Strategies for Using Detailed Kinetics in Engine Simulations Ellen Meeks ERC Symposium: Fuels for Future IC Engines June 6-7, 2007 Madison, Wisconsin
Outline Role of simulation in design Importance of chemical kinetics Challenges of using detailed kinetics Strategies that bridge the gap between design simulation and kinetics 2
Simulation reduces cost of development Cost Testing Simulation Complexity, Capability, Time There is a growing opportunity to: Reduce risk Improve use of testing Speed development Facilitate innovation 3
How effective can simulation be? According to our customers, it depends on the accuracy: Needs to be predictive to Allow exploration and discovery Guide design and testing Must provide deep understanding of dependencies to Manage complexity of tradeoffs Facilitate innovative solutions 4
Computational fluid dynamics (CFD) is already widely used for engine design Geometry A Example Goal: Maximize uniformity in piston bowl Geometry B 10º BTDC 10º BTDC TDC 20º ATDC CFD Simulation: Determine effects of port flow distribution, injector design, injector timing, piston bowl geometry TDC 20º ATDC STAR-CD simulations with simplified chemistry 5
An engine system is a chemical plant Fuel Air (O 2 + N 2 ) Horsepower CO 2 + H 2 O CO, NO X, PM Unburned HC Pt / Rh CO 2 + H 2 O Air (O 2 +N 2 ) 6
There is a gap between CFD simulation and testing requirements Goal: Minimize emissions while maximizing efficiency Workflow is incomplete CFD users cannot predict chemistry effects adequately CFD Simulation: Determine effects of port flow distribution, injector design, injector timing, piston bowl geometry 10º BTDC TDC 20º ATDC Ignition delay, CO, UH, PM, etc. Test: Determine emissions, efficiency, performance 7
Detailed chemistry is required to address many important issues Ignition timing Diesel HCCI/CAI Knock reduction Emissions control NO x, CO, UHC, PM Fuel flexibility Exhaust-gas recirculation System control 8
Challenges for using detailed kinetics Real fuels are too complex Validated detailed mechanisms are scarce Simulations are too time-consuming Companies often lack chemistry expertise 9
Addressing the challenges Real fuels are too complex Validated detailed mechanisms are scarce Simulations are too time-consuming Companies often lack chemistry expertise 10
Real fuel components can be categorized 50 40 30 20 10 0 Example: Gasoline* *http://www.atsdr.cdc.gov/toxprofiles/tp72-c3.pdf alkanes alkenes isoalkanes cycloalkanes cycloalkenes aromatics miscellaneous Classes of chemical compounds have Common molecular structures Similar chemical behavior Biofuels have different compositions Methyl-ester structures Contain oxygen 11
Surrogate fuel mixtures can be used to represent real fuels in simulations 1 or 2 molecules represent each significant chemical class, e.g.: Real Fuel Component iso-paraffins normal paraffins single ring aromatics cyclo-paraffins olefinic species multi-ring aromatics oxygenates Surrogate Fuel Candidate iso-octane, hepta-methyl nonane n-heptane, n-hexadecane toluene methylcyclohexane 1-pentene alpha-methyl napthalene methyl stearate, methyl linoleate Detailed chemistry models are built for each molecule Use elementary reactions Allow merging to form mixtures 12
Different model fuels can be assembled for different applications Tailor to prediction of desired combustion and physical properties: Ignition delay Knocking tendency Flame speeds Pollutant emissions Sooting tendency & particle size distributions 13
Example shows prediction of ignition delay with 5-component mixture Surrogate mixture compositions defined to match class composition or RON #* All do reasonably well in capturing ignition behavior with idealized model of engine, detailed kinetics *Reported by: C. V. Naik, et al SAE 2005-01-3742 14
With a database of molecules, we can assemble an appropriate surrogate aromatics Real Fuel Characteristics Class composition Heat-release rate Octane / Cetane # H/C ratio, O content U.S. E15 Gasoline n-paraffins olefins c-paraffins i-paraffins n-heptane Match Properties Select Molecules Set Composition 19% n-heptane 45% 15% 3% 1% 15% Iso-octane 1-pentene mchexane m-xylene ethanol Merge Mechanisms Species Reactions Surrogate Fuel Composition Chemical Model for Simulation 15
Addressing the challenges Real fuels are too complex Validated detailed mechanisms are scarce Simulations are too time-consuming Companies often lack chemistry expertise 16
Mechanism generation for a molecule can be fully automated Reaction Patterns Properties Stored Data Groups Group Contribution Quantum Chemistry Determine Reactions and Molecules Determine Properties of Molecules Determine Kinetic Rate Parameters Surrogate Fuel & Oxidizer Experimental Data Kinetic Rates Rules Based on work in collaboration with the Dow Chemical Company No Simulate Target Experiments OK? Yes Chemical Mechanism n-hexadecane aromatics olefins c-paraffins i-paraffins n-paraffins 17
Validation focused on mechanismgeneration rules is key to building database Well studied molecules used to refine rules Assures consistency between surrogate fuel component mechanisms Allows easy assembly of mixtures 18
Get full benefit from molecules that have been well studied experimentally For example: n-heptane Shock-tube, JSR, Flames, RCM, and Engine data Ignition delay time (sec) 1 0.1 0.01 0.001 0.0001 0.00001 0.5 0.7 0.9 1.1 1.3 1.5 1000/Temperature (1/K) Shock-tube and RCM Data Pressure Range: 0.07 to 60 atm Stoichiometry: ϕ = 0.1 to 4 Shock-tube data RCM data 19
Validation involves simulations of well controlled experiments with full chemistry CHEMKIN simulation of shock-tube show detailed kinetics ability to predict ignition Ignition delay time (sec) 0.1 0.01 0.001 0.0001 0.00001 0.6 0.8 1 1.2 1.4 1.6 1000/Temperature (1/K) Ciezki et al., 3 atm Simulation, 3 atm Ciezki et al., 13.3 atm Simulation, 13.3 atm Ciezki et al., 40 atm Simulation, 40 atm 20
Improvement of rules creates solid foundation for mechanism generation Enables full power of mechanism generation Assures consistency in mixtures Example: Improvement of of MCH MCH mechanism involved updated reaction rate rate rules rules for for RO RO 2 chemistry 2 Ignition time (sec) 0.10 0.01 Model, Original 10 atm, model, Year 101 mec atm Original model, 15 atm Model, 15 atm, Year 1 mec Model, 20 atm, Year 1 mec Data, 10 10 atm atm Data, 15 15 atm atm Data, 20 20 atm atm Model, 10 atm, updated m Model, 15 atm, updated m Model, 20 atm, updated m Original model, 20 atm Revised model, 10 atm Revised model, 15 atm Revised model, 20 atm 650 750 850 Temperature (K) 950 1050 21 Work performed in collaboration with C. Westbrook and W. Pitz, et al., LLNL
Addressing the challenges Real fuels are too complex Validated detailed mechanisms are scarce Simulations are too time-consuming Companies often lack chemistry expertise 22
Several methods allow linking to CFD with limited impact on simulation time + global chemistry CFD Model of cylinder geometry + reduced chemistry Multi-zone model Automated Zone Zone Mapping + progress variables Table Table look-up look-up vs. vs. Progress variable Detailed Chemistry Model Automated Chemistry Reduction Single-zone or 1-D flame model 23
Multi-zone models allow detailed kinetics Multiple, homogeneous regions Core, boundary layer, crevice Connected through work/heat Can be auto-extracted from CFD Can include full particle-formation chemistry Approach shows good prediction of heat-release, pressure, ignition, emissions From: S. M. Aceves, et al., SAE 2001-01-1027 24
Automated mechanism reduction can be very effective for skeletalization Ignition delay example: 5-component gas. surrogate 60% reduction, ~ 5X Faster Equivalence ratio 0.1-2.0 T= 600-1800K P= 0.5-60atm < 4% error Flame-speed example: n-heptane diesel surrogate 80% reduction, ~ 30X Faster Equivalence ratio 0.7-1.7 T= 300-700K < 2% error Flame Speed (cm/sec) Ignition Delay (sec) 1.00E+00 1.00E-01 1.00E-02 1.00E-03 1.00E-04 1.00E-05 1.00E-06 Equiv Ratio 0.5 and 20 atm Skeletal Master 0.0000 0.5000 1.0000 1.5000 1000/T (1/K) Flame speed at 500K 140 120 Skeletal 100 Master 80 60 40 20 0 0.4 0.8 1.2 1.6 2 Equivalence Ratio 25
More severe reduction can also be achieved Lumping of species or reactions Remove requirement of elementary reactions Use of partial equilibrium assumptions Remove some species from flow equations Identification of manifolds Separate out timescales 26
Addressing the challenges Real fuels are too complex Validated detailed mechanisms are scarce Simulations are too time-consuming Companies often lack chemistry expertise 27
The Model Fuels Consortium is addressing many of these issues Industry funded and Directed Built on commercial offerings Advised by leading science experts 28
Overall goal is to bridge gap between chemistry and engine design simulation Cost per Test / Simulation TEST CFD MFC Kinetics Simulation Make better use of expensive tests Improve accuracy of design simulations Provide bridge between detailed chemistry and design tools Build fuel-chemistry knowledge base Number of Tests / Simulations 29
Focus is on building a validated database and engineering analysis tools Knowledge-base Fuel Mixture Representation Fuel Component Mechanisms Master Mechanism Assembly Software Tools Mechanism Evaluation and Validation Development of Mechanism Analysis and Engine Simulation Tools Reduced Mechanisms Mechanism Reduction Development of Mechanism Reduction Tools 30
MFC Technical Advisory Team Dr. Charles Westbrook Chief Technical Advisor A pioneer in combustion modeling while at the Lawrence Livermore National Laboratory Prof. Mitsuo Koshi, Tokyo University Expert in combustion kinetics and mechanism generation Prof. Anthony Dean, Colorado School of Mines Expert kineticist; formerly lead scientist at Exxon Prof. William Green, Massachusetts Inst. of Technology Expert in numerical methods for model reduction and mechanism generation techniques Prof. Ulrich Maas, Universität Karlsuhe Expert in engine combustion simulation and numerical methods Prof. Hiromitsu Ando, Fukui University Former deputy general manager of engine research at Mitsubishi Motors 31
Summary and Conclusions There is increasing need for detailed chemistry simulation for engine design Advances in technology facilitate use of kinetics in simulation Automation of mechanism generation Automation of mechanism reduction Advanced overlay techniques with CFD More powerful computers 32
Contributors C. V. Naik K. V. Puduppakkam C. Wang Thank You 33