76 Int. J. Powertrains, Vol. 6, No. 1, 2017 Natural gas/diesel RCCI CFD simulations using multi-component fuel surrogates Andrew G. Hockett* Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado, USA Email: [email protected] *Corresponding author Greg Hampson Woodward Inc., Loveland, Colorado, USA Email: [email protected] Anthony J. Marchese Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado, USA Email: [email protected] *Corresponding author Abstract: Previous attempts to model natural gas/diesel reactivity controlled compression ignition (RCCI) engines using single fuel component chemical kinetics have demonstrated difficulties with reproducing the gradual increase in combustion rate observed experimentally. This study investigates whether employing a multi-component vaporisation and chemical kinetics model for diesel fuel can yield closer agreement with experimental combustion rates. Multi-dimensional CFD simulations are compared against an injection timing sweep from a GM 1.9 L diesel engine modified with port injected natural gas. Using the multi-component model for both diesel vaporisation and diesel chemical kinetics resulted in a closer match with experimental heat release rate than using single component diesel chemical kinetics. However, the overly fast combustion rates at ignition could not be completely eliminated. In addition, a parameter study revealed that the simulation results are strongly sensitive to the ratio of components in the diesel fuel surrogate, the injected mass, and the injection velocity. Keywords: RCCI engine; CFD model; dual fuel; chemical kinetic mechanism; multi-component surrogate; natural gas; diesel. Reference to this paper should be made as follows: Hockett, A.G., Hampson, G. and Marchese, A.J. (2017) ‘Natural gas/diesel RCCI CFD simulations using multi-component fuel surrogates’, Int. J. Powertrains, Vol. 6, No. 1, pp.76–108. Copyright © 2017 Inderscience Enterprises Ltd. Natural gas/diesel RCCI CFD simulations 77 Biographical notes: Andrew G. Hockett is a Practicing Simulation Engineer for an internal combustion engine research consulting company. He received his PhD in Mechanical Engineering at Colorado State University in 2015. Greg Hampson is a senior principal engineer at Woodward, Inc. He received his PhD in Mechanical Engineering from the University of Wisconsin, Madison. Anthony J. Marchese is a Professor of Mechanical Engineering at Colorado State University. He received his PhD in Mechanical Engineering from Princeton University. 1 Introduction Engine manufacturers face increasingly stringent emissions regulations due to concerns over human health and the environment, specifically with regards to emissions of carbon monoxide (CO), unburned hydrocarbons (UHC), particulate matter (PM), and oxides of nitrogen (NOX). Additionally, the US Environmental Protection Agency (EPA) recently initiated regulations on greenhouse gases (GHG), such as carbon dioxide (CO2), and raised fuel efficiency standards for light duty vehicles by 2018 and heavy duty vehicles by 2025 (US Environmental Protection Agency, 2011, 2012). Therefore, engine designers are facing difficult challenges to meet emissions regulations and increase engine efficiency. One promising strategy that has received considerable attention over the years is low temperature combustion, wherein fuel premixed with excess air generates lower combustion temperatures and less NOX compared to combustion of diesel sprays and stoichiometric spark ignited (SI) mixtures (Dec, 2009). Additionally, lean premixed combustion avoids the rich regions within diesel sprays where soot is formed. Homogeneous charge compression ignition (HCCI) is a low temperature combustion method whereby compression heating of a lean premixed fuel air mixture in a high compression ratio engine initiates ignition reactions throughout the chamber nearly simultaneously. This method generates very fast combustion rates over a short duration yielding high efficiencies, but also has a narrow operating range and difficulties in combustion phasing control (Dec, 2009). In recent years some of the obstacles of HCCI have been overcome by stratifying the charge mixture with two fuels of different reactivity, known as reactivity controlled compression ignition (RCCI) (Kokjohn et al., 2009). This strategy uses a premixed low reactivity fuel (e.g., gasoline or natural gas) and stratification of a high reactivity fuel (e.g., diesel) via early direct injection. By stratifying the high reactivity fuel, ignition proceeds from areas of high mixture reactivity to areas of low reactivity, which lengthens the combustion duration, lowers the pressure rise rate and peak pressure, and allows for high loads to be achieved. Additionally, the load range can be extended by using a higher percentage of high reactivity fuel at low load and more low reactivity fuel at high load. Splitter et al. (2011) were able to achieve high efficiency RCCI operation using gasoline and diesel in a heavy duty single cylinder engine between 4 and 14.5 bar IMEP while meeting EPA emission standards for NOX without after treatment. 78 A.G. Hockett et al. The use of natural gas for RCCI operation is advantageous for multiple reasons. Firstly, as shown by Nieman et al. (2012), the difference in reactivity between natural gas and diesel is wider in comparison to that between gasoline and diesel. This attribute enables further lengthening of the combustion duration, which consequently results in lower pressure rise rate, lower peak pressures, and extension to higher engine loads. Secondly, natural gas can produce less CO2 emissions than gasoline or diesel on an energy equivalent basis, because it is composed primarily of methane (CH4), which has higher hydrogen to carbon ratio and a higher heating value. Thirdly, natural gas is expected to remain less expensive than gasoline or diesel on an energy equivalent basis for many decades to come (US Energy Information Administration, 2014). Therefore, utilising natural gas in RCCI combustion allows for a significant range of the operating map in a diesel engine to achieve higher efficiency with significantly reduced emissions and lower fuel cost. A greater understanding of RCCI combustion physics can be obtained through multi-dimensional modelling, which is necessary to explore emissions formation and develop optimal control strategies that can overcome the difficulties of combustion phasing and large pressure rise rate. Previous modelling work with gasoline/diesel RCCI has been used for optimisation of injection strategies, fuel ratios, and exploring the effects of EGR (Splitter et al., 2011; Kokjohn et al., 2009; Hanson et al., 2010; Puduppakkam et al., 2011). Many of these simulations have used n-heptane as a single component surrogate for diesel fuel, which is based on similarity incetane number and heating value. However, previous modelling studies on natural gas/diesel RCCI are limited and have demonstrated additional modelling challenges due to the wider reactivity difference between diesel and natural gas. Nieman et al. (2012) used reduced chemical kinetics in a multi-dimensional CFD simulation coupled with a genetic sorting algorithm to optimise natural gas/diesel RCCI operation over a wide range of speeds and loads, including 23 bar IMEP. The diesel fuel was modelled using single component fuel surrogates where n-heptane represented diesel fuel and the natural gas was modelled as pure methane. However, the study by Neiman et al. did not compare simulated pressure and heat release rates with experiments and so it is not possible to ascertain the accuracy of the model when using single component chemistry. HCCI and RCCI combustion is characterised by both low temperature heat release (LTHR) and high temperature heat release (HTHR) (Hanson et al., 2010). Doosje et al. (2014) showed experimentally that the increase in combustion rate for natural gas/diesel RCCI should be gradual and continuous during HTHR at high natural gas to diesel ratios. However, the modelling results in the study by Nieman et al. showed apparent heat release rate (AHRR) profiles with multiple periods of increasing heat release separated by periods of nearly constant combustion rate during what appears to be HTHR. Dahodwala et al. (2015) used multi-dimensional CFD to model natural gas diesel RCCI at 6 bar BMEP to investigate how the mixture changes as the percentage of natural gas is increased and the causes for high methane and CO emissions. The study by Dahodwala et al. also used n-heptane to model diesel fuel and methane to model natural gas. Their comparison between simulation and experiment showed that the CFD model predicted a noticeably steeper increase in heat release rate at the start of combustion than observed in experiment. Puduppakkam et al. (2011) showed that for gasoline/diesel RCCI simulations Natural gas/diesel RCCI CFD simulations 79 using multi-component gasoline chemistry was able to achieve better agreement with the AHRR profile in comparison to using a single component. It is therefore of interest to explore whether a multi-component chemistry and vaporisation model for diesel fuel and multi-component chemistry model for natural gas can produce a more gradual increase in combustion rate similar to that observed experimentally. This study seeks to explore the difficulties and sensitivities associated with multi-dimensional CFD modelling of natural gas/diesel RCCI. Simulations are compared against experimental engine data acquired from a GM 1.9 L light duty turbo-charged diesel engine modified with port injection of natural gas. Comparisons are made between using single component and multi-component diesel surrogates for vaporisation and chemical kinetics. To perform these computations, an existing multi-component diesel reaction mechanism has been combined with a newly reduced mechanism for methane, ethane, and propane, resulting in a new reduced natural gas/diesel dual fuel mechanism. A parameter study was also performed to investigate which inputs have the strongest effect on the AHRR profile. Parameters investigated include the liquid surrogate, the ratio of diesel components, injected mass, and injection velocity. Finally, the effectiveness of adding multi-zone chemistry and adaptive mesh refinement are explored. 2 Reduced chemical kinetic mechanism development Natural gas and diesel combustion chemistry was modelled using reduced chemical kinetic mechanisms. Previously, the authors presented a reduced natural gas/diesel chemical mechanism for conventional dual fuel engines, which used n-heptane for diesel fuel and methane, ethane, and propane for natural gas (Hockett et al., 2016). This mechanism, referred to as CSU141, is initially used herein to model RCCI combustion. The natural gas chemistry in CSU141 was reduced from the detailed methane through n-pentane mechanism from Healy et al. (2010) at the National University of Ireland Galway and is referred to in this work as the NUIG mechanism. The n-heptane chemistry in CSU141 was reduced from the detailed n-heptane mechanism from Curran et al. (1998, 2002). Recently Pei et al. (2015) presented a reduced chemical kinetic mechanism for a two component diesel surrogate consisting of n-dodecane and m-xylene, which consisted of 163 species and 887 reactions, and will be hereafter referred to as the LLNL163 mechanism. To use the LLNL163 diesel mechanism in the RCCI engine simulations, the natural gas chemistry from CSU141 was added. The reduction and combination procedure is shown schematically in Figure 1. The natural gas chemistry consisted of two reductions of NUIG using the direct relation graph with error propagation and sensitivity analysis (DRGEPSA). The details of each reduction are listed in Table 1, which are reproduced here from Hockett et al. (2016). The second reduction uses a target mixture with higher propane than ethane content, which was necessary to match the behaviour of the NUIG mechanism for pure propane in air. The species and reactions from these two reduced natural gas mechanisms were then appended to the LLNL163 diesel mechanism. Any duplicate reactions between the three reduced mechanisms were given the rate constant values from the LLNL163 mechanism to ensure that the kinetics of diesel ignition was preserved. Care was taken to find and add reactions from the detailed NUIG mechanism that were not in any of the individual 80 A.G. Hockett et al. reduced mechanisms, but whose species were all present after combining the reduced mechanisms. The result was a multi-component mechanism for natural gas/diesel combustion consisting of 186 species and 1,014 reactions, referred to hereafter as CSU186. Figure 1 Schematic diagram illustrating the formulation of the CSU186 multi-component dual fuel diesel/natural gas mechanism (see online version for colours) Table 1 Conditions used for reducing the detailed natural gas mechanism using DRGEPSA Base mechanism NUIG natural gas NUIG natural gas Target species CH4, C2H6, C3H8 CH4, C2H6, C3H8 Fuel composition (mole frac.) XCH4 = 0.85, XCH4 = 0.93, XC2H6 = 0.1, XC2H6 = 0.02, XC3H8 = 0.05 XC3H8 = 0.05 Temperature (K) 750, 800, 850, 900, 950, 750, 800, 850, 900, 950, 1,000, 1,050, 1,100, 1,150, 1,200 1,000, 1,050, 1,100, 1,150, 1,200 10, 30, 80 10, 30, 80 Equivalence ratio, φ 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4 Ignition delay error tolerance 0.1 0.1 Sensitivity analysis fraction 0.5 0.5 Pressure (bar) Figure 2 shows comparisons of computed ignition delay period between the CSU186 mechanism and the LLNL163-base mechanism for a diesel surrogate mixture of 70% n-dodecane and 30% m-xylene at lean and stoichiometric equivalence ratios. The excellent agreement across the range of temperatures and pressures demonstrates that the behaviour of the n-dodecane and m-xylene chemistry from LLNL163 has been retained after addition of the natural gas chemistry. A comparison with the CSU141 n-heptane mechanism for the 80 bar condition, which is most relevant to engine TDC pressure, is also shown in Figure 2. The 70% n-dodecane/30% m-xylene mixture has shorter ignition delay periods compared to n-heptane. Natural gas/diesel RCCI CFD simulations Figure 2 81 Zero-dimensional adiabatic ignition delay calculations comparing the dual fuel CSU186 to the base LLNL163 mechanism for a diesel surrogate of 70% n-dodecane/30% m-xylene in air at (a) φ = 0.5 and (b) φ = 1.0 also shown are the ignition delay periods for n-heptane at 30 and 80 bar using the csu141 mechanism (see online version for colours) (a) (b) A similar ignition delay comparison between the reduced CSU186 mechanism and the detailed NUIG natural gas mechanism is shown in Figure 3 for an equivalence ratio of 0.5 at two different natural gas compositions. One composition consists of 90% methane, 6.7% ethane, and 3.3% propane and the other is a mixture of 70% methane, 20% ethane, and 10% propane. Close agreement between the two mechanisms across a range of pressures and temperatures shows that the reduced mechanism can replicate the behaviour of the detailed mechanism at conditions indicative of engine top dead centre (TDC) conditions for different natural gas compositions. Figure 3 Zero-dimensional adiabatic ignition delay calculations comparing the reduced dual fuel CSU186 mechanism to the detailed NUIG-base mechanism for two different natural gas compositions of methane/ethane/propane in air, (a) 90%CH4/6.7%C2H6/3.3%C3H8 in air, φ = 0.5 (b) 70%CH4/20%C2H6/10%C3H8 in air, φ = 0.5 (see online version for colours) (a) (b) 82 A.G. Hockett et al. 3 Experimental procedure Engine experiments were conducted using a four cylinder light-duty General Motors (GM) 1.9 L common rail, turbo-charged diesel engine. The specifications of the engine are listed in Table 2, the diesel injector specifications are listed in Table 3, and the measured gas composition is listed in Table 4. For the experiments described herein, the engine was modified to allow multi-port injection of natural gas. This modification was accomplished by building a natural gas rail with Woodward natural gas injectors attached to the rail and connecting tubing from each injector to the intake manifold runners, such that the gas was injected perpendicular to the air flow direction. Table 2 Engine specifications Cylinder arrangement 4 Inline Displacement volume 1.9 L Bore 82 mm Stroke 90.4 mm Connecting rod length 161 mm Compression ratio Injection system Max injection pressure Valves per cylinder 17.5 Bosch common rail 1,600 bar 4 DOHC Rated power 110 kW (147.5 hp) @ 4,000 RPM Rated torque 315 Nm (232 ft-lbf) @ 2,000 RPM Table 3 Diesel injector specifications Name Bosch CRIP 2-MI Nozzles 7 Nozzle orifice diameter Included spray angle Flow specification Discharge coefficient Table 4 Species 141 micron 148 440 mm3/30 sec 0.86 Measured natural gas composition Mole fraction CH4 0.9461 C2H6 0.0285 C3H8 0.0031 CO2 0.0125 N2 0.0093 The engine was controlled by a Woodward engine control unit. The diesel fuel consumption for the engine was measured using Micro Motion CMF025 Coriolis mass flow meters on the supply and return of the fuel tank, which have an accuracy of ±0.1% of the measured rate. Natural gas consumption was measured using a Micro Motion Natural gas/diesel RCCI CFD simulations 83 CMF010 Coriolis mass flow meter, which has an accuracy of ±0.35% of the measured rate. The air mass flow rate was measured with an automotive hot wire anemometer, which has an accuracy of ±3%. In-cylinder pressure transducers (Kistler 6058A) were installed in the glow plug hole of each cylinder and pressure data were acquired using a National Instruments data acquisition system. Crank angle data were acquired with a resolution of 0.25 crank angle degrees. Pressure traces were averaged over 100 cycles for each cylinder and used to calculate the AHRRs for each cylinder, which assumed a constant ratio of specific heats of 1.3. A three-point moving average was applied to smooth out small amplitude noise in the AHRR profiles. Table 5 Experimental operating conditions. Diesel volume per cylinder (μL/st) 4.6 ± 0.2 Natural gas per cylinder (mg) 13.5 Natural gas energy share (%) 78 Engine speed (RPM) 2,000 Diesel rail pressure (bar) 700 Injection duration (ms) 0.34 Commanded start of injection (°ATDC) Boost pressure (kPa abs) –46 –48 –52 141.1 141.8 141.0 Manifold temperature (°C) 37.5 38.8 38.7 Air mass per cylinder (mg) 617.8 623.2 623.2 Net IMEP for cylinder 2 (bar) 8.77 8.81 8.72 NOx (ppm) 220 131 75 The experimental operating conditions are listed in Table 5. Only cylinder 2 was operated in RCCI mode, while the other cylinders operated using conventional dual fuel combustion, whereby the injection is near TDC. This was done to avoid the inherent controls issues with maintaining steady state with RCCI. An injection timing sweep at 8.8 bar net IMEP for cylinder 2 at 2,000 rpm using a single diesel injection was performed while keeping both fuel masses constant. The injected diesel volume listed in Table 5 is from the injector characterisation experiments described in the computational method section. The mass from the injector characterisation is preferred as a model input because it is specific to each injector and avoids the inherent inaccuracies from averaging the total engine consumption over all four cylinders. Also the injector characterisation is a direct measurement of fuel flow as opposed to fuel consumption which measures a supply and return flow, which are both much higher flow rates compared to the consumption rate. Therefore, the uncertainty in mass per cylinder from the injector characterisation experiments is much lower than the uncertainty from the consumption rate. The injector characterisation used an AVL PLU131 mass flow meter, which has a precision of 0.1%, to directly measure the mass per stroke of the injector needle. The density of the fuel is then found from a correlation with injected fuel temperature and used with the mass measurement to calculate a volume per stroke. This volume can then be used with the engine’s fuel temperature to calculate density and then estimate the mass per injection. Fuel temperature was measured in the rate tube near the injector. The actual mass estimates used are described in the computational section. The mass measured from the injector characterisation was within uncertainty of the average diesel mass per 84 A.G. Hockett et al. cylinder calculated from the engine fuel consumption. Small fluctuations in the rate tube pressure caused the mass flow measurement to fluctuate and resulted in an overall injected volume uncertainty of ±0.2 μL/st. With the uncertainty in the injected fuel temperature on the engine, the uncertainty in injected mass is estimated to be ±0.5 mg/st. 4 Computational method Computational modelling of the in-cylinder physics was performed using CONVERGE™ 2.2.0. A 1/7th sector of the cylinder geometry was used for all simulations in this work, wherein each sector included one of the injector’s seven nozzles as depicted in Figure 4. All engine sector simulations presented herein began at intake valve closure (IVC) and were run until 20 crank angle degrees ATDC. A preliminary GT-POWER simulation incorporating the experimental AHRR profile was performed to obtain the initial conditions at IVC, such as the mass of exhaust residual, wall temperatures, and the initial pressure and temperature. This 1-D simulation tool is useful for matching the experimental compression pressure when using homogenous initial conditions and constant uniform wall temperatures. The initial velocity field was obtained from a gas exchange simulation using the full cylinder geometry with intake and exhaust ports and valve motions. This provided a mapping file that was used to initialise the velocity and turbulence fields in the sector geometry. The wall temperatures in the sector simulation were adjusted slightly to better match the start of combustion. The CONVERGE™ code uses a fixed and uniform Cartesian grid and a cut cell method at the boundaries. The simulations used a maximum cell width of 1 mm. Adaptive mesh refinement down to 0.5 mm was used during the compression stroke up until start of injection (SOI). Fixed embedding with 0.25 mm cells was used throughout the entire domain from SOI to the end of the simulation. Second order central differencing was used for the surface fluxes and a fully implicit time marching method was used with a variable time step. For the single component diesel surrogate, the gas phase chemistry was modelled using n-heptane and the CSU141 mechanism. For the multi-component surrogate, diesel fuel was represented with a blend of n-dodecane and m-xylene as required by the CSU186 mechanism. For all simulations, natural gas was represented with a methane, ethane, and propane mixture, whereby all higher carbon number species were represented with propane. The assumption of a well stirred reactor (WSR) in each cell was used along with detailed chemical kinetics and a Reynolds averaged Navier-Stokes (RANS) turbulence model. The RANS turbulence model used was the renormalisation group (RNG) κ-ε model (Han and Reitz, 1995) and the turbulent heat transfer model used the law of the wall model from Amsden (1997). While the WSR assumption does not account for many of sub-grid turbulence-chemistry interactions (TCI), Pomraning et al. (2014) found that the WSR assumption with a RANS turbulence model is able to achieve grid convergence for premixed and non-premixed combustion at cell sizes of 0.25 mm and smaller and that the converged solution agreed well with experimental measurements for non-premixed jet flames and premixed propagating flames. Pomraning et al. concluded that while the TCI commutation error between cells averaged temperature and cell averaged reaction rate cannot be eliminated, in a well-resolved RANS simulation the magnitude of this error must not be as significant as other sources of uncertainty. However, this study did not investigate the TCI error for RANS simulations of Natural gas/diesel RCCI CFD simulations 85 volumetric premixed auto-ignition characteristic of HCCI and RCCI engines. Resolving the turbulent eddies may be necessary to replicate the smooth increase in combustion rate for these types of engines, but that requires a large eddy simulation (LES), which carries a very large increase in computational time. This work attempts to work with RANS types models to reduce computational cost and therefore the 0.25mm recommendation of Pomraning et al. is used. Figure 4 Computational sector geometry used for engine simulations (see online version for colours) The spray model considered in this study was that of the Lagrangian drop Eulerian fluid type. The dynamics of the spray droplets were modelled using a statistical approach where parcels are used to represent a group of identical droplets. Droplet breakup was modelled using the modified KH-RT droplet breakup model, as described in the CONVERGE™ theory manual (Convergent Science, 2014), which does not use a defined breakup length. The initial liquid surface disturbances responsible for the primary KH breakup are modelled using the KH-ACT method of Som and Aggarwal (2010), which includes effects of aerodynamics, cavitations, and turbulence. Droplet/wall interactions were modelled using the CONVERGE™ hybrid film model, which uses both particle and film height quantities to model wall impingement (Convergent Science, 2014). Collision and coalescence were modelled using the no time counter collision model of Schmidt and Rutland (2000). Collision outcomes included reflexive and stretching separation as described by Post and Abraham (2002). The Frossling correlation (Amsden et al., 1989) was used to model droplet vaporisation. The droplet surface temperature used in the Frossling correlation is found from an energy balance that assumes a homogeneous droplet temperature. For multi-component liquid vaporisation, the composition of the vapor at the droplet surface is found via Raoult’s law and the vapour pressure used in the Frossling correlation is found through Dalton’s law of partial pressures. Each liquid component vaporised into its gas phase species. Other properties for multi-component liquids are mass weighted averages. The spray model was validated against liquid and vapour penetration measurements from experiments done by the Engine Combustion Network (ECN) (2012), which consisted of a vaporising, but non-reacting spray injected into a constant volume chamber at typical TDC temperature and pressure conditions. Spray simulations were performed 86 A.G. Hockett et al. for the 500 bar injection pressure spray A condition, which is an n-dodecane spray, and the 1,500 bar injection pressure spray H condition, which is an n-heptane spray. Comparisons of liquid and vapour penetration between the simulations and the experiments demonstrated close agreement within the first millisecond (Hockett, 2015). These findings are consistent with those of Senecal et al. (2012), who employed many of the same spray sub-models and found grid convergent behaviour at a cell size of 0.25 mm. To model the diesel injection, CONVERGE™ requires a rate of injection profile or rate shape, injection duration, a discharge coefficient, and a total injected mass to determine how much mass to inject per time step. Rate shape profiles, applied current traces, time averaged mass flow, and fuel temperature were collected for each injector across sweeps of electronic pulse width at fixed rail pressure using a Bosch style rate of injection tube apparatus (Hockett, 2015). This sweep was repeated for rail pressures from 400 to 1,200 bar. These measurements also provided the dwell time between applied injector current and start of fuel flow, which determined the SOI timing in the simulations. The injector characterisation data also provided a means to determine the volume per injection for a specific electronic pulse width at a specific rail pressure. These injection experiments were conducted using an industry standard calibration fluid for diesel injection equipment, which was provided with a correlation for density with temperature to determine the volume flow rate from the mass flow measurement. For the experimental SOI timing sweep described in Table 5, an electronic pulse width of 400 μs was used for all diesel injections and the diesel rail pressure was held at 700 bar. At this condition, the injector characterisation predicts that the volume of diesel fuel per injection is 4.6 ± 0.2 microlitres per stroke (μL/st). Figure 5 Comparison of fuel liquid density with fuel temperature for the diesel liquid surrogates investigated (see online version for colours) In this study different diesel surrogate liquid properties and different injected liquid temperatures were used to determine the density and therefore the mass of diesel to use as an input to the model. Figure 5 shows a plot of density as a function of temperature for the different diesel surrogates used. The CONVERGE™ liquids property library has a built-in single component surrogate for diesel fuel called DIESEL2. However, the DIESEL2 density does not vary with temperature, unlike the other properties. To remedy this issue, the DIESEL2 surrogate was modified by adding the density correlation used for the calibration fluid in the rate of injection experiments, which is shown in Figure 5. For the multi-component diesel surrogate, the liquid density was represented by a mass Natural gas/diesel RCCI CFD simulations 87 weighted average between n-dodecane and m-xylene. The initial two-component diesel surrogate investigated consisted of 30% m-xylene and 70% n-dodecane and its density is shown in Figure 5. Figure 6 Comparison of experimental rate of injection profiles for two different injection durations (see online version for colours) Note: The rate shapes have been normalised by the steady state injection rate. Table 6 Calculated injected mass and velocity for different diesel surrogates investigated Injected volume (μL/st) Fuel temp (K) Fuel density (kg/m3) Injected mass (mg/st) Peak injection velocity (m/s) Heating value (kJ/kg) ModifiedDIESEL2 liquid, n-heptane chemistry 4.6 500 643 2.96 192 44560 70% n-dodecane/30% m-xylene 4.6 500 604 2.78 194 43190 70% n-dodecane/30% m-xylene 4.6 400 700 3.22 192 43190 75% n-dodecane/25% m-xylene 4.6 500 600 2.76 190 43350 65% n-dodecane/35% m-xylene 4.6 500 607 2.79 193 43040 70% n-dodecane/30% m-xylene 5.46 500 604 3.3 229 43190 Diesel surrogate The rate shape for the experimental condition investigated is compared with the steady state injection rate in Figure 6. The rate shape for this condition shows an injection duration of 3.5 ± 0.5 crank angle degrees. Dual fuel combustion has been shown to have higher injector tip temperatures than conventional diesel operation at high substitution due to lower diesel flow rates providing less cooling. Königsson and Stalhammar (2012) measured injector tip temperatures as high as 600 K in a dual fuel engine operating at 80% substitution and 13 bar BMEP. This high tip temperature can cause the first injected fuel to have higher temperature than that in the rail. For short injection durations such as that in Figure 6, this can imply that the average fuel temperature used as an input should 88 A.G. Hockett et al. be closer to the tip temperature (Leuthel et al., 2008). While the temperatures are lower in RCCI, the short injection duration used and high substitution is reason to use a higher fuel temperature than the measured fuel return temperature as a model input. The initial fuel temperature used in the simulations was 500 K. The injected mass and peak injection velocities for the various liquid surrogates, fuel temperatures, and injected volumes investigated in the simulations are shown in Table 6. The steady state injection velocity from the rate shape in Figure 6 was 213 m/s. 5 Results The experimental pressure and AHRR plots for the injection timing sweep are shown in Figure 7. The six crank angle degree advancements resulted in a four degree retard in CA10 timing, a six degree retard in CA50 timing, and a 7.5 degree increase in CA10-90 combustion duration. This behaviour is in agreement with other experimental studies on RCCI combustion (Splitter et al., 2011; Hanson et al., 2010). The peak LTHR and HTHR decreases as combustion phasing retards. Figure 7 (a) Experimental pressure and AHRRs for the SOI timing sweep (b) AHRRs during LTHR for the same SOI sweep (see online version for colours) (a) (b) 5.1 Single component diesel model Computational modelling was first performed for the –46° ATDC diesel injection timing case. The simulation results using the modified DIESEL2 liquid properties and n-heptane chemistry from CSU141 to represent diesel fuel are compared against experimental pressure and AHRR in Figure 8. The phasing of LTHR is slightly later in comparison to experiment, but the start of the HTHR is in close agreement. However, the CA10-90 combustion duration is longer than the experimental and the CA50 has later phasing. Additionally, there is a significant difference in the rate of change in the AHRR profile at the start of HTHR with the simulation predicting a larger and faster rise in AHRR. After the initial fast combustion rate at main ignition the simulation shows a period of nearly constant heat release rate between –8° and –6°ATDC. This is followed by a second rise in heat release rate to a maximum that is less than the experimental peak and occurs at a more retarded phasing, which causes the lower peak pressure. The simulated AHRR Natural gas/diesel RCCI CFD simulations 89 profile therefore shows two distinct events where the heat release rate is increasing, while the experimental shows a single continuous increase in combustion rate. For the single component n-heptane diesel chemistry surrogate, the faster rise in AHRR at main ignition could not be eliminated with further tuning of such parameters as injected mass, natural gas mass, exhaust residual mass, wall temperatures, turbulent kinetic energy, or swirl ratio. Figure 8 Comparison between experimental and simulated pressure and AHRR for the SOI = –46° ATDC case with the single component n-heptane chemistry used to model diesel chemical kinetics (see online version for colours) 5.2 Multi-component diesel surrogate vaporisation and chemical kinetic model The initial fast rise in AHRR at ignition could be a consequence of modelling the diesel fuel as a single component liquid, both in terms of vaporisation and the gas phase chemical kinetics. To separate these two competing effects, the simulation was initially performed using a two-component liquid surrogate of 70% n-dodecane/30% m-xylene, while the gas phase kinetics were modelled with n-heptane. The simulation was then repeated using both the two-component liquid surrogate and the CSU186 two-component chemical mechanism. Figure 9 is a comparison of simulated pressure and AHRR between the single component n-heptane diesel surrogate model from Figure 8, the two-component liquid surrogate/single component gas phase surrogate, and the two-component liquid/two-component gas phase surrogate. For all three simulations the natural gas mixture is the same. 90 Figure 9 A.G. Hockett et al. Experimental and simulated pressure and AHRR for SOI = –46° ATDC (see online version for colours) Notes: Simulation results include single component n-heptane diesel surrogate model (red lines), the two-component liquid surrogate/single component gas phase kinetics model (green lines), and the two-component liquid/two-component gas phase kinetics model (blue lines). A comparison of the red and green curves in Figure 9 shows that adding a two-component liquid surrogate changed the start of combustion phasing by one crank angle degree and consequently lowered the peak pressure. Other than the small change in start of combustion phasing, the AHRR profiles are very similar in shape and magnitude. The LTHR is still under predicted and has later phasing than the experiment. Therefore, the two-component vaporisation model did not have a dramatic effect on the AHRR. In contrast, the addition of both the two-component liquid surrogate and the two-component diesel gas phase surrogate, as shown by the blue curve, results in a LTHR profile that is in much closer agreement with experiment and also results in a peak heat release rate that is closer to the experimental peak AHRR, which leads to a closer match in peak pressure. The start of combustion phasing also has closer agreement with experiment. The initial fast rise in combustion rate at ignition is still present. However, the transition to the second period of increasing heat release rate occurs sooner and increases at a faster rate, which is closer to the experimental result. The fact that the fast increase in combustion rate at ignition could not be reduced with the two-component liquid surrogate/ two-component gas phase surrogate for diesel fuel suggests that it is caused by other effects not accounted for in this model, such as turbulence chemistry interactions and error in the convection and diffusion rates of the injected fuel affecting the air/fuel ratio distribution. In-cylinder images of local equivalence ratio and temperature from the simulations in Figure 9 are compared in Figure 10. The top row of images shows a cross-section plane coloured by equivalence ratio just before the start of LTHR at –20° ATDC for each of the three diesel surrogate models. The middle and bottom rows show equivalence ratio and temperature, respectively, for each of the three diesel surrogate models at –12° ATDC, which is just prior to HTHR. For all three diesel surrogate cases, a vapour plume of injected fuel travels towards the liner wall it rotates counter-clockwise through the periodic boundaries due to the swirl motion. A volume of rich mixture is apparent at the leading edge of the injected fuel. The presence of injected fuel in the squish region prior to ignition agrees with optical observations of RCCI made by Kokjohn et al. (2012). Natural gas/diesel RCCI CFD simulations 91 Figure 10 Comparisons of planar cross-sections of local equivalence ratio and temperature for the three diesel surrogates investigated (see online version for colours) Notes: The top row of images shows equivalence ratio prior to LTHR. The middle and bottom rows show equivalence ratio and temperature, respectively, prior to HTHR. At –20° ATDC, the single component model has the largest volume of rich mixture, which is located closer to the liner showing greater vapour penetration. The larger volume of rich mixture can be explained by referring to Table 6, where it is seen that, for liquid fuel temperature of 500 K, the mass of injected fuel is highest for the modified DIESEL2 liquid surrogate due to its higher density. The greater injected fuel mass creates more LTHR as indicated by the higher gas temperature in the bottom row of images of Figure 10. Comparing the two-component liquid surrogate cases shows that the fuel distributions are nearly identical, but the case with the 70% n-dodecane gas phase kinetics has a larger region of high temperature than the case with n-heptane gas phase kinetics. This result is due to n-dodecane being more reactive than the n-heptane as indicated by the ignition delay comparison in Figure 2, which results in more energy released during LTHR. Therefore, the higher reactivity of n-dodecane in the two-component gas phase surrogate results in closer agreement with the experimental AHRR profile during LTHR in terms of phasing and magnitude. 92 A.G. Hockett et al. Figure 11 (a) Planar cross-section of equivalence ratio distribution with a line showing where data was extracted. Variation in (b) equivalence ratio, (c) temperature, and (d) heat release rate are presented at different crank angles as a function of distance along the white line with the origin at the white dot in the lower right hand side of the plane (see online version for colours) (a) (b) (c) (d) Figure 11(a) shows a line through the rich region of injected fuel for the two-component liquid and gas phase diesel surrogate model. Figure 11(b) and Figure 11(c) plot the equivalence ratio and temperature across this line prior to ignition at –12° ATDC and just after ignition at –10° ATDC. The white dot marks the origin for distance in the plots. The peak in temperature (1,150 K) along this line at –12° ATDC is a result of the LTHR as explained in Figure 10. The temperature of the natural gas far from any injected diesel fuel at this time is approximately 900 K due to compression. Because the natural gas is assumed to be homogeneous, the gradient in equivalence ratio is a consequence of the gradient in diesel fuel concentration and therefore mixture reactivity. The results suggest that just prior to ignition, large spatial gradients exist in the diesel fuel concentration (and therefore equivalence ratio) and large homogeneous regions of diesel fuel concentration are not observed. Therefore, the overly fast combustion rate at ignition is not likely due to the model’s inability to generate a gradient in gas mixture reactivity. Figure 11(d) shows profiles of local heat release rate along the same line from Figure 11(a) at different crank angles during ignition. During this period, the gas is slowly travelling to greater distances Natural gas/diesel RCCI CFD simulations 93 along the line due to the counter-clockwise swirl motion. Heat release begins at the richest location as seen by the profile at –11.5° ATDC. Then at –11° ATDC, the heat production grows to a nearly constant value from 7 to 9 mm. The heat release rate near 7 mm stays constant at about 50 W/mm3 between –11.5° and –11° ATDC, but then increases at –10.5°. Between –10.5° and –10° ATDC, the gas between 8 and 10 mm releases all of its energy from combustion reactions thereby demonstrating a nearly simultaneous volumetric ignition. The profile at –10° ATDC also shows two thin reaction zones, which subsequently propagate into the unburned gas. It is the fast combustion between –10.5° and –10° ATDC that is responsible for the sudden rise in global AHRR at ignition seen in Figure 9. The flat heat release rate profile at –11° ATDC and the nearly simultaneous ignition between 8 and 10 mm in Figure 11(d) are unexpected behaviours because there is a gradient in temperature and equivalence ratio across this region that should create a more sequential ignition process. The equivalence ratio and temperature profiles in Figure 11(b) and Figure 11(c) show that the gas located near the highest concentration of injected fuel is between 900 and 1100 K and has an equivalence ratio between 0.9 and 1.1. Referring to the ignition delay plots in Figure 2 shows that the ignition delay periods decrease substantially at temperatures greater than 1,000 K and for near stoichiometric mixtures at this pressure (55 bar) the ignition delay periods become close to one crank angle at 2,000 rpm (0.08ms). Moreover, the difference in ignition delay between the stoichiometric mixture and the φ = 0.5 mixture at these temperatures and pressures is a fraction of a crank angle. While the ignition delay calculations are not truly representative of the time temperature history of the gas in the engine, the ignition delay calculations demonstrate that having a volume of the mixture near stoichiometric and at temperatures greater than 1,000 K can create very similar chemical kinetic reaction rates that are fast enough relative to one crank angle degree to behave like a simultaneous ignition. Because such a simultaneous ignition was not observed in experiment, it is possible that the model could be over predicting the maximum equivalence ratio at the time of ignition. Optical RCCI engine experiments by Kokjohn et al. (2012) did not observe any fuel rich regions in the squish region for a similar injection timing of –50° ATDC and instead measured a maximum equivalence ratio of 0.5. It is unclear if the mixture differences between the optical experiments of Kokjohn et al. and this model are due to differences in engine geometry and operating conditions or a consequence of shortcomings in the RNG κ-ε turbulence model to accurately capture turbulent mixing rates in the squish region. Figure 12 shows the progression of a heat release rate iso-surface of 30 W/mm3, which represents the boundary between the burned and unburned regions of the gas. The period of nearly constant heat release rate after ignition occurs from –8° to –6° ATDC. The image at –8°ATDC in Figure 12 shows the burned region has consumed most of the region where the injected fuel was concentrated. The image at –5° ATDC shows that burned region has expanded into the bowl region and therefore increased in surface area significantly. A larger surface area over which combustion reactions occur generates higher global AHRR, which is confirmed by the acceleration in AHRR in Figure 9 beginning around –5° ATDC. Therefore, the period of nearly constant AHRR is caused by a lack of injected fuel available to burn and continues until the burned region expands into the piston bowl. This suggests that the model could be over predicting the vapour penetration into the squish region. 94 A.G. Hockett et al. Figure 12 Time progression of heat release rate iso-surfaces for different diesel surrogates (see online version for colours) The two-component chemistry model had a higher peak AHRR than the other two models which was in closer agreement with the experimental peak AHRR. The explanation for the faster heat release can be explained in Figure 13. The image at –12° ATDC depicts the distribution of the injected fuel prior to HTHR and clearly shows injected fuel present in bowl region due to the tail end of the vaporising spray. The local heat release rate and equivalence ratio are shown at –1° ATDC, which is when the differences in global AHRR are apparent in Figure 9. The model with the two-component diesel chemical kinetics surrogate clearly shows ignition reactions occurring in the gas between the burned regions whereas the two models using n-heptane for diesel chemical kinetics does not. The equivalence ratio image shows that three diesel surrogate models have essentially equal concentration of injected fuel in the bottom of the bowl at this time. This additional volumetric heat release causes faster propagation of the burned region. Therefore, the faster acceleration in AHRR and higher peak AHRR observed for the two-component gas phase surrogate is due to the more reactive chemical kinetics of n-dodecane in comparison to n-heptane causing volumetric heat release rate throughout the bottom of the piston bowl. Natural gas/diesel RCCI CFD simulations 95 Figure 13 Cross-sectional planes showing the distribution of injected fuel at –12° ATDC and local heat release rate and equivalence ratio in the bottom of the piston bowl at –1° ATDC for the different diesel surrogates (see online version for colours) 5.3 Ratio of diesel fuel components The effect of changing the ratio of n-dodecane to m-xylene in the diesel surrogate is investigated in Figure 14. The 70% n-dodecane/30% m-xylene simulation from Figure 9 was used as base case. A comparison was made between the base case and a 65% n-dodecane/35% m-xylene diesel surrogate and a 60% n-dodecane/30% m-xylene surrogate. The volume of injected fuel was kept constant and the injected mass varied by less than 1% as detailed in Table 6. As more n-dodecane is added to the diesel surrogate the start of combustion advances and there is greater energy released during the fast combustion rate at ignition. This leads to a larger difference in pressure rise rate at start of combustion. The experimental peak AHRR, CA50 timing, peak pressure, and combustion duration are most closely matched by the 75% n-dodecane/25% m-xylene case. In addition, 75% n-docecane surrogate provides the closest prediction to the experimental AHRR between –5° and 5° ATDC, which represents the majority of the combustion duration. Furthermore, the peak AHRR during LTHR is more closely matched by the 75% n-dodecane case even though the phasing of the LTHR peak is about 1 crank angle degree more advanced than the experimental peak. The 65% n-dodecane/ 96 A.G. Hockett et al. 35% m-xylene has the most gradual increase in combustion rate at ignition, which tends to agree with the experimental rise in AHRR. However, the 65% n-dodecane case has a more retarded start of combustion and CA50 phasing and consequently has lower peak AHRR and peak pressure. For the purpose of matching general engine performance metrics, such as CA50, peak pressure, peak AHRR, and combustion duration, the 75% n-dodecane/25% m-xylene case predicts the closest match with experiment. The results from Figure 14 demonstrate that the combustion phasing and consequently the engine performance predicted by this RCCI model is sensitive to a 5% change in the ratio of diesel surrogate components. This presents a challenging obstacle for RCCI modelling as the ideal ratio of n-dodecane to m-xylene to use as a surrogate could vary from one diesel fuel sample to another. Figure 14 Comparison of pressure and AHRR as the ratio of n-dodecane to m-xylene in the diesel surrogate is changed (see online version for colours) The mass fraction distribution of the two diesel fuel components for the n-dodecane percentage sweep are shown in Figure 15 at –20° ATDC, which is prior to start of LTHR, and at –14°ATDC, which is after LTHR but before HTHR. The image at –20° ATDC clearly shows that the location of the richest region is nearly equivalent in all three cases with the peak concentration of n-dodecane scaling with the percentage of n-dodecane used for liquid diesel surrogate. Examination of the equivalence ratio at –20° ATDC shows equivalent distribution between all three surrogate mixtures, thereby indicating that differences in combustion behaviour are due to mixture reactivity and not air/fuel ratio. The images at –14° ATDC shows that as the n-dodecane percentage in the diesel surrogate increases the volume of n-dodecane consumed in the richest region during Natural gas/diesel RCCI CFD simulations 97 LTHR increases and so too does the peak temperature. This agrees with the greater peak AHRR during LTHR seen in Figure 14. The higher peak temperature at this location then leads to the more advanced phasing of the HTHR ignition. Furthermore, the model with 75% n-dodecane has a larger volume of gas with a temperature greater than 1,000 K, which creates a larger volume of nearly simultaneous auto-ignition and results in the larger rise in AHRR at ignition seen in Figure 14. The images in Figure 15 demonstrates how the reactivity of the diesel surrogate employed strongly affects the energy released during LTHR, which in turn determines the start of combustion phasing for HTHR and consequently the engine performance predicted. Figure 15 Cross-section planes showing the distribution of injected fuel and temperature showing where LTHR has occurred as the ratio of n-dodecane to m-xylene is changed (see online version for colours) 98 A.G. Hockett et al. The simulation results demonstrated that as percentage of n-dodecane in diesel surrogate increased, there was a faster acceleration in AHRR that resulted in a higher peak AHRR. This trend can be explained in Figure 16, which depicts a cross-sectional plane of temperature at –5° ATDC when the global AHRR begins accelerating. The unburned gas temperature is higher for a higher percentage of n-dodecane because the start of combustion phasing is more advanced and therefore a greater portion of the combustion occurs during compression. The greater temperatures in the piston bowl cause earlier volumetric heat release throughout the bottom of the piston bowl as seen in the image at – 2° ATDC. With earlier onset of heat release throughout the piston bowl, compression heating helps keep the temperatures high, yielding faster reaction rates and faster propagation of the burned region into the piston bowl. Therefore, differences in the acceleration of AHRR and resulting peak AHRR between the three simulations are due to differences in combustion phasing, which is a result of the differences in reactivity between the three diesel surrogates. Figure 16 Distributions of temperature and local heat release rate as the ratio of n-dodecane to m-xylene is changed (see online version for colours) 5.3 Sensitivity to injected diesel mass The injector characterisation experiments allow for the injected mass to be known within ±0.5 mg thus allowing for a range of values to use as a model input. Figure 17 explores the sensitivity of the injected mass by comparing the base case of 4.6 μL per injection to a case with 5.4 6 μL per injection. Using the density at 500 K, these injected volumes correspond to 2.78 mg and 3.3 mg per injection respectively, which spans the range of uncertainty in injected mass. Both of these cases used the two-component diesel surrogate with 70% n-dodecane and 30% m-xylene. Because the injection duration was Natural gas/diesel RCCI CFD simulations 99 kept constant, the 3.3 mg/st case has a higher peak injection velocity of 229 m/s compared to the 194 m/s for the 2.78 mg/st case. The greater diesel injection has greater LTHR, which leads to earlier start of HTHR and greater energy released at ignition. Later the AHRR profiles for the two simulations become nearly equal to each other from –8° to –1° ATDC. In general, a small change of 0.5 mg, which is within the uncertainty of the measurement, has advanced the start of combustion by 1.5 crank angle degrees and increased the peak pressure by 13 bar, thereby demonstrating that this RCCI model is sensitive to the injected mass. Figure 17 Comparisons of pressure and AHRR as injected mass is increased and injection duration is held constant (see online version for colours) The fuel and temperature distribution between LTHR and HTHR is shown in Figure 18 at –14° ATDC. The fuel in the 3.3 mg/st case has penetrated farther due to the greater injection velocity. The further penetration leads to the highest concentration of fuel being near the liner wall in the squish region. Once the rich volume of the plume reaches the wall there is further enrichening as upstream fuel continues to travel towards the same location. The larger volume of rich mixture leads to more LTHR at this location. Also the injected fuel is distributed over a larger volume. The iso-surface of local heat release rate at TDC shows that more of the squish region has been consumed and the surface area of the burned region is larger. Therefore, the higher peak AHRR for the 3.3 mg/st case is due to faster consumption of the natural gas in the squish region. 100 A.G. Hockett et al. Figure 18 In-cylinder images for the two different injected masses (see online version for colours) 5.4 Sensitivity to injection velocity The model has been shown to be sensitive to the injected mass, but as injected mass was changed the injection velocity also changed because the injection duration was held constant. Therefore, it is of interest to explore how sensitive the model might be to injection velocity when the injection mass is held constant. In order to achieve higher injection velocity the discharge coefficient in the base case was lowered from 0.86 to 0.7, which changed the peak injection velocity from 194 m/s to 228 m/s. Attempts to increase the discharge coefficient were not able to appreciably increase the injection velocity. Therefore, the injection duration was increased from 3.5 crank angle degrees to 4.2 crank angle degrees, which lowered the injection velocity from 194 m/s to 160 m/s. This change in injection duration is only slightly greater than the reported uncertainty range of ±0.5 crank angle degrees. A comparison of the pressure and AHRR between the simulations is presented in Figure 19. The combustion rates during LTHR are essentially equal between the cases as is the main combustion timing. However, the period of nearly constant combustion rate after ignition is very different between the three simulations, with the higher injection velocity having a longer period of constant AHRR. The differences in the period of constant AHRR also causes the CA50 timing to vary between the three simulations. The slowest injection velocity case appears to have the closest agreement with the gradual increase in AHRR and therefore the closest agreement in pressure rise rate. The results of Figure 19 demonstrate that the model is sensitive to injection velocity. Natural gas/diesel RCCI CFD simulations 101 Figure 19 Comparisons between pressure and AHRR as injection velocity is changed and injection mass is held constant (see online version for colours) Note: The base case uses the 70% n-dodecane/30% m-xylene diesel surrogate. In order to explore the cause for the difference in the predicted CA50 timings, a comparison of heat release rate iso-surfaces of 30 W/mm3 are presented in Figure 20. The image at –11° ATDC shows the ignition location which is located where the richest mixture of injected fuel exists. As the injection velocity is increased the vapour penetration into the squish region increases. The ignition location for the slowest injection velocity case does not occur in the squish region and instead occurs near the bowl rim. This leads to a dramatic difference in how the rest of the burned region grows. The image at –5°ATDC shows that the highest injection velocity case has a burned region that is beginning to expand into the piston bowl, the base case has expanded further into the bowl, and the slowest injection velocity case has grown significantly into the bowl, but has not yet burned into the squish region. These differences in surface area of the burned region correspond to the differences in global AHRR seen in Figure 19. Examination of Figure 20 at 5°ATDC, when the AHRR is decreasing for all three cases, shows that the highest injection velocity case burned the furthest into the squish region. At this time, all three cases have burned a similar volume of gas in the bowl region. Therefore, the highest injection velocity case has the largest surface area and consequently a higher global AHRR. The differences in burn behaviour between the highest and lowest injection velocity cases are significant, because the initial growth in the burned region occurs in the squish region for the highest injection velocity case and in the bowl region for the lowest injection velocity case. Given that the lowest injection velocity case has closer agreement with the experimental rise in AHRR, it is likely that 102 A.G. Hockett et al. injection duration of 4.2 degrees might be more accurate than the 3.5degree measurement taken from the rate shape. This is possible as the uncertainty range allows for injection duration as long as 4.0 degrees. The results of Figure 20 show that the location of ignition is an important factor that it is sensitive to the injection velocity. For the short injection duration used in this experiment, the value for injection duration needs to be known with greater accuracy than ±0.5 crank angle degrees in order to predict the injection velocity correctly. Figure 20 Comparison between heat release rate iso-surfaces of 30 W/mm3 at different injection velocities (see online version for colours) 5.5 Use of multi-zone chemistry and adaptive mesh refinement The simulations presented thus far have not included any speed up techniques and so the computational time has been expensive. One such speed up technique is multi-zone chemistry whereby cells with similar temperature and composition are solved for simultaneously by the chemistry solver and the solution is remapped to the cells. This procedure reduces the number of calls to the chemistry solver, which can often be the most computationally expensive part of each time step. However, because RCCI is a chemical kinetics dominated combustion regime, the simulation results can be sensitive to how the multi-zone chemistry method chooses cells to be similar in temperature and composition. Another speed-up technique is the use of adaptive mesh refinement (AMR), Natural gas/diesel RCCI CFD simulations 103 which only refines the grid where gradients in temperature, velocity, and/or species mass fraction are above a specified tolerance. Thus a larger-base cell width can be used throughout large portions of the domain and the total cell count is reduced. To test these speed-up techniques the same base case from Sections 5.4 and 5.5 was repeated first with multi-zone chemistry, then with AMR, and finally with both multi-zone and AMR. Figure 21 Comparison between simulations with and without multi-zone chemistry and adaptive mesh refinement (see online version for colours) Table 7 A multi-zone chemistry strategy for RCCI simulations Multi-zone bin: temperature (K) 5 Multi-zone bin: progress φ 0.001 Multi-zone bin: n-C12H26 mass fraction 0.0001 Multi-zone bin: m-xylene mass fraction 0.0001 Multi-zone bin: HO2mass fraction 0.0001 Multi-zone bin: OH mass fraction 0.0001 Table 8 An adaptive mesh refinement strategy for RCCI simulations AMR dimension Sub-grid error tolerance velocity (m/s) 0.1 temperature (K) 0.5 n-dodecane mass fraction 0.0001 Methane mass fraction 0.0001 m-xylene mass fraction 0.0001 Figure 21 shows the base simulation compared to a simulation using the multi-zone chemistry strategy in Table 7 and the AMR strategy in Table 8. The simulations using only multi-zone and only AMR are not presented for conciseness, but the agreement was similar that in Figure 21. The agreement between the two simulations in Figure 22 is excellent and the computational time was reduced from 47.2 hours to 16.4 hours when using 64 cores. The AMR strategy reduced the maximum cell count from 986,290 cells to 313,868 cells. The agreement in Figure 21 was obtained after multiple iterations using different multi-zone bin sizes and bin dimensions and using different AMR refinement tolerances. It was found that species-based AMR was necessary to obtain agreement in the start of combustion phasing. Also including more species in the multi-zone strategy 104 A.G. Hockett et al. did not always yield more accurate results. Therefore the model can be sensitive to the multi-zone and AMR strategy employed. Future work is still needed to verify that the speedup strategies presented will perform well at other diesel to natural gas fuel ratios and different overall equivalence ratios. Figure 22 Comparison between experimental data and simulation prediction for the SOI = –52° ATDC case using the 75% n-dodecane/25% m-xylene diesel surrogate (see online version for colours) 6 Simulation Results for SOI = –52° ATDC The 75% n-dodecane/25% m-xylene surrogate in Figure 14 had the closest agreement with the experimental start of combustion, peak pressure, CA50, and combustion duration. For this reason the 75% n-dodecane surrogate was used to model the more advanced injection timing case of SOI = –52° ATDC, which is compared with experimental pressure and AHRR in Figure 22. A slight variation in initial temperature and pressure was applied to the inputs files to reflect the slight change in trapped air mass as described in Table 5. The piston and head temperatures were reduced by 30 K because this more retarded combustion phasing had lower load and therefore lower peak gas temperatures. The lower gas temperatures also result in less heat transfer to the injector tip and so the injected fuel temperature was lowered to 400 K. Figure 22 shows that the phasing and magnitude of LTHR are well captured by the model. The start of combustion is close to the experimental, but again the simulation shows a much more rapid increase in combustion rate at ignition than seen in experiment. The pressure rise rate and the combustion duration agree well with experiment. The peak heat release is lower than Natural gas/diesel RCCI CFD simulations 105 experiment leading to a 4 bar lower peak pressure and lower pressure during expansion. Overall, the agreement with experimental pressure and AHRR shows that the model is able to capture the changes in pressure and AHRR due to injection timing change. 7 Conclusions This study investigated multi-dimensional CFD modelling of natural gas/diesel RCCI by incorporating multi-component fuel surrogates for both diesel and natural gas. This necessitated the formulation of a new chemical kinetic mechanism for natural gas and diesel dual fuel combustion, which represents diesel fuel as a blend of n-dodecane and m-xylene and natural gas as a mixture of methane, ethane, and propane. The n-dodecane and m-xylene chemistry was taken from an existing reduced mechanism from Pei et al. (2015) and the natural gas chemistry was reduced from the detailed mechanism from Healy et al. (2010). Experimental RCCI data from a SOI timing sweep on a light duty diesel engine was modelled using different combinations of the multi-component and single component vaporisation and chemical kinetics models for diesel fuel. Model sensitivity studies were then performed on the ratio of diesel surrogate components, the injected mass, and the injection velocity. Finally, a multi-zone chemistry and adaptive mesh refinement strategy was investigated to assess the accuracy of adding these speed up techniques. These comparison studies resulted in the following conclusions. 1 Introducing a two-component diesel surrogate did not eliminate the large sudden increases in combustion rate at ignition. In-cylinder images from the simulations showed a volume of concentrated diesel fuel vapour in the squish region that demonstrated a nearly simultaneous ignition throughout. While, there was a gradient in equivalence ratio and therefore mixture reactivity throughout this volume, the equivalence ratios were close to stoichiometric causing fast reaction rates with differences in ignition time across the volume of less than one crank angle. Therefore, the model employed in this work could be under predicting the turbulent mixing rates of the injected fuel vapour and consequently over predicting the peak concentration of high reactivity fuel. 2 Applying the two-component liquid vaporisation and two-component gas phase chemical kinetics diesel surrogate resulted in closer agreement with the experimental peak AHRR, combustion duration, and phasing of LTHR compared to the single component vaporisation and single component gas phase chemical kinetics model. Including multi-component chemical kinetics had a more appreciably change in the predicted AHRR than including multi-component vaporisation. 3 The simulations showed a period of nearly constant combustion rate after ignition. This period becomes longer as the location of ignition begins further into the squish region because the combustion zone must propagate a further distance to reach the piston bowl, where the combustion surface area is able to expand greatly. 4 Volumetric heat release occurs in the bottom of the piston bowl when using the two-component diesel chemistry model. This was not seen when using the single component surrogate due to the lower reactivity of n-heptane compared to n-dodecane. This volumetric heat release was responsible for the closer agreement with the experimental peak AHRR. 106 A.G. Hockett et al. 5 The RCCI model was shown to be sensitive to the ratio of diesel surrogate components. A 5% change in the ratio of diesel components resulted in a two crank angle degree advancement in start of combustion timing. This advancement in start of combustion increased caused faster volumetric heat release in the piston bowl resulting in higher peak AHRR. The 75% n-dodecane mixture yielded the closest agreement with start of combustion phasing, peak AHRR, combustion duration, and peak pressure for both injection timings. 6 The RCCI model was shown to be sensitive to injected mass. A 0.5 mg change in injected mass resulted in a 1.5 crank angle degree advancement in ignition timing and an increase in peak pressure of 13 bar. Given that the injected mass measurement has an uncertainty of ±0.5 mg/st, greater accuracy in the diesel mass measurement is needed at the condition investigated. 7 The RCCI model was shown to be sensitive to injection velocity, because it strongly effects the location of ignition. A lower injection velocity obtained using a longer injection duration of 0.75 crank angles degrees resulted in the initial growth of the burned zone occurring in the piston bowl region rather than the squish region, which yielded closer agreement with the experimental rise in AHRR. Given that the injection duration has an uncertainty of ±0.5 crank angle degrees, the accuracy of the injection duration measurement needs improvement at the condition investigated. 8 A multi-zone chemistry and AMR strategy is presented which yielded very small error in the pressure and AHRR and had a computational time savings of 65%. This required dimensions of chemical species in both the multi-zone and AMR strategies. The accuracy of this strategy needs further validation at other diesel to natural gas ratios, different loads, and different air/fuel ratios. Future work should investigate the validity of the spray model to predict vapour penetration with long mixing times when using short duration pulse injections, because the transient behaviour of the injector has a large impact on the distribution of the high reactivity fuel. This will require experimental spray data from high speed imaging and laser diagnostics of short pilot injections with long mixing times. 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