elva

haUnirsity6G 2

Keywords:

ristionI) e

the cylinder pressure trace and heat release rates of the experiments. The unburned hydrocarbon emis-

nd renion fue

engines is the ability to combust a wide variety of fuels rangingfrom low caloric value fuels such as biomass derived gases to nat-ural gas, alcohols, gasoline, and diesel [15]. Furthermore, many ofthese fuels can be combined as blends such as n-butanol/n-heptane,ethanol/n-heptane, and n-butanol/gasoline [611].

els due to rapid Heat Release Rates (HRRs) at high loads and a rel-atively narrow engine operating range [1].

To improve ignition and combustion processes and to reducethe environmental impacts, various engine control strategies havebeen implemented on HCCI engines. These techniques includevarying the level of the Exhaust Gas Recirculation (EGR), adjustingthe equivalence ratio, changing the intake temperature, and imple-menting Variable Valve Actuation (VVA). Negative valve overlap(NVO) is a specic form of VVA where the exhaust valve is closedbefore all the exhaust has been completely evacuated from the cyl-inder, which traps variable amounts of hot combustion products

Corresponding author. Tel.: +1 519 888 4567x38362; fax: +1 519 885 5862.E-mail addresses: spvisakh@engmail.uwaterloo.ca (S. Visakhamoorthy), john.

wen@uwaterloo.ca (J.Z. Wen), sivoththaman@uwaterloo.ca (S. Sivoththaman),

Applied Energy 94 (2012) 166173

Contents lists available at

Applied

lsebob.koch@ualberta.ca (C.R. Koch).Charge Compression Ignition (HCCI) engines are a promising exten-sion of the current internal combustion engine technology in thatthey offer a design where emission levels of Spark Ignition (SI) en-gines can be attained with the thermal efciency of CompressionIgnition (CI) engines [1]. In HCCI engines, which can be consideredas hybrid engines between SI and CI engines, a premixed fuelaircharge is compressed until it autoignites. This technology can helpreduce the environmental impacts of the increased transportationfuel usage by offering reduced NOx (nitric oxide NO and nitrogendioxide NO2) and soot emissions while operating at near diesel-engine efciencies. Another major benet offered by these HCCI

or localized high-temperature regions in the cylinder. Moreover,higher thermal efciencies are possible due to the higher compres-sion ratios required to autoignite these dilute airfuel mixtures.However, one of the main challenges is that combustion phasingof HCCI engines is controlled by chemical kinetics and as suchthere is no specic ignition timing event as there is in SI or CI en-gines. Another challenge is the relatively high levels of UnburnedHydrocarbon (UHC) and CO (carbon monoxide) emissions [1214]. These emissions primarily arise from the charge in the creviceregions and from a thin thermal boundary layer that forms alongthe in-cylinder surfaces. Other challenges include higher noise lev-HCCIButanolHeptaneMulti-zoneModelNegative valve overlap

1. Introduction

Alternative energy technologies able ways to reduce the transportat0306-2619/$ - see front matter 2012 Elsevier Ltd. Adoi:10.1016/j.apenergy.2012.01.047sion is predicted to a reasonable level while NOx (nitric oxide NO and nitrogen dioxide NO2) formation isunder-predicted. The difculty in mapping the NOx emission is attributed to the fact that the combustionmodel operates within the closed cycle period of engines and therefore does not capture the complexityof the charge stratication within the NVO equipped engine. Nevertheless, the trend of increasing NOxlevels with the increasing fraction of butanol in the fuel mixture is captured and the model is able to pre-dict the pressure, heat release rates, and combustion phasing for the three fuel blends tested.

2012 Elsevier Ltd. All rights reserved.

ewable fuels are possi-l usage. Homogeneous

In HCCI engines a homogeneous airfuel mixture is compres-sion ignited with a relatively large amount of charge dilution. Thisresults in low-temperature combustion, which subsequently pro-duces low NOx and less soot due to a lack of fuel-rich ame regionsAccepted 17 January 2012Available online 16 February 2012

The model is calibrated using one experimentally characterized operating point and by taking intoaccount the major features of NVO. The model simulations at other four operating points closely matchNumerical study of a butanol/heptane fuIgnition (HCCI) engine utilizing negative

Sona Visakhamoorthy a, John Z. Wen a,, Siva SivothtaDepartment of Mechanical and Mechatronics Engineering, University of Waterloo, 200bDepartment of Electrical and Computer Engineering, University of Waterloo, 200 UnivecDepartment of Mechanical Engineering, University of Alberta, Edmonton, AB, Canada T

a r t i c l e i n f o

Article history:Received 10 November 2011Received in revised form 16 January 2012

a b s t r a c t

The calibration and compamodel for simulating operaCompression Ignition (HCC

journal homepage: www.ell rights reserved.led Homogeneous Charge Compressionlve overlap

man b, Charles Robert Koch c

versity Avenue West, Waterloo, ON, Canada N2L 3G1Avenue West, Waterloo, ON, Canada N2L 3G1G8

on to experimental data of a parallel computing multi-zone combustional characteristics of an n-butanol/n-heptane fuelled Homogeneous Chargengine utilizing the negative valve overlap (NVO) technology is described.

SciVerse ScienceDirect

Energy

vier .com/ locate/apenergy

model [8] and two n-heptane models [29,30]. To predict the NOxformation, the NOx mechanism from GRI-Mech 2.11 is added to

plied[15]. The actual NVO period is dened as the time during whichboth the inlet and exhaust valves are closed. This control strategyfor HCCI engines allows for charge preheating by the trapped ex-haust gases, which promotes autoignition and improves perfor-mance [16]. The complicated chemical and physical processesintroduced by recycling the trapped exhaust gases do bring aboutnew challenges to theoretical and modeling studies.

To understand the details of the combustion process or to de-sign model based control strategies for HCCI engines, appropriatesimulation models must be developed. These simulation modelscan differ widely in terms of complexity and computational costand can be broadly categorized into one of three groups: singlezone models with chemical kinetics [17], multi-zone models withchemical kinetics [18,19], and Computational Fluid Dynamics(CFD) based models with chemical kinetics [20]. The simplest ofthese three groups of models is the single zone model with chem-ical kinetics where the entire charge mass is treated as a singlelumped zone of homogeneous temperature, pressure, and speciesconcentration. Reaction rates and species evolution are solvedusing chemical kinetics. These models tend to over-predict HRRand pressure rise rates while under-predicting certain emissionsdue to the assumptions inherent to such single zone models. Forexample, they lack any means of representing crevice regionsand thermal boundary layers within the engine which are a signif-icant source of CO and UHC emissions. To better differentiateamong the bulk charge, thermal boundary layer, and crevice re-gions, multi-zone models are used. Here, the in-cylinder chargeis broken down into concentric ring-like or individually lumpedzones which are treated as stirred reactors. Each zone is homoge-neous in terms of species concentrations and temperature andpressure distributions, but the zones are stratied from one an-other. This treatment allows for capturing the species and temper-ature gradients throughout the cylinder but requires more zones ifa larger temperature gradient exists across the cylinder.

Since single and multiple zone models operate within the closedperiod of the engine cycle (between the Inlet Valve Closing (IVC)and the Exhaust Valve Opening (EVO)) the initial conditions ofthe combustion chamber (including the chemical compositionsand temperature) at IVC are set by the user. Due to the difcultyin ascertaining the exact values of these initial conditions and thesensitive nature of HCCI combustion, this can lead to some inaccu-racy in the model prediction. To reduce this error and improve theinputs to the model, single zone models may be augmented withfull engine cycle simulations that calculate conditions up to IVC,at which the single zone model takes over until EVO [21]. Similarly,multi-zone models may be linked to a CFD solver such as KIVAwhich has been used to determine the initial gas mixture and tem-perature distribution prior to combustion. Typically at a certain pis-ton position, the multi-zone model takes over based on thetemperature prole that is calculated by the CFD code. Finally, thereare pure CFD models where the chemical kinetics is fully imple-mented throughout the closed cycle period [20]. In these casesmassand heat transfer can be solved along with accurate turbulencemodeling of the compressed gases. The CFD modeling provides in-sight into the specic engine features which promote turbulencesuch as in the squish area of the pistons. However, due to the levelof details involved and depending on the mesh size, these simula-tions can be very computationally intensive. In addition, signicantefforts are needed to produce complicated meshes for different en-gine geometries. Nevertheless, some research has been done to par-allelize these models across computing clusters to reduce thecomputational time [20]. More fundamental work needs to be done,however, for implementing the CFD code in designing model based

S. Visakhamoorthy et al. / Apcontrol strategies for HCCI engines.As mentioned earlier, a major benet of HCCI engines is their

ability to combust a wide range of fuels. Fuels that have beenthe above mechanism and this creates a reaction mechanism con-sisting of 200 species and 1805 reactions. The GRI-Mech 2.11 NOxmechanism is selected as opposed to the that from GRI-Mech 3.0since previous work [31,32] have indicated that GRI-Mech 3.0over-predicts NOx levels in partially premixed ames.

For this study, a 10-zone model with the detailed chemicalkinetics is used to numerically solve the species, temperatureand pressure evolution within the cylinder. It has been reported[27,33] that 10 zones are sufcient for solving the in-cylinder pres-sure trace, which is a major indicator of the engine performance.The selection of the number of zones is exible but subject to theavailability of the number of CPUs for parallel computing. Thismodel operates within the closed cycle period of the engine andsteps through species, temperature and pressure evolution at eachincremental crank angle of one engine cycle. Heat transfer betweenzones and the cylinder walls is considered but the mass transferbetween zones is not. There is, however, a mass loss due toblow-by and this is implemented by an equivalent loss of massfrom all zones. Heat transfer coefcients are determined usingthe Woschni correlation which accounts for bulk gas velocitycaused by the piston movement. Specically, in order to describethe temperature gradient across the cylinder, a scaling factor ischosen with the selected volume of the hottest core zone. The ori-ginal multi-zone numerical model is parallelized using the domaindecomposition method and Fortran MPI (Message Passing Inter-face). This approach shares the computationally intensive step ofsolving the species evolution for each zone across multiple proces-experimentally tested and numerically simulated include PrimaryReference Fuels (PRFs), natural gas, diesel, and methanol/ethanol/dimethyl-ether blends [1,2224]. In this study, a HCCI enginefuelled with three n-butanol/n-heptane blends is used to validatea parallelized multi-zone model coupled with the detailed chemi-cal kinetics. Since the fuel tested contains a fraction of large hydro-carbons (n-heptane), it exhibits low-temperature heat release [25].This initial heat release occurs due to an increase in initial reactionrates caused by increasing temperature. Under such conditions,hydrogen is abstracted by oxygen from the fuel creating alkyl (R)and hydroperoxyl (HO2) radicals. The alkyl radicals are consumedvia two pathways which become dominant in the low-temperatureheat release: alkylperoxy radical (RO2) production and olen pro-duction. At all temperatures, alkyl radical conversion to RO2 is fas-ter than olen production. However, starting at approximately700 K at 10 atm, the reverse RO2production reaction becomesdominant over the forward reaction thus increasing the productionof alkyl radicals [26]. These alkyl radicals are then consumed viathe olen pathway. This overall increase in olen productionreduces the fuel consumption leading to the end of the low-temperature heat release region.

The objective of this work is to test and calibrate a recentlydeveloped multi-zone HCCI model [27] on a NVO equipped HCCIengine fuelled with varying ratios of bio-butanol/n-heptane andto compare the predictions with experimental data. The utility ofthe model for predicting the in-cylinder pressure, HRR, NOx andunburned hydrocarbon emissions is examined.

2. Numerical simulation

The chemical mechanism chosen for this study is the one devel-oped by Dagaut and Togbe [28]. This mechanism is based on theamalgamation and reduction of two discrete models: an n-butanol

Energy 94 (2012) 166173 167sors. It is found that running the parallelized model on a quad-coreIntel i7 workstation results in runtimes reduced by approximatelyhalf in comparison to single-core computation of the serial model

runner. The intake air is preheated to a set temperature using a

ture, mainly consisting of the residual gas, with a relatively uni-form temperature in the cylinder. Between this core and the

Intake temperature of the air fuel mixture

lied600W electrical band-type heater. The in-cylinder pressure ismeasured using a Kistler piezoelectric transducer and a K-typethermocouple with an accuracy of 2 C is installed in the intakemanifold and measures the intake temperature. Exhaust emissionsare characterized using a 5-gas emissions test bench (Horriba CLA-510SS emission analyzer, which measures the gas concentrationsusing the chemiluminescent method). The exhaust emissions aresampled 5 cm downstream of the exhaust ports. NOx emissionsare measured with 1 ppm resolution, UHCs with 10 ppm resolu-tion, and CO with 0.01% resolution. The crank angle position is re-corded with 0.1 resolution using a BEI optical encoder.

Three Butanol Volume Percentages (BVPs) of the fuel at variousequivalence ratios are tested with the intake temperature and en-gine speed held constant. They are BVP 12 (representing a volumeon the same workstation. Typical serial mode runtimes are 80 minwhile parallelized runs take 40 min.

The model is calibrated against a given set of experimental datawith a number of parameters adjusted. Exact experimental valuesfor some of these parameters are generally unknown or difcult todetermine from the existing experiment which is why they need tobe calibrated. Once a suitable set of calibrated parameters has beendetermined for a single engine operating point, these parametersare left unchanged when the model is used as a predictive toolfor other operating points. Table 1 shows a list of the adjustableparameters and their brief descriptions. Note: the geometric ratio,which is dened as the ratio of the thicknesses of the neighboringzones [34], is always set to 1 in this study.

3. Experimental data

The experimental apparatus is a single cylinder HCCI enginecombusting n-butanol and n-heptane blends [25]. The engine testbed is a Ricardo Hydra Mark III using a Mercedes E550 cylinderhead with variable valve timing. The fuel is injected into the intake

Cylinder wall temperature assumed to remain spatially andtemporally constant

Heat transfer scaling factor applied in the Woschni correlationBlow-by lossesFraction of the cylinder volume allocated to the core zoneFraction of the trapped residual gasesGeometric ratio how rapidly zones get thinner as they approach the wallThermal width the temperature difference between the outermost and

core zonesTable 1Description of adjustable model parameters.

Parameters

168 S. Visakhamoorthy et al. / Appfraction of 12% butanol in the fuel mixture), BVP17 and BVP 22. Tocalibrate and validate the simulation, ve experimental operatingpoints are selected, i.e., BVP 12 (u = 0.332, 0.346), BVP 17(u = 0.345, 0.357) and BVP 22 (u = 0.366). One operating point(BVP 17 at u = 0.357) is used to calibrate the model while the otherfour are used for model validation. The experimental operatingconditions of the engine are presented in Table 2. Some of theseparameters (listed in Table 1) are adjusted during the model cali-bration, as shown later.

4. Results and discussion

4.1. Model calibration

Asmentioned earlier, the parameters listed in Table 1 need to becalibrated using available experimental data since their values arenot directlymeasured and cannot be easily determined. The operat-ing pointwithBVP17atu = 0.357 is used to calibrate themodel. Thecalibration processmatches pressure traces during the compressionstroke by adjusting blow-by andmatches ignition timing by varyingthe intake temperature. Then other parameters such as the temper-ature stratication, fraction of residual gases, and heat transfer scal-ing factor are modied to achieve a small error between the modelprediction andexperimentpressure traceover the entireCAD (CrankAngle Degree) range. During calibration, the intake temperature isnot restricted to the measured intake manifold temperature sincethe signicant amount of trapped gas, due to NVO, could cause pre-heating of the fresh airfuel mixture charge. Blow-by is found to benegligible for this case and this is conrmed by literature [15]. It isimportant to note that this initial calibration of model parametersis conducted using the measured pressure trace only. The same setof model parameters is then used to investigate the trend of HRRand the formation of NOx and UHC, which have required a furthermodel calibration by adjusting the equivalence ratio, as discussedin Section 4.2.

The uncalibrated and calibrated pressure traces from the initialcalibration are shown in Fig. 1 with the calibrated pressure tracematching experimental data quite well. The uncalibrated pressuretrace does not take into account residual gases, blow-by losses, andthe temperature stratication. The calibrated model parametersare specied in Table 3. It should be noted that the high residualgas fraction of 0.29 (29%) is caused by early closing of the exhaustvalve to trap hot residuals. The cylinder wall temperature is not di-rectly measured, but is estimated to be 20 above the coolant tem-perature. A relatively large core zone volume is selected with agreater heat transfer scaling factor. This implies that for this initialcalibration case, there is a large volume of the hot fuel/air/gas mix-

Table 2Engine operating parameters.

Parameter (unit) Value

Compression ratio () 12:1Bore (mm) 97Stroke (mm) 88.9Connecting rod to crank arm ratio () 3.5996Displacement volume (l) 0.653IVO, IVC (deg, after the bottom dead center) 151, 21EVO, EVC (deg, after the bottom dead center) 100, 130Engine speed (RPM) 1021EGR (%) 0Coolant temperature (C) 6970Intake temperature (C) 80Equivalence ratio () 0.332, 0.345, 0.346, 0.357, 0.366BVP (%) 12, 17, 22

Energy 94 (2012) 166173cylinder wall, there is a signicant temperature gradient.Using the calibrated pressure curve above, the HRR curve is cal-

culated and shown in Fig. 2. Using the heat release analysis methodoutlined in [35], the net HRR is calculated by accounting for anyheat transfer through the walls of the cylinder. The HRR curve gen-erally shows adequate matching although the rst-stage ignition issomewhat early and larger than experimental data. Experimen-tally, the rst-stage ignition peaks at around 15CAD bTDC (beforethe Top Dead Center) whereas the simulation data indicates a peakat 20CAD bTDC. Discrepancies in predicting the correct ignitionperiod may be due to the reaction mechanism or result from errorsin setting initial conditions. It can be attributed to the model lim-itation in fully describing the NVO effects as well, as discussedlater.

The measured NOx level for the calibration case is 5.4 ppm [25]while the simulation result is 0.7 ppm. This under-prediction of theNOx level can be explained by the thermal NOx formation. In fact,

plied0

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S. Visakhamoorthy et al. / Apthe predicted peak temperature in the cylinder ranges from 2100 Kto 2250 K for individual cases and all these values are above thethreshold temperature of the thermal NOx formation. The NVOused in the experimental engine can introduce signicant amountsof the inhomogeneity into the charge prior to ignition [36]. Thisinhomogeneity occurs in both the temperature distribution andspecies concentrations. Any fuel-rich regions caused by this inho-mogeneity could cause localized hot spots and a higher productionof NOx. Those hot spots, especially when they occur across the cyl-inder and in individual zones, are not captured by the multi-zonemodel due to its underlying assumption of complete mixing be-tween trapped gasses and the inducted fuel/air charge. The modelalso assumes the trapped residual gas with the same temperatureof the intake charge (although the intake temperature is adjustedto account for the total energy balance). Since the in-cylinder tem-perature prole is difcult to measure, it is not available from the

to better represent the temperature variation in cylinder. This

CAD

Fig. 1. Uncalibrated (left) and calibrated (right) predictions of the pressure trace for BVPdata.

Table 3Calibrated model parameters.

Parameter (unit) Value

Intake temperature (C) 122Wall temperature (C) 90.0Heat transfer scaling factor () 40.0Blow by loss NeglectedCore zone volume () 0.30Residual gas fraction () 0.29Thermal width (C) 0.0

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BVP = 17exp = 0.357simul = 0.357

Fig. 2. The HRR curves for the calibration case BVP 17. The solid line is themeasured data and the dashed line is the simulation result.could result in a better prediction of NOx formation.The predicted UHC emission of 226 ppm is much lower than the

measured 1780 ppm in experiment. This difference is likely due tothe thermal boundary layer in the numerical model being too thin.In other words, combustion is more complete in the numericalsimulation and hence produces less UHC. This is observed duringthe model calibration as the expansion stroke of the simulationshowed a slight deviation from the experiment. The deviation islikely caused by the excess energy remaining in the charge andcan be reduced by increasing the size of the quenched thermalboundary layer. However, increasing the size of this layer in thesimulation reduces the mass available for combustion and inter-feres with the proper combustion. Under experimental conditions,the implementation of NVO facilitates combustion with fuel injec-tions occurring during the NVO period [16]. With NVO, trappedresiduals can be oxidized during the NVO period and the fuel in-jected during this time facilitates combustion [16]. However, thesimulation model only operates between IVC and EVO and cannotcapture the NVO period and its associated phenomena. Thus, thecombustion facilitation effect of NVO is not fully captured in themodel and an increase in the size of the thermal boundary layeris also restricted.

4.2. Model limitations in predicting NVOexperiments. Otherwise, the locations of model zones could be set

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BVP = 17exp = 0.357simul = 0.357

17. The dashed lines are the simulation results and the solid lines are the measured

Energy 94 (2012) 166173 169Previous work [27] involving similar combustion modeling hasnot been used to study experimental data from the NVO equippedHCCI engines. Due to the implementation of NVO, there could bevariable amounts of trapped residual gases inside the cylinderdepending on the valve timing. Using the experimental valve tim-ing and engine specications, the trapped gas percentage is deter-mined using the ratio of the volume at the Exhaust Valve Closing(EVC) to the volume at IVC and is found to be 29%. This level oftrapped gas is quite high but it is conrmed by other work usingthis same engine [25]. Such high levels of the trapped gas are pos-sible because the exhaust valve is closed before the piston reachesTDC thus preventing all the exhaust gas from being evacuated. Thistrapped gas could have a fairly high temperature [36] and thisshould result in signicant charge preheating. In addition, thetrapped gas will modify the initial chemical compositions of thefuel/air mixture in the cylinder. The inclusion of such a large per-centage of exhaust gases in the subsequent intake charge has differ-ent effects with regards to themodel development. Firstly, since themulti-zone model does not account computationally for the tem-perature of trapped residual gases (it assumes the trapped gases

are at the same temperature of the intake charge), the intake chargetemperature is specied as a manual input to the model. Secondly,the initial calibration assumes at IVC, there is a homogeneous mix-ture throughout the cylinder and there is no any temperature strat-ication in the computational domain. However, in practice, NVO

but it also represents the increase in equivalence ratio caused bytrapped UHCs and any fuel-rich regions which may ignite early.Normally, for engines not implementing NVO, the equivalence ra-tio is not adjusted and is set to the experimental value [27]. Admit-tedly, a 14.8% increase is fairly large. Nevertheless, this increase,along with an increased mass in the outer zone, is the main adjust-ment between the new calibration and the original calibratedresults.

Some other minor changes in model parameters are listed inTable 4. Note that the wall temperature and the residual gas frac-tion remain unchanged. The smaller heat transfer scaling factor isused together with a smaller core zone volume and a greater ther-mal width. The new calibration results in excellent pressure tracematching for the calibration case as shown in Fig. 3. Expansionstroke pressure trace matching is also improved compared to theoriginal calibration. Using the pressure data, the HRR curve is cal-

From the measured data, for a given BVP, HRR curves for various

Table 4Comparison of the model parameters before and after taking the NVO into account.

Original calibration New calibration accounts for NVO

Parameter (unit) Value Parameter (unit) Value

Intake temperature (C) 122 Intake temperature (C) 132Heat transfer scaling factor () 40.0 Heat transfer scaling factor () 1.0Core zone volume () 0.30 Core zone volume () 0.01Thermal width (C) 0.0 Thermal width (C) 30.0

170 S. Visakhamoorthy et al. / Applied Energy 94 (2012) 166173could result in the signicant inhomogeneity and stratication ofthe charge temperature and trapped residual gas at IVC [36]. Thiscan lead to fuel-rich regions which will ignite sooner than the as-sumed homogeneous charge. Alternatively, increasing the overallequivalence ratio of the model could account for the earlier ignitioncaused by fuel-rich regions and applying the temperature stratica-tion could better mimic the experimental situation. Finally, thismodel assumes that the residual gas is a complete combustionproducts constituting of CO2, H2O and N2. However, the actualresidual gas contains also NOx and UHCs. These recycled NOx andUHCs may contribute to combustion by effectively creating aslightly richer mixture in the subsequent cycle. This effect may beminimal for most operating points, but for points near the misrelimit, the recycling of unburned and partially burned reactantscan signicantly inuence subsequent cycles [37]. The energycontent of the intake charge should be increased when NVO ispresent due to the heat energy of the trapped residuals and anyunburned or partially oxidized reactants.

With the increased energy content accounted, the model isrecalibrated for the case (BVP 17, u = 0.357) to improve matchingof the predicted cylinder pressure with experimental data whilerelaxing constraints on the model parameters. The main differencebetween the experimental and original simulation pressure curvesis a slight deviation during the expansion stroke likely due to moreenergy remaining in the charge during expansion in the simula-tion. This can be improved by forcing more mass into the outerzone to reduce the amount of mass which reacts. Doing so hastwo effects: it increases total unburned reactant emissions and re-duces the peak pressure since there is less mass available for com-bustion. To further improve the model predictions, the equivalenceratio is increased by 14.8%. Not only does this aid in combustion,

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exp = 0.3570

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simul = 0.410

Fig. 3. Pressure and HRR curves for the improved calibration case. The solid linequivalence ratios exhibit nearly identical low-temperature HRR,but have varying high-temperature HRR. In other words, the equiv-alence ratio has little effect on the cool ame activity for a givenBVP. This same trend is observed in the simulation predictions.

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BVP = 17exp = 0.357simul = 0.410culated and matches the experimental results quite closely. Inaddition to exhibiting a good peak behavior, the HRR curve showsa signicantly improved rst-stage combustion timing and magni-tude when compared to the experiment.

4.3. Model predictions

With this new set of calibrated parameters listed in Table 4, theother four operating points of the engine are simulated. No otherparameters are changed during simulation except for BVP andthe equivalence ratio. All equivalence ratios are raised by 14.8%above the experimental values.

For the four validation points the simulation results match clo-sely to the experimental results except for a quite small deviationduring the expansion strokes of the predicted pressure traces, asshown in Fig. 4. These simulation results capture the overall trendsof the system with a good prediction of the cool ame activity. Forexample, changing from u = 0.332 to u = 0.346 with BVP12 showsan increase in the peak pressure which is expected due to the ri-cher mixture, although the low-temperature HRR remains con-stant, as shown in Fig. 5. All HRR curves in Fig. 5 show excellentmain-ignition timing and magnitudes and very good low tempera-ture heat release timing and magnitudes. The effect of increasingequivalence ratios on the cool ame activity for a given BVP willbe discussed later.es are the measured data and the dashed lines are the simulation results.

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BVP = 17exp = 0.345simul = 0.396

BVP = 22exp = 0.366simul = 0.420

-40 -20 0 20 40CAD

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Fig. 4. Predicted pressure traces for the four validation points. The solid lines are the measured data and the dashed lines are the simulation results.

Fig. 5. HRR curves for the four operating points. The solid lines are the measured data and the dashed lines are the simulation results.

S. Visakhamoorthy et al. / Applied Energy 94 (2012) 166173 171

lied172 S. Visakhamoorthy et al. / AppIn addition to the two cases of u = 0.332 and 0.346 at BVP 12, threemore cases of u = 0.320, 0.340, and 0.360 are simulated. The HRRcurves for these ve cases are shown in Fig. 6, which shows thatvarying the equivalence ratios for a xed BVP of 12 does not bringabout signicant changes in the low-temperature heat release.

The UHC predictions for the four predictive cases are on the or-der of 500 ppm. These levels are close to the experimentwhen com-pared to the rst set of calibration results and this is attributed tothe increasedmass in the outer zone (i.e., the cooler thermal bound-ary layer). UHC levels are however still lower than the measureddata of around 1800 ppm. NOx emissions for the four validation

Fig. 6. The simulated HRR curves with varying equivalence ratios for BVP 12.

Table 5Experimental and simulation emissions.

NOx UHC

BVP u Simulation(ppm)

Experimental(ppm)

Simulation(ppm)

Experimental(ppm)

12 0.322 0.60 5.5 492 185112 0.346 0.54 5.4 503 174617 0.345 0.55 5.3 521 187717 0.357 0.53 5.4 531 178022 0.366 0.52 5.1 558 1845

5

5.1

5.2

5.3

5.4

5.5

5.6

5.7

5.8

5.9

6

0.5

0.52

0.54

0.56

0.58

0.6

10 15 20 25

Expe

rim

enta

l NO

x (p

pm)

Sim

ulat

ion

NO

x (p

pm)

Butanol Volume Percentage

Simulation

Experimental

Fig. 7. NOx trends in experimental data and simulation for a number of engineoperating points listed in Table 5. The linear lines are plotted to show trends in boththe simulation (dotted) and the experiment (solid).cases are approximately one order of magnitude lower than thereported experimental values (about 5 ppm) as shown in Table 5.As previously mentioned the model cannot capture the inhomoge-neity in species concentrations, which leads to part of the modeldiscrepancy in predicting chemical species and temperature inthe engine. Considering the three mechanisms of NOx formation,namely fuel, prompt, and thermal, NOx formation from fuel sourcesis unlikely in this study as there is no fuel bound nitrogen. Theprompt NOx is not found in the simulation results and this agreeswith the previous nding that this mechanism was primarilyobserved in fuel-rich ames where hydrocarbon groups react withnitrogen to formHydrogen Cyanide (HCN). The only effectivemech-anism, therefore, is thermal NOx. It is interesting to see that as BVPis increased, NOx levels decrease (Fig. 7) and this is likely due to areduction in peak in-cylinder temperatures although no experi-mental temperature measurements are available to conrm this.This same trend is observed in the simulation results. The NOx trendcan be also affected by the discrepancy in predicting certain chem-ical species such as N2 and O2 under engine conditions with NVO.Again, the model has limitations in describing NVO equipped HCCIengines. Nevertheless, even though the simulation results under-predict NOx levels, the model is useful for capturing overall trendsas shown in Fig. 7.

5. Conclusions

A numerical multi-zone combustion model of an n-butanol/n-heptane fuelled HCCI engine utilizing NVO is studied. After cali-brating the model to one operating point the simulations are run topredict the pressure traces and heat release rates for a number ofoperating points which are then compared to experimental data.The model is calibrated to account for major effects of NVO onthe initial model conditions at IVC. The resulting calibrated modelproduces pressure traces and the HRR curves that closely matchthe experimental results at all ve operating points. However sim-ulated NOx emissions are under-predicted compared to the exper-iment and this is attributed to the model limitation of not beingable to capture the signicant inhomogeneity of temperature andchemical compositions in the practical engine equipped withNVO. Similarly, the unburned hydrocarbon emissions are under-predicted but their predictions are improved when the majoreffects of NVO are accounted for during model calibration. Sincethe combustion model only operates for the closed valve periodof the engine cycle, it cannot completely capture the combustionand emission effects of NVO. Nevertheless, calibration of the inputparameters of the model to account for some aspects of NVO doesresult in excellent pressure trace and HRR predictions. This sug-gests that the model can be used as a predictive tool for investigat-ing in-cylinder pressure, heat release, and combustion phasing inNVO equipped HCCI engines fuelled with the different n-butanol/n-heptane blends.

Acknowledgements

The authors of this paper would like to acknowledge the nan-cial support provided through the Auto21 project D301-DHC andthrough NSERC.

References

[1] Yao M, Zheng Z, Liu H. Progress and recent trends in homogeneous chargecompression ignition (HCCI) engines. Progress Energy Combust Sci2009;35:398437.

[2] Yamasaki Y, Kanno M, Taura Y, Kaneko S. Study on biomass gas HCCI engine.

Energy 94 (2012) 166173SAE Int 2009;32:66.[3] HaggithD, SobiesiakA,Miller L, PrzybylaG. Experimental indicatedperformance

of a HCCI engine fuelled by simulated biomass gas. SAE Int 2010;01:1081.

[4] Nathan SS, Mallikarjuna JM, Ramesh A. An experimental study of the biogas-diesel HCCI mode of engine operation. Energy Convers Manage2010;51:134753.

[5] Maurya RK, Agarwal AK. Experimental study of combustion and emissioncharacteristics of ethanol fuelled port injected homogeneous chargecompression ignition (HCCI) combustion engine. Appl Energy 2011;88:116980.

[6] Saisirirat P, Foucher F, Chanchaona S, Mounaim-Rousselle C. Spectroscopicmeasurements of low-temperature heat release for Homogeneous CombustionCompression Ignition (HCCI) n-Heptane/Alcohol mixture combustion. EnergyFuels 2010;24:54049.

[7] Saisirirat P, Togbe C, Chanchaona S, Foucher F, Mounaim-Rousselle C, Dagaut P.Auto-ignition and combustion characteristics in HCCI and JSR using 1-butanol/n-heptane and ethanol/n-heptane blends. Proc Combust Inst2011;33:300714.

[8] Dagaut P, Togbe C. Oxidation kinetics of butanol-gasoline surrogate mixtures ina jet-stirred reactor: experimental and modeling study. Fuel 2008;87:331321.

[9] Maurya RK, Agarwal AK. Statistical analysis of the cyclic variations of heatrelease parameters in HCCI combustion of methanol and gasoline. Appl Energy2012;89:22836.

[10] Fathi M, Saray RK, Checkel MD. The inuence of Exhaust Gas Recirculation(EGR) on combustion and emissions of n-heptane/natural gas fueledhomogeneous charge compression ignition (HCCI) engines. Appl Energy2011;88:471924.

[11] Wu H-W, Wang R-H, Ou D-J, Chen Y-C, Chen T-y. Reduction of smoke andnitrogen oxides of a partial HCCI engine using premixed gasoline and ethanolwith air. Appl Energy 2011;88:388290.

[12] Oakley A, Zhao H, Ladommatos N, Ma T. Experimental studies on controlledauto-ignition (CAI) combustion of gasoline in a 4-stroke engine. SAE Int2001;01:1030.

[13] Christensen M, Johansson B, Einewall P. Homogeneous charge compressionignition (HCCI) using isooctane ethanol and natural gas a comparison withspark ignition operation. SAE Int 1997:972874.

[20] Wang Z, Shuai S-J, Wang J-X. Multi-dimensional simulation of HCCI engineusing parallel computation and chemical kinetics. SAE Int 2008;01:0966.

[21] Ogink R, Golovitchev V. Gasoling HCCI modeling: computer programcombining detailed chemistry and gas exchange processes. SAE Int2001;01:3614.

[22] Mack JH, Aceves SM, Dibble RW. Demonstrating direct use of wet ethanol in ahomogeneous charge compression ignition (HCCI) engine. Energy2009;34:7827.

[23] Atkins MJ, Koch CR. The effect of fuel octane and dilutent on homogeneouscharge compression ignition combustion. Proc Inst Mech Eng Part D JAutomob Eng 2005;219:66575.

[24] Chen Z, Yao M, Zheng Z, Zhang Q. Experimental and numerical study ofMethanol/Dimethyl Ether dual-fuel compound combustion. Energy Fuels2009;23:271930.

[25] Shahbakhti M, Ghazimirsaied A, Audet A, Koch CR. Combustion characteristicsof Butanol/n-Heptane blend fuels in an HCCI engine. in: Proceedings ofcombustion institute Canadian section spring technical meeting. Ottawa;2010.

[26] Zhao F, Asmus TW, Assanis DN, Dec JE, Eng JA, Najt PM. Homogeneous chargecompression ignition (HCC) engines: key research and development issues.SAE Int 2003;03:646.

[27] Tzanetakis T, Singh P, Chen J-T, Thomson MJ, Koch CR. Knock limit predictionvia multi-zone modelling of a primary reference fuel HCCI engine. Int J Veh Des2010;54:4772.

[28] Dagaut P, Togbe C. Experimental and modeling study of the kinetics ofoxidation of Butanol-n-Heptane mixtures in a jet-stirred reactor. Energy Fuels2009;23:352735.

[29] Curran HJ, Gaffuri P, Pitz WJ, Westbrook CK. A comprehensive modeling studyof n-heptane oxidation. Combust Flame 1998;114:14977.

[30] Seiser R, Pitsch H, Seshadri K, Pitz WJ, Curran HJ. Extinction and autoignition ofn-heptane in counterow conguration. Proc Combust Inst 2000;28:202937.

[31] Naha S, Aggarwal SK. Fuel effects on NOx emissions in partially premixed

S. Visakhamoorthy et al. / Applied Energy 94 (2012) 166173 173[14] Stanglmaier RH. TWR, Souder JS. HCCI operation of a dual-fuel natural gasengine for improved fuel efciency and ultra-low NOx emissions at low tomoderate engine loads. SAE Int 2001;01:1897.

[15] Audet AD. Closed loop control of HCCI using camshaft phasing and dualfuels. Edmonton: University of Alberta; 2008.

[16] Aroonsrisopon T. Analysis of stratied charge operation and negative valveoverlap operating using direct fuel injection in homogeneous chargecompression ignition engines. Madison: University of Wisconsin-Madison;2006.

[17] Flowers D, Aceves A, Smith R, Torres J, Girard J, Dibble R. HCCI in a CFR engine:experiments and detailed kinetic modeling. SAE Int 2000;01:0328.

[18] Aceves SM, Flowers DL, Westbrook CK, Smith JR, Pitz W. A multi-zone modelfor prediction of HCCI combustion and emissions. SAE Int 2000;01:0327.

[19] Visakhamoorthy S, Tzanetakis T, Haggith D, Sobiesiak A, Wen JZ. Numericalstudy of a homogeneous charge compression ignition (HCCI) engine fueledwith biogas. Appl Energy 2012;92:43746.ames. Combust Flame 2004;139:90105.[32] Barlow RS, Karpetis AN, Frank JH, Chen JY. Scalar proles and NO formation in

laminar opposed-ow partially premixed methane/air ames. Combust Flame2001;127:210218.

[33] Flowers DL, Aceves SM, Martinez-Frias J, Dibble RW. Prediction of carbonmonoxide and hydrocarbon emissions in iso-octane HCCI engine combustionusing multizone simulations. Proc Combust Inst 2002;29:68794.

[34] Tzanetakis T. Multi-zone modeling of a primary reference fuel HCCIEngine. Toronto: University of Toronto; 2006.

[35] Heywood JB. Internal combustion engine fundamentals. New York: McGraw-Hill; 1988.

[36] Rothamer DA, Snyder JA, Hanson RK, Steeper RR, Fitzgerald RP. Simultaneousimaging of exhaust gas residuals and temperature during HCCI combustion.Proc Combust Inst 2009;32:286976.

[37] Shahbakhti M, Koch CR. Characterizing the cyclic variability of ignition timingin a homogeneous charge compression ignition engine fuelled with n-heptane/iso-octane blend fuels. Int J Engine Res 2008;9:36197.

Numerical study of a butanol/heptane fuelled Homogeneous Charge Compression Ignition (HCCI) engine utilizing negative valve overlap1 Introduction2 Numerical simulation3 Experimental data4 Results and discussion4.1 Model calibration4.2 Model limitations in predicting NVO4.3 Model predictions

5 ConclusionsAcknowledgementsReferences