performed at three different FIPs (500, 750, and 1000 bars). Results showed that the MD10 fueled engine resulted in superior combustion compared with baseline mineral diesel, which was further improved by DPI at higher FIPs. The use of DPI strategy was found to be more effective at higher FIPs, resulting in higher brake thermal efficiency (BTE), lower exhaust gas temperature (EGT), and reduced oxides of nitrogen (NOx) emissions compared with SPI strategy. Detailed investigations showed that the addition of methanol in mineral diesel reduced particulates, especially the accumulation mode particles (AMP). Different statistical analysis and qualitative correlations between fuel injection parameters showed that higher FIP and advanced SoMI timings were suitable for particulate reduction from the MD10 fueled engine.

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Modelling analysis of multiple diesel injection strategies with one-dimensional simulation coupled with artificial neural networks

Thermal Science ◽

10.2298/tsci160504223o ◽

2017 ◽

Vol 21 (1 Part B) ◽

pp. 413-425

Author(s):

Orkun Ozener ◽

Muammer Ozkan ◽

Levent Yuksek

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Optimization Problems ◽

Thermodynamic Simulation ◽

Model Parameters ◽

Pilot Injection ◽

Injection Strategy ◽

Diesel Injection ◽

Main Injection ◽

Artificial Neural

In the modern Diesel injection systems the phasing of injection in the same cycle gives a high flexibility to engineers from the perspective of engines performance and emission optimization. Basically, the injection is separated in to three phases: the pilot, main, and post injection phases. The focus of this study is based on pilot injection strategy implementation, which can be used for emission control effectively. In this work, reference main and pilot + main injection strategy experiments were realized in a modern Diesel engine. The logged data groups were used to model the engine at 1-D thermodynamic simulation AVL BOOST. In the second stage of this work, the engine operating points which are not realized at test bench are made run at BOOST programme. The new model parameters of simulation are identified with artificial neural network technique. The results showed that the implementation of appropriate mass of pilot injection at the appropriate injection advance will reduce the NOx emissions compared to reference main injection strategy. For reducing CO emissions the pilot injection mass should also be kept in the same range with higher injection pressure that can be achieved. Usage of 1-D simulation programme coupled with artificial neural network was found useful up to a certain extent especially for parametric analyses and optimization problems via with validation of calibration parameters at a huge experimental data.

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