Click to edit Master subtitle style - LOGEsoft...Click to edit Master subtitle style [1] Lehtiniemi,...
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[1] Lehtiniemi, H., Mauss, F., Balthasar, M., Magnusson, I., “Modeling Diesel Spray Ignition using Detailed Chemistry with a Progress Variable Approach”, Comb. Sci. and Tech. 178: 1977-1997 (2006). [2] LOGE AB, www.logesoft.com, 2018. [3] Matrisciano, A., Franken, T., Perlman, C., Borg, A. et al.., “Development of a Computationally Efficient Progress Variable Approach for a Direct Injection Stochastic Reactor Model”, SAE Technical Paper: 2017-01-0512 (2017). [4] Seidel, L., Moshammer , K., Wang, X., et al., „Comprehensive kinetic modeling and experimental study of a fuel -rich, premixed n heptane flame”, Combustion and Flame 162: 2045-2058 (2015). [5] Zeuch, T., Moréac, G., Ahmed, S.S., Mauss, F., “A comprehensive skeletal mechanism for the oxidation of n-heptane generated by chemistry-guided reduction”, Combustion and Flame 155: 651-674 (2008). [6] Engine Combustion Network, „ECN Data Search page." [online]. available: https://ecn.sandia.gov/ecn -data-search/?nam=1. Last checked: 29.11.2018. [7] Richards, K.J., Senecal, P.K., and Pomraning, E., "CONVERGE (v2.4), Convergent Science, Inc. ", Madison, WI, 2018. [8] Vishwanathan, G., Reitz, R. D., “Development of a practical soot modeling approach and its application to low-temperature diesel combustion”, Combust. Sci. Technol. 182:1050-1082 (2010). Introduction A reaction mechanism describes the combustion of a surrogate fuel and accounts for chemical and physical properties of the commercial fuel. The use of complex detailed reaction mechanisms in 3D Computational Fluid Dynamic (CFD) simulations can lead to a high demand of computational costs. One possible solution to reduce these costs is to use tabulated chemistry methods. In this work, two applications predicted using the detailed chemistry solver SAGE and the tabulated combustion progress variable (CPV) approach are presented. Good agreement between the two models are found for a diesel engine sector case and the Spray A from the Engine Combustion Network (ECN). Figure 2 shows the temperature and O2-mass fraction profile of SAGE and CPV at three different time steps. The overall behaviour is similar. The ignition behaviour is unexpected. Both cases ignite next to the spray cone, not at the tip of the spray. The CPV predicts higher temperatures and less O2 in the same regions. A possible reason for this discrepancy is the thermodynamic treatment within CPV, where only 19 species are available. This treatment will be investigated further in future. Figure 3 shows the prediction of the pressure and rate of heat release (RoHR) for the engine sector case for both EGR levels and combustion models. The prediction of both combustion models agrees well. As expected, a higher EGR amount leads to a lower mean pressure and RoHR. The temperature profile (Figure 4) shows an owerall good agreement. 1 st INTERNATIONAL CONFERENCE ON SMART ENERGY CARRIERS, Napoli, Italy, 21 – 23 January, 2018 Further Application of the Fast Tabulated CPV Approach Adina Werner 1 , Andrea Matrisciano 2,4 , Corinna Netzer 1 , Harry Lehtiniemi 2 , Anders Borg 2,4 , Lars Seidel 3 , Fabian Mauss 1 1 Brandenburg University of Technology, Cottbus, Germany| 2 LOGE AB, Lund, Sweden | 3 LOGE Deutschland GmbH, Cottbus, Germany | 4 Chalmers University of Technology, Gothenburg, Sweden Conclusions The tabulated combustion progress variable approach leads to good and reasonable results compared to the SAGE detailed chemistry solver and the experiment. The tabulated approach decreases the CPU times by factor 13 for the n-dodecane mechanism and by factor 4 for the smaller n-heptane mechanism. Future work will include emission prediction and validation. The CPV model The CPV model assumes that a progress variable C can be used for the reconstruction of the thermo-chemical state on the whole reaction trajectory. C is defined as a function of the chemical enthalpy h 298 [1]: = ℎ 298 −ℎ 298,0 ℎ 298, −ℎ 298,0 The look-up tables were generated with LOGEtable [2] using adiabatic homogeneous constant pressure reactors. The generated table replaces the chemistry solver in the CFD code [3]. Only 19 CPV species are transported and used to calculate the thermo- dynamics of the gas phase. Figure 1: Comparison of the predicted pressure and liquid penetration length using SAGE and CPV versus experiment. Figure 2: Temperature and O 2 -mass fraction profile of SAGE (left) and CPV (right) at 0.2 ms, 0.425 ms and 0.725 ms. Figure 3: Predicted pressure and chemical RoHR of SAGE and CPV for different EGR amounts (no EGR: 0%; EGR: 30%). Correspondence to: [email protected] 0 CFD Code Transport of • Chemical enthalpy H 298 • Mixture fraction Z • Mass fraction EGR Y EGR • Species for thermodynamics • Emissions Extra output species are assigned as passives CPV Model Combustion Well-stirred reactor model [8, 9] with source terms from the CPV table Soot Method of Moments M 0 and M 1 [10, 11, 12] NO x Thermal NO Table look-up parameters • p, T, ϕ , Y EGR Update of sources • H 298 , Z, Y EGR Species / emission update n-dodecane [4] n-heptane [5] Species 487 121 Reactions 2331 594 Property Range Grid points Range Grid points EGR [%] 0.0 – 40.0 5 0.0 – 40.0 5 Equivalence ratio [-] 0.2 - 10.0 25 0.2 - 10.0 25 Pressure [bar] 1.0 - 200.0 18 1.0 - 200.0 24 Unburnt temperature [K] 250.0 - 1500.0 101 300.0 - 1500.0 89 Simulation Setup The Spray A from the ECN [6] is modelled using the 3D CFD Code CONVERGE [7]. The chosen geometry is a cube with an edge length of 10.8 cm. The turbulence is predicted using the Reynolds Averaged Navier-Stokes (RANS) model. To different combustion models are applied: the SAGE detailed chemistry solver [7] to solve the chemistry on the fly and the CPV model [3]. Further, a diesel engine sector case (137 mm bore, 165 mm stroke and 263 mm connecting rod) is modelled. The engine operates at 1600 rpm and the fuel is injected as single injection at 9° CA bTDC. Two different cases with no EGR and 30 % EGR amount were compared. Table 1: Used reaction schemes and CPV table ranges. Number of species SAGE CPU time [h] CPV CPU time [h] 487 169.98 13.74 121 7.66 / 8.23 2.00 / 2.16 Table 2: Comparison of CPU times SAGE vs CPV and 487 species vs 121 species. Results and Discussion Figure 1 shows the predicted pressure of the detailed chemistry solver SAGE and the CPV model versus the experimental pressure for Spray A. The pressure is well predicted by both combustion models. The first differences occur at 0.2 ms, where the first liquid parcels leave the fixed cell cone. The deviation between SAGE and CPV may be caused by differences in the mesh refinement and time stepping. [mm] SAGE CPV 2200 Temp [K] 2100 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 900 800 700 600 4 8 12 16 24 28 32 36 0.16 yO2 [-] 0.135 0.11 0.085 0.06 0.035 0.01 CPV SAGE 12 fix mesh end 20 [mm] 36 32 28 24 8 4 0 SAGE CPV SAGE CPV CA 10 CA 50 CA 10 CA 50 Figure 4: Temperature profile of SAGE and CPV for no EGR (left) and 30% EGR (right) at two different time steps. Temp [K] 2500 2275 2050 1825 1600 1375 1150 925 700 [9] Pang, K. M., Jangi, M., Bai, X.-S. and Schramm, J., “Evaluation and optimization of phenomenological multi -step soot model for spray combustion under diesel engine-like conditions”, Combust. Theor. Model. 19(3):279-308 (2015). [10] Mauß, F., Entwicklung eines kinetischen Modells der Rußbildung mit schneller Polymerisation. PhD thesis, RWTH Aachen, (1998). [11] Pasternak, M., Mauss, F., Perlman, C., Lehtiniemi, H., “Aspects of 0D and 3D Modeling of Soot Formation for Diesel Engines”, Combust. Sci. Technol. 186:10 -11, 1517-1535 (2014). [12] Frenklach, M., Harris, S. J., “Aerosol dynamics modelling using the method of moments” J. Colloid Interface Sci.118:252–261 (1987).
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Click to edit Master subtitle style
[1] Lehtiniemi, H., Mauss, F., Balthasar, M., Magnusson, I., “Modeling Diesel Spray Ignition using Detailed Chemistry with a Progress Variable Approach”, Comb. Sci. and Tech. 178: 1977-1997 (2006).
[2] LOGE AB, www.logesoft.com, 2018. [3] Matrisciano, A., Franken, T., Perlman, C., Borg, A. et al.., “Development of a Computationally Efficient Progress Variable Approach for a Direct Injection Stochastic
Reactor Model”, SAE Technical Paper: 2017-01-0512 (2017). [4] Seidel, L., Moshammer, K., Wang, X., et al., „Comprehensive kinetic modeling and experimental study of a fuel-rich, premixed n heptane flame”, Combustion and
Flame 162: 2045-2058 (2015). [5] Zeuch, T., Moréac, G., Ahmed, S.S., Mauss, F., “A comprehensive skeletal mechanism for the oxidation of n-heptane generated by chemistry-guided reduction”,
Combustion and Flame 155: 651-674 (2008). [6] Engine Combustion Network, „ECN Data Search page." [online]. available: https://ecn.sandia.gov/ecn-data-search/?nam=1. Last checked: 29.11.2018. [7] Richards, K.J., Senecal, P.K., and Pomraning, E., "CONVERGE (v2.4), Convergent Science, Inc. ", Madison, WI, 2018.[8] Vishwanathan, G., Reitz, R. D., “Development of a practical soot modeling approach and its application to low-temperature diesel combustion”, Combust. Sci.
Technol. 182:1050-1082 (2010).
IntroductionA reaction mechanism describes the combustion of a surrogate fuel andaccounts for chemical and physical properties of the commercial fuel. Theuse of complex detailed reaction mechanisms in 3D Computational FluidDynamic (CFD) simulations can lead to a high demand of computationalcosts. One possible solution to reduce these costs is to use tabulatedchemistry methods. In this work, two applications predicted using thedetailed chemistry solver SAGE and the tabulated combustion progressvariable (CPV) approach are presented. Good agreement between the twomodels are found for a diesel engine sector case and the Spray A from theEngine Combustion Network (ECN).
Figure 2 shows the temperature and O2-mass fraction profile of SAGE andCPV at three different time steps. The overall behaviour is similar. Theignition behaviour is unexpected. Both cases ignite next to the spray cone,not at the tip of the spray. The CPV predicts higher temperatures and lessO2 in the same regions. A possible reason for this discrepancy is thethermodynamic treatment within CPV, where only 19 species are available.This treatment will be investigated further in future.Figure 3 shows the prediction of the pressure and rate of heat release(RoHR) for the engine sector case for both EGR levels and combustionmodels. The prediction of both combustion models agrees well. Asexpected, a higher EGR amount leads to a lower mean pressure andRoHR. The temperature profile (Figure 4) shows an owerall goodagreement.
1st INTERNATIONAL CONFERENCE ON SMART ENERGY CARRIERS, Napoli, Italy, 21 – 23 January, 2018
Further Application of the Fast Tabulated CPV ApproachAdina Werner1, Andrea Matrisciano2,4, Corinna Netzer1, Harry Lehtiniemi2, Anders Borg2,4, Lars Seidel3, Fabian Mauss1
1Brandenburg University of Technology, Cottbus, Germany| 2LOGE AB, Lund, Sweden | 3LOGE Deutschland GmbH, Cottbus, Germany | 4Chalmers University of Technology, Gothenburg, Sweden
ConclusionsThe tabulated combustion progress variable approach leads to good andreasonable results compared to the SAGE detailed chemistry solver andthe experiment. The tabulated approach decreases the CPU times by factor13 for the n-dodecane mechanism and by factor 4 for the smaller n-heptanemechanism. Future work will include emission prediction and validation.
The CPV modelThe CPV model assumes that a progress variable C can be used for thereconstruction of the thermo-chemical state on the whole reactiontrajectory. C is defined as a function of the chemical enthalpy h298 [1]:
𝐶 =ℎ298 − ℎ298,0
ℎ298,𝑚𝑖𝑛 − ℎ298,0The look-up tables were generated with LOGEtable [2] using adiabatichomogeneous constant pressure reactors. The generated table replacesthe chemistry solver in the CFD code [3].
Only 19 CPV species are transported and used to calculate the thermo-dynamics of the gas phase.
Figure 1: Comparison of the predicted pressure and liquid penetration length using SAGE and CPV versus experiment.
Figure 2: Temperature and O2-mass fraction profile of SAGE (left) and CPV (right) at 0.2 ms, 0.425 ms and 0.725 ms.
Figure 3: Predicted pressure and chemical RoHR of SAGE and CPV for different EGR amounts (no EGR: 0%; EGR: 30%).
Correspondence to: [email protected]
0
CFD Code
Transport of• Chemical enthalpy H298
• Mixture fraction Z• Mass fraction EGR YEGR
• Species for thermodynamics
• Emissions Extra output species are assigned as passives
CPV Model
CombustionWell-stirred reactor model [8, 9] with source terms from the CPV table
SootMethod of Moments M0
and M1
[10, 11, 12]
NOx
Thermal NO
Table look-up parameters• p, T, ϕ , YEGR
Update of sources • H298, Z, YEGR
Species / emission update
n-dodecane [4] n-heptane [5]
Species 487 121
Reactions 2331 594
Property Range Grid points Range Grid points
EGR [%] 0.0 – 40.0 5 0.0 – 40.0 5
Equivalence ratio [-] 0.2 - 10.0 25 0.2 - 10.0 25
Pressure [bar] 1.0 - 200.0 18 1.0 - 200.0 24
Unburnt temperature [K] 250.0 - 1500.0 101 300.0 - 1500.0 89
Simulation SetupThe Spray A from the ECN [6] is modelled using the 3D CFDCode CONVERGE [7]. The chosen geometry is a cube withan edge length of 10.8 cm. The turbulence is predicted usingthe Reynolds Averaged Navier-Stokes (RANS) model. Todifferent combustion models are applied: the SAGE detailedchemistry solver [7] to solve the chemistry on the fly and theCPV model [3].Further, a diesel engine sector case (137 mm bore, 165 mmstroke and 263 mm connecting rod) is modelled. The engineoperates at 1600 rpm and the fuel is injected as singleinjection at 9° CA bTDC. Two different cases with no EGR
and 30 % EGR amount were compared.
Table 1: Used reaction schemes and CPV table ranges.
Number of species SAGE CPU time [h] CPV CPU time [h]
487 169.98 13.74
121 7.66 / 8.23 2.00 / 2.16
Table 2: Comparison of CPU times SAGE vs CPV and 487 species vs 121 species.
Results and DiscussionFigure 1 shows the predicted pressure of the detailed chemistry solverSAGE and the CPV model versus the experimental pressure for Spray A.The pressure is well predicted by both combustion models. The firstdifferences occur at 0.2 ms, where the first liquid parcels leave the fixedcell cone. The deviation between SAGE and CPV may be caused bydifferences in the mesh refinement and time stepping.
[mm]
SAGE CPV2200
Temp [K]
210020001900180017001600150014001300120011001000900800700600
4
8
12
16
24
28
32
36
0.16
yO2 [-]
0.1350.110.0850.060.0350.01
CPVSAGE
12
fix mesh end
20
[mm]
36
32
28
24
8
4
0
SAGE CPV SAGE CPV
CA10
CA50
CA10
CA50
Figure 4: Temperature profile of SAGE and CPV for no EGR (left) and 30% EGR (right) at two different time steps.
Temp [K]
2500
2275
2050
1825
1600
1375
1150925
700
[9] Pang, K. M., Jangi, M., Bai, X.-S. and Schramm, J., “Evaluation and optimization of phenomenological multi-step soot model for spray combustion under diesel engine-like conditions”, Combust. Theor. Model. 19(3):279-308 (2015).
[10] Mauß, F., Entwicklung eines kinetischen Modells der Rußbildung mit schneller Polymerisation. PhD thesis, RWTH Aachen, (1998).[11] Pasternak, M., Mauss, F., Perlman, C., Lehtiniemi, H., “Aspects of 0D and 3D Modeling of Soot Formation for Diesel Engines”, Combust. Sci. Technol. 186:10-11,
1517-1535 (2014).[12] Frenklach, M., Harris, S. J., “Aerosol dynamics modelling using the method of moments” J. Colloid Interface Sci.118:252–261 (1987).
