Strict Emission Regulations Research Articles

Simulation study of NO2-Assisted regeneration performance of variable cell geometry catalyzed diesel particulate filter

With the increasingly strict emission regulations of countries around the world, reducing the particulate emissions of diesel engines is the focus of research. In this paper, the optimal asymmetry ratio was researched by comparing pressure drop under different operation conditions through the validated model. When soot loading is 0 g/L, the optimal asymmetric ratio is 1.1. When soot loading is 4 g/L, the optimal asymmetric ratio is 1.2. And performances of passive regeneration of catalyzed diesel particulate filters with different channel shapes were also studied. Based on the remaining soot loading, regeneration efficiency and pressure drop, the hexagonal channel has obvious advantages in NO2 assisted passive regeneration, followed by the asymmetrical square channel, and the octagonal channel is the worst.

Research on Simulation System Model of Diesel Engine Applied to Virtual Calibration Development

In order to meet the increasingly strict emission regulations, more complex control systems are used in the engine, which increases the calibration workload by several times. Virtual test bed is a method to greatly reduce the calibration cycle, workload and cost by replacing the real engine and vehicle for the hardware in the loop virtual calibration research. Virtual calibration process mainly includes experiment design, virtual experiment, model construction, data optimization, MAP generation and calibration result test. This paper mainly describes the establishment method of the simulation model of diesel engine of China 6 applied to virtual calibration based on the AVL CRUISETM M and MoBEOTM. The differences between the test results and the simulation results under the steady-state and transient conditions of the model were compared. The results of stationary mode and cold WHTC show that the model is of good quality and can be used in the research of virtual calibration development.

Parametric Investigation on Single Cylinder Spark Ignition Engine Fueled Methanol Blends; Water-Based Micro Emulsions and Conventional Gasoline

In this contribution, the investigation conducted on alternative fuels includes methanol 20% blended with gasoline 80% and emulsion-based fuel with the composition of gasoline 80%, ethanol 15%, and H2O 5% are compared with 100% conventional gasoline fuel. These fueled single-cylinders spark ignition engine is studied for checking their performance and emission characteristics as per future emission norms. This work is performed on One-dimensional AVL Boost Simulation Software. The simulations predicted the performance and emission characteristics were far lesser than conventional 100% gasoline. These fuels meet the strict emission regulations of Euro VII. The main purpose of this investigation is to use alternative fuels to improve the performance and emission characteristics of the single- cylinder spark ignition engine and reduce the consumption of fossil fuel reserves. This investigation led to the conclusion that by using methanol 20% in 80% gasoline and micro-emulsion, fuel improves the power, BSFC (brake specific fuel consumption), thermal efficiency and combustion properties of the single-cylinder spark-ignition engine. The CO, HC and NOx emissions were also reduced for alternative fuel than 100% gasoline fuel. The novel water-based emulsion fuel showed the lowest value of NOx emissions as compared to blended 20% methanol with 80% gasoline and 100% gasoline fuel.

Application of machine learning tools for constrained multi-objective optimization of an HCCI engine

Because of strict emission regulations, significant efforts are made by researchers to reduce the pollutants from internal combustion engines. Homogeneous mixture of fuel and air in Homogeneous Charge Compression Ignition (HCCI) engine results in lower amounts of thermal NOx while CO and HC emissions would be higher in this process. Besides, performance instability due to knock and misfire is another problem that HCCI engines are facing. In this paper, machine learning tools have been implemented to learn the behavior of a single-cylinder engine using three different fuels with the HCCI strategy. Changes in six objective functions with engine Inlet Valve Closing (IVC) temperature and air-fuel equivalence ratio are studied with the help of an Artificial Neural Network (ANN). Those objective functions are thermal efficiency, combustion efficiency, IMEP, together with NOx, CO, and HC emissions. A multi-objective optimization algorithm based on a decomposition method and genetic algorithm is used to identify the best operating condition of studied fuels. Results show that HCCI engine fueled with Ethanol and Methanol could produce significantly lower HC and CO emissions at their optimum point. However, the produced NOx would be higher in these cases comparing to the engine fueled with CNG. The genetic algorithm indicates that the optimum point of operation for CNG-fueled engine is TIVC ≈ 445 K while λ ≈ 2.25. In contrast, for the Ethanol-fueled case, this optimum point is located in the IVC temperature of ≈450 K and λ ≈ 2.75. The TOPSIS point is located in minimum air-fuel equivalence ratio and lower IVC temperatures (TIVC ≈ 420 K) when the engine is being operated with Methanol as fuel.

Electric Drivetrain Optimization for a Commercial Fleet with Different Degrees of Electrical Machine Commonality

At present, the prevalence of electric vehicles is increasing continuously. In particular, there are promising applications for commercial vehicles transferring from conventional to full electric, due to lower operating costs and stricter emission regulations. Thus, cost analysis from the fleet perspective becomes important. The study of cost competitiveness of different drivetrain designs is necessary to evaluate the fleet cost variance for different degrees of electrical machine commonality. This paper presents a methodology to find a preliminary powertrain design that minimizes the Total Cost of Ownership (TCO) for an entire fleet of electric commercial vehicles while fulfilling the performance requirements of each vehicle type. This methodology is based on scalable electric machine models, and particle swarm is used as the main optimization algorithm. The results show that the total cost penalty incurred when sharing the same electrical machine is small, therefore, there is a cost saving potential in higher degrees of electrical machine commonality.

Dual fuel ethanol port injection in a compression ignition diesel engine: Technical analysis, environmental behavior, and economic viability

The increase in prices, dependence on fossil fuels, and concerns about greenhouse gas emissions, and strict emissions regulations have motivated researchers to look for low carbon renewable fuels for internal combustion engines. Ethanol is one of these fuels due to its presence in the energy matrix of several countries. Among the methods applied to use ethanol in compression ignition engines, the primary technique currently studied is port fuel injection (PFI). This research studies the feasibility of using ethanol as a partial fuel in a generator set analyzed beyond the technical and environmental parameters and the economic aspect. The experiments were conducted in a diesel single-cylinder engine, with its original diesel fuel direct injection system, and added an auxiliary PFI system for the ethanol supply. The research gap is based on the lack of studies that include, in addition to the analysis of performance parameters and gaseous emissions, the economic viability assessment for the studied engine, which is also a fundamental factor in an actual application. The engine was tested under different conditions of six progressive replacements of diesel fuel by ethanol, starting only with diesel fuel (baseline test) and increasing ethanol mass fraction up to 0.634. The measured data showed that ethanol effectively increased ignition delay and reduced combustion duration, which did not lead to increased efficiency and reduced fuel consumption. The maximum in-cylinder pressure was 71.3 bar in the baseline test and dropped progressively to 51.6 bar when in the most significant substitution. The rate of heat release increased by about 92.5% with ethanol concerning the baseline test. When ethanol was used, the CO emissions decreased by up to 43%, resulting in about 2.4 g/kWh, while NOx emissions reduced by up to 20% compared to the baseline test, achieving 0.69 g/kWh. The economic analysis made it clear that the operational cost increased when the engine worked with ethanol replacing partially diesel fuel. However, with a slight increase in operational cost, especially in São Paulo (SP) state, it is possible to reduce pollutant emissions. For the baseline test, the operating cost was 0.354 U$/kWh. The specific fuel cost reaches a maximum of 0.441 U$/kWh, 25% higher than the respective baseline test, increasing ethanol fraction.

Joint optimization of charging facility investment and pricing in automobile retail supply chain and coordination

In terms of energy-saving and emission reduction, and the low cost of using new energy vehicles (NEVs), it’s expected to have a huge potential market. To gain more market share and meet the increasingly strict emissions regulations, auto manufacturers and distributors as the main stakeholders of the automobile retail supply chain start to make charging facility investments (CFI) to promote the adoption of new energy vehicles (NEVs). However, there is no clearly dominant strategy regarding whether and how the auto manufacturer or the retailer should create a joint optimization between their CFI and pricing strategies. Thus, we investigate a four-stage automobile retail supply chain consisting of the government, one manufacturer, one retailer, and customers. The manufacturer and the retailer contemplate CFI while setting the wholesale and retail prices with consideration of the subsidy policy and the heterogeneous customer value towards NEVs. We use Stackelberg game paradigms to model the interactions between players in the automobile retail supply chain. Besides, we derive the thresholds which determine whether the CFI should be or carried out by the manufacturer or the retailer. We find the retailer has a last-mover advantage to conduct CFI. In addition, the manufacturer or the retailer should abandon CFI by himself to have a lift in certain cases. Furthermore, the manufacturer investment cost-sharing contract is designed to coordinate the automobile retail supply chain to make Pareto optimization. It means both the manufacturer’s and the retailer’s profits can improve.

Prediction of Engine-Out Emissions Using Deep Convolutional Neural Networks

<div class="section abstract"><div class="htmlview paragraph">Analysis-driven pre-calibration of a modern automotive engine is extremely valuable in significantly reducing hardware investments and accelerating engine designs compliant with stricter emission regulations. Advanced modelling tools, such as a Virtual Engine Model (VEM) using Computational Fluid Dynamics (CFD), are often used within the framework of a Design of Experiments for Powertrain Engineering (DEPE) with the goal of streamlining significant portions of the calibration process. The success of the methodology largely relies on the accuracy of analytical predictions, especially engine-out emissions. Results show excellent agreements in engine performance parameters (with R<sup>2</sup> > 98%) and good agreements in NOx and combustion noise (with R<sup>2</sup> > 87%), while the Carbon Monoxide (CO), Unburned Hydrocarbons (HC) and Smoke emissions predictions remain a challenge even with a large n-heptane mechanism consisting of 144 species and 900 reactions and refined mesh resolution. In this study, a Machine Learning (ML) approach is presented to correlate in-cylinder images of Equivalence Ratio, Temperature, Velocity field and Turbulent Kinetic Energy (TKE) at Exhaust Valve Opening (EVO) to engine-out emissions of CO, HC and Smoke measured experimentally. The images generated from CFD simulations and experimentally measured emissions data were used to train a deep Convolutional Neural Network (CNN) based on the VGG16 architecture. The prediction performance of the trained model was evaluated on held-out data. The deep learning model led to a significant improvement in prediction of all emissions species and captured the qualitative trends as well. This model could be used as an emissions prediction sub-model in the Virtual Engine Model framework leading to significantly lower computational costs by avoiding the use of expensive chemistry solvers. Gradient-weighted Regression Activation Mapping (Grad-RAMS) was used to draw insights into image features that had a strong influence on the emissions predictions.</div></div>

State-of-the-Art of Strategies to Reduce Exhaust Emissions from Diesel Engine Vehicles

Compression ignition engines play a significant role in the development of a country. They are widely used due to their innate properties such as high efficiency, high power output, and durability. However, they are considered one of the key contributors to transport-related emission and have recently been identified as carcinogenic. Thus, it is important to modify the designs and processes before, during, and after combustion to reduce the emissions to meet the strict emission regulations. The paper discusses the pros and cons of different strategies to reduce emissions of a diesel engine. An overview of various techniques to modify the pre-combustion engine design aspects has been discussed first. After that, fuel modifications techniques during combustion to improve the fuel properties to reduce the engine-out emission is discussed. Finally, post-combustion after-treatment devices are briefly discussed, which help improve the air quality of our environment.

Urea-SCR System Development in the Mitigation of NOx Emissions from Diesel Engines – A Review Study

Introduction: Due to energy demand concerns, diesel engines have gained much attraction recently compared to petrol engines because of their higher thermal efficiencies. However, they emit a larger amount of NOx emissions into the atmosphere. Objective: Nitrogen oxides are known as important ambient air pollutants that are responsible for health problems, forest damage, and buildings corrosion. Therefore, using emissions control strategies for diesel engines is required in order to have a cleaner environment. Urea-SCR (selective catalytic reduction of NOx by urea) after-treatment system is considered as one of the most efficient techniques available to reduce engine-out NOx emissions sufficiently. Conclusion: This review article discusses all the methods suggested to diminish nitrogen oxides emissions and then presents a comprehensive survey on developing urea-SCR unit -whether from catalyst development aspect or from injection system modification point of view- in diesel engines to meet strict emissions regulations.

Experimental and numerical investigation of the ignition process in a large bore dual fuel engine

Dual fuel engines, powered by natural gas and ignited by a small quantity of diesel fuel, have emerged in marine and power generation sectors as extremely attractive solutions due to their low emissions and fuel flexibility. However, the stricter emissions regulations and the high demanding market requests for more efficient dual fuel engines require ever shorter time for engine development.Reliable phenomenological fast running 0D/1D combustion models can therefore be used as effective tools for reducing the time and cost of testing activities. Nevertheless, the simulation of the dual-fuel combustion process is particularly challenging, due to diesel/methane chemical interaction and combustion mode transition from diesel autoignition into premixed flame propagation. The correct simulation of the diesel injection, air entrainment and autoignition process is thus crucial for the combustion process prediction and for reliable estimations of the engine performance.The aim of the paper is therefore to investigate the ignition process in dual fuel engines through a detailed experimental analysis and to provide a suitable 0D predictive combustion model for the ignition process simulations. An ignition delay model was developed based on 0D detailed chemistry reactions mechanism and it was validated against experimental data from an extensive test campaign. Several diesel injection strategies in terms of injected quantity, injection pressure and duration and different in-cylinder conditions were considered. The proposed model was shown to be capable of providing reliable predictions of the ignition process with an error of ignition delay prediction limited on average in the +/− 1 crank angle degree.

Influence of palmitic and oleic acid mixtures on combustion evaluation of a diesel engine

Diesel engines are commonly known for its high-power output and high performance. However, the rise in energy consumption, the gradual depletion of petroleum fuels and compliance with strict emission regulations are of great interest to researchers to produce alternative fuels from renewable energy resources. Of all the different alternative fuels, vegetable oil replaces traditional diesel fuels. The transesterification process is frequently used in the process of extracting biodiesel from vegetable oils. In this process, triglycerides are converted into monoglycerides using alcohol and a catalyst at a specific reaction temperature. In this experimental work, pongamia and mahua oil having a higher composition of Palmitic and oleic fatty acids are used to extract biodiesel through a transesterification using methanol and KOH catalyst. Since the combustion process in diesel engine is very complicated because of heterogeneous spray combustion. The physiochemical properties of biodiesel vary when compared to diesel fuel. It is essential to study the influence of Palmitic and oleic fatty acids on combustion parameters of diesel engines. Palmitic and oleic acids are the main fatty acids of pongamia and mahua biodiesel. This was the key inspiration for this research to understand and examine the combustion behaviour of CI engines fuelled with methyl ester containing a higher composition of Palmitic and oleic acid mixture. The experiment was performed with a diesel engine for diesel and pongamia and mahua biodiesel blends (PME25, PME50, PME75 and PME) as a fuel. The combustion parameters namely, in-cylinder pressure, the MRPR, HRR, cumulative HRR, ID and CD were evaluated. It was observed that the ID and the MRPR reduced for biodiesel than diesel due to higher cetane number of biodiesel blends showing a smoother operation of the engine. The longer combustion duration was detected for all methyl ester blends, resulting in poor engine performance for biodiesel blends.

Study on the influence of alumina nanomethanol fluid on the performance, combustion and emission of DMDF diesel engine

With the increasingly strict domestic emission regulations, how to reduce diesel emission without affecting its output power has become a hot and important research topic. Due to their unique physical and chemical properties, the combined use of methanol and Al2O3nanoparticles plays a unique role in promoting combustion and reducing emissions. In this study, Al2O3methanol nanofluid fuel was injected into the inlet and diesel fuel was injected into the cylinder to explore the influence of Al2O3nanoparticles on the performance, combustion and emissions of diesel methanol dual fuel (DMDF) entered. The experienced results showed that with the addition of Al2O3nanoparticles in methanol, the peak pressure and heat release rate in the cylinder of the diesel engine were improved, the combustion delay period and the combustion duration were shortened, the fuel consumption rate was reduced by up to 10.8%, and the braking thermal efficiency was increased by 12.11% at most. With the addition of Al2O3nanoparticles, NOx, CO, HC and soot ratio emissions of the engine were reduced, among which the NOx reduction ratio was small, and the maximum reduction ratio of the last three was 28.82%, 83.33% and 29.27% respectively.

A new scheme for large marine vessels LNG cold energy utilization from thermodynamic and thermoeconomic viewpoints

With the implementation of International Maritime Organization's new emission regulations in 2020, it has a huge impact on shipping industry. To meet the strict emission regulations and policies, liquefied natural gas, as a promising fuel, can replace the traditional heavy oil, and effectively and practically act as the main power source of marine engine. When liquefied natural gas is vaporized from −162 °C to room temperature, a large amount of cold energy is released to the outside world. To reuse the cold energy efficiently and economically, a new scheme integrated with organic Rankine cycle, seawater desalination, subzero storage and air-conditioning is proposed through comparison of six solutions. With a series of analysis and comparison, R290, NH3, R32 and R152a are determined as the best working fluids for organic Rankine cycle, subzero storage, seawater desalination and air-conditioning modules, respectively. After optimizing the parameters of the scheme by genetic algorithm, the exergy efficiency, net work and payback period reach 55.91%, 210.3 kW and 4.27 years, respectively, showing excellent thermoeconomic performance. Therefore, the proposed method provides a new idea for cold energy recovery of natural gas engine on ships.

Effects of Oxygenated Biomass Fuels on the Performance of Diesel Engine and After-Treatment System

Abstract Oxygenated biomass fuels have attracted significant attention due to their contributions in reducing environmental pollution and fossil fuel consumption. In view of stricter emission regulations, the use of these alternative fuels cannot fully meet the requirements, and it needs to be combined with an after-treatment system. In this article, polymethoxy dimethyl ether (PODE) and n-pentanol were blended with diesel (D100 (pure diesel)) at 15% and 20% by volume, respectively, referred to as D85P15, D80P20, D85A15, and D80A20, while the effects of the addition of two new oxygenated biomass fuels on the performance of diesel engine and diesel oxidation catalyst (DOC) and catalytic diesel particulate filter (CDPF) after-treatment system were experimentally investigated. Results show that the addition of oxygenated biomass fuels can improve combustion and reduce carbon monoxide (CO) and soot emissions. At heavy load conditions, when D80P20 was used, compared with D100 and D80A20, the conversion efficiency of CO emissions in DOC + CDPF system is always the highest, close to 100%. It shows that the addition of oxygenated biomass fuels can effectively improve the exhaust oxygen concentration. Besides, there is nearly no increase in CDPF pressure drop at each tested engine speed when D80P20 is used. This has greatly improved in CDPF performance.

Experimental and Simulation Analysis of Natural Gas-Diesel Combustion in Dual-Fuel Engines

The recent strict emission regulations and the necessity to reduce the global CO2 levels have promoted research studies over alternative fuel combustion technologies. Dual fuel technology is a fascinating approach as it could combine the benefits of two fuels to achieve high thermal efficiency with lower harmful emissions. Natural gas burns cleaner than other fossil fuels and its recent low price makes it an attractive fuel as a near-term solution. The utilization of natural gas with diesel in a compression ignition engine can result in high engine performance with lower CO2 emissions. However, at low engine loads, natural gas-diesel operation suffers from low combustion efficiency with high unburned hydrocarbon and carbon monoxide emissions. The present paper collates the work of experimental and simulation studies on natural gas-diesel dual fuel operation over three different engine set-ups. Two single-cylinder, with and without optical access, and a multi-cylinder engine were used to analyze the effects of varying diesel injection and air-path strategies on the combustion performance, with a focus on the low load operation zone. The results showed that the injection timing, as well as intake charge dilution, can severely promote clean combustion at the low load zone. An early diesel injection can enhance the diesel-natural gas homogeneity within the cylinder and improve the combustion efficiency of the engine. On the other hand, high levels of exhaust gas recirculation (EGR) are mandatory to suppress NOx and further enhance the brake thermal efficiency of the engine.

Design of a virtual test cell based on GMDH-type neural network for a heavy-duty diesel engine

The engine development process faces big challenges from new strict emission regulations in addition to the need for fuel efficiency improvements. The Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) environments decreases the required time during engine development, calibration, verification, and validation of the product. An accurate and easy to build dyno-engine model with real-time operational ability is required for this purpose. Artificial Neural Networks (ANN) have shown ability to model dynamic and complex systems like internal combustion engines. In this paper, the Group Method of Data Handling (GMDH) algorithm was utilized to build an ANN model of a heavy-duty diesel engine. One objective is to reduce the amount of manual labor on the results during the ANN model development process. The GMDH algorithm is a self-organizing process that will find the system laws and optimize the model structure automatically in one iteration. The GMDH model results were compared with a model developed by Levenberg-Marquardt Backpropagation (LM-BP) algorithm. The ANN models used actuator signals from an Engine Management System (EMS) to simulate the engine operation parameters. As revealed by the simulation results, the ANN models successfully predicted engine torque, fuel flow, and NOx concentration. The GMDH model as a self-organized model reduced lead time, had slightly higher transient cycle accuracy, had fewer inconsistent predictions, and demonstrated better extrapolation capability. The prediction accuracy for transient operation was improved by shifting the predicted value by calculating time delay and a decrease of 76.66% for fuel flow and 66.51% for NOX concentration in model accuracy were achieved. The GMDH dyno-engine model can be effectively applied as a virtual test cell instrument for testing, calibration, and optimization purposes.

Evaluation of Mechanism for EGR Deposit Formation Based on Spatially- and Time-Resolved Scanning-Electron-Microscope Observation

<div class="section abstract"><div class="htmlview paragraph">Exhaust gas recirculation (EGR) is widely used in diesel engines to reduce nitrogen oxide emissions. To meet the strict emission regulations, e.g., Real Driving Emissions, the EGR system is required to be used at temperatures lower than the present ones. However, under cool conditions, an adhesive deposit forms on the EGR valve or cooler because of the particulate matter and other components present in the diesel exhaust. This causes sticking of the EGR valve or degradation of the heat-exchange performance, which are serious problems. In this study, the EGR deposit formation mechanism was investigated based on spatially- and time-resolved scanning electron microscopy (SEM) observation. The deposit was formed in a custom-made sample line using real exhaust emitted from a diesel engine. The exhaust including soot was introduced into the sample line for 24 h (maximum duration), and the formed deposit was observed using SEM. The deposit was formed on the surface of the sample line below 300 °C (wall temperature) and had two layers: the deposit formed near the surface of the sample line had hard and dense structures, such as a lacquer (called a hard deposit), and the deposit formed on the hard deposit was powdery and exhibited a “grape-like” shape similar to particulate matter (called a powdery deposit). The amounts of both types of deposits increased in proportion to the duration of the exhaust supply. According to the SEM observations of the hard deposit, powdery deposit, and boundary layer between the positions where the hard and powdery deposits were formed, the hard deposit was formed by taking in the powdery deposit, when the exhaust including soot was supplied.</div></div>

Optimization of the propulsion plant of a Liquefied Natural Gas transport ship

Stricter emission regulations and variability of fuel prices pose the focus on the optimization of steam turbine based propulsion plants of Liquefied Natural Gas (LNG) ships. The efficiency of such a propulsion plant has been improved in this work by studying the introduction of reheating and preheating stages in the onboard regenerative Rankine cycle. A thermodynamic model of the propulsion plant has been developed from the facility diagrams, being validated afterwards with available experimental data from actual ship operation. The predictions of different scenarios obtained by the model when introducing modifications in the power propulsion cycle showed promising results. It was found that a combination of preheating and reheating stages was found to increase the cycle efficiency up to 33.71%, reducing fuel consumption in around 20 t/day and CO2 emissions in more than 20,000 t per year. An exergy analysis of the impact of cycle modifications and an economic assessment of the proposed investment plan were performed. It was found that the boiler was the main contributor to exergy destruction, fact that justifies the cycle modifications performed. The economic analysis of the investment plan of implementing the selected alternative provided benefits even in a conservative scenario, with an Internal Rate of Return higher than 12% and a Pay-Back Period less than 9 years for all the studied scenarios. In summary, a practical industrial application of thermodynamic and exergy analysis to the propulsion plant of a LNG ship has been shown, allowing an efficiency, economic and environmental improvement.

Microscopic characteristics of near-nozzle spray at the initial and end stages

It is demonstrated that the fuel deposit has a significant influence on the NOx and particular matter (PM) emissions, especially with the consideration of the more and more strict emission regulations issued by the governments, which results in that the near-nozzle spray has been received the increased attention, recently. In this study, the near-nozzle spray characteristics at the initial and end stages were investigated by a high-resolution camera with a laser as the light source. At the initial stage, microscopic behaviors of the near-nozzle spray were observed at upstream, midstream and downstream, respectively. Moreover, the injection frequency was changed to check the effect on the near-nozzle spray morphology. Next, the micro spray length, width and angle were obtained from the processed images. While, at the end stage, the near-nozzle spray evolutions were observed and characterized at 0.5, 0.3, 0.2 and 0.1 ms before the end of injection (BEOI), the end of injection (EOI), 0.05, 0.1, 0.15 and 0.2 ms after the end of injection (AEOI), respectively. Furthermore, the droplet area, spray area without droplets, droplet number and averaged diameter were calculated. Results show that three structures of the near-nozzle spray at the initial stage can be classified as mushroom, steeple and cylinder shapes. And the injection frequency has an influence on their formations owing to the residual fuel and sucked air AEOI. Moreover, although more droplets can be identified at the end stage with time, it does not indicate the better atomization. On the contrary, these droplets AEOI are responsible for the injector deposit in the real engine.