A model investigation of fuel and operating regime impact on HCCI

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A model investigation of fuel and operating regime impact on HCCI engine performance Mattia Bissoli, Alessio Frassoldati, Alberto Cuoci, Eliseo Ranzi, and Tiziano Faravelli Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b00893 • Publication Date (Web): 08 Jan 2018 Downloaded from http://pubs.acs.org on January 11, 2018

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A model investigation of fuel and operating regime impact on HCCI engine performance

Bissoli, M., Frassoldati, A., Cuoci, A., Ranzi, E., Faravelli, T.*

Department of Chemistry, Materials and Chemical Engineering “G. Natta”, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy

Author 1 Mattia Bissoli Dipartimento di Chimica, Materiali e Ingegneria Chimica “G. Natta” Politecnico di Milano P.zza Leonardo da Vinci 32, 20133 Milano - ITALY Phone: (039) 0223993243 Email: [email protected]

Author 2 Alessio Frassoldati Dipartimento di Chimica, Materiali e Ingegneria Chimica “G. Natta” Politecnico di Milano P.zza Leonardo da Vinci 32, 20133 Milano - ITALY Phone: (039) 0223993286 Email: [email protected]

Author 3 Alberto Cuoci Dipartimento di Chimica, Materiali e Ingegneria Chimica “G. Natta” Politecnico di Milano P.zza Leonardo da Vinci 32, 20133 Milano - ITALY Phone: (039) 0223993283 Email: [email protected]

Author 4 Eliseo Ranzi Dipartimento di Chimica, Materiali e Ingegneria Chimica “G. Natta” Politecnico di Milano P. zza Leonardo da Vinci 32, 20133 Milano - ITALY Phone: (039) 0223993250 Email: [email protected]

Author 5 Tiziano Faravelli Dipartimento di Chimica, Materiali e Ingegneria Chimica “G. Natta” Politecnico di Milano P.zza Leonardo da Vinci 32, 20133 Milano - ITALY Phone: (039) 0223993282 Email: [email protected]

*Corresponding Author: Tiziano Faravelli Dipartimento di Chimica, Materiali e Ingegneria Chimica “G. Natta” Politecnico di Milano P.zza Leonardo da Vinci 32, 20133 Milano - ITALY Phone: (039) 0223993282 Email: [email protected]

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Abstract The aim of this paper is to investigate the fundamental role of chemical kinetics on the performance maps of HCCI engines in terms of operability limits, engine efficiency, and emissions. The work focuses on a Ricardo E6 engine, highlighting the impact of different fuels (PRF80, PRF100, and ethanol) on ringing, misfire and partial burn limits, as well as on several performance variables and pollutant emissions. The operability maps are calculated assuming proper criteria to identify the limits of the map in terms of ringing, misfire and partial burn. Sensitivity Analysis and Rate of Production Analysis highlight the role of H2O2 in sustaining the combustion of ethanol at high EGR and air dilution with respect to PRF100 and PRF80 mixtures. The multi-zone model confirms that thermal stratification and crevices are the main responsible for the emissions of CO and unburned species. NOx are produced mainly via thermal mechanism. Interaction of N2O with H and O radicals plays also a role, while prompt mechanism does not significantly affect NOx emissions. Ethanol shows greater flexibility, lower pollutant emissions, and wider operability conditions with respect to engines fed with primary reference fuels. The paper highlights the potentials of this multi-zone model in reproducing the engine performance. Non-reacting CFD simulations are first used to estimate heat and mass transfer coefficients. Then, the proposed model does not require further empirical or tuning parameters. Only the thresholds defining the operability maps are derived from the experiments, and are the same for all the fuels and operating conditions investigated. The extensive comparison with a large set of experimental data shows the capability of the model to describe the effect of fuel composition and EGR the operability map, highlighting how such a tool can play an important role in understanding the chemistry controlling fuel reactivity and pollutant emissions in the different conditions. These information can support not only fuel and engine operation selection, but also their optimal design. As an example, the effects of boost and engine speed on the HCCI combustion are critically investigated, in terms of the extension of the operability region, engine thermal efficiency and exhaust emissions.


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Nomenclature

Acronyms AFR BMEP

Air-fuel ratio Brake Mean Effective Pressure

CA

Crank Angle

CoV

Coefficient of Variation

CR

Compression Ratio

∆ Lower Heating Value of the fuel EGR

Exhaust Gas Recirculation

EVO

Exhaust Valve Opening

HCCI

Homogeneous Charge Compression Ignition

HRR

Heat Release Rate

IC IMEP IVC λ

Internal Combustion Indicated Mean Effective Pressure Intake Valve Closing Ratio between the effective air mass and the stoichiometric air mass

MON

Motor Octane Number

PRF

Primary Reference Fuel

PRR

Pressure Rise Rate

RBG

Residual Burnt Gas (RBG)

RON

Research Octane Number

RoPA

Rate of Production Analysis

SA

Sensitivity Analysis

SI

Spark Ignition


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1

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Introduction

Homogeneous Charge Compression Ignition (HCCI) engines have received great attention in the last few decades as an alternative Internal Combustion (IC) engine technology, because of the possibility to obtain high efficiency with a cleaner combustion. HCCI combines characteristics of both diesel and Spark Ignition (SI) engines. The charge is ignited through compression, relying on its auto-ignition characteristics, and power output control is not performed using throttle valves, ensuring high operating efficiency like diesel engines. The use of a premixed air-fuel charge ensures cleaner combustion with reduced soot emissions, like SI engines. Nevertheless, using a premixed charge in a compression ignition engine, causes High Heat Release (HRR) and Pressure Rise Rates (PRR) leading to ringing events 1, which are one of the primary constraints governing the high power output limit. Charge dilution by means of lean equivalence ratio and Exhaust Gas Recirculation (EGR) allow preventing these phenomena. Although these solutions enable low NOx emissions by reducing the in-cylinder temperature, excessive charge dilution leads to very low power output and unburned hydrocarbon emissions. Despite these drawbacks, the actual interest in HCCI engines is well demonstrated by the extensive and recent literature on this subject, which analyzes several different features, such as fuel effect 2, the thermal stratification inside the engine 3, optimal operating strategies with both traditional

4, 5

and bio-fuels

5-8

, as well as the

combustion control 9. Chemical kinetics plays a fundamental role in controlling the combustion in HCCI engines, since local conditions of the charge govern auto-ignition timing 1. Thus, HCCI engines have a narrow operating range compared to traditional SI and diesel engines, especially at high load 10. Operating maps are usually represented in terms of air-fuel dilution versus EGR ratio, or Indicated Mean Effective Pressure IMEP (or Brake Mean Effective Pressure, BMEP) versus engine speed, and they tabulate steady state operating data of different performance variables (fuel consumption, efficiency, emissions, etc.) across the whole range of engine operating conditions. Numerous and extensive literature works investigate the HCCI engine operating ranges by means of maps, both experimentally and theoretically. Thring

11

mapped the operating range of a single cylinder four-stroke test engine with intake air

heating at different air-fuel and EGR ratios. He found that the operating range was very narrow, and highlighted the difficulties in controlling the auto-ignition process. Following Thring’s work11, Oakley and co-workers

12, 13

systematically explored the air-fuel ratio (AFR) versus EGR operating

range of a Ricardo E6 single-cylinder engine at fixed speed and compression ratio (CR) for different fuels: gasolines, primary reference fuel mixtures, methanol, and ethanol. Three zones that limit ACS Paragon Plus 4 Environment

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the stable HCCI operation were identified: the first one at low loads, called partial burn, characterized by low engine efficiencies; the knocking region, observed at high loads, where high pressure rise rates occur, and the misfire zone, at high dilutions, where high cycle-to-cycle variations are registered. Furthermore, the authors found different performance for the fuel tested. Koopmans et al.

14

investigated the feasible range of a naturally aspirated HCCI engine,

using three different valve timing to cover the entire range of the unthrottled HCCI engine. Zhao and co-workers 15 built performance maps for a four cylinders Ford Zetec engine running at dilute, stoichiometric conditions using only EGR to control combustion. Maps showed a stable HCCI operation for loads between 0.5 and 4 bar BMEP and speeds from 1000 to 3500 rpm. Knocking was not observed, although misfire and partial burn were. Hyvönen et al.

16

explored the

load/speed HCCI operating range too, using a five-cylinder engine with variable compression ratio and inlet air heating via exhaust heat recovery. Peng and co-workers 17 investigated the effects of intake temperature and compression ratio on the AFR-EGR ratio maps for the HCCI combustion of n-heptane. In particular, they observed that the high-load limit can be extended by lowering the intake temperature or increasing the EGR ratio, in order to delay the auto-ignition timing. The extension of the HCCI high-load limit is a very active area of research 1, and recent studies

18-20

showed that engine boost can significantly extend the high-load limit of HCCI engines, potentially into ranges comparable with turbo-diesel engines. They also pointed out that for HCCI engines, ringing (rather than knock) more correctly describes the high-load limit. In order to investigate the performance of boosted HCCI engines, Kulzer et al. 21 and Sun et al. 22 showed maps for different operating parameters, fuel consumption and emissions for HCCI engines with high load limits up to 8 bar. Allen and Law

23

and Haraldsson et al.

24

also experimentally investigated the operating

range of HCCI engines. Another possible way to investigate the HCCI operating range is by means of computational models, which provide a time- and cost-effective solution to analyze and optimize the engines. Martinez-Frias et al. 25 used a single-zone model based on a Perfectly Stirred Reactor (PSR) model coupled with the Woschni correlation to predict performance maps for a natural-gas HCCI engine. Yelvington and Green

26

modeled the performance maps of gasoline HCCI combustion measured

by Oakley et al. 12, defining a criterion to predict also the limits of these maps. While the high-load limit was well captured in terms of engine knock, misfire and partial burn events were not satisfactorily reproduced since cycle-to-cycle variations were not accounted for by the model. In a successive work, Yelvington et al. 27 extended this analysis to the data of Yang et al. 28, highlighting the importance of accurate heat transfer models and the need of detailed kinetic mechanisms in HCCI modeling. Bhave and co-workers 29 also modeled the data obtained on the Ricardo E6 engine ACS Paragon Plus 5 Environment

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fueled with a PRF95 mixture

13

by means of a Probability Density Function (PDF) model coupled

with a 1-D engine cycle simulator. Model predictions showed the usefulness of negative valve overlapping in controlling the combustion timing and the need of coupling it with intake charge heating methods at low loads. Aichlmayr et al. 30 mapped the operating space for miniature HCCI engines, while Pinheiro and co-workers 31 investigated boost effects on the performance of a VW TDI engine modified to operate in HCCI conditions and fueled with ethanol. Other noticeable applications of engine models for investigating the HCCI operating range are showed in 32, 33. Despite the well-recognized fundamental role of kinetics in the HCCI combustion, a general lack of knowledge about the chemical kinetics ruling the operability maps of HCCI engine can be observed. This is true not only for the fuel effect on the operative limits, but also on how chemical kinetics affects the efficiency and the emissions over the engine operability region. As a result, this paper aims to investigate the HCCI engine maps emphasizing the fundamental role of chemical kinetics on reactivity, engine efficiency, and emissions. First, the paper predicts the operative maps of a Ricardo E6 engine with a multi-zone model in terms of ringing, misfire and partial burn limits. These operability maps are evaluated assuming proper threshold values for combustion efficiency, PRR and cycle-to-cycle variability in accordance to what was done in the experiments. These threshold values are the same for all the fuels and operating conditions investigated in this paper. The model simulates several engine cycles to predict the large cycle-to-cycle variations which characterize the misfire region of the map. Distribution of several performance variables and pollutant emissions are also discussed. In particular, the impact of different fuels (PRF80, PRF100, and ethanol) and engine configurations (speed and boost) on the operability maps are investigated from the chemical point of view and under real engine conditions, by means of advanced tools like Sensitivity Analysis and Rate of Production Analysis. This approach allows identifying the most effective solutions to maximize engine performance and reduce pollutant emissions. Finally, The extensive comparison with a large set of experimental data performed in this work shows the capability of the model to describe the effect of fuel composition and ERG the operability map, highlighting how such a tool can play an important role in understanding the chemistry controlling fuel reactivity and pollutant emissions in the different conditions. The focus of this work is not the validation of the model in terms of its ability to describe a specific working point of the engine, but rather to verify its ability to predict trends and in particular the fuel effect on the operability maps. This work also highlights the differences in the chemistry controlling fuel reactivity and pollutant emissions in the different engine conditions.


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2

Multi-zone model of HCCI engine

2.1 Engine simulation model The multi-zone model of HCCI engine developed and extensively described and validated by Bissoli et al.

34

is here briefly summarized. The model has been validated using experimental

measurements taken in six different engines, covering a wide range of fuels, engine geometries and working conditions (Engine Speed=705÷2400 rpm, displacement=377÷981 cm3, CR=4.5÷16, and boost pressure=100÷240 kPa). Measurements used in the validation

34

included pressure

traces in reactive and motoring conditions, in-cylinder speciation and temperature profiles, heat fluxes to the piston head and critical compression ratios for low and high temperature ignition. This model follows the so-called ‘‘onion-skin” approach 35 and describes only the compression and expansion phases of a four-stroke cycle. The simulation cycle starts at Intake Valve Closing (IVC) and ends at Exhaust Valve Opening (EVO). Being the focus on combustion, valve dynamics effects are neglected, while gas flows to and from the crevices are accounted for. During the cycle simulation, the model evaluates system reactivity in each zone, and heat and mass exchange between adjacent zones. Neighboring zones can interact through work, heat and mass transfer at interfaces (both laminar and turbulent), accounting in this way thermal and composition stratification in the charge. An adiabatic polytropic transformation is adopted to evaluate the effect of the RBG expansion process that occurs after the EVO point, since the pressure moves from the value reached at BDC towards the atmospheric pressure

34

. Furthermore, it is also

possible to specify the EGR-fresh charge ratio at the beginning of the new cycle, if an external EGR is present. A complete and adiabatic mixing between the Residual Burnt Gas (RBG) trapped in the cylinder and the fresh charge, possibly including EGR, is assumed. Multiple simulation cycles allow to update the RBG and EGR composition and temperature at the discharging phase, thus affecting the initial conditions of the new cycle. Model equations derive from different assumptions. First, in-cylinder mixture is described as an ideal gas. The spatial discretization of the in-cylinder volume is carried out by defining several zones as ideal reactors with uniform temperature and composition, and time-variable volume. Figure 1 shows the zone configuration. Uniform pressure is assumed in the entire engine, except for the crevices. In fact, crevices are described as a constant-volume variable-mass zone, characterized by a small pressure difference with respect to the engine pressure. This pressure difference, proportional to a constant , drives the mass flux between the crevice and the external zone. depends on system geometry and its value is estimated through a non-reactive ACS Paragon Plus 7 Environment

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CFD simulation by matching the pressure difference between the two zones predicted during the cycle. Crevices and the external zone are also responsible for the thermal interactions with cylinder walls and piston head. An innovative heat transfer model derived from CFD is applied, improving temperature description in the near-wall region with respect to the predictions of Woschni-like models 34, 36.

34

Figure 1: Multi-zone model configuration .

The multi-zone model was specifically conceived for simulations with detailed kinetic mechanisms and allows performing kinetic investigation by means of Rate of Production Analysis (RoPA) and Sensitivity Analysis (SA)

34

, in order to identify the effect of different fuels and different engine

operating conditions on the chemical paths controlling auto-ignition, engine performance, and pollutant formation. All the mathematical details about the model formulation can be found in the previous paper

34

, while here we simply discuss the role of engine-specific parameters and

summarize the methodology adopted for their definition. Mass and heat fluxes between adjacent zones depend on diffusivity and conductivity. While laminar contributions are evaluated from the pure species transport properties using proper mixture-averaging rules, turbulent contribution are calculated from the turbulent viscosity, which depends on the friction velocity ∗ . The friction velocity is considered proportional to the mean piston speed through an engine-specific parameter 35, 37. A further parameter modulates the heat exchanged with the surroundings , , which is evaluated by means of the wall-function model of Han and Reitz

38

. In this wall-function, the

∗ friction velocity at the walls is considered proportional to the friction velocity ∗ through the

engine specific parameter . The three parameters , and are evaluated using nonreactive CFD calculations. Furthermore, they are defined only once for each specific engine, and they are valid for the whole range of operating conditions 34.


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The required fluid dynamic simulations are performed with the open-source OpenFOAM® software coupled with the Lib-ICE libraries 39, specifically tailored for engine simulations.

2.2 Kinetic mechanism The general POLIMI_1412 kinetic model is used in this work. This detailed mechanism describes the oxidation of hydrocarbons and oxygenated species up to diesel and bio-diesel fuels

40, 41

. It

consists of more than 480 species and 19,000 reactions. A core mechanism featuring the detailed chemistry model of C1-C4 hydrocarbons is coupled with a semi-detailed mechanism for the primary propagation reactions of larger species. This lumped and effective approach enables an accurate description of the complexity of liquid hydrocarbon mixtures and their pyrolysis and oxidation mechanisms using a limited number of chemical species. Moreover, the extensive use of structural analogies and similarities within the different reaction classes, easily allows the extension of the mechanism to new reaction classes esters

43

42

and large species such as heavy methyl-

. Thermodynamic properties are taken from literature databases

44

or evaluated on the

basis of Benson additivity rules 45. Mechanism validation was carried out in a wide range of operating conditions through extensive comparisons with experimental data in well controlled reaction environments (jet stirred reactors, rapid compression machines, shock tubes, flow reactors, etc.)

46-49

, and more complex

applications, such as auto-ignition experiments of n-heptane droplets in microgravity conditions 50 and IC engines over a wide range of operating conditions 41, 51, 52. Lumped and derived skeletal kinetic mechanisms have been successfully applied to evaluate the auto-ignition propensities of pure components, surrogate mixtures, and real transportation fuels 53

. The complete kinetic mechanism in CHEMKIN format (with thermodynamic and transport

properties) is available on the CRECK Modeling web site 54.

3

Operability and performance maps of HCCI engines

This work focuses on experimental data obtained at Brunel University in a Ricardo E6 singlecylinder, four-stroke research engine

55

, hereinafter referred as Ricardo E6. The engine runs at

1500 rpm and has a displacement of 504 cm3 with a compression ratio of 11.5. An exhaust gas recirculation system allows modifying the mass ratio of EGR to fresh charge, while a pre-heater regulates the desired intake mixture temperature within ±1 °C. At the exhaust, gases are analyzed in order to quantify pollutants, as well as residual O2 and CO2 concentrations.


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Displacement [cm3]

504

Bore [mm]

76.2

Stroke [mm]

111.1

Rod Length [mm]

241.3

Compression Ratio

11.5

Speed [rpm]

1500

Intake Valve Closing [°BTDC]

137

Exhaust Valve Opening [°ATDC]

144

Intake Charge Temperature [°C]

320

Coolant Temperature [°C]

80

Table 1: Ricardo E6

55

engine characteristics.

Table 1 reports the complete engine specifications. All the experimental operability maps showed in this work refer to this engine. Following the experimental procedure, aimed at exploring the two-dimensional region in which CAI (Controlled Auto Ignition) combustion can be successfully attained, predicted maps are generated by independently varying the EGR ratio (mass-basis) and λ, considered as the global incylinder AFR after mixing EGR with the fresh feed

55

. More than 150 λ-EGR configurations are

investigated for each fuel, as showed in Figure 4. 50 simulation cycles are performed for each point, in order to account for the effect of EGR on cycle-to-cycle variability. Iso-contours of several different variables (like ignition timing, IMEP, exhaust emissions, etc.) are plotted inside the different maps by averaging data from the last 10 simulation cycles. A total of 15 zones are adopted for the simulations: 14 zones to reproduce the in-cylinder volume, plus a crevices zone (with a volume equal to 2% of the clearance one already shown in

34

56

) to account for all the dead-volumes. As

, this number of zones allows for a good description of the phenomena

involved, maintaining a reasonable computational effort. As already mentioned, the three enginespecific parameters , and (see section 2.1) are obtained by means of comparison with non-reactive CFD simulations. The procedure is not reported here for the for the sake of brevity, but further information are available in

34

. It has to be underlined that this process does not

represent a fitting procedure on the experiments, rather than an a-priori way for assessing the relation between geometry and turbulence/wall heat transfer intensity in the engine. The values of these parameters are engine-specific and do not depend on operating conditions or modeling ACS Paragon Plus 10 Environment

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assumptions. They are independent on CRs, speeds, initial conditions (pressure, temperature and composition) and fuels, highlighting the predictive capability of this multi-zone model as discussed in a previous work 34. Table 2 summarizes the values of , and adopted for the Ricardo E6 engine. These values, as well as the other parameters in Table 2, are used in all the simulations of this engine, without modifications or adjustments.

Number of Zones

15

Engine cycles for each condition

50

Twall [K]

450

Cuz

0.12

Cuw

0.58

Cx [kg/s/Pa0.5]

1.5e-4

Table 2: multi-zone model setup adopted in this work.

Appendix A shows that model predictions are scarcely affected by the wall temperature of this engine. Because of the lack of experimental data, a value of 450 K was adopted for the wall temperature for the entire spectrum of operating conditions. This value is in line with typical wall temperatures in HCCI engines 56-58. In his work, Oakley mentioned that the fresh mixture and EGR are mixed approximately one meter upstream of the inlet port 12. In this way, a very good mixing and homogenization before entering the cylinder is achieved. Moreover, this setup allows to control the EGR-fresh charge mixture inlet temperature by using the air heater. It is then reasonable to assume a homogeneous initial temperature of the charge. The typical stratification observed during the engine cycle is then described by the model, which accounts for heat exchange with the walls and heat and mass transfer between zones. Following the usual practice reported in literature

59, 60

, the initial

temperature in the multi-zone model is estimated to match the experimental combustion phasing, in terms of the crank angle where 10% of the total heat is released (CA10). This solution already demonstrated its effectiveness 34. In particular, for each fuel the initial temperature is defined in order to reproduce the experimental phasing for any λ, when no EGR is added. This value is then used for all the conditions across the entire operating map. The result, as can be observed in Table 3, shows that ethanol and PRF 80 require 510 K, while PRF 100 needs a higher initial temperature (525 K).


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Fuel

Temperature IVC [K]

Ethanol

510

PRF 80

510

PRF 100

525

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Table 3: IVC temperatures adopted for the fuels showed in this work.

3.1 HCCI operating limits As already mentioned, 2-D operative maps represent the regions where the engine shows a stable operability. As shown in Figure 2, three different zones, knock, misfire and partial burn, define and limit the operating region. In line with recent studies on HCCI combustion 1, we refer to the high load limit in terms of ringing (undesired noise due to strong pressure waves 10, 18) instead of knock.

Figure 2: Typical λ-EGR operative map of a HCCI engine, together with critical limits of different combustion regions.

In a qualitative way, Figure 3 highlights the features of these three different combustion regimes, for an engine fueled with PRF100. High loads and low EGR dilutions cause the ringing phenomena, characterized by intense HRR and PRR

61-63

. On the other side, partial burn limits the low-load

region of HCCI operability, and it is characterized by an incomplete combustion occurring during the engine cycle, with a consequent reduction of combustion efficiency 55:

! ∆


(1)

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RINGING

PARTIAL BURN

MISFIRE

Figure 3: Critical conditions limiting the operability region of HCCI engines. Solid lines shows how these critical combustion regimes can be observed on different variables. Dashed lines represent conditions of stable combustion.

Finally, misfire is observed at high EGR dilutions, and represents an unstable condition, where the engine goes towards periodical oscillating cycles. For this reason, simulations of several cycles are required in order to average predicted engine performance. Of course, the cycles with low peak temperatures affect engine performance. The cycle-to-cycle variability is quantified in terms of variation of the indicated mean pressure (CoV IMEP) along the cycles, defined as follows:

CoV "#$%

& ' ( *+,-

(2)

+ *+,-

where IMEP is calculated as:

"#$%

./0 0' 1

Misfire is then characterized by a strong CoV IMEP increase

(3)

55

, which affects not only the in-

cylinder variables like pressure and temperature, but also fuel conversion and exhaust emissions. As discussed by Sjöberg and Dec 64, this cycle-to-cycle variability near the misfire regime is mainly due to the fluctuation of the fuel concentration in the EGR. As a matter of fact, when ignition fails to occur in a cycle, EGR gases contain a high amount of unburned fuel. Thus, the inlet mixture of the next cycle will contain a higher fuel concentration and may result in a firing cycle. Figure 3 shows that the model is able to capture this behavior. The limits defining the operative HCCI maps are evaluated by means of simple rules based on literature suggestions and experiments. The region of stable HCCI combustion is defined as the set of different λ-EGR conditions, which satisfy all the constraints reported in Table 4. Experimentally, ACS Paragon Plus 13 Environment

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the misfire limit was defined when misfire was observed at least in 1% of the engine cycles investigated. Large oscillations of engine's speed and torque output were observed under misfire conditions. The calculated quantity CoV IMEP (coefficient of variation of IMEP), with a threshold value of 60 %, is used to define this limit in the model. The partial burn was experimentally defined by an excessive rise of unburned hydrocarbons in the exhaust gases, while knocking occurred if at least 10% of the engine cycles recorded large vibrations. A threshold value of 80% for the calculated combustion efficiency is used to define the partial burn limit in the model, since it is the value that gives the best match of the partial burn limit for the different fuels investigated in this work. Since the model does not compute vibrations, a threshold value of 6 bar/deg for the Pressure Rise Rate (PRR) is used to define the knocking/ringing limit in the model. These threshold values are deduced directly (see experimental maps of CoV IMEP and PRR in the next paragraph) or indirectly (combustion efficiency to match the partial burn limit) from the experiments

55

(see

experimental maps of CoV IMEP and PRR in the next paragraph) and are comparable with those adopted by other authors in different engines

13, 17, 29, 64

. It is worth noting that the calculated

operative maps show a limited sensitivity to the numerical values of threshold limits reported in Table 4. It is also important to emphasize that the definition of these threshold values are the same for all the fuels and conditions investigated in this paper.

Combustion Efficiency [%] CoV IMEP [%] PRR [bar/deg] ≥ 80

≤ 60

≤6

Table 4: Ricardo E6 engine. Threshold values of critical parameters defining the stable operability region of HCCI combustion.

Figure 4 compares the operating limits predicted by the multi-zone model (solid lines) with the experiments (dashed lines) for three different fuels, namely PRF80, PRF100, and ethanol. A qualitative analysis of Figure 4 highlights that the fuel type affects the operating region. Model predictions are in reasonable agreement with the experiments: the model correctly describes ringing and partial burn, both in terms of values and trends. Ethanol is more flexible to both air and EGR dilution, and it is the only tested fuel able to reach stoichiometric combustion with EGR ratios between 50% and 70%. Moreover, it also extends the operability region to lower loads, i.e. higher λs. PRFs behave in a similar way, but they are less flexible to EGR dilution. They can only operate up to 50-55% of EGR, and the presence of the reactive n-heptane in PRF80 slightly decreases the misfiring tendency. All the fuels show similar ringing region, with differences on the dilution conditions where transition between ringing and misfire is observed.


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Energy & Fuels

Figure 4: Ricardo E6. Stable HCCI operating region for PRF80, PRF100, and ethanol. Comparison between experiments lines) and model predictions (solid lines). Points show the different lambda-EGR combinations simulated.

4

55

(dashed

Fuel effects on HCCI operability maps

In order to better highlight the behavior of investigated fuels, Figure 5 directly compares the three different predicted HCCI operating regions of Figure 4.

Figure 5: Ricardo E6. Comparison of predicted HCCI operating limits for different fuels.

As already mentioned, ringing is scarcely affected by the fuel type. All the fuels exhibit the hot ignition around TDC reaching similar ringing limits (see Figure 5) and this fact confirms the similarity of the high temperature combustion mechanisms for all these fuels. Figure 6 shows the good agreement between experimental and predicted maps of the Pressure Rise Rates (PRR) for the different fuels. PRR rapidly increases approaching ringing limit, thus confirming the scarce sensitivity to the threshold value. As already discussed, it is worth noting that the knocking/ringing limit was experimentally defined using engine vibration measurements and not the measured PRR. The satisfactory agreement between measured (about 5÷5.5 [bar/deg]) and predicted PRR (6 [bar/deg]) at the knocking/ringing limit supports the choice of the PRR to evaluate the knocking/ringing limit. ACS Paragon Plus 15 Environment

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Experimental

Model Prediction

Figure 6: Experimental 55 and predicted maps of maximum Pressure Rise Rate (PRR) [bar/deg].

The higher flexibility of ethanol to EGR dilution is mainly related to the flammability limits and induction times of the in-cylinder mixtures. In order to verify this fact, the reactivity of different mixtures is investigated around the conditions of misfire at λ=4 and 20 bar. Ignition delay times in adiabatic constant-volume batch reactor are predicted by using the OpenSMOKE++ Suite reported in Figure 7.

Figure 7: Ignition delay times at different EGR dilutions.


65

and

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Energy & Fuels

At high temperatures, Figure 7 shows that the two PRFs mixtures have very similar ignition delay times. This fact confirms the similar reactivity of PRF80 and PRF100 observed in HCCI engine. Despite the high pressure, the ignition delay times are quite high because of the air and EGR dilution (these operating conditions are those of representative of the misfire region). Figure 7 also shows the effect of different EGR dilutions on ethanol delay times. The reactivity of ethanol is higher than the one of PRFs in the temperature range 900-1000 K, explaining why ethanol can support higher EGR dilutions. Figure 7 confirms also the role of EGR in decreasing the reactivity. Conversely, moving to lower temperatures, it is possible to observe not only the higher reactivity of PRF80 compared to PRF100, but also the lower reactivity of ethanol. This behavior is consistent with the RON=107 and MON=89 values of ethanol 66, because of the different severity of the two standard specifications. Moreover, the comparison between the operability regions of PRF80 and PRF100 indicates the higher reactivity of PRF80 able to reach 55% EGR dilutions, while PRF100 already enters the misfire region at 50% EGR ratios. This predicted behavior is also experimentally confirmed. The sensitivity analysis performed by the HCCI multi-zone model allows to better understand the different behavior of ethanol and PRF100 directly in the engine. At λ=4, two EGR conditions (10% and 30%) are analyzed for both fuels. Figure 8 shows that the reactivity of ethanol is mainly driven by hydrogen peroxide (H2O2) and hydroperoxy radicals (HO2). Hydroperoxy radicals enhance the system reactivity above 900-1000 K by producing H2O2, which accumulates in the system up to the transition to the hot ignition. As shown in Figure 9, a higher EGR dilution does not change significantly the system reactivity, because the system is still far from the limits of the operative map.

Figure 8: Ethanol combustion at λ=4. Sensitivity analysis of the temperature in the inner zone at 10% and 30% EGR ratios.


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Figure 9: Ethanol combustion at λ=4 with 10% (dashed) and 30% (solid) of EGR. Temperature and H2O2 concentration profiles in the inner zone along the cycle.

The behavior of PRF100 is different. Figure 10 not only shows a large sensitivity of the system to the isomerization reaction of alkyl-hydroperoxide radicals (IC8H17OO) in forming hydroperoxy-alkyl radicals and their successive propagation reactions, but also indicates the relevant effect of the EGR ratio. Here the system is closer to the misfire limits. Figure 11 shows the large effect of the EGR variations, mainly in terms of the peak temperatures. Moreover, the lower formation of H2O2 with respect to the concentration values of Figure 9 is also noteworthy. From all these considerations it is possible to conclude that higher resistance of ethanol to EGR dilution is due to the capability of producing enough quantities of HO2 and H2O2, which sustain the reactivity also at high EGR ratios. On the other hand, the higher sensitivity of peroxide species to EGR dilution explains the weaker reactivity of PRF before the hot ignition, as already observed by Sjöberg and Dec 66.

Figure 10: PRF100 combustion at λ=4. Sensitivity analysis of temperature in the inner zone at 10% and 30% EGR ratios.


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Energy & Fuels

Figure 11: PRF100 combustion at λ=4 with 10% (dashed) and 30% (solid) of EGR. Temperature and H2O2 concentration profiles in the inner zone along the cycle.

Due to the similarities between PRF100 and PRF80, only ethanol and PRF100 are shown and discussed in the next paragraphs, while detailed results of PRF80 are reported in Appendix B.

4.1 Ignition timing and combustion duration Figure 12 shows the combustion behavior of PRF100 in terms of pressure traces, highlighting that the maximum pressure increases when λ decreases from about 5 to 2.5. Peak pressure ranges from ~20 bar (close to the partial burn limit) to ~35 bar, when approaching the ringing limit. Figure 13 analyzes ethanol combustion, showing lower pressure peaks when EGR increases. Maximum pressure moves from ~35 bar down to ~25 bar when 60% of EGR is used. Furthermore, differently from λ variations, the change of EGR ratio affects not only the pressure peak, but also the combustion phasing and duration, as already observed in literature 1.

Figure 12: Pressure traces predicted by the model for different λ.


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Figure 13: Pressure traces predicted by the model for different dilution and EGR dilutions.

Figure 14 presents how maximum pressure varies inside the HCCI operability range for the two fuels. Pressure trends follow the same behaviors shown in Figure 12 and Figure 13, highlighting the direct dependence on the amount of fuel fed to the system.

Figure 14: Maps of predicted in-cylinder pressure peak [bar].

Figure 15 compares predicted and measured ignition timing in terms of CA10, i.e. the crank angle where 10% of the total heat is released. Experiments with both fuels show that the reduction of λ delays ignition timing, because of the lower O2 concentration. Similarly, the increase of EGR leads to an increase of CA10, because of the higher amount of inert species (CO2, H2O).

PRF100


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Energy & Fuels

Ethanol

Figure 15: Ignition timing maps reported as CA10 [°CA]. Comparison between experiments (right panels). CA=360 is the TDC.

55

(left panels) and model predictions

Model predictions reproduce well the ignition timings, both in terms of values and trends, in the whole λ/EGR conditions, for both fuels. While ethanol ignition moves in the range 356-362 °CA, the PRF ignition is delayed in the range 362-370 °CA. This feature further explains not only the possibility of ethanol to extend the operability range, but also the weak effect of λ at low EGR, where system reactivity remains high. To further highlight the different behavior of ethanol and PRF100, Figure 16 shows the ignition timing as a function of the EGR ratios for a fixed λ=3. The model successfully predicts the weaker variations of CA10 with EGR for ethanol, and the greater changes predicted and experimentally observed for PRF100.

Figure 16: Effect of EGR ratios on predicted CA10. Comparison between experiments (symbols) and model predictions (lines). Solid line: ethanol, dashed line: PRF100. CA=360 is the TDC.


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

PRF100

Ethanol

Figure 17: Combustion duration maps reported as CA10-90 [°CA]. Comparison between experiments 55 (left panels) and model predictions (right panels).

Figure 17 shows the combustion duration in terms of CA10-90. Experiments with both fuels show that higher λ and EGR increase the combustion duration, which typically moves from 10 to 20 °CA. As a matter of fact, combustion duration becomes longer, due to the larger dilution. Comparisons between predicted and experimental duration confirm the reasonable model predictions, both in terms of values and trends. Nevertheless, the model slightly overestimates the CA10-90 at high EGR ratio for PRF100. Model predicts duration values of 24-28 °CA at the limiting EGR dilutions of 45-50%, while experimental data already indicate misfire conditions. The same behavior can be observed in Figure 12 and Figure 13.

4.2 Engine performance parameters Engine load, cycle-to-cycle variability, and thermal engine efficiency are here analyzed. Figure 18 presents the load maps. As expected, load increases when air and EGR ratios decrease, since stoichiometric and undiluted conditions are approached. PRF100 and ethanol have similar trends in the whole range, with also a similar maximum IMEP of ~2.5 bar, although PRF100 shows slightly higher IMEP values. While the trends are correctly predicted, there are slightly underACS Paragon Plus 22 Environment

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Energy & Fuels

predicted, mainly for PRF100. These deviations are related to the longer combustion durations already observed in Figure 15. PRF100

Ethanol

Figure 18: Load maps reported as IMEP [bar]. Comparison between experiments 55 (left panels) and model predictions (right panels).

As already mentioned, combustion stability is also important, since it defines the misfire limits. Figure 19 show that for both fuels CoV IMEP sharply increases with air and EGR dilution, when approaching misfire region. Model predictions agree well with experiments, thanks to the model capabilities of reproducing the effects of residual burned gases and EGR on the system and the cycle-to-cycle variability. This allows to satisfactorily defining the misfire region. The multi-zone model reproduces only the effects of EGR on combustion stability, neglecting all the remaining sources of experimental variability that may lead to further cyclic variations. For this reason, model predictions of Figure 19 simply describe the cycle-to-cycle variability close to the misfire region.


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Page 24 of 50

PRF100

Ethanol

Figure 19: Combustion stability maps reported as CoV IMEP [%]. Comparison between experiments 55 (left panels) and model predictions (right panels).

Engine performance is also analyzed in terms of indicated thermal efficiency, defined as the work done on the piston with respect to the energy introduced with the fuel:

2

3

"#$% ! 0' 1

! ∆

(4)

Figure 20 highlights that efficiency increases with fuel amount in the intake charge, while air dilution and large EGR ratios reduce it. This behavior is the same for both fuels, and shows that in these conditions, thermal efficiency ranges from about 26% to 36%. Then, when approaching the misfire limit, the efficiency rapidly decreases due to higher combustion instability. Lastly, it is possible to note that for an assigned λ-EGR ratio, efficiency is slightly higher for ethanol.


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Energy & Fuels

PRF100

Ethanol

Figure 20: Maps of indicated thermal efficiency [%]. Comparison between experiments 55 (left panels) and model predictions (right panels).

Predictions in general agree with measurements especially in terms of trends for both fuels. Furthermore, the comparison of Figure 20 and Figure 18 shows that the minimum efficiency occurs in the low-load, partial burn region, while the maximum efficiency corresponds with the high-load one. The model highlights that these trends can be related to the different loss mechanisms in the engine. Prior studies suggested that partial combustion events and the heat lost to the walls are the main loss mechanisms 67-69. Model results show that in the operating conditions located in the middle of the operating map for PRF100 (lambda=3, EGR=30%), about 38% of the total heat from the combustion chamber is exchanged with the piston head, while 62 % is lost to the cylinder walls and head. The relatively small contribution of cylinder walls is due to the fact that the heat losses mainly take place at a CAD close to the TDC, since the gas temperature is close to the maximum value. In these conditions, the area of the cylinder walls is relatively small compared to piston and cylinder head. Figure 21 shows that percentage of heat lost with respect to the total energy introduced with the fuel has a significant impact on the global energy balance of the system, and ranges from ~33% to 41% for PRF100 and 46% for ethanol. This figure also clarifies the trends of thermal efficiency ACS Paragon Plus 25 Environment

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Page 26 of 50

observed in Figure 20. In particular, the region of maximum efficiency is represented by the area where high combustion efficiency (Figure 22) combines with low heat losses, i.e. the high-load region.

Figure 21: Predicted maps of heat losses [%] with respect to the energy introduced with the fuel.

Figure 22: Predicted maps of combustion efficiency [%].

These trends are confirmed also by Pinheiro and co-workers

31

in a different HCCI engine. They

also suggested that friction losses might also affect engine efficiency, especially at high engine speeds, low loads, and low combustion efficiency. For these reasons, they are expected to be negligible in the conditions here analyzed.

4.3 Exhaust emissions Thanks to the use of a detailed kinetic mechanism, the model is able to predict the chemical evolution of the system, of course including the different pollutants, not only CO and NOx, but also unburned hydrocarbons and intermediate components, such as aldehydes. Exhaust emissions are reported as:


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Energy & Fuels

"4/5678/ 9.865:56 $55;4 <

2 1 = A

>?@ ! ∆ ! 2

(5) 3

Figure 23 compares predicted and experimental NOx emissions for PRF100 and ethanol. NOx emissions mainly depend on O2 availability and system temperature: for a fixed λ, higher EGR rate means lower system temperature (due to dilution), resulting in lower NOx emissions. Similarly, at a fixed EGR rate, higher λ decreases in-cylinder temperatures and lower NOx emissions. Consequently, the experimental trends are of difficult explanation, since both fuels show increasing NOx emissions with λ at low EGR ratios. In terms of peak values, PRFs show higher emissions with respect to ethanol. This is due to the higher dilution required by the alcohol to avoid ringing, which contributes lowering the in-cylinder temperature.

PRF100

Ethanol

Figure 23: Maps of NOx exhaust emissions [g/kWh]. Comparison between experiments panels).

55

(left panels) and model predictions (right

Figure 23 highlights the capabilities of the present multi-zone model in predicting NOx emissions inside the whole engine operating region. First, the model confirms what mentioned above regarding the unexpected trend of NOx emissions with λ at low EGR rates. Furthermore, the model captures the lower NOx emissions when n-heptane is added to the PRF mixture, as evident from ACS Paragon Plus 27 Environment

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the comparison between Figure 23 panel A with Figure B22 in Appendix B. This behavior is due to the higher ringing resistance of PRF100 with respect to PRF80, which allows reaching higher loads and, consequently, higher in-cylinder temperatures. Figure 24 compares predicted and experimental CO emissions for ethanol and PRF100. A direct comparison between the two fuels shows that CO emissions are very similar, with ethanol showing higher maximum values due to its wider operability range. The model is able to quantify the increasing CO emissions approaching misfire and partial burn regions. This is more evident and further confirmed in Figure 25. As expected, CO emissions directly influence the thermal efficiency reported in Figure 20, with the regions of low efficiency showing high CO emissions. It is important to notice that the region close to the boundary of the operability map (partial burn limit), does not represent the normal engine operation. In fact, also the author55 of the experimental work clarified that they extended the analysis to conditions which are outside normal engine operation.

PRF100

Ethanol

Figure 24: Maps of CO exhaust emissions [g/kWh]. Comparison between experiments 55 (left panels) and model predictions (right panels).


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Energy & Fuels

Figure 25: Effect of air dilution on CO exhaust emissions. Comparison between experiments (symbols) and model predictions (lines).

Formaldehyde (CH2O) and acetaldehyde (CH3CHO) are two typical pollutants deriving from incomplete combustion. Figure 26 shows the similarity of their maps, with larger emissions close to the misfire and partial burn regions for both fuels. PRF100

Ethanol

Figure 26: Predicted maps of formaldehyde (left panels) and acetaldehyde (right panels) emissions [g/kWh].

The sensitivity analysis shown in Figure 27 explains the higher acetaldehyde emissions observed for ethanol combustion. In facts, acetaldehyde is directly formed by the decomposition of primary ethanol radicals (CH3CHOH). Therefore, the competition of metathesis reactions to produce primary and secondary ethanol radicals controls the concentration of CH3CHO. On the other hand, ACS Paragon Plus 29 Environment

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the reactions of HO2 with ethanol reduce acetaldehyde concentration because they enhance the system reactivity, favoring the complete acetaldehyde oxidation, thus reducing aldehyde emissions.

Figure 27: Ethanol combustion at λ=4, EGR 30%. Sensitivity analysis on acetaldehyde concentration in the inner zone.

The model provides also detailed information on the distribution of these intermediate species inside the different engine zones. CO concentrations reported in Figure 28 clearly shows that combustion initially occurs in the inner zone around TDC, and then it proceeds towards the outer zones 70-72. Here, the lower temperature does not allow for a complete combustion, producing CO, which diffuses towards the inner region during the expansion phase. Crevices also contribute to increase the emission of CO unburned species, mostly through the accumulation of unburned and partially oxidized species (such as aldehydes) which are released to the cylinder zone during the expansion phase. These compounds, especially the ones released to the cylinder towards the end of the cycle, cannot undergo complete oxidation. Figure 28 also highlights the role of EGR in recirculating partial combustion products, showing a non-zero concentration of CO before the ignition. The distribution of CH3CHO concentration shows a different behavior. Because of its higher reactivity, CH3CHO is rapidly consumed also in the outer zones. For this reason, crevices play a relevant role in controlling the emissions of acetaldehyde and other combustion intermediates.


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Energy & Fuels

Figure 28: Ethanol combustion at λ=4, EGR 30%. Model prediction of CO (left panel) and acetaldehyde (right panel) mole fractions in different regions.

Similar considerations can be repeated for NOx emissions already showed in Figure 23. Figure 29 presents NOx profiles for ethanol combustion at λ=2.6 and 10% of EGR ratio, highlighting the presence of NO, N2O and NO2 at the end of the cycle. These species are emitted at the tailpipe and also recycled in the cylinder through the EGR, justifying the initial concentration different from zero. Figure 29 highlights that in the initial low-temperature region, HO2 radicals convert NO to NO2, producing the more reactive OH radicals. These reactions have an enhancing effect on system reactivity 73. Then, during the hot ignition, NO and N2O are produced. In particular, nitrous oxide (N2O) is rapidly formed in the early stages of the high temperature ignition through the reaction N2+O+M=N2O+M, and then rapidly decomposed as temperature raises via the reverse reaction. The sensitivity analysis in Figure 30 clarifies that N2O is also converted to NO through the reactions with H and O radicals. As system temperature decreases, NO is then partially converted to NO2 (Figure 29).

Figure 29: Ethanol, λ=2.6, EGR=10%. Model prediction of NOx mass fraction profiles in the core region during an engine cycle.

Sensitivity analysis in Figure 30 highlights the role of the different mechanisms on NO production. The thermal mechanism (O+N2=NO+N and N+O2=NO+O) is the main source of NO during the engine cycle. NOx emissions are also sensitive to the decomposition of H2O2 and the reactions


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between fuel and HO2, which control system reactivity. Prompt mechanism does not show a significant role in these conditions. Moving to more diluted conditions, NOx emissions decrease, as well as the role of the thermal mechanism, because of the lower peak temperatures. As an example, at λ=2.6 about 77% of the NOx emissions from ethanol combustion derives from thermal mechanism, while the remaining 23% is due to the N2O path. Moving to λ=3, emissions from thermal NOx becomes ~58%. Similar considerations can be repeated for PRFs.

Figure 30: Ethanol, λ=2.6, EGR=10%. Sensitivity analysis on NO in the inner zone.

5

Optimal operative conditions and fuel selection.

The maximum load in HCCI engines is significantly lower with respect to the load of SI and diesel engines 1. In order to overcome this limitation, several studies 1 investigated the role of boost in increasing the high-load and extending the operability region of HCCI engines. Since the power output is determined not only by load, but also by engine speed, the sensitivity of performance maps to engine speed and intake pressure (i.e. boost level) are shown in this Section. Yet again, these results refer to the Ricardo E6 engine and are obtained by independently varying the engine speed and intake pressure, while keeping fixed all the other initial and boundary conditions. Left panel of Figure 31 shows the effect of engine speed on operability limits for ethanol, highlighting the reduction of the extension of operability region as engine speed increases. Misfire and partial burn regions become larger, as a consequence of the lower time available to complete fuel oxidation. This is in line with the measurements of Maurya and Akhil 74 who observed large CO and unburned hydrocarbons emissions at high engine speeds in HCCI combustion conditions. At high RPMs, delayed combustion moves the ringing limits toward stoichiometric conditions. Because of the larger volume available when auto-ignition occurs, there is a reduction of heat release rate, thus lowering peak in-cylinder pressure, temperature, and ringing phenomena 1. ACS Paragon Plus 32 Environment

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Energy & Fuels

It is important to observe that in this engine and these conditions, PRF100 is not able to sustain a stable combustion at speeds higher than 1500 rpm because of its lower reactivity. Right panel of Figure 31 analyzes the boost effect for PRF100. Boost enlarges the operability map of HCCI by reducing misfire and partial burn regions, similarly to what observed by Dec and coworkers 10, 18. Ringing becomes more critical, due to the higher pressures and higher loads. Further details on the effect of engine speeds and boost pressure on engine performance and emissions are reported in Appendix A of the Supplemental Material.

Figure 31: Effect of engine speed and IVC pressure on the predicted HCCI operability region.

It is evident that all the detailed information on engine performance supplied by the model become very useful in defining and selecting the desired optimal engine conditions. As a simple example, let us assume the maximum thermal efficiency as target variable to define these conditions. Figure 32, together with Table 5 and Table 6, summarize the optimal conditions for the examples of Figure 31. For ethanol combustion, the rise of engine speed moves the optimal conditions towards lower EGR ratios and higher λs, i.e. towards less diluted mixtures. For PRF100 combustion, higher intake pressures force the optimum towards higher EGR dilutions and lower λs.


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Figure 32: Optimal engine conditions. Effect of engine speed on ethanol combustion (left panel) and intake pressure on PRF100 combustion (right panel).

As expected, the change in optimal conditions due to higher intake pressures increases pressure peaks, which are marginally affected by the engine speed. CO emissions decreases with both higher engine speeds and boost pressures. The effect of engine speed can be explained on the basis of higher oxygen availability and system temperatures. For the same reason, also NOx emissions tend to increase. On the other hand, an increase of boost pressure reduces λ and thus oxygen concentrations, causing a reduction in NOx emissions. Lower CO emissions are observed thanks to the higher system pressure, which favors successive oxidation to CO2. Nevertheless, emissions remain quite high due to the relatively low temperatures. A similar increasing trend with both engine speed and intake pressure is observed for thermal efficiency. As already observed by Pinheiro et al.

31

, engine speed has a positive impact on thermal efficiency, which moves from

~36.7% to ~42.6% at 3500 rpm. This higher efficiency is mainly due to the inverse relation between engine speed and heat loss. The higher the engine speed is, the lower the heat losses are, because the system has less time to exchange heat with the surroundings. Moreover, engine speed also affects the engine efficiency by changing the ignition timing and the relative amount of compression and expansion work.

Engine

Maximum

EGR

Speed

Thermal

[rpm]

Efficiency [%]

1500

36.7

1.2

2500

41.0

3500

42.6

Pressure

Temperature

CO

NOx

Peak [bar]

Peak [K]

[g/kWh]

[g/kWh]

56.5

26

1630

10

0.06

2.3

33.3

29

1830

6.0

0.05

2.4

10.0

30

1860

0.3

0.1

λ

ratio [%]

Table 5: Ricardo E6. Effect of engine speed on optimal ethanol combustion.


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Energy & Fuels

Maximum

EGR

P IVC Thermal

λ

[bar] Efficiency [%]

Pressure

Temperature

CO

NOx

Peak [bar]

Peak [K]

[g/kWh]

[g/kWh]

ratio [%]

0.93

35.8

2.5

32.7

26

1710

12

0.12

1.2

37.2

2.3

46.0

36

1700

8

0.10

1.4

37.7

2.0

53.5

42

1690

4

0.06

Table 6: Ricardo E6. Effect of intake pressure on optimal PRF100 combustion.

While these couple of simple examples already indicate the capability of the model to select optimal operating conditions, it is clear that more effective and useful coupled conditions can be investigated and the model can become a useful tool both for the selection of optimal operating conditions and for the optimal engine design. Moreover, the possibility to analyze the effect of different fuels make this engineering tool suitable not only to select the best fuel for specific HCCI applications, but even the design of optimal fuels within specified targets and/or engine characteristics.

6

Conclusions

This work presents and discusses a comprehensive model of HCCI combustion with an extensive analysis of operability maps. The satisfactory comparisons with detailed experimental data obtained in a Ricardo E6 engine

55

support the validity of the multi-zone model not only for

describing temperature and composition distribution inside the engine, but also for obtaining reliable predictions of thermal efficiency and maximum in-cylinder pressure, together with combustion properties such as ignition, combustion duration, HRR, and pollutant emissions. Based on multi-cycle simulations, the multi-zone model properly accounts for EGR composition and correctly reproduces several operability maps adopting threshold limits for partial burn, ringing, and misfire that are derived from their experimental definition. These threshold values are the same for all the different conditions investigated with the model. Results highlighted that fuel affects the operability region of stable HCCI combustion, with ethanol being the most flexible fuel, while PRF100 and PRF80 have a smaller, and similar, operative region. Ignition delay times and sensitivity analysis under real HCCI conditions reasonably clarify this behavior, highlighting the role of H2O2 production from ethanol at ~1000 K. On the contrary, the lower amount of H2O2 from iso-octane results in a lower resistance of PRF100 when rising EGR ratios. This behavior is consistent with the RON and MON values of ethanol. ACS Paragon Plus 35 Environment

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

The structure of the model allows to obtain not only temporal, but also spatial information in the system, confirming the role of thermal stratification and crevices in the emissions of CO and unburned species. NOx are produced mainly via thermal mechanism. Interaction of N2O with H and O radicals plays also a role, while prompt mechanism does not significantly affect NOx emissions. The model allowed to calculate the effect of varying engine speed and boost pressure on performance and pollutant emissions. The model shows that increasing the engine speed significantly reduces the extension of the operability region. For this reason, differently from the low speed conditions initially investigated, only ethanol shows a stable operative area for speeds up to 3500 rpm. The model also shows that thermal efficiency increases with the engine speed and that the area of the maximum efficiency moves towards less diluted conditions. In fact, increasing the engine speed reduces the heat losses, globally resulting in higher thermal efficiencies. The lower heat losses and the higher fuel concentration lead to higher in-cylinder pressures and temperatures, with benefits on the IMEP and thus engine efficiency. Moreover, higher in-cylinder temperatures allow a reduction in CO and partial combustion products emissions, but with higher NOx production. On the contrary, boost enlarges the operability region for HCCI by reducing misfire and partial burn, while ringing becomes more critical due to the higher pressures. The higher loads positively affect the thermal efficiency, even if in a lower amount compared to the previous case. Both CO and NOx emissions decrease. Ethanol offers greater flexibility, lower pollutant emissions, and wider operability conditions with respect to engines fed with primary reference fuels. Furthermore, as already observed by other authors, boost techniques have the potential of extending the HCCI operative range, also to highload conditions when coupled with delayed combustion. Lastly, this work confirms the fundamental role of chemistry in explaining engine efficiency, and emissions, thus showing the need of proper tools, like multi-zone models, able to a-priori predict optimal engine and fuel solutions and to support their optimal design. The proposed model already proved to be a useful tool to investigate the optimal engine operating conditions and the different performance of different fuels. The possibility to analyze the effect of different fuels make this engineering tool suitable not only to select the best fuel for specific HCCI applications, but even the design of optimal fuels within specified targets and/or engine characteristics.


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Supplemental Material The supplemental material attached to this paper contains: Appendix A. Sensitivity of operability limits to wall temperature, engine speeds, and boost pressure. Appendix B. Operability maps of PRF80.

Acknowledgement Authors gratefully acknowledge the useful discussions with Dr. David Vuilleumier.


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using new reduced ethanol oxidation mechanism", Energy Conversion and Management 2016,

121, 55-70.


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List of Figures Figure 1: Multi-zone model configuration 34. ................................................................................... 8 Figure 2: Typical λ-EGR operative map of a HCCI engine, together with critical limits of different combustion regions....................................................................................................................... 12 Figure 3: Critical conditions limiting the operability region of HCCI engines. Solid lines shows how these critical combustion regimes can be observed on different variables. Dashed lines represent conditions of stable combustion. .................................................................................................. 13 Figure 4: Ricardo E6. Stable HCCI operating region for PRF80, PRF100, and ethanol. Comparison between experiments

55

(dashed lines) and model predictions (solid lines). Points show the

different lambda-EGR combinations simulated. ............................................................................ 15 Figure 5: Ricardo E6. Comparison of predicted HCCI operating limits for different fuels. ............... 15 Figure 6: Experimental 55 and predicted maps of maximum Pressure Rise Rate (PRR) [bar/deg].... 16 Figure 7: Ignition delay times at different EGR dilutions. ............................................................... 16 Figure 8: Ethanol combustion at λ=4. Sensitivity analysis of the temperature in the inner zone at 10% and 30% EGR ratios. .............................................................................................................. 17 Figure 9: Ethanol combustion at λ=4 with 10% (dashed) and 30% (solid) of EGR. Temperature and H2O2 concentration profiles in the inner zone along the cycle. ...................................................... 18 Figure 10: PRF100 combustion at λ=4. Sensitivity analysis of temperature in the inner zone at 10% and 30% EGR ratios. ...................................................................................................................... 18 Figure 11: PRF100 combustion at λ=4 with 10% (dashed) and 30% (solid) of EGR. Temperature and H2O2 concentration profiles in the inner zone along the cycle. ...................................................... 19 Figure 12: Pressure traces predicted by the model for different λ. ................................................ 19 Figure 13: Pressure traces predicted by the model for different dilution and EGR dilutions. ......... 20 Figure 14: Maps of predicted in-cylinder pressure peak [bar]. ....................................................... 20 Figure 15: Ignition timing maps reported as CA10 [°CA]. Comparison between experiments 55 (left panels) and model predictions (right panels). CA=360 is the TDC. ................................................. 21 Figure 16: Effect of EGR ratios on predicted CA10. Comparison between experiments (symbols) and model predictions (lines). Solid line: ethanol, dashed line: PRF100. CA=360 is the TDC. ......... 21 Figure 17: Combustion duration maps reported as CA10-90 [°CA]. Comparison between experiments 55 (left panels) and model predictions (right panels). ................................................ 22 Figure 18: Load maps reported as IMEP [bar]. Comparison between experiments

55

(left panels)

and model predictions (right panels). ............................................................................................ 23 ACS Paragon Plus 48 Environment

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Figure 19: Combustion stability maps reported as CoV IMEP [%]. Comparison between experiments 55 (left panels) and model predictions (right panels). ................................................ 24 Figure 20: Maps of indicated thermal efficiency [%]. Comparison between experiments

55

(left

panels) and model predictions (right panels). ............................................................................... 25 Figure 21: Predicted maps of heat losses [%] with respect to the energy introduced with the fuel. ..................................................................................................................................................... 26 Figure 22: Predicted maps of combustion efficiency [%]. .............................................................. 26 Figure 23: Maps of NOx exhaust emissions [g/kWh]. Comparison between experiments

55

(left

panels) and model predictions (right panels). ............................................................................... 27 Figure 24: Maps of CO exhaust emissions [g/kWh]. Comparison between experiments

55

(left

panels) and model predictions (right panels). ............................................................................... 28 Figure 25: Effect of air dilution on CO exhaust emissions. Comparison between experiments (symbols) and model predictions (lines). ....................................................................................... 29 Figure 26: Predicted maps of formaldehyde (left panels) and acetaldehyde (right panels) emissions [g/kWh]......................................................................................................................................... 29 Figure 27: Ethanol combustion at λ=4, EGR 30%. Sensitivity analysis on acetaldehyde concentration in the inner zone. .......................................................................................................................... 30 Figure 28: Ethanol combustion at λ=4, EGR 30%. Model prediction of CO (left panel) and acetaldehyde (right panel) mole fractions in different regions. ..................................................... 31 Figure 29: Ethanol, λ=2.6, EGR=10%. Model prediction of NOx mass fraction profiles in the core region during an engine cycle........................................................................................................ 31 Figure 30: Ethanol, λ=2.6, EGR=10%. Sensitivity analysis on NO in the inner zone. ........................ 32 Figure 31: Effect of engine speed and IVC pressure on the predicted HCCI operability region. ...... 33 Figure 32: Optimal engine conditions. Effect of engine speed on ethanol combustion (left panel) and intake pressure on PRF100 combustion (right panel).............................................................. 34


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List of Tables Table 1: Ricardo E6 55 engine characteristics. ................................................................................ 10 Table 2: multi-zone model setup adopted in this work. ................................................................. 11 Table 3: IVC temperatures adopted for the fuels showed in this work........................................... 12 Table 4: Ricardo E6 engine. Threshold values of critical parameters defining the stable operability region of HCCI combustion............................................................................................................ 14 Table 5: Ricardo E6. Effect of engine speed on optimal ethanol combustion. ................................ 34 Table 6: Ricardo E6. Effect of intake pressure on optimal PRF100 combustion. ............................. 35


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A model investigation of fuel and operating regime impact on HCCI

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