Applied Energy 113 (2014) 848–863

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Applied Energy

journal homepage: www.elsevier .com/ locate/apenergy

Dynamic Adaptive Chemistry applied to homogeneous and partiallystratified charge CI ethanol engines

0306-2619/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.apenergy.2013.08.002

⇑ Corresponding author. Tel.: +39 0971 205204.E-mail addresses: annarita.viggiano@unibas.it (A. Viggiano), vinicio.magi@

unibas.it (V. Magi).URLs: http://www.unibas.it/utenti/viggiano/viggiano.htm (A. Viggiano), http://

www.unibas.it/utenti/magi/vmagi.html (V. Magi).

Annarita Viggiano ⇑, Vinicio MagiSchool of Engineering, University of Basilicata, viale dell’Ateneo Lucano 10, Potenza 85100, Italy

h i g h l i g h t s

� DAC technique is extended to the simulations of ethanol HCCI/PSCCI engines.� The specific choice of the DAC parameters is carefully analyzed.� Ethanol as the only species for the graph search gives very accurate results.� As regards NOx, ethanol–N2O as search-initiating set gives the best results.� DAC speed-up depends on the level of detail of the full mechanism.

a r t i c l e i n f o

Article history:Received 6 March 2013Received in revised form 31 July 2013Accepted 1 August 2013Available online 7 September 2013

Keywords:Dynamic Adaptive ChemistryEthanolHCCI/PSCCI enginesNOx emissions

a b s t r a c t

The Dynamic Adaptive Chemistry (DAC) technique is extended in this work to multidimensional simula-tions of ethanol HCCI/PSCCI engines. Several DAC computations have been performed by using twokinetic reaction mechanisms of ethanol with different levels of detail, that include 57 species and 135species, respectively. The specific choice of the DAC parameters, i.e. the set of search-initiating speciesand the tolerance value, has been carefully analyzed. The simulations show that very accurate results,in terms of pressure and heat release rate profiles and CO, CO2 and UHC emissions, are obtained with eth-anol as the only species for the graph search both with the fuel uniformly distributed and by directlyinjecting liquid fuel in the combustion chamber. As regards NOx, specific attention has been addressedto the analysis of the NOx formation in order to correctly reproduce the paths that lead to NOx emissionsfor the different cases. The choice of ethanol–N2O as search-initiating set has given the best results withnegligible errors with respect to the full mechanism. For the single-zone computations, the use of DACprovides a speed-up of the 135-species full mechanism more than 9, whereas, with respect to the 57-spe-cies mechanism, about 50% of the computational time is saved in the multidimensional simulations.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

In order to enhance the efficiency and reduce the emissions ofInternal Combustion Engines (ICEs), new combustion strategiesare currently under investigation. Among them, the HomogenousCharge Compression Ignition (HCCI) combustion strategy includesthe main advantages of Compression Ignition (CI) combustion(high thermal efficiency) and of Spark Ignition (SI) combustion(low emissions). However, the main drawbacks of HCCI enginesare the lack in the control of combustion phasing and the narrowoperating range [1]. These limitations may be overcome by using

a Partially Stratified Charge Compression Ignition (PSCCI) engine[2] and blends of non-conventional fuels with specific chemical–physical properties [3]. Under the PSCCI combustion mode, the de-gree of charge stratification and the single or multiple direct fuelinjection timing become the controlling parameters. By increasingthe fuel stratification, the heat release rate can be controlled andbounded, thus widening the operating range. As regards non-con-ventional fuels, ethanol is a very promising choice [4–6] based onits peculiar chemical–physical properties, i.e. high octane number,high latent heat of vaporization and the presence of atomic oxygenin its molecule.

Dealing with numerical simulations of ICEs, detailed kineticreaction mechanisms are strictly required in the effort to accu-rately characterize the fuel oxidation process. However, the com-putational cost of the simulations increases with the number ofchemical species and reactions involved in the kinetic reactionmechanism. For instance, in Ref. [7] a detailed analysis of the issues

A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863 849

that affect the computational cost of reacting flow simulations ispresented, thus showing that the computational time scales line-arly, quadratically and cubically with the number of species forthe computation of chemical rates, for the solution of a mixture-averaged diffusion model and for the Jacobian factorization,respectively. Therefore, different techniques are currently underinvestigation to reduce the computational cost of such simulationsby simplifying the kinetic reaction mechanisms [7].

The basic idea is that the minimum set of fundamental chemicalspecies and reactions depends on the operating conditions, interms of temperature, pressure and mixture composition [8], thatcharacterize the specific engine application. Several different ther-modynamic conditions may occur in an engine combustion cham-ber during engine operation. For instance, dealing with HCCIengines, fuel mixtures are mainly under lean conditions and a skel-etal simplified mechanism may be adequate. Kinetics simplifica-tion can be obtained starting from a detailed mechanism bymeans of reliable techniques, for example those based on time-scale analysis, as the Computational Singular Perturbation (CSP)methodology [9,10]. Such a procedure has already been employed[11,12] to study ethanol kinetics and ethanol HCCI engines withgood success.

In the case of PSCCI engines, the direct fuel injection produces astratification of mixture composition and temperature. In this case,a dynamic approach to simplify the kinetic mechanisms on-the-flywith low computational overhead is required. Among such ap-proaches, the Dynamic Adaptive Chemistry (DAC) technique [13],based on the Directed Relation Graph (DRG) [14] and the DirectedRelation Graph with Error Propagation (DRGEP) [15] techniques, isvery promising.

DAC technique has already been employed with success for sin-gle zone computations of HCCI engines fed by conventional fuels,i.e. n-heptane [13] and gasoline surrogates [16], that includetwo- and three- components blends of Primary Reference Fuels(PRF) and Toluene Reference Fuels (TRF). In Refs. [13,16] interest-ing conclusions are drawn for an efficient and accurate applicationof such a technique. Specifically, for all cases, a search-initiating setthat includes fuel species, CO and HO2 and a value for the searchterminating threshold equal to 10�4 are recommended to be a goodchoice in order to accurately simulate pressure and species massfraction profiles. Besides, no additional chemical species has tobe included in the search-initiating set to correctly evaluate bothNOx emissions and the acceleration of hydrocarbon ignition dueto the presence of NO in the unburned gas mixture. In [17] DAChas been employed for the simulation of an n-heptane direct injec-tion engine by considering fuel-CO–HO2–NO as major species andby selecting, among them, the search-initiating set according to thespecific thermo-chemical conditions attained during the simula-tion. A computational time saving up to 30% and 50% is obtainedby using, as starting point, kinetic reaction mechanisms with 34species and 61 species, respectively, whereas the differences interms of emissions between the results obtained with DAC andwith the full mechanism are lower than 10%. DAC methodologycan be coupled with the In Situ Adaptive Tabulation (ISAT) method[18], the so called TDAC (Tabulation of Dynamic Adaptive Chemis-try) methodology, to increase the speed-up of the simplificationprocedure. TDAC technique has been used to simulate an n-hep-tane HCCI engine in two-dimensional configuration with differentkinetic mechanisms and grid resolution [19]. The same set ofsearch-initiating species and the same tolerance value as in Refs.[13,16], i.e. fuel-CO–HO2 and 10�4 respectively, are used to getthe same average pressure, heat release rate and major speciesprofiles obtained by using the full mechanisms. Furthermore, theauthors show that the speed-up of the simulations increases withthe size of the reaction mechanism and of the computational grid.Besides, TDAC has been used with good success [20] to analyze the

effect of NO on the ignition delay time in an iso-octane HCCIengine.

The contribution of this work is to show how the DAC method-ology can be extended to the simulation of ethanol-fueled HCCIand PSCCI engines. The operating conditions of ethanol enginesare related to the specific chemical–physical properties of such abiofuel. Specifically, such engines can operate with high values ofthe compression ratio, lean mixtures and preheating of the intakeair, in order to reach the fuel autoignition temperature. Besides, asregards NOx emissions, previous works [6,21] have shown thatthermal NOx formation has a secondary role in ethanol HCCI en-gines. This suggests that different mechanisms of NOx formationmust be investigated and selected in HCCI engines with respectto conventional engines. From this discussion follows that, dealingwith HCCI engines, the choice of DAC parameters must be carefullydone in order to get accurate results. In this work, several compu-tations will be performed in order to select the most appropriateset of search-initiating species and tolerance for the graph search.

An in-house multidimensional numerical solver will be used,whose capability to accurately simulate the fluid dynamic andchemical processes in ethanol-fueled engines has already beenshown in Refs. [3,6,22], where a skeletal mechanism for ethanoloxidation [23] has been employed. DAC methodology has beenimplemented in such a solver and, in this work, it is employedfor single-zone, two-dimensional and three-dimensional computa-tions. Single-zone simulations have been performed to select theset of major chemical species and the tolerance value, by a compar-ison of the DAC results with those obtained by using the full mech-anism. Then, two-dimensional and three-dimensional simulationshave been carried out to analyze the performance of HCCI andPSCCI engines, respectively. The results with DAC are validatedby comparing them with the results obtained by using the fullmechanism and, in the case of HCCI engine, also with availablemeasurements [24]. Specific attention has been addressed to theuse of ethanol as fuel and to what are the inferences of this choiceon the application of DAC methodology.

This work is organized as follows: in the next section the com-putational model will be described followed by the computationalset-up, then the results will be discussed and finally the conclu-sions will be summarized.

2. The computational model

The three-dimensional Reynolds-Averaged Navier–Stokes(RANS) equations for transient, turbulent, chemically reactiveflows with liquid sprays are solved by using the REC-2000 code[25]. The governing equations are coupled with the equation ofstate and are written in the strong conservation-law form in mov-ing generalized curvilinear coordinates. Turbulence is described byusing a standard two-equation k–� model [26]. Near walls, heatand momentum fluxes are computed by a wall function modeloriginally proposed by Launder and Spalding [26], by accountingfor both the viscous laminar sub-layer, with a linear velocity pro-file, and the fully turbulent layer, with a log-law velocity profile.

As regards the liquid fuel injection, a Lagrangian approach isused for the fuel droplets, that are collected in a discrete numberof parcels [27] and are solved by using a Monte Carlo technique[28]. The spray model consists in the injection of drops from a linesource, that represents the intact core [29,27]. The exchange ofmass, momentum and heat between the gas and liquid phases,the turbulent dispersion of drops, the collisions, coalescence anddrop breakup are computed by solving the energy, momentumand mass conservation equations at the liquid–gas interface[28,30,31]. The atomization model is described in [32], where theexpressions for initial spray angle, intial Sauter Mean Diameter

850 A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863

and intact liquid core length are given. As far as the drop collisionand coalescence are concerned, the models proposed in [28] areused. After collision, the drops may coalesce or separate. The sec-ondary drop breakup is simulated by using the Taylor–Analogy–Breakup (TAB) model [33].

As far as the combustion model is concerned, the chemicalsource terms are computed by employing detailed kinetic reactionmechanisms for ethanol oxidation. Besides, the influence of theturbulent timescale on the kinetic timescale has been accountedfor in the evaluation of the new species concentrations by usingthe formulation given in Ref. [34]. Two different kinetic reactionmechanisms for ethanol oxidation, with different levels of detail,have been considered in this work. The first mechanism [23] in-cludes 235 reaction steps and requires the solution of the conser-vation equation of 43 chemical species. A NOx sub-mechanismwith 53 reaction steps and 14 additional chemical species is alsoincluded to compute NOx emissions. Hereafter, this mechanismwill be referred to as the 57-species mechanism. This is a skeletalmechanism, that, as reported in Ref. [23], has been developed bynarrowing its range of validity. For instance, such a mechanism isnot able to capture cool flames or soot formation. The 57-speciesmechanism has already been validated in a previous work [6]where the same HCCI engine of this work has been considered.The computational results of REC-2000 code obtained by employ-ing the 57-species mechanism are in very good agreement withmeasurements in terms of average pressure, heat release rateand emissions. This is the reason why such mechanism has alsobeen extensively used in this work. Nevertheless, the use of DACwith the 57-species mechanism is not expected to provide a largereduction of the computational time, because it is already a skele-tal mechanism. However, in the case of ethanol blended with con-ventional fuels, the 57-species mechanism must be linked to otherspecific oxidation mechanisms, thus resulting in much more com-plex kinetics that will surely gain speed-up benefit from DAC. Thisjustifies the interest in tuning the DAC methodology with the 57-species mechanism.

Nevertheless, in order to show the advantages of the DAC meth-odology, another mechanism for ethanol oxidation [35], that in-cludes NOx kinetics and comprises 1349 elementary reactionsand 135 chemical species, has been considered. In this case, a sig-nificant increase of the speed-up is expected. This mechanism, re-ferred to as the 135-species mechanism, has been setup for theethanol oxidation at high pressure and intermediate/high temper-ature, starting from other mechanisms available in the literature[36–41].

Both mechanisms have been simplified dynamically in timeand, in the case of multidimensional simulations, in space basedon the local thermo-chemical conditions in the combustion cham-ber. DAC procedure employs the DRGEP method [13,15], that is anextension of the Directed Relation Graph (DRG) method [14]. Theprocedure is based on the definition, at each time step and in eachcomputational cell, of the dependence matrix, rij, which measuresthe error introduced to the production/consumption rate of theith species when the jth species is removed from the chemical ki-netic mechanism, with i, j = 1, . . . , Ns, where Ns is the number ofthe chemical species of the full mechanism. The dependence ma-trix is defined as follows:

rij ¼P

k¼1;Nrjmikqkdjkj

Pk¼1;Nr

jmikqkj;ð1Þ

where k is the reaction index, Nr is the number of reactions, mik is thestoichiometric coefficient of the ith species in the kth reaction, qk isthe rate of progress variable of the kth reaction and djk is equal to1 if the kth reaction involves the jth chemical species, otherwise itis equal to 0. Hence, a graph can be constructed with the chemical

species as nodes where the edges between nodes are weighted withthe dependence coefficient.

For each path l that connects species i and j in the graph, a pathdependent coefficient rij,l can be defined, as the product of thedependence coefficients encountered on the path, that gives ameasure of the overall error as it propagates through the differentspecies encountered along the path. The path dependent coeffi-cient is a more accurate measure of the error introduced to the pro-duction/consumption rate of species i by the elimination of speciesj. The maximum value of the path dependent coefficients betweenspecies i and j, defined as

Rij ¼ maxl¼1;Npfrij;lg ð2Þ

where Np is the number of possible paths connecting species i and j,is called the generalized dependence coefficient of species i fromspecies j.

At the start of the simplification procedure, the major chemicalspecies, which have a key role in the chemical kinetics and whosetime evolution must be accurately predicted by the kinetic mech-anism, are identified. Hence, for each ith major species, only thechemical species j with Rij higher than a defined threshold � are re-tained as active species. The final simplified skeletal mechanismincludes only the chemical reactions which do not involve re-moved chemical species. However, the non-active chemical speciesare still considered as third body in the respective reactions.

In this work, different values of � and different sets of search-initiating chemical species are selected.

As far as the numerical model is concerned, REC solver employsan implicit finite volume numerical scheme with an overall secondorder spatial accuracy, whereas a backward first-order temporaldiscretization is employed for the time derivatives. The pressureequation, obtained by manipulating the continuity and momentumequations with the equation of state, is solved by using Stone’sstrongly implicit iterative method [42] for multidimensional par-tial differential equations. The source terms due to chemical reac-tions are solved explicitly by using a variable coefficient ODEssolver, named DVODE [43], due to the stiffness of the equations.The solver is used with a relative and an absolute error toleranceequal to 10�6 and 10�15, respectively.

3. The test case and the computational set-up

The ethanol HCCI engine of Ref. [24] is considered in this workin order to compare the numerical results with experimental mea-surements. The engine specifications and the operating conditionsare summarized in Table 1, where Tair and pair are the air intaketemperature and pressure, respectively. In order to reach theautoignition temperature of ethanol during the compressionstroke, the inlet air is preheated. Based on the high octane numberof ethanol, the engine compression ratio can be higher than that ofstandard engines and in the computations it is set to the geomet-rical value, i.e. 21:1. The engine runs lean by using an extra-amount of inlet fresh air with respect to the stoichiometriccondition.

The computations start at Intake Valve Closing (IVC), by consid-ering uniform thermodynamic properties, and end at ExhaustValve Opening (EVO). The homogeneous conditions at IVC areachieved in the intake pipe of the experimental apparatus by sup-plying the fuel through two solenoid valves placed upstream of amixing tank [44]. The kinematic eddy viscosity at IVC, mt, is set to57 cm2/s based on the semi-empirical equations for k and � sug-gested by Hayder et al. [45]. In order to correctly model the heattransfer at walls, a parametric analysis has been carried out in aprevious work [6] in order to determine the piston and cylindertemperature, Twall, which gives a good agreement with the experi-

Table 1Specifications of the engine and operating conditions.

Engine specificationsDisplaced volume 1600 cm3

Bore 12.065 cmStroke 14 cmConnecting rod 26 cmCompression ratio 21:1

Operating conditionsEngine speed 1000 rpmTair 393 Kpair 0.1 MPaIntake valve close 13� ABDC @ 1 mm liftExhaust valve open 39� BBDC @ 1 mm liftTwall 480 Kmt 57 cm2/s

HCCIEquivalence ratio 0.2TIVC 406.10 KpIVC 0.1002 MPa

PSCCIEquivalence ratio at IVC 0.261TIVC 406.69 KpIVC 0.1002 MPaDI ratio 23%Tinj 350 Kdinj 0.018 cm

A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863 851

mental data, in terms of average pressure and heat release rate.Following this study, Twall is assumed equal to 480 K and constantin space and time.

r (cm)

z(c

m)

0 2 4 6

0

5

10

r (cm)

z(c

m)

4.5 5 5.5 6

0

0.2

0.4

0.6

0.8

Fig. 1. Two-dimensional computational mesh at IVC (on the top) and detail of themesh at TDC (on the bottom).

As regards the HCCI engine geometry, since the combustionchamber is axis-symmetric with a flat piston crown, a computa-tional domain with a 1� sector in the azimuthal direction is consid-ered to reduce the computational time of the simulations. Thecomputational mesh includes 26 cells in the radial direction, whilein the axial direction the number of cells ranges from 48 at IVC to32 at TDC (Top Dead Center) by removing rows of cells during thecompression stroke. The grid is stretched to cluster grid points nearthe piston and the cylinder walls to accurately solve the details ofthe boundary layer. The resolution in the regions close to the wallsis kept to be constant with a grid-size of 0.15 mm in both the radialand the axial directions for all simulations, which ensures that theviscous laminar sublayer is well captured. In this layer, velocityprofile is assumed to be linear, whereas in the fully turbulent layerthe velocity follows the well-known log-law profile. Fig. 1 showsthe computational grid at IVC and a detail of the grid at TDC.

A sensitivity analysis of the results to mesh refinement has al-ready been carried out in Ref. [6], by increasing the resolution inboth the radial and the axial directions. The results do not show

Fig. 2. Three-dimensional computational mesh at TDC.

crank angle [deg]

aver

age

pres

sure

[MP

a]

-20 0 20 400

2

4

6

8DAC fuel, ε = 10-1

DAC fuel, ε = 10-2

DAC fuel, ε = 10-3

DAC fuel, ε = 10-4

full mechanism

crank angle [deg]

aver

age

pres

sure

[MP

a]

-20 0 20 400

2

4

6

8

DAC fuel-CO-HO2, ε = 10-1

DAC fuel-CO-HO2, ε = 10-2

DAC fuel-CO-HO2, ε = 10-3

DAC fuel-CO-HO2, ε = 10-4

full mechanism

Fig. 3. Single-zone computations of the HCCI engine with the 57-species kineticmechanism [23]: average pressure vs. crank angle by using DAC with fuel/fuel-CO–HO2 as major chemical species on the top/bottom and different values of tolerance.

852 A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863

relevant differences with respect to the grid selected in this workin terms of average pressure, heat release rate and emissions.

The equivalence ratio, /, is set equal to 0.2. In order to set theinitial conditions at IVC, in terms of temperature, pressure andcomposition of the mixture, single-zone computations of the fullengine cycle, including the intake/exhaust process, have been per-formed [6]. Therefore, the REC-2000 code has been used in non-dimensional configuration, by solving only the mass and energyconservation equations and by considering the intake and exhaustmass flow rates during the intake/exhaust strokes. The computedvalues of temperature, TIVC, and pressure, pIVC, at IVC are 406.10 Kand 0.1002 MPa, respectively. Besides, the computations show thatat IVC, in addition to air and fuel, there is a significant amount ofcarbon dioxide, water vapor and acetaldehyde, whose values, interms of mass fraction, are 1.6 � 10�3, 1.0 � 10�3 and 5.8 � 10�6,respectively. The single-zone computations are not expected togive an accurate estimation, for example, of CO concentration,since CO is mainly produced at walls. For instance, the CFD simu-

crank angle [deg]

heat

rele

ase

rate

[J/C

A]

-15 -10 -5 0 5

0

200

400

600

800

DAC fuel, ε = 10-1

DAC fuel, ε = 10-2

DAC fuel, ε = 10-3

DAC fuel, ε = 10-4

full mechanism

crank angle [deg]

heat

rele

ase

rate

[J/C

A]

-15 -10 -5 0 5

0

200

400

600

800

DAC fuel-CO-HO2, ε = 10-1

DAC fuel-CO-HO2, ε = 10-2

DAC fuel-CO-HO2, ε = 10-3

DAC fuel-CO-HO2, ε = 10-4

full mechanism

Fig. 4. Single-zone computations of the HCCI engine with the 57-species kineticmechanism [23]: heat release rate vs. crank angle by using DAC with fuel/fuel-CO–HO2 as major chemical species on the top/bottom and different values of tolerance.

lation, carried out with the mechanism in Ref. [23], gives the fol-lowing composition of exhaust gases in terms of the massfraction of the most relevant species: 4.07 � 10�2 (CO2),2.55 � 10�2 (H2O), 4.60 � 10�4 (CO), 1.83 � 10�4 (UHC), of which1.41 � 10�4 is the acetaldehyde mass fraction, 2.36 � 10�7 (NO),1.16 � 10�6 (NO2) and 7.86 � 10�6 (N2O), whereas the single-zonecomputation gives: 4.15 � 10�2 (CO2), 2.55 � 10�2 (H2O),5.09 � 10�8 (CO), 1.50 � 10�4 (UHC), which is almost entirely gi-ven by acetaldehyde mass fraction, 1.14 � 10�6 (NO), 1.13 � 10�7

(NO2) and 7.79 � 10�7 (N2O). Nevertheless, the use of single-zonemodel for the estimation of IVC conditions is accurate enough inorder to correctly predict the engine performance and emissions,as it is shown by the good agreement between numerical resultsand experimental data [6].

As far as the PSCCI combustion mode simulations are con-cerned, the computational domain is a three-dimensional 72� sec-tor, because of an axial centered located 5-hole injector. The grid atTDC is shown in Fig. 2. It consists of 24 and 10 cells uniformly dis-tributed along the radial and azimuthal directions respectively,while the number of cells ranges from 46 at IVC to 8 at TDC alongthe axial direction. Based on previous engine computations [3],such a grid resolution is expected to be adequate to qualitatively

crank angle [deg]

CO

[g]

CO

2 [g]

-10 -5 0 5 100

0.01

0.02

0.03

0.04

0.05

0.06

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

DAC fuel, ε = 10-1

DAC fuel, ε = 10-2

DAC fuel, ε = 10-3

DAC fuel, ε = 10-4

full mechanism

crank angle [deg]

CO

[g]

CO

2 [g]

-10 -5 0 5 100

0.01

0.02

0.03

0.04

0.05

0.06

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

DAC fuel-CO-HO2, ε = 10-1

DAC fuel-CO-HO2, ε = 10-2

DAC fuel-CO-HO2, ε = 10-3

DAC fuel-CO-HO2, ε = 10-4

full mechanism

Fig. 5. Single-zone computations of the HCCI engine with the 57-species kineticmechanism [23]: CO and CO2 emissions vs. crank angle by using DAC with fuel/fuel-CO–HO2 as major chemical species on the top/bottom and different values oftolerance.

crank angle [deg]

NO

[g]

NO

2 [g]

-100 0 1000

1E-06

2E-06

3E-06

4E-06

-1.5E-07

-1E-07

-5E-08

0

5E-08

1E-07

1.5E-07

DAC fuel, ε = 10-1

DAC fuel, ε = 10-2

DAC fuel, ε = 10-3

DAC fuel, ε = 10-4

full mechanism

crank angle [deg]

NO

[g]

NO

2 [g]

-100 0 1000

1E-06

2E-06

3E-06

4E-06

-1.5E-07

-1E-07

-5E-08

0

5E-08

1E-07

1.5E-07

DAC fuel-CO-HO2, ε = 10-1

DAC fuel-CO-HO2, ε = 10-2

DAC fuel-CO-HO2, ε = 10-3

DAC fuel-CO-HO2, ε = 10-4

full mechanism

crank angle [deg]

NO

[g]

NO

2 [g]

-100 0 1000

1E-06

2E-06

3E-06

4E-06

-1.5E-07

-1E-07

-5E-08

0

5E-08

1E-07

1.5E-07

DAC fuel-NO-NO2, ε = 10-1

DAC fuel-NO-NO2, ε = 10-2

DAC fuel-NO-NO2, ε = 10-3

DAC fuel-NO-NO2, ε = 10-4

full mechanism

crank angle [deg]

NO

[g]

NO

2 [g]

-100 0 1000

1E-06

2E-06

3E-06

4E-06

-1.5E-07

-1E-07

-5E-08

0

5E-08

1E-07

1.5E-07

DAC fuel-N2O, ε = 10-1

DAC fuel-N2O, ε = 10-2

DAC fuel-N2O, ε = 10-3

DAC fuel-N2O, ε = 10-4

full mechanism

(a) (b)

(c) (d)Fig. 6. Single-zone computations of the HCCI engine with the 57-species kinetic mechanism [23]: NO and NO2 emissions vs. crank angle by using DAC with several sets ofmajor chemical species and different values of tolerance.

crank angle [deg]

NO

prod

uctio

nra

te[m

ole/

(cm

3 s)]

-6 -4 -2 0-2E-06

0

2E-06

4E-06 totalN2OpromptthermalNO2

Fig. 7. Single-zone computations of the HCCI engine with the 57-species kineticmechanism [23]: NO production rate vs. crank angle.

A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863 853

provide correct trends of emissions and specific fuel consumption.Therefore, this resolution is sufficient to assess the advantages ofthe DAC methodology when applied to direct injection engine.

However, a more refined mesh must be employed near walls in or-der to accurately quantify the amount of heat loss and, conse-quently, engine performance and emissions. The equivalenceratio of the homogeneous charge at IVC is set to 0.261, with an en-gine fuel load of 0.04057 g, and the values of temperature andpressure at IVC are determined as in the HCCI case. At 30�CA BTDC,a fuel mass equal to 23% of the total fuel mass (DI ratio) is directlyinjected into the chamber during an injection period of 6� CA. Li-quid ethanol is injected at Tinj = 350 K throughout a 5-hole injectorwith each hole diameter, dinj, equal to 0.18 mm.

4. Results

First, single-zone computations of the ethanol HCCI engine willbe discussed by applying the DAC methodology to the two kineticreaction mechanisms. Different values of tolerance and several setsof major species will be considered in order to get the best compro-mise between the accuracy and the computational cost of the DACsimulations. The results of this analysis will be used to set the sim-ulations in two- and three-dimensional configurations. Then, two-dimensional computations of the axis-symmetric combustionchamber will be considered under HCCI combustion mode and

crank angle [deg]

num

ber

of s

peci

es-150 -100 -50 0 50 100

0

20

40 fuel, ε = 10-1

fuel, ε = 10-2

fuel, ε = 10-3

fuel, ε = 10-4

crank angle [deg]

num

ber

of s

peci

es

-150 -100 -50 0 50 1000

20

40 fuel-CO-HO2, ε = 10-1

fuel-CO-HO2, ε = 10-2

fuel-CO-HO2, ε = 10-3

fuel-CO-HO2, ε = 10-4

crank angle [deg]

num

ber

of s

peci

es

-150 -100 -50 0 50 1000

20

40 fuel-NO-NO2, ε = 10-1

fuel-NO-NO2, ε = 10-2

fuel-NO-NO2, ε = 10-3

fuel-NO-NO2, ε = 10-4

crank angle [deg]

num

ber

of s

peci

es

-150 -100 -50 0 50 1000

20

40 fuel-N2O, ε = 10-1

fuel-N2O, ε = 10-2

fuel-N2O, ε = 10-3

fuel-N2O, ε = 10-4

Fig. 8. Single-zone computations of the HCCI engine with the 57-species kinetic mechanism [23]: number of active chemical species vs. crank angle by using different sets ofmajor chemical species and tolerance values.

crank angle [deg]-100 0 100

NO2

NH3

N2H

N2

H

O

H2

N

NH

NH2

HNO

N2O

activeinactive

activeinactiveactiveinactiveactiveinactiveactiveinactiveactiveinactive

activeinactiveactiveinactiveactiveinactiveactiveinactiveactiveinactiveactiveinactive

DAC fuel, ε = 10-4

DAC fuel-N2O, ε = 10-4

Fig. 9. Single-zone computations of the HCCI engine with the 57-species kineticmechanism [23]: major differences in terms of active chemical species when DAC isused with fuel or fuel-N2O as search-initiating set and with � = 10�4.

ε

spee

d-up

10-4 10-3 10-2 10-10

2

4

6

8fuelfuel-CO-HO2

fuel-NO-NO2

fuel-N2O

Fig. 10. Single-zone computations of the HCCI engine with the 57-species kineticmechanism [23]: speed-up of DAC computations with respect to full mechanism vs.tolerance by using different sets of major chemical species.

854 A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863

A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863 855

the results will be compared with measurements available in theliterature [24]. Finally, a partial stratification of the charge ob-tained by directly injecting liquid ethanol into the chamber willbe considered, in order to assess the reliability and effectivenessof the DAC technique with this type of engine.

4.1. HCCI engine: single-zone computations

Single-zone computations of the engine, running under HCCIconditions, have been performed by using both the kinetic reactionmechanism of Saxena and Williams [23] and the detailed mecha-nism developed by Cancino et al. [35]. The results obtained byemploying the full mechanisms have been compared with thoseattained with the DAC simplification. Different sets of search-initi-ating chemical species have been employed, i.e. fuel, fuel-CO–HO2,fuel-NO, fuel-NO–NO2 and fuel-N2O, with different values of thethreshold, �, in the range 10�1–10�4.

First, the results obtained with the 57-species kinetic mecha-nism are considered. Fig. 3 shows on the top the average pressure

Table 2Computational times and DAC overheads with � = 10�4 in the case of single-zonecomputations with the 57-species kinetic mechanism [23].

Full mechanism DAC

Search-initiating set Fuel Fuel-CO–HO2 Fuel-NO–NO2 Fuel-N2O

CPU time (s) 163.1 64.3 76.1 69.6 75.0DAC overhead (s) 4.3 4.9 5.1 4.8DAC overhead (%) 6.7 6.4 7.3 6.4

crank angle [deg]

aver

age

pres

sure

[MP

a]

-20 0 20 400

2

4

6

8DAC fuel, ε = 10-1

DAC fuel, ε = 10-2

DAC fuel, ε = 10-3

DAC fuel, ε = 10-4

full mechanism

crank angle [deg]

aver

age

pres

sure

[MP

a]

-20 0 20 400

2

4

6

8

DAC fuel-CO-HO2, ε = 10-1

DAC fuel-CO-HO2, ε = 10-2

DAC fuel-CO-HO2, ε = 10-3

DAC fuel-CO-HO2, ε = 10-4

full mechanism

Fig. 11. Single-zone computations of the HCCI engine with the 135-species kineticmechanism [35]: average pressure vs. crank angle by using DAC with fuel/fuel-CO–HO2 as major chemical species on the top/bottom and different values of tolerance.

versus crank angle obtained by employing DAC with only fuel assearch-initiating species and different values of tolerance. Thecomparison of the results with those obtained by using the fullmechanism shows that DAC gives a very high accuracy except withthe highest value of �. In this latter case, combustion does not oc-cur. The most accurate results are obtained by using � equal to 10�3

or less.As expected, with a larger set of major chemical species, i.e.

fuel-CO–HO2, higher values of tolerance can be used withoutreducing the accuracy of the results, as shown on the bottom ofFig. 3. In this case, the profiles obtained by using � equal or lowerthan 10�2 are on top of each other.

These outcomes are confirmed by the analysis of the results interms of heat realease rate and of CO and CO2 emissions, shownin Figs. 4 and 5, respectively. The figures show that, with fuel-CO–HO2 as major chemical species and tolerance equal to 10�2,the peak of heat release rate is somewhat overestimated, while alower value of tolerance gives a very good agreement with the fullmechanism.

crank angle [deg]

heat

rele

ase

rate

[J/C

A]

-20 -15 -10 -5 0 5

0

200

400

600

800

DAC fuel, ε = 10-1

DAC fuel, ε = 10-2

DAC fuel, ε = 10-3

DAC fuel, ε = 10-4

full mechanism

crank angle [deg]

heat

rele

ase

rate

[J/C

A]

-20 -15 -10 -5 0 5

0

200

400

600

800

DAC fuel-CO-HO2, ε = 10-1

DAC fuel-CO-HO2, ε = 10-2

DAC fuel-CO-HO2, ε = 10-3

DAC fuel-CO-HO2, ε = 10-4

full mechanism

Fig. 12. Single-zone computations of the HCCI engine with the 135-species kineticmechanism [35]: heat release rate vs. crank angle by using DAC with fuel/fuel-CO–HO2 as major chemical species on the top/bottom and different values of tolerance.

crank angle [deg]

CO

[g]

CO

2 [g]

-15 -10 -5 0 5 100

0.01

0.02

0.03

0.04

0.05

0.06

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

DAC fuel, ε = 10-1

DAC fuel, ε = 10-2

DAC fuel, ε = 10-3

DAC fuel, ε = 10-4

full mechanism

crank angle [deg]

CO

[g]

CO

2 [g]

-15 -10 -5 0 5 100

0.01

0.02

0.03

0.04

0.05

0.06

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

DAC fuel-CO-HO2, ε = 10-1

DAC fuel-CO-HO2, ε = 10-2

DAC fuel-CO-HO2, ε = 10-3

DAC fuel-CO-HO2, ε = 10-4

full mechanism

Fig. 13. Single-zone computations of the HCCI engine with the 135-species kineticmechanism [35]: CO and CO2 emissions vs. crank angle by using DAC with fuel/fuel-CO–HO2 as major chemical species on the top/bottom and different values oftolerance.

856 A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863

Nevertheless, the use of either fuel or fuel-CO–HO2 as majorchemical species does not guarantee accurate evaluation of NOx

emissions, as shown in Figs. 6a and b. In the figures, the grams ofNO and NO2 species are shown as a function of crank angle andboth amounts are underestimated with respect to the full mecha-nism, even with the lowest value of �.

This is in agreement with Ref. [15], where the authors claimthat NO or NO2 must be added to the search-initiating set to pre-dict NOx emissions. However, in Refs. [13,16,19,20], it has beenshown that it was not necessary to include NOx species in thesearch-initiating set to get accurate results in the case of conven-tional fuels, except for the case of fuel direct injection of Ref.[17]where NO is added as major species when temperature is higherthan 1800 K. For instance, in Ref. [20], where a very detailed mech-anism for iso-octane oxidation is used as starting point, the authorsclaim that NO and NO2 are retained as active species mostlythrough the strong links with HO2 and OH.

As far as HCCI engines are concerned, in Refs. [6,21] it is shownthat thermal NOx has a secondary role. In order to clarify the role ofthe different mechanisms of NOx formation in ethanol HCCI en-gines, five production mechanisms [46] involved in the NOx pro-duction in hydrocarbon flames, i.e. thermal, prompt, reburn, NO2

and N2O mechanisms, have been considered and the contributionsof each of them to the NO production rate have been separatelyexamined. The analysis has been performed by using the full etha-nol oxidation kinetic reaction mechanism. Specifically, the reburnmechanism has been found to be irrelevant in the NOx formation.The contributions of the other mechanisms, together with the totalNO production rate, are reported versus crank angle in Fig. 7. Thefigure shows that the N2O mechanism has a major role with re-spect to prompt, thermal and NO2 mechanisms. Specifically, thereaction of N2O with O to form NO is dominant. This could bedue to the specific operating conditions of such an engine, i.e. leanmixture and low temperature combustion, and to the presence of asignificant amount of N2O (mass fraction of 3.1 � 10�8) which istrapped in the chamber at IVC. Hence, the most immediate choicefor the set of major species is to add NO and NO2 to such a set.However, since the analysis of the NO formation has shown theprimary role of N2O, such a chemical species is another candidateto be included as major species in order to improve the results.

Fig. 6c shows the results obtained with DAC by adding both NOand NO2 to the set of search-initiating species. A better agreementis achieved by using � = 10�4, although the values of emissions atEVO are somewhat lower than those obtained with the full mechanism.

Finally, when fuel-N2O are used as major chemical species, avery good agreement with the full mechanism is obtained withthe lowest value of tolerance, as given in Fig. 6d.

The number of retained chemical species as a function of crankangle is shown in Fig. 8 for all sets of major species and all valuesof tolerance. The number of active species is stored every 50 timeiterations. As expected, a lower value of tolerance and a larger setof species for the DRGEP graph search correspond to a larger num-ber of active species. In the first phase, from the IVC to the time in-stant of the autoignition of the mixture, the number of retainedchemical species is relatively low, since chemical kinetics has a sec-ondary role. When ignition starts and heat is released, chemistrybecomes controlling and the number of active species significantlyincreases. In this phase, the number of retained chemical speciesvaries in the ranges 20 � 44, 28 � 44, 24 � 52 and 26 � 50 whenfuel, fuel-CO–HO2, fuel-NO–NO2 and fuel-N2O are used as search-initiating set, respectively, with � = 10�4. Finally, during the lastpart of the expansion stroke, the number of species remains nearlyconstant.

The sets of retained chemical species, that are relevant in theevaluation of NOx emissions, have been compared during the fullengine cycle in the cases with only fuel and with fuel-N2O as major

chemical species, and with � = 10�4. This analysis has shown that,during some phases of the engine cycle, the chemical species N2, H,O, H2, N, NH, NH2, HNO, N2O, N2H, NH3 and NO2 are active in thecase fuel-N2O are used for the DRGEP graph search, while theyare not when only fuel is used as major chemical species. Such dif-ferences are shown in Fig. 9 where each chemical species, specifiedon the right of the same figure, is active/inactive when the corre-sponding line is in the upper/lower position. For instance, N2O, thatis the major responsible of NO production, is clearly always activeif it is included in the set of search-initiating species, whereas it isinactive in the first phase of engine cycle when only fuel is chosenas major species.

The efficiency of DAC is measured by using a speed-up param-eter, i.e. the ratio between the computational time of the simula-tions required with the full mechanism and the computationaltime with DAC, and is plotted versus tolerance in Fig. 10. In the fig-ure, all simulations are included except for the case with fuel as theonly major chemical species and � = 10�1, since this case is unableto predict combustion. As expected, the speed-up increases with �up to about 5.6 with � = 10�1. For a given tolerance, the computa-tional time is minimum with fuel as the only major chemical spe-cies. In order to accurately predict average pressure, heat releaserate and CO and CO2 emissions, it is sufficient to use fuel as

crank angle [deg]

NO

[g]

NO

2 [g]

-100 0 1000

1E-06

2E-06

3E-06

4E-06

-1.5E-07

-1E-07

-5E-08

0

5E-08

1E-07

1.5E-07

DAC fuel, ε = 10-1

DAC fuel, ε = 10-2

DAC fuel, ε = 10-3

DAC fuel, ε = 10-4

full mechanism

crank angle [deg]

NO

[g]

NO

2 [g]

-100 0 1000

1E-06

2E-06

3E-06

4E-06

-1.5E-07

-1E-07

-5E-08

0

5E-08

1E-07

1.5E-07

DAC fuel-CO-HO2, ε = 10-1

DAC fuel-CO-HO2, ε = 10-2

DAC fuel-CO-HO2, ε = 10-3

DAC fuel-CO-HO2, ε = 10-4

full mechanism

crank angle [deg]

NO

[g]

NO

2 [g]

-100 0 1000

1E-06

2E-06

3E-06

4E-06

-1.5E-07

-1E-07

-5E-08

0

5E-08

1E-07

1.5E-07

DAC fuel-NO-NO2, ε = 10-1

DAC fuel-NO-NO2, ε = 10-2

DAC fuel-NO-NO2, ε = 10-3

DAC fuel-NO-NO2, ε = 10-4

full mechanism

crank angle [deg]

NO

[g]

NO

2 [g]

-100 0 1000

1E-06

2E-06

3E-06

4E-06

-1.5E-07

-1E-07

-5E-08

0

5E-08

1E-07

1.5E-07

DAC fuel-N2O, ε = 10-1

DAC fuel-N2O, ε = 10-2

DAC fuel-N2O, ε = 10-3

DAC fuel-N2O, ε = 10-4

full mechanism

(a) (b)

(c) (d)Fig. 14. Single-zone computations of the HCCI engine with the 135-species kinetic mechanism [35]: NO and NO2 emissions vs. crank angle by using DAC with several sets ofmajor chemical species and different values of tolerance.

A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863 857

search-initiating species and � = 10�3, as discussed above, with aspeed-up of about 3.4. In order to accurately predict NOx emissions,fuel-N2O are required as major chemical species with � = 10�4, andthis reduces the speed-up to about 2.2.

The DAC overhead is very low in all cases. The computationaltimes and the DAC overheads obtained by using an Intel (R) Core(TM) i7 CPU, with � = 10�4 and different sets of search-initiatingspecies, are summarized in Table 2. DAC overhead slightly changesdepending on the cases, being lower/higher when fuel/fuel-NO–NO2 are used as major species, and varies in the range 6.4–7.3%with respect to the total computational time.

A similar analysis has been performed by using the 135-speciesmechanism [35]. Fig. 11 shows the average pressure versus crankangle obtained by employing DAC with only fuel and fuel-CO–HO2 as search-initiating species and different values of tolerance.In both settings, the comparison of the results with those obtainedby using the full mechanism shows that DAC gives very good re-sults except with the highest value of �.

The heat release rate vs. crank angle, shown in Fig. 12, suggeststhat lower values of tolerance, i.e. � 6 10�3, should be used in order

to accurately get the correct heat release profile. This is more clearwhen only fuel is used for the DRGEP graph search, whereas in theother case the results obtained with � = 10�2 can be acceptable.These findings are confirmed by the amount of CO and CO2 inthe combustion chamber, as shown in Fig. 13.

The NOx profiles, given in Fig. 14, show that fuel-NO–NO2 andfuel-N2O can be used as search-initiating sets, with � 6 10�3, in or-der to correctly evaluate NOx emissions. The former set is also ableto reproduce the NO2 mass profile versus crank angle at the timewhen ignition occurs, i.e. about 20� CA BTDC.

The number of retained chemical species as a function of crankangle is shown in Fig. 15 for all sets of major species and all toler-ances. When only fuel is used for the search graph, few species,lower than 10, are retained in the first part of the engine cyclefor all values of tolerance, whereas more species are active whenadditional major species are considered. In all cases, the numberof active species suddenly increases when combustion takes placeup to 64, 60, 67 and 69 when fuel, fuel-CO–HO2, fuel-NO–NO2 andfuel-N2O are used as search-initiating set, respectively, with� = 10�4.

crank angle [deg]

num

ber

ofsp

ecie

s-150 -100 -50 0 50 100

0

20

40

60fuel, ε = 10-1

fuel, ε = 10-2

fuel, ε = 10-3

fuel, ε = 10-4

crank angle [deg]

num

ber

ofsp

ecie

s

-150 -100 -50 0 50 1000

20

40

60fuel-CO-HO2, ε = 10-1

fuel-CO-HO2, ε = 10-2

fuel-CO-HO2, ε = 10-3

fuel-CO-HO2, ε = 10-4

crank angle [deg]

num

ber

ofsp

ecie

s

-150 -100 -50 0 50 1000

20

40

60fuel-NO-NO2, ε = 10-1

fuel-NO-NO2, ε = 10-2

fuel-NO-NO2, ε = 10-3

fuel-NO-NO2, ε = 10-4

crank angle [deg]

num

ber

ofsp

ecie

s

-150 -100 -50 0 50 1000

20

40

60fuel-N2O, ε = 10-1

fuel-N2O, ε = 10-2

fuel-N2O, ε = 10-3

fuel-N2O, ε = 10-4

Fig. 15. Single-zone computations of the HCCI engine with the 135-species kinetic mechanism [35]: number of active chemical species vs. crank angle by using different setsof major chemical species and tolerance values.

ε

spee

d-up

10-4 10-3 10-2 10-10

5

10

15

20

25

30fuelfuel-CO-HO2

fuel-NO-NO2

fuel-N2O

Fig. 16. Single-zone computations of the HCCI engine with the 135-species kineticmechanism [35]: speed-up of DAC computations with respect to full mechanism vs.tolerance by using different sets of major chemical species.

858 A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863

The speed-up of DAC versus tolerance is plotted in Fig. 16 forall the simulations with different sets of search-initiating species.As expected, DAC efficiency is higher in this case, i.e. 135-speciesmechanism, with respect to the 57-species mechanism, since amore complex kinetic mechanism is considered as a startingpoint. The speed-up ranges from about 6 to about 25 and ismore than 9 with � = 10�3 and fuel-NO–NO2 as search-initiatingset.

4.2. HCCI engine: axially symmetric two-dimensional computations

In all the multidimensional computations, the 57-species reac-tion mechanism [23] is used, since it provided results in goodagreement with measurements [6]. On the basis of the analysisperformed by employing a single-zone configuration, in the two-dimensional configuration DAC is employed by setting � = 10�4,and fuel, fuel-N2O and fuel-NO–NO2 as search-initiating sets.

Two more cases have been included in the analysis of the aver-age pressure and heat release rate profiles by decreasing, / = 0.152,and increasing, / = 0.261, the value of the equivalence ratio withrespect to the baseline case / = 0.2. The values of TIVC and pIVC, com-puted by using the single-zone analysis, are 403.29 K and0.1002 MPa, respectively, in the former case and 408.93 K and0.1002 MPa, respectively, in the latter case. The estimation of thewall temperature takes into account the fact that the gas temper-ature in the combustion chamber decreases with the equivalence

ratio, therefore the walls are expected to be colder. For this reason,Twall is set equal to 460/500 K with / equal to 0.152/0.261. This is-sue is further discussed in Ref. [6].

crank angle [deg]

aver

age

pres

sure

[MP

a]

heatreleaserate

[J/deg]

-20 0 20 400

2

4

6

8

0

50

100

150

200

250

300

350

full mechanismDAC fuelmeasurements

φ =0.2

crankangle[deg]

aver

age

pres

sure

[MP

a]

heatreleaserate

[J/deg]

-20 0 20 400

2

4

6

8

0

50

100

150

200

250

full mechanismDAC fuelmeasurements

φ =0.152

crankangle[deg]

aver

age

pres

sure

[MP

a]

heatreleaserate

[J/deg]

-20 0 20 400

2

4

6

8

10

0

200

400

600

800

1000

full mechanism

DAC fuelmeasurements

φ =0.261

Fig. 17. Multidimensional computations of the HCCI engine: average pressure andheat release rate vs. crank angle by using the full mechanism and DAC with fuel assearch-initiating species. The measured profiles [24] are also shown for comparison.

crank angle [deg]

CO

mas

s[g

]

-20 -15 -10 -5 0 5 100

0.01

0.02 full mechanismDAC fuel

crank angle [deg]

CO

2m

ass

[g]

-20 -15 -10 -5 0 5 100

0.02

0.04

0.06

crank angle [deg]

UH

Cm

ass

[g]

-20 -15 -10 -5 0 5 100

0.02

0.04

Fig. 18. Multidimensional computations of the HCCI engine: CO, CO2 and UHC massvs. crank angle by using the full mechanism and DAC with fuel as search-initiatingspecies.

crank angle [deg]

NO

mas

s[g

]

NO

2 mass

[g]

-50 0 50 100

0

5E-07

1E-06

1.5E-06

2E-06

2.5E-06

-1E-06

0

1E-06

full mechanismDAC fuelDAC fuel-NO-NO2

DAC fuel-N2O

Fig. 19. Multidimensional computations of the HCCI engine: NO and NO2 vs. crankangle by using the full mechanism and DAC with different sets of search-initiatingspecies.

A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863 859

Fig. 17 shows the results, in terms of average pressure and heatrelease rate, obtained by using the full mechanism and DAC simpli-fication with only fuel as major species. The measured profiles [24]

are also shown for comparison. The figures show that the com-puted profiles are on top of each other in all cases. This confirmsthe accuracy of the DAC simplification even if only fuel is used assearch-initiating chemical species. Besides, the computations arein a very good agreement with measurements.

For the sake of conciseness, the following analysis is limited tothe baseline case. In Fig. 18, CO, CO2 and UHC profiles versus crankangle are given. The figure shows that the profiles are on top ofeach other and the differences in terms of emissions at EVO be-tween the DAC technique and the full mechanism are negligible,since they are equal to 0.13%, 0.0025% and 0.025% for CO, CO2

and UHC emissions, respectively. The numerical value of CO emis-sions, equal to about 6.6 � 10�4 g, is in good agreement with the

860 A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863

experimental data [24], equal to about 7.4 � 10�4 g. An extensivediscussion of the validation of computed emissions and of theinfluence of crevices is given in Ref. [6].

As far as NOx emissions are concerned, the use of fuel for thesearch graph does give different results with respect to the fullmechanism, as shown in Fig. 19. The results noticeably improveby adding NO and NO2 to the set of search-initiating species, witha discrepancy, compared to the full mechanism, equal to 7.5% andto 5.9% in terms of NO and NO2 emissions, respectively. Besides,the results fully match those obtained with the full mechanismwhen DAC is used with fuel-N2O as major species, thus confirmingthe findings of the single-zone computations. In the latter case, thediscrepancy in terms of NO and NO2 emissions, with respect to thefull mechanism, is equal to 0.25% and 0.23%, respectively.

The speed-up of the DAC computations with respect to thoseusing the full mechanism is equal to 2.04, 1.92 and 1.83 when fuel,fuel-NO–NO2 and fuel-N2O are used as search-initiating set,respectively. This means that the saved time, with respect to thesimulations with the full mechanism, is equal to 51%, 48% and

2

700 750 800 850 900

25 CA BTDC

T [K]

700 750 800 850 900

25 CA BTDC

T [K]

Fig. 20. Multidimensional computations of the PSCCI engine: spray and

45%, respectively. Dealing with multidimensional simulations, thisis a relevant result. In fact the computation by employing the fullmechanism requires 253 h on an Intel (R) Core (TM) i7 CPU. Be-sides, with more complex reaction mechanisms as starting point,the speed-up of the simulations is expected to improve even more.

4.3. PSCCI engine: multidimensional computations

As in the case of HCCI engine, the computations under the PSCCIcombustion mode have been performed by using the 57-speciesreaction mechanism and by employing the DAC approach with� = 10�4 and fuel, fuel-NO–NO2 and fuel-N2O as search-initiatingsets.

Fig. 20 shows the temperature and fuel mass fraction contourplots at 25� CA BTDC in the middle axial plane of the domain cor-responding to the injector location and the location of the liquidparcels in the combustion chamber. In the spray region, the gasmixture becomes reacher and colder, due to both the vaporization

5 CA BTDC

0.028 0.09 0.152

25 CA BTDC

Yf

0.028 0.09 0.152

25 CA BTDC

Yf

temperature and fuel mass fraction contour plots at 25� CA BTDC.

crank angle [deg]

aver

age

pres

sure

[MP

a]

-20 0 20 400

5

10 full mechanismDAC fuel

crank angle [deg]

heat

rele

ase

rate

[J/d

eg]

heatrelease[J]

-20 -10 0 10 200

200

400

600

800

1000

1200

1400

1600

-1500

-1000

-500

0

500

1000

1500

full mechanismDAC fuel

Fig. 21. Multidimensional computations of the PSCCI engine: average pressure,heat release rate and total heat release vs. crank angle by using the full mechanismand DAC with fuel as search-initiating species.

crank angle [deg]

CO

mas

s[g

]

-30 -20 -10 0 100

0.02

full mechanismDAC fuel

crank angle [deg]

CO

2m

ass

[g]

-30 -20 -10 0 100

0.05

0.1

crank angle [deg]

UH

Cm

ass

[g]

-30 -20 -10 0 100

0.02

0.04

0.06

Fig. 22. Multidimensional computations of the PSCCI engine: CO, CO2 and UHCmass vs. crank angle by using the full mechanism and DAC with fuel as search-initiating species.

crank angle [deg]

NO

mas

s[g

]

NO

2 mass

[g]

0 50 1000

0.001

0.002

0.003

0.004

-4E-05

-2E-05

0

2E-05

4E-05

full mechanismDAC fuelDAC fuel-NO-NO2

DAC fuel-N2O

Fig. 23. Multidimensional computations of the PSCCI engine: NO and NO2 vs. crankangle by using the full mechanism and DAC with different sets of search-initiatingspecies.

A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863 861

of the liquid parcels and the energy transfer with the injectedcharge.

The results, in terms of average pressure, heat release rate andtotal heat release versus crank angle, of both the full mechanismand DAC with only fuel as search-initiating species, are shown inFig. 21. The figure shows that the profiles are on top of each other,

thus confirming once again the effectiveness of the DAC approachand suggesting the appropriate set of initiating species for theDRGEP graph search. A slight difference in the fluctuations of heatrelease rate in its descending part is found, but the total heat re-lease profiles are in very good agreement.

The CO, CO2 and UHC profiles are shown as a function ofcrank angle in Fig. 22. Once more, the DAC approach is able toaccurately predict the production/consumption of these speciesand the total amount of emissions at EVO. The differences be-tween the full mechanism and DAC results are equal to 0.2%,practically 0% and 0.2% in terms of CO, CO2 and UHC emissions,respectively.

As regards NOx emissions, they are expected to be higher thanin the case of the HCCI engine, since the charge is partially strati-fied and the total amount of fuel is higher, thus resulting in highervalues of the maximum temperature in the combustion chamber[3]. Indeed, under the PSCCI combustion mode, temperature inthe chamber increases up to 2780 K, whereas in the HCCI engineit is lower than 1760 K. Consequently, in the PSCCI engine thermalNOx are expected to be relevant with respect to N2O mechanism.

Such a consideration explains the results of Fig. 23 in terms ofNO and NO2 profiles versus crank angle obtained by employingboth the full mechanism and DAC with fuel, fuel-NO–NO2 andfuel-N2O as search-initiating sets. NO emissions are much higherthan NO2 emissions. Besides, the use of only fuel as major specieswith the DAC simplification gives results with relatively small dis-crepancies, with respect to the full mechanism simulation, equal to1.6% and 2.7% in terms of NO and NO2 emissions, respectively. Suchdiscrepancies can be easily removed by adding NO–NO2 or N2O tothe set of search-initiating species.

The speed-up of the DAC computations with respect to thoseusing the full mechanism is equal to 2.30, 1.86 and 2.10 when fuel,fuel-NO–NO2 and fuel-N2O are used as search-initiating set,respectively.

5. Conclusions

In this work, Dynamic Adaptive Chemistry methodology, basedon the Directed Relation Graph with Error Propagation technique,has been extended to the simulations of ethanol HCCI/PSCCI en-gines and its accuracy and performance have been carefully as-sessed. This is an interesting subject for the research community,since the thermo-physical properties of ethanol have been suc-

862 A. Viggiano, V. Magi / Applied Energy 113 (2014) 848–863

cessfully exploited in innovative engines to improve their operat-ing conditions and reduce emissions. The fuel combustion kineticsmodeling is fundamental in order to simulate the use of differentfuels or blends of fuel. In this respect, DAC technique may be a use-ful approach in order to reduce the computational time of thesimulations.

In order to get the best compromise between accuracy andcomputational cost of the DAC simulations, single-zone computa-tions have been performed for tuning the tolerance value, �, andto gain insights into the choice of major species for the graphsearch. Two kinetic reaction mechanisms for ethanol oxidation,with 57-species and 135-species, respectively, have been consid-ered. An ethanol HCCI engine has been analyzed, that typically runswith lean mixtures and with very low emissions, especially NOx.

The simulations have shown that very good results are ob-tained, in terms of average pressure, heat release rate, CO andCO2 profiles versus crank angle by using � = 10�3 or less and byusing only fuel as major species. The use of fuel-CO–HO2 assearch-initiating set enables to get more accurate results with low-er values of tolerance.

As far as NOx emissions are concerned, the primary role ofthe N2O mechanism with respect to the other mechanisms offormation of NOx has been assessed. In order to correctlysimulate the N2O mechanism, and consequently NO and NO2

emissions, fuel-N2O have to be used as major species with� = 10�4 in the case of the 57-species mechanism. On the otherhand, when the 135-species mechanism is employed as fullmechanism, either fuel-NO–NO2 or fuel-N2O as majorspecies provide good results with � = 10�3 and even more with� = 10�4.

The two-dimensional simulations of an HCCI engine, per-formed by using the 57-species mechanism and � = 10�4, haveconfirmed the conclusions of the single-zone computations, i.e.fuel is sufficient as major species to simulate, with very highaccuracy, average pressure, heat release rate, CO, CO2 and UHCprofiles versus crank angle, whereas it is needed to add N2O tothe set of search-initiating species to correctly get NO and NO2

profiles.As far as the PSCCI engine computations are concerned, the

amount of fuel is increased both by increasing the equivalence ra-tio in the homogeneous mixture to the upper limit to get a reason-ably slow combustion and by directly injecting additional fuel inthe combustion chamber. In this case, higher temperatures areachieved in the combustion chamber, so thermal NOx formationbecomes relevant. Therefore, the computations show that the useof � = 10�4 and fuel as major species for DAC simplification givesvery good agreement with the full mechanism in terms of averagepressure, heat release rate, CO, CO2 and UHC profiles and a reason-able agreement in terms of NO and NO2 profiles. Besides, a verygood agreement is obtained by adding NO–NO2 or N2O to thesearch-initiating set.

The speed-up of DAC depends on the level of detail of the fullmechanism, which is used as a starting point, as well as on the va-lue of tolerance and on the number of major species. By using the135-species reaction mechanism, very accurate results are ob-tained with a speed-up of about 9.4. Dealing with multidimen-sional simulations and with the 57-species mechanism, 45% and52% of the computational time is saved under HCCI and PSCCI com-bustion mode, respectively, with negligible discrepancies with re-spect to the full mechanism even in terms of NOx emissions. Thisis a very interesting result due to the large computational cost ofthese simulations. Besides, the efficiency of the DAC approach inmultidimensional simulations of HCCI/PSCCI engines is expectedto considerably improve when ethanol is blended with conven-tional fuels due to the increased complexity of the reaction mech-anisms to be considered.

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