Meeting EURO6 emission regulations by multi-objective optimization of the injection strategy of two direct injectors in a DDFS engine [229]

Table of contents :
Meeting EURO6 emission regulations by multi-objective optimization of the injection strategy of two direct injectors in a D ...
1. Introduction
2. Computational approach and methodology
2.1. CFD model and validation
2.2. Artificial neural network (ANN) methodology
2.3. Non-dominated sorting genetic algorithm (NSGA-II) methodology
3. Results and discussion
4. Conclusions
Credit author statement
Declaration of competing interest
Appendix
A.1. Mesh sensitivity study
A.2. CFD model validation for other loads and strategies
A.3. First law energy distribution formulas
A.3. Second law exergy distribution formulas
Nomenclature
References

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Energy 229 (2021) 120737

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Energy journal homepage: www.elsevier.com/locate/energy

Meeting EURO6 emission regulations by multi-objective optimization of the injection strategy of two direct injectors in a DDFS engine Sasan Shirvani a, *, Saeid Shirvani a, Amir H. Shamekhi a, Rolf Reitz b, Fatemeh Salehi c a

Mechanical Engineering Department, K.N. Toosi University of Technology, Tehran, Iran Engine Research Center, University of WisconsineMadison, Madison, WI, USA c School of Engineering, Macquarie University, Australia b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 October 2020 Received in revised form 11 April 2021 Accepted 22 April 2021 Available online 27 April 2021

Direct Dual Fuel Stratification (DDFS) is a novel LTC strategy among other strategies which uses two direct injectors in the combustion chamber, similar to Reactivity-Controlled Compression Ignition (RCCI), but resulting in more authority over the combustion process and the rate of heat release. DDFS has comparable thermal efficiency to RCCI and HCCI, as well as extra-low NOx and soot emissions, and it also is able to meet the EURO6 emission mandate without using aftertreatment under optimized conditions. Thus, it is crucial to optimize the injection strategy of both injectors in a DDFS engine. Artificial Neural Networks (ANNs) are used to develop a model for predicting engine performances and pollution. A multi-objective optimization analysis was performed to minimize NOX, soot and fuel consumption simultaneously using the non-dominated sorting genetic algorithm (NSGA-II) for the injection parameters of the gasoline and diesel direct injectors. The optimal solutions met the EURO6 mandate for NOX and soot, offered lower fuel consumption up to 8 g/kW-h, and also had about 2% higher thermal efficiency than the base case. Thermodynamic evaluation based on the first and second laws were performed for seven selected candidates on the Pareto Front and compared with the base case. © 2021 Elsevier Ltd. All rights reserved.

Keywords: Direct dual fuel stratification (DDFS) ANN Genetic algorithm (GA) Optimization NSGA-II CFD

1. Introduction Internal Combustion Engines (ICEs) play a dominant role in supplying the world's energy demands and mobility applications. Generally, ICEs are operated with fossil fuels; however, recently, owing to global warming concerns, using bio-derived fuels, which are considered carbon-neutral, has received tremendous attention. Alongside ICEs, Electric Vehicles (EVs) are promising future generations of vehicles. However, EVs are restricted by challenges that may postpone their utility to the later decades. For instance, massive infrastructure and tremendous expenses is needed to replace all ICEs with EVs. Moreover, batteries are still cumbersome and expensive with limited capacity, and also the lithium resources in the earth's crust are limited, which is challenging for manufacturing lithium-ion batteries. As a result, it is crucial to continue improving and developing new technologies in ICEs to make them cleaner and more efficient [1]. Low-Temperature Combustion (LTC) is a practical pathway to

* Corresponding author. E-mail address: [email protected] (S. Shirvani). https://doi.org/10.1016/j.energy.2021.120737 0360-5442/© 2021 Elsevier Ltd. All rights reserved.

make ICEs more efficient, combined with ultra-low engine emissions. LTC can reduce nitrogen oxides (NOX) and Particulate Matter (PM) emissions to almost zero levels, but at the expense of higher levels of Unburned Hydro Carbon (UHC) and carbon monoxide (CO). The most well-known LTC strategies are categorized as Homogenous Charge Compression Ignition (HCCI), introduced in the 1980s, Premixed Charged Compression Ignition (PCCI) in 1996, Reactivity Controlled Compression Ignition (RCCI), in 2006, and Direct Dual Fuel Stratification (DDFS), in 2015. HCCI was introduced by Onishi et al. [2] in 1979, and as the name implies, a fully homogenous charge enters the combustion chamber whose is burning completely kinetically controlled. In this strategy, the heat addition occurs very fast in a short duration, so there is very limited control over the combustion process. Engine noise levels can be rather high in HCCI engines due to the fast combustion, and the operating range is also limited. The combustion control levers in HCCI are mainly the EGR rate, equivalence ratio, intake pressure, and temperature [3]. Nowadays, many scholars are still researching HCCI strategies to overcome its limitations and make it more practical for ICEs. Aydogan [4] performed an experimental investigation on the fuel mixture to have more control over HCCI combustion. N-heptane, n-hexane, and their

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showed that the formation of particles could be divided into two processes, including in-cylinder and out of cylinder processes. Soot agglomerates were mainly produced during the in-cylinder process, while complex materials were found in the analyzer and outcylinder process. One of the main drawbacks of LTC strategies is low control over the rate of heat release because LTC combustion is kinetically controlled, and it is sensitive to initial conditions. A novel LTC strategy was introduced in 2015 by Wissink and Reitz called Direct Dual Fuel Stratification (DDFS) [18]. They implemented two direct injectors in the combustion chamber, one specifically for gasoline and the other one specifically used for diesel. In this strategy, an early injection provides a partially premixed charge of gasoline in the combustion chamber, and then by injecting a little portion of diesel, reactivity gradient is achieved (RCCI concept), then a nearTDC injection provides direct control over the rate of heat release which is similar to the PPC concept; thus, DDFS benefits from RCCI and PPC simultaneously. According to the experiments performed by Wissink et al. [19e21], DDFS has comparable thermal efficiency to RCCI, and it produces low emissions. It also was reported that DDFS has a rather high PM compared to RCCI owing to the near-TDC injection, which is mixing-controlled combustion. One practical pathway to address this problem is using oxygenated fuels like ethanol blends with gasoline. Shirvani et al. [22] performed a numerical study showing that by using E10 (10% ethanol blended in gasoline by volume) as the low-reactivity fuel, it is possible to decrease PM up to 40%; however, NOX increased by 2%. Ethanol improved fuel oxidization, so the gross thermal efficiency slightly increased in the E10 case compared to gasoline/diesel DDFS. In another attempt, they showed that by using conventional diesellike piston profiles (omega-type), NOX and CO was reduced by 16% and 27%; respectively, compared to the modified-piston [23]. DDFS's thermal efficiency and CO2 emissions are comparable to the RCCI strategy, while it provides direct control over the rate of heatrelease [24]. The DDFS strategy was also compared with other LTC strategies (RCCI, HCCI, and PPC) from the perspectives of the first and second laws of thermodynamics. It was found that DDFS has comparable thermal efficiency to HCCI and RCCI, and unlike HCCI and RCCI, it has a great potential to use aftertreatment with high conversion efficiency. DDFS has a great potential to be considered for mobility purposes and the future of ICEs [25]. In a study carried out by Li et al. [26], they implemented two direct injectors in the combustion chamber of an engine to benefit from the DDFS concept for their tests, and they denoted the strategy as Intelligent Charge Compression Ignition (ICCI). The engine operated with gasoline/diesel managed to reach about 50% thermal efficiency with about 0.12 g/kW-h NOX emissions. Higher gasoline ratios resulted in a retarded combustion phasing and an increase in UHC and CO. They showed that the engine under ICCI strategy had the best performance at 8 bar IMEP, and the measured NOX was less than 0.1 g/kW-h (met the EURO6 limits). In another attempt, they demonstrated that the ICCI engine operated by methanol/biodiesel could achieve up to 53% thermal efficiency with low UHC and CO emissions (less than 0.1 g/kW-h), but at the expense of an increase in fuel consumption. The optimal methanol injection timing was between
340 and
300 ATDC, and biodiesel injection was at
48 to
42 ATDC [27]. Ansari et al. [28] performed a two objective optimization methodology in a light-duty engine operated at both RCCI (natural gas/diesel) and CDC strategies to minimize Brake Specific Fuel Consumption (BSFC) and Brake Specific Urea Consumption (BSUC). New parametric empirical models were developed to predict the experimental results of the tests. They found that single diesel fuel injection mode was the optimum combustion mode for 3e6 bar IMEP compared to RCCI. The cost of BSUF for the RCCI mode was

blends were used in the experiments. It was found that n-hexane is more knock-resistant. By utilizing 75% of n-hexane and 25% of nheptane, the operating range was maximized. Besides, UHC was measured at its minimum levels about 309 ppm, when 50% of nhexane and 50% of n-heptane were applied in the fuel structure. Moradi et al. [5] performed a numerical study on the effects of hydrogen and oxygen additions on emissions and engine performance of an HCCI engine fueled with natural gas. Hydrogen addition managed to extend the combustion duration and led to faster combustion. At the low-load operating conditions, 30% additional oxygen could extend the operating range up to 48%; however, the thermal efficiency decreased by about 2.3%, and CO and UHC increased. PCCI is another LTC strategy in ICEs and was introduced by Aoyama et al. [6] in 1996. In this strategy, a direct injector in the combustion chamber supplies a partially homogenous charge by sequential injections to stratify the charge in the combustion chamber. This stratification leads to a longer combustion duration than HCCI, which means better control over the combustion process. PCCI is still a challenging strategy due to its cyclic variation, sensitivity to initial conditions, and limited operating range, and many scholars are trying to improve it. Kalghati et al. [7] reported that an early pilot injection with the main injection near Top Dead Center (TDC) could enhance combustion stability and reduce cyclic variation. They also indicated that PM was decreased by using gasoline instead of diesel fuel. This strategy is also called Partially Premixed Combustion (PPC). At higher operating loads, the need for high-octane fuels and EGR rate will be increased to maintain combustion phasing. Other drawbacks of PCCI are engine noise and high levels of UHC and CO emissions, like in HCCI and RCCI strategies [8e10]. Zhang et al. [11] performed an experimental investigation on the effects of injection strategy on combustion characteristics in a PPC engine. Results showed that by adopting a double injection strategy, the interaction between main and pilot jets could advance the combustion phasing near the TDC, and thermal efficiency improved. In the triple injection strategy, advanced and retarded post injections resulted in poor combustion efficiency. The triple injection case, which had
38/-24/-6 ATDC injection timings, showed the best results for the thermal efficiency. To overcome the limitations of HCCI and PCCI strategies, in 2006, Inagaki et al. [12] introduced RCCI. They used two different fuels with different reactivity to provide better control over the combustion process by reactivity gradient in the combustion chamber. Iso-octane (or gasoline) was supplied by a port fuel injector as the low-reactivity fuel, and diesel fuel was injected directly in the combustion chamber as the high-reactivity fuel. This makes a reactivity gradient in the charge, and combustion starts from the regions with higher reactivity and extends towards lowreactivity regions. As a result, combustion duration increased compared to HCCI and PCCI, and more control over the combustion phasing was achieved. RCCI has lower engine noise levels than HCCI and PCCI, and similarly, it has ultra-low emissions [13,14]. In some studies, the gross thermal efficiency of RCCI was shown to reach up to 60% under optimized conditions [15]. Other scholars investigated the effects of other alternative fuels to gasoline to extend the RCCI operating range. Li et al. [16] conducted a numerical study on the effects of Intake Valve Closing (IVC) and EGR rate on the control of PPRR in a gasoline/biodiesel RCCI engine. Results showed that delaying IVC timing and increasing EGR improved PPRR. Two-stage optimization indicated acceptable results for emission and performance. NOX, soot, and CO reduced by 62%, 44%, and 28%, respectively, and at the optimum condition, the thermal efficiency reached 44%. Han et al. [17] performed an experimental investigation focusing on PM production in an RCCI engine. The results 2

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30e35% lower than the CDC mode at higher loads (IMEP>6 bar). At medium loads (7e9 bar), RCCI showed better results for fuel consumption and emissions, and the conversion efficiency of the UHC was about 80e95% and could meet the EPA2010 emission mandate. Liu et al. [29] conducted a multi-objective optimization analysis for a diesel/natural gas dual-fuel engine using GA. It was reported that soot emissions met EURO6 in all optimal solutions, while for NOX, optimal solutions met the EURO5 emission limit. The retarded injection timing was very influential for reducing NOX. For the combustion chamber design, a more open chamber yielded better results than re-entrant ones. Li et al. [30] performed energy and exergy analyses on different combustion regimes: CDC, RCCI, and HCCI. It was found that the primary source of exergy destruction is due to chemical reactions during the combustion process. In addition, the transition from the low-temperature heat release to high-temperature heat release was another source of exergy destruction. They also reported that a premixed charge was more favorable than a direct injection in the combustion chamber to minimize exergy destruction. In the HCCI and RCCI strategies, by increasing the equivalence ratio to the stoichiometric values, exergy destruction decreased. In the present study, a 3D-CFD model was first developed and validated against experimental data to simulate the combustion process in a DDFS engine. An artificial neural network was trained based on 576 samples of DDFS combustion with the sweeps of gasoline and diesel injection parameters. Non-dominated sorting genetic algorithm (NSGA-II) was used to find the optimal solutions based on a multi-objective optimization analysis for minimizing NOX, soot, and indicated specific fuel consumption. Seven candidates were selected from different regions of the Pareto Front for further evaluations and making a comparison with the base case. Finally, the optimal candidates were analyzed based on the first and second laws of thermodynamics.

Table 1 Engine geometry and injector specifications for the experimental setup. Engine type

Caterpillar 3401E Single Cylinder Oil Test Engine (SCOTE)

Piston type Modified-piston (wide shallow) Displacement (L) 2.44 Bore (mm) 137.2 Stroke (mm) 165.1 Connecting rod length (mm) 261.6 Squish height (mm) 1.57 Number of valves per cylinder 4 IVO ( ATDC) 335 IVC ( ATDC)
143 Intake valve opening duration 242 (CAD)  EVO ( ATDC) 130 EVC ( ATDC)
355 Exhaust valve opening 235 duration (CAD) Valve overlap (CAD) 30 Swirl ratio 0.7 Compression ratio 14.88:1 Diesel injector specifications Body style Bosch CRI2 series Nozzle angle ( ) 148 Hole diameter ðmmÞ 141 Number of holes 7 Gasoline injector specifications Body style Bosch CRI2 series Nozzle angle ( ) 143 Hole diameter ðmmÞ 117 Number of holes 10

Table 2 Experimental operating conditions used for the DDFS combustion. Experimental operating parameters

2. Computational approach and methodology A 3D-CFD model was developed and validated against experimental data from the tests at the engine research center of the University of Wisconsin Madison for the DDFS combustion at the load of 9.41 bar gross IMEP and engine speed 1300 rpm. In the second phase of the study, an ANN model was developed to train the behavior of the engine and a comparison of the CFD results, and the ANN outputs are provided to examine the accuracy of the ANN model. In the third phase, a multi-objective optimization analysis was performed using the non-dominated sorting genetic algorithm (NSGA-II) to predict the optimal results for injection parameters to minimize three objectives of NOX, soot and Indicated Specific Fuel Consumption (ISFC).

EGR (%) TEGR ( C) Tin ( C) Pin (kPa) (supercharged) Equivalence ratio Gross IMEP (bar) Qfuel (kJ/cyc)

39.5 90.3 49.3 186.2 0.57 9.41 4.71

Fuel1

Diesel

Injection pressure (bar) SOI2 ( BTDC) Injection duration (ms) Total energy ratio (%) Fuel2 Injection pressure (bar) SOI1 ( BTDC) Injection duration 1 (ms) SOI3 ( BTDC) Injection duration 2 (ms)

500 60 0.6 7.0 Gasoline 1000 340 1.4 4 0.8

2.1. CFD model and validation Generalized Re-Normalization Group (GRNG) k
ε model was used owing to the study performed in Ref. [31], and the coefficients for the calculations of the turbulence simulation were taken from Ref. [32]. The GRNG k
ε model has supremacy over other k
ε models because it showed close results to experimental data for reactive spray cases. In addition, the GRNG model provides higher accuracy trade-off for swirl and shear flows, and it also shows better fuel vapor jet structure and fuel jet penetration behavior for simulating the compressible flows in diesel engines [33]. For simulating the combustion process, the SAGE solver with the combination of the multi-zone model was employed because the multi-zone model reduces the computational time up to 10 times with accurate results [34,35]. For spray modeling, the Kelvin-

The CFD model simulates closed-cycle DDFS combustion from Intake Valve Closing (IVC ¼
143 ATDC) to Exhaust Valve Opening (EVO ¼ 130 ATDC) in a heavy-duty single-cylinder research engine. Geometrical engine specifications and operating conditions are listed in Tables 1 and 2. Experimental data of the engine, operating conditions, cylinder pressure, and emissions are taken from Ref. [19]. SOI1 is the timing of an early gasoline injection to provide a relatively homogenous charge in the cylinder, SOI2 is the diesel injection timing to make a reactivity gradient in the chamber (to benefit from the RCCI concept), and SOI3 is the gasoline nearTDC injection timing. The injection and valve timings of the DDFS engine are shown in Fig. 1. For modeling turbulence transport of the chamber, the 3

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Fig. 1. DDFS injection and valve timings.

Fig. 3 shows the comparison of numerical results and experimental data for cylinder pressure, AHRR and emissions. In addition, the CFD model was used for other loads and strategies to examine its accuracy under other operating loads, as presented in the Appendix.

Helmholtz and Rayleigh-Taylor (KH-RT) hybrid model was used to simulate primary and secondary break-ups in all cases [36]. The Frossling correlation was used for spray evaporation [37], and the No Time Counter (NTC) model was applied for the spray collision because it is faster than the O'Rourke model. In contrast to the O'Rourke, which has a second-order correlation between the number of parcels and computational time, the NTC model has a linear correlation. For the wall interactions, the Wall Film O'Rourke was adopted [38]. To simulate the combustion process, the SAGE detailed chemistry solver model was employed to solve the detailed chemical kinetics during the combustion process. A reduced reaction mechanism for iso-octane and n-heptane combustion with 108 species and 435 reactions was used [39]. The Zeldovich and Fenimore Mechanisms [40,41] were used for thermal and prompt NOX formation, respectively, while the Hiroyasu model was utilized for soot formation [42]. Boundary conditions with no wall roughness were considered for the head cylinder, cylinder wall and piston surface. Dirichlet boundary conditions for the wall temperature were chosen from the experimental tests given in Ref. [43]. Fig. 2 illustrates the combustion chamber with two direct injectors of the CFD model at TDC. A grid sensitivity study was performed for the model with different mesh sizes, including 2 mm (coarse mesh), 1.4 mm (medium mesh) and 1 mm (fine mesh) describe in the Appendix, and the medium mesh size was chosen for further simulations based on its accuracy and computational time. For some critical regions such as the spray and wall zones, mesh refinement was done to have better CFD results. According to Ref. [44], mesh sizes for spray regions should not exceed 0.25 mm, so this criterion was applied for the 3D-CFD model in this study.

2.2. Artificial neural network (ANN) methodology Artificial Neural Network (ANN) is a popular machine learning technique that has shown its functionality and reliability in various fields of study. ANN has the ability to predict the input-output relationships of a given system for large numbers of datasets. The inspiration of the ANN model was initially introduced by McCulloh and Pitts in 1943, which was an inspiration of the biological nature of the nervous system [45]. ANN can solve various ranges of problems in science and engineering, particularly in areas in which the numerical techniques are time-consuming and need tremendous computational time. In this study, a common configuration of a feed-forward multi-layer perceptron (MLP) network was used for the prediction of the most considerable DDFS engine outputs, including NOX, soot, ISFC, UHC þ CO, and Peak Pressure Rise Rate (PPRR). In the ANN, the activation function gives a correlation between inputs and outputs, and training techniques which rely on minimizing Mean Squared Error (MSE) or Root Mean Squared Error (RMSE), which are defined as follows:

Error ¼ Yi
Ybi

Fig. 2. Piston geometry profile with the crevice volume for the combustion chamber at TDC. 4

(1)

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Fig. 3. Comparison of experimental data and numerical results for cylinder pressure, AHRR and emissions.

MSE ¼

n  2 1X Yi
Ybi n

(2)

i¼1

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n  2 u1 X Yi
Ybi RMSE ¼ t n

(3)

i¼1

where; Yi is the CFD output and Ybi is the ANN predicted value. MSE, RMSE, and correlation coefficient (R) indicate the performance of the network. For testing, validation and training of the model, 15% of the data sets belonged to testing (86 samples), 15% for validation (86 samples), and 70% for training (404 samples). The ANN model used in this study is a two-layer feed-forward network with a tan-sigmoid transfer function at the first and second layers, and a linear transfer function for the output layer. There are five inputs, including diesel energy fraction (Ed), diesel start of injection (SOI2), gasoline energy fraction (Eg), gasoline start of injection (SOI3), and gasoline injection pressure (Pg) as the main injection parameters for the injection strategy of DDFS. The output of the ANN was considered NOX, soot, ISFC, UHC þ CO, and PPRR. The network topology is illustrated in Fig. 4 for inputs and targets. Fig. 5 shows the comparison of ANN outputs and CFD results for all targets (NOX, soot, ISFC, UHC þ CO and PPRR). MSE, RMSE and correlation coefficient (R) for all targets shows the good performance of the ANN, and the magnitude of errors, defined in Eq. (1), is also negligible. Fig. 6 shows the output results of the ANN for diesel and gasoline injection strategy sweeps. In Case (a), diesel energy fraction (Ed) and timings (SOI2) were swept with considering gasoline energy fraction (Eg) and timings (SOI3) with constant values like the base model. In Case (b), gasoline injection strategy (Eg and SOI3) was swept while considering Ed and SOI2 as constant values like the base model. The main purpose of providing Fig. 6 is the examination of the ANN performance and its outputs for NOX, soot and ISFC as three objectives in optimization. As shown in Fig. 6, the trade-off between NOX and soot is obvious, and in regions where the combustion has a mixing-controlled nature (high amounts of Ed and Eg, late injection of SOI2), ISFC increases. The results are in consistence with the experimental data in Refs. [18e21].

Fig. 4. ANN topography, inputs, outputs, neuron numbers, and hidden layer.

multi-objective optimization of the injection strategy for both injectors in the DDFS engine. According to Darwin's theory of evolution, a new generation is created from the previous generation based on mutation, crossover, and natural selection. After some generations, the algorithm converges toward the optimal solution called the Pareto Front [47]. Five important effective parameters for injection strategy (Ed, SOI2, Eg, SOI3, and Pg) for the two-direct injectors in the combustion chamber of a DDFS engine were considered as design parameters. The main objectives of the optimization are NOX, soot and ISFC (or fuel consumption). Table 3 presents the ranges for the design parameters used in this study for optimization. The upper and lower ranges for the design parameters are taken from the experimental tests performed by Wissink et al. [18e21]. Ed more than 11%, the higher amount of diesel fuel makes the combustion mixing-controlled and CA50 can occur before TDC. For late SOI2s after
30 ATDC, the similar behavior occurs and the DDFS strategy does not benefit over the RCCI concept. For Ed less than 4%, a suitable reactivity gradient is not be achieved to benefit over the RCCI concept. Gasoline energy fractions of the near-TDC injection must be between 20% and 40% because for Eg more than 40%, combustion moves toward mixingcontrolled combustion, which has high levels of emissions and

2.3. Non-dominated sorting genetic algorithm (NSGA-II) methodology Non-dominated Sorting Genetic Algorithm two (NSGA-II) was developed by Deb et al. [46] and was adopted in this study for the 5

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Fig. 5. Comparison of the ANN outputs and CFD targets for 576 samples. Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and correlation coefficient (R) are shown as performance indexes of the ANN.

6

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Fig. 6. Diesel and gasoline injection strategy sweeps of the DDFS combustion model.

pressure in kPa, and V is cylinder volume in m3.

Table 3 Ranges of design parameters used for multi-objective optimization. Diesel energy fraction (Ed) (%) Diesel start of injection (SOI2) ( ATDC) Gasoline energy fraction (Eg) (%) Gasoline start of injection (SOI3) ( ATDC) Gasoline injection pressure (Pg) (bar)

4 to 11
120 to
30 20 to 40
8 to 0 750 to 1500

3. Results and discussion The solution approach for optimizing the injection strategy for both direct injectors in the DDFS engine is illustrated in Fig. 7. The initial population for the Genetic Algorithm (GA) was considered to have 500 members, and after 32 generations, the optimal solutions on the Pareto Front were obtained, and the results are depicted in Fig. 8. It can be seen that soot and ISFC have a linear correlation, which means when soot particles are not oxidized, fuel consumption must increase to generate the same power for the engine. On the other hand, the trade-off between NOX and soot is also illustrated in the Pareto Front, showing the rationale behind the mixing-controlled combustion in DDFS. Fig. 9 shows seven selected candidates from all locations of the Pareto Front for further consideration. All the Pareto Front members meet the EURO6 emission limits for NOX and soot which are 0.4 and 0.01 g/kW-h. Two candidates were selected from the upper end of the front, three were selected from the middle of the front, and two of them were selected from the lower end of the front to fully cover all regions and thus have candidates from all locations. The candidates are shown as P1 to P7 in the Pareto Front in Fig. 9. Table 4 presents the optimal values for the design parameters of both gasoline and diesel injectors, and the results of emissions, ISFC, and PPRR of the base case and the optimal candidates are presented. NOX emission of the optimal candidates is reduced from 20% to 62%, and is soot reduced from 54% to 84% compared to the base case. Compared to the base case, the fuel consumption (i.e. ISFC) decreased from 3.2% to 5.4% for the optimal candidates. PPRR is an Index of engine noise level, and in the optimal candidates, PPRR level increased owing to the fact that the injection strategy

fuel consumption. On the other hand, for Eg less than 20%, direct control over the rate of heat-release is not possible, which is one of the most significant benefits of DDFS compared to RCCI. Diesel energy fraction (Ed), gasoline energy fraction (Eg), ISFC in g/kW-h, and Peak Pressure Rise Rate (PPRR) in bar/degree are defined as follows:

Ed ¼

Eg ¼

md  LHVd md  LHVd þ mg  LHVg   mg  LHVg near
TDC injection

ISFC ¼

md  LHVd þ mg  LHVg nR  60 mf H N pdV 

PPRR ¼

 dp dq max

(4)

(5)

(6)

(7)

where; mi is the mass of each fuel, LHVi is the lower heating value of each fuel, mf is the fuel mass flow rate in g/h, nR equals two for four-stroke engines, N is engine speed in rpm, p is the cylinder 7

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Fig. 7. Solution approach for optimizing injection strategy of a DDFS engine using two direct injectors.

Fig. 8. Initial values and Pareto Front of the DDFS injection strategy optimization for both gasoline and diesel direct injectors.

mixing-controlled combustion. P6 and P7 have more diesel energy fraction (Ed) than other optimal candidates, and as it is evident in Fig. 9, these points have higher levels of ISFC and soot emissions. This is because of the nature of diesel fuel and a large amount moves combustion towards a mixing-controlled one; higher levels of soot, and fuel consumption are the characteristics of mixingcontrolled combustion. The gasoline injection strategy (Eg and SOI3) is also shown in Fig. 10. The optimal ranges for gasoline energy fractions and timings are about 23% < Eg