Selection of an Appropriate Pre-Injection Pattern in a Marine Diesel Engine Through a Multiple-Criteria Decision Making Approach

Abstract

In the present work, a numerical model was developed to analyze a commercial diesel engine. The adequacy of this model was validated using experimental results. This model was employed to study several pre-injection strategies. Particularly, the pre-injection rate, duration and starting instant were analyzed in the ranges 5% to 25%, 1° to 5° and −22° to −18°, respectively. The effect on consumption and emissions of NO_x, CO, and HC wereas evaluated. Since some of these configurations have opposite effects on consumption and/or emissions, it is necessary to develop a formal tool to characterize the most appropriate injection pattern. To this end, a multiple-criteria decision making approach was employed. It was found that the injection duration must remain as low as possible due to significant reductions in NO_x. The most appropriate injection pattern resulted 1° pre-injection duration, 20% pre-injection rate, and −19° pre-injection starting instant. This configuration leads to increments of 6.7% in consumption, 3.47% in CO, and 3.83% in HC but reduces NO_x by 34.67% in comparison with the case without pre-injection.

1. Introduction

It is well known that diesel engines are efficient thermal machines but emit significant levels of harmful gases such as CO₂, Particulate Matter (PM), NO_x, CO, HC and SO_x [1]. Between these, PM and NO_x are especially important in diesel engines. Smoke emissions, related to PM, mostly composed of soot, are related to several diseases but are not globally regulated [2]. On the other hand, ever increasing NO_x emission limits have been imposed by regional and international authorities [3]. According to this, several procedures have been developed in the recent years to reduce NO_x emissions in diesel engines such as EGR, water injection, modification of the injection parameters and after-treatments [4,5,6,7]. Between these measurements, this paper focuses on multiple injection strategies using pilot injections. This strategy affects mixing and combustion [8]. Several works about the advantages and disadvantages of pilot injections can be found in literature. One of the main disadvantages is that soot can increase because pilot injections aggravate spray characteristics and particulates can be formed at the rich region [9,10]. In order to avoid this problem, some authors recommend combining multiple injections with other systems such as EGR [11] or high injection pressures [12], while other researchers indicate that it is possible to obtain both NO_x and particulate reduction using only multiple injections. In this regard, Carlucci et al. [13] analyzed the pilot injection timing and duration and found that NO_x emission levels are mainly influenced by the pilot duration, whereas smoke emission is influenced by both variables. Tanaka et al. [14] analyzed the time and quantity of the pilot injection. Benajes et al. [15] analyzed the effect of the dwelling time, i.e., the time between two consecutive injections, obtaining reductions in noise, NO_x and particulates. Pierpont et al. [16] varied the fuel distribution in each pulse and the dwelling time between pulses and obtained a reduction in NO_x and particulates but also a 3%–4% increase in consumption. Hotta et al. [17] analyzed the number of pre-injections and obtained reductions in both NO_x and HC. Fang et al. [18] analyzed the effect of the injection angle when using multiple injections. Other works such as those of Ishida et al. [19] and Minani et al. [20] obtained NO_x and consumption reductions but recommended employing a small amount of pilot quantity in order to maintain soot emissions. In addition to these aforementioned works based on diesel engines, other similar studies were realized using biodiesel [21], gasoline [22,23,24], dual fuel [25,26,27,28] and other fuels [29,30,31,32,33,34].

Numerical simulations also provide interesting information to analyze multiple injections. Particularly, in the field of Computational Fluid Dynamics (CFD), one can refer to the work of Lechner et al. [35], who analyzed advanced injection strategies to achieve partially premixed combustion in a diesel engine; Sun and Reitz [36], who analyzed injection strategies to optimize two-stage combustion; Verbiezen et al. [37], who analyzed the effect of injection timing; Zehni and Jafarmadar [38], who analyzed the effect of split injection in a direct-injection diesel engine; Abdullah et al. [39], who analyzed the effect of injection pressure; Coskun et al. [40], who analyzed second injection timings; Wang et al. [41], who analyzed several fuel injection strategies on a gasoline engine; Zhaojie et al. [42], who analyzed two-stage fuel injection with EGR; Lamas et al. [43,44], who analyzed several configurations of pilot injections; and Sencic [45], who analyzed alternative injection patterns.

Despite this literature about multiple injections, it is important to develop a reliable tool to characterize the most appropriate injection pattern. In this regard, this work presents a Multiple-Criteria Decision Making (MCDM) approach to characterize the most appropriate injection pattern. MCDM methodologies constitute a formal tool to handle complex decision-making situations, providing coherence in the decision process. Three pre-injection parameters such as pre-injection rate (R), duration (D) and starting instant (S) were analyzed. The emissions and consumption analyzed in the present work were obtained through a CFD model previously validated with experimental results.

2. Materials and Methods

The engine analyzed in the present work is the Wärtsilä 6 L 46. This is a four-stroke diesel engine with six cylinders. Each cylinder presents two intake and two exhaust valves. The fuel injector has 10 holes and is placed at the center of the cylinder head. The fuel is injected directly into the cylinder since it is a direct injection engine.

The geometry and mesh were realized using SolidEdge and Gambit softwares, respectively. A grid independence study was carried out to provide its adequacy. The software OpenFOAM was employed for the CFD simulations. OpenFOAM was chosen because it is an open software which allows a complete manipulation of the code. The simulation started at 360° Crank Angle After Top Dead Center (CA ATDC) and the whole cycle was analyzed. As boundary conditions, the heat transfer from the cylinder was modelled as a combined convection–radiation. The RANS (Reynolds-averaged Navier-Stokes) equations of conservation of mass, momentum and energy were solved. The k-ε was employed as the turbulence model due to its robustness and reasonably accuracy for a wide range of turbulent flows. As fuel droplet breakup, the Kelvin–Helmholtz and Rayleigh–Taylor breakup models [46] were employed, and the Dukowicz model [47] for the heat-up and evaporation. Regarding the chemical kinetics, a reaction mechanism was programmed by combining the following three kinetic schemes:

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Ra and Reitz’s [48] kinetic scheme, based on 131 reactions and 41 species, for combustion.

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Yang et al.’s [49] kinetic scheme, based on 20 species and 43 reactions, for NO_x formation.

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Miller and Glarborg’s [50] kinetic scheme, based on 24 species and 131 reactions, for NO_x reduction.

This numerical model was validated using experimental measurements. For this purpose, experimental measurements obtained elsewhere [51,52,53,54,55] were employed. The emissions and Specific Fuel Consumption (SFC) obtained numerically and experimentally at several loads are indicated in Figure 1. This figure shows a reasonable correspondence between numerical and experimental results. The in-cylinder pressure obtained numerically and experimentally at 100% load is shown in Figure 2. This figure also shows a satisfactory correspondence between experimental and numerical results. A certain error was inevitable due to both numerical and experimental handicaps. On the one hand, the instruments employed to characterize experimental measurements have a certain tolerance and, on the other hand, numerical errors are introduced due to the discretization processes and the hypothesis assumed to simplify the governing equations.

3. Results and Discussion

Once the numerical model was validated with experimental measurements, it was used to analyze the results of several pre-injection patterns. Five pre-injection rates were employed: 5%, 10%, 15%, 20% and 25%; five pre-injection durations: 1° Crank Angle (CA), 2° CA, 3° CA, 4° CA and 5° CA; and five pre-injection starting instants: −22° CA ATDC, −21° CA ATDC, −20° CA ATDC, −19° CA ATDC and −18° CA ATDC. These ranges were chosen according to a previous paper [43], in which it was obtained that wider ranges lead to excessive increments in NO_x emissions or consumption. Taking into account these values of pre-injection rates, durations and starting instants, a total of 125 cases were analyzed. These are summarized in Figure 3. SFC and emissions of NO_x, CO and HC were characterized for these 125 cases.

As indicated previously, a MCDM method was employed. The first step in MCDM approaches consists on defining the decision tree with the relative importance of each parameter,

Table 1. Decision tree for the MCDM approach.

Requirement	Sub-Requirement
SFC (50%)	SFC (100%)
Emissions (50%)	NOx (33.3%)
	CO (33.3%)
	HC (33.3%)

. Two requirements (general aspects) were considered: consumption and emissions. The second level represents the sub-requirements (specific aspects). The relative importance of each requirement and sub-requirement must be defined too. In the present work, this importance was distributed equally, i.e., the same importance was given to SFC and emissions: 50% (α = 0.5 in per unit basis) for each one. Regarding emissions, the same importance was given to NO_x, CO and HC: 33.3% (β = 0.333 in per unit basis) for each one. It is worth mentioning that other scenarios could also be possible in case it is necessary to provide other levels of importance.

The next step was to calculate the variation of each sub-requirement respect to the case without pre-injection, in per unit basis, Equation (1).

$$V_i=\frac{X_i-X_{ref}}{X_{ref}}$$

(1)

where X_i is the value of the sub-requirement i and X_ref the value corresponding to the case without pre-injection, i.e., SFC = 172 g/kWh, NO_x = 13.3 g/kWh, CO = 4.5 g/kWh and HC = 5.5 g/kWh. Once each indicator was transformed into its variation in per unit basis, the global adequacy index, AI, was computed by Equation (2).

$$AI={\displaystyle \sum _{i=1}^{125}{\alpha }_i{\beta }_i{V}_i}$$

(2)

where α_i is the weight of each requirement and β_i the weight of each sub-requirement in per unit basis.

The minimum value of the AI is the most adequate solution. It was determined using an in-house code programmed using the open software GNU Octave. For the parameters analyzed in the present work, the minimum AI is −0.0122, corresponding to −19° CA ATDC pre-injection starting instant, 20% pre-injection rate and 1° CA pre-injection duration. The results are detailed in Appendix A. As can be seen in this appendix, the minimum AI value was obtained in the ninety-first case analyzed. On a per-unit basis, SFC varies by 0.067; NO_x −0.3467, CO 0.0347 and HC 0.0383, corresponding to an SFC increment of SFC 6.7%; NO_x reduction of 34.7%, CO increment of 3.47% and HC increment of 3.83% in comparison with the case without pre-injection. Despite the increments in SFC, CO and HC, these solution results are the most appropriate because the increments are low and the NO_x reduction is too significant.

The results shown in Appendix A indicate that the influence of CO and HC in the AI is not too important due to the low variation of these emissions with the pre-injection rate, duration and starting instant. On the other hand, SFC and NO_x emissions are more sensitive. A representative quantity of pre-injection is necessary, specifically 20%, due to the important reduction on NO_x emissions. Nevertheless, if this pre-injection rate is increased, it leads to important increments in SFC and, to a lesser extent, CO and HC emissions. Regarding the pre-injection instant, early pre-injections are appropriate to reduce NO_x but the increase increment in SFC is considerable; for this reason, a -19° CA ATDC was obtained. Finally, the injection duration mainly affects to NO_x and short injections lead to considerable NO_x reductions. The goal of NO_x reduction using pre-injections is to control the combustion temperature. It is well known that high combustion temperatures promote NO_x formation [56]. In order to control the combustion temperature, it is necessary to rigorously inject the fuel at the optimum instant. It is worth mentioning that in practical applications it is not possible to reduce the injection duration at the desirable level. For instance, the present engine is a four-stroke medium-speed model which runs at 500 rpm, which corresponds to 0.00033 s to reach 1° CA. Some current piezo-injectors are able to switch on and off in tens of microseconds but solenoid or electromagnetic injectors have longer response times. For this reason, pre-injections shorter than 1° CA were not analyzed in the present work. Using 1° CA pre-injection duration, the AI is represented in Figure 4. As can be seen, the minimum value of AI, −0.0122 is reached using 20% pre-injection rate and −19° CA ATDC pre-injection starting instant, as indicated above.

The consumption against the pre-injection rate and starting instant is shown in Figure 5. This figure refers to 1° CA pre-injection duration. As can be seen, early pre-injections promote considerable increase increments in SFC, as indicated above. On the other hand, the pre-injection rate also promotes increase increments in SFC considerably.

NO_x emissions against the pre-injection rate and starting instant are indicated in Figure 6. This figure refers to 1° CA pre-injection duration. As can be seen, early pre-injections promote reductions in NO_x, but increment SFC, as indicated above. On the other hand, the pre-injection rate considerably reduces NO_x but also increments SFC.

Figure 7 and Figure 8 show the CO and HC emissions against the pre-injection rate and starting instant. These figures refer to 1° CA pre-injection duration. As can be seen, both CO and HC are sensitive to the pre-injection rate and starting instant. Nevertheless, the influence of CO and HC in the results is low due to their low variation.

4. Conclusions

The present work proposes a numerical model to analyze the commercial diesel engine Wärtsilä 6 L 46. Once validated, the numerical model was employed to analyze 125 injection patterns with different values of pre-injection rate, duration and starting instant in the ranges 5% to 25%, 1° CA to 5° CA and −22° CA ATDC to −18° CA ATDC, respectively. Due to this large number of results and the fact that these parameters can lead to different and opposite effects, it is difficult to determine the most adequate injection pattern. According to this, a MCDM approach was employed to select the most appropriate injection pattern. The effects on consumption and emissions of NO_x, CO and HC were characterized. It was found that the injection duration must remain as lower as possible due to significant reductions in NO_x. The most appropriate injection pattern was shown in the 1° CA pre-injection duration, 20% pre-injection rate and −19° CA ATDC pre-injection starting instant. This injection pattern leads to increases in 6.7% in SFC, 3.47% in CO and 3.83% in HC but reduces NO_x by 34.67% in comparison with the case without pre-injection. Future works will focus on analyzing other parameters such as number of pre-injections, dwelling time and injection angle, as well as other engines.