1. 1 GT-POWER TRAINING Engine Performance Analysis Gamma Technologies, Inc. All information contained in this document is confidential and cannot be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose, without the express written permission of Gamma Technologies, Inc.

2. 2 GT-POWER Content (Basic) GT-POWER Applications Solution Method Discretizing a Model Orifice Connections Pipes Cylinder Ports Valves Injection In-Cylinder Heat Transfer Combustion Cylinder Pressure AnalysisCylinder Pressure Analysis Flow Splits Convergence Advanced Topics

3. 3 GT-POWER Content (Advanced) Pipe Equations (ref) Turbochargers Heat Exchangers SI Fuel Puddling Model EGR SI Turb Model DI Jet Model Exhaust Aftertreatment Acoustics (Non-linear) Acoustics (Linear) Model Correlation Transient Simulation 3-D Combustion (KIVA) Mean Value / Real Time CFD Coupling Basic Topics

4. 4 GT-POWER Applications

5. 5 Benefits Of Simulation Saves Time and Money –Shortened development cycle –Reduces number of prototypes required –Optimization of design with minimal prototyping and laboratory testing Excels Where Traditional Methods Lack –Proof of concept –Validation and sensitivity studies –Component matching when no hardware yet available –Analysis of stubborn performance problems –Simulation of unusual ambient conditions: composition, temperature, pressure

6. 6 Model Fidelity Model Detail Real-Time Models (controls modeling) 1-D Gas Exchange (GT-POWER) 3-D CFD (KIVA) 1 10 100 1000 x Real Time CPU Time vs. Model Type Black Box Models 0.1 1-D Mean Value Model (GT-POWER) Original Figure provided by FEV

7. 7 GT-POWER Applications Engine performance analysis Based on 1-D fluid dynamics Very flexible, to allow studies of advanced concepts Detailed thermodynamics Many combustion & emissions models Thermal analysis Acoustics Engine control analysis via SIMULINK CFD flow analysis via STAR-CD, fluent and KIVA

8. 8 Solution Method

9. 9 Engine Cycle Simulation Solves 1-Dimensional equations to predict the flow rates in the intake and exhaust systems In-Cylinder modeling of combustion, pressure, heat transfer to cylinders, work Detailed sub-models: turbos, acoustics, catalyst, etc. Enable user to find best balance between complexity and accuracy, taking into account available input data and desired outputs

10. 10 Flow Solution Whole system is discretized into many small sub- volumes, connected by boundaries Each engine cycle is divided into many small time steps Scalar Variables density internal energy pressure temperature etc. Vector Variables mass flux velocity etc. Staggered Grid

11. 11 Flow Solution Simultaneous solution of three equations at each time step: –Continuity (conservation of mass) –Energy (conservation of energy) –Momentum (conservation of momentum) Mass & Energy solved in sub-volumes (scalars) –Pressure, Temperature, Species concentrations, etc. –Calculated at the centroid of each sub-volumes –Considered uniform (1D) throughout the sub-volume Momentum solved at boundaries (vectors) –Velocity, Mass flux, Mass fraction fluxes, etc.

12. 12 Flow Solution Example

13. 13 Solution At Each Timestep The solution is NOT an iterative numerical process (as is the case in a CFD type simulation) The solution is based on the state of the system at time t 0 and is calculated for a new time t 1 The new time t 1 must be close enough to time t 0 to ensure the solution is valid This maximum time step is always calculated at each time step

14. 14 Time Steps GT-POWER remains stable by choosing its timesteps such that the Courant number is less than or equal to.8, where the Courant number is defined as:  t=Time Step  x=Discretized Element Length u= Fluid Velocity c= Speed of Sound Time step is calculated for each subvolume: smallest one is applied to entire system

15. 15 Prediction Methodology Steps similar to physical processes in engine Solve flow equations –Predicts air and fuel in the cylinder Combustion –Model fuel energy release into pressure and temperature Calculate IMEP MEP = Work per cycle / Cylinder Volume Displaced per Cycle Subtract friction and auxiliary losses Calculate BMEP, torque & power

16. 16 Discretizing a Model

17. 17 Intake Manifold Components

18. 18 Model Discretization Discretization is the splitting of large parts into smaller sections in order to improve the model’s accuracy. –Coarse discretization results in larger timesteps and faster execution at the expense of model accuracy –Finer discretization results in better accuracy and frequency resolution at the expense of execution time –Beyond a certain limit, further reducing the sub-volume size does not bring benefits.

19. 19 DISCRETIZATION PRACTICES General performance analysis: –Intake system discretization ~= 0.4*cylinder bore –Exhaust system discretization ~= 0.55*cylinder bore Detailed acoustic analysis – Use ½ discretization length from general Do not build models with 1 or 2 very short elements.

20. 20 Short Cone Discretization Model a short cone as a sharp contraction with an orifice connection if either of the following criteria are true: – L 1 < DX – Half Angle > 15 deg. Flow coefficient Cd=1 in the converging direction; Cd=“def” in the diverging direction.

21. 21 Exercise Discretization.gtm

22. 22 Orifice Connections

23. 23 Orifice Connections Orifice connections allow fluid to flow between two adjacent components Discharge coefficients in either direction may be user defined or set to “def”. –“def” discharge coefficients are calculated assuming that the parts are connected as shown below. –Calculated discharge coefficients can be seen in the GT- POST RLT-Viewer Mode

24. 24 Orifice Connections (Cont.) A diameter restriction, d, may be specified or set to “def” if there is no restriction. Orifices may be specified with multiple holes D1 d D2D1 d D2

25. 25 Discharge Coefficients The discharge coefficient is the ratio of the effective area to the reference area. Reference area is derived from the diameter for throttles, orifices and valves. (more later on valves) C D = A EFFECTIVE / A REFERENCE

26. 26 Expansion and Contractions When modeling, always pay close attention to any point of area change –Large source of pressure drop in typical manifolds –Pressure waves reflect off of these area changes due to the change in velocity –Important to manifold wave dynamics—both for performance (breathing) AND acoustic results

27. 27 Flow Contractions At discontinuous contractions, velocity toward the center results in a vena contracta. Cd in flow connections characterizes the vena contracta Discontinuous transition of upstream area to boundary area results in a flow contraction loss. Cd = def assumes discontinuity.

28. 28 Flow Contractions Smooth transition from upstream area to boundary area results in no contraction loss. Cd = def Cd ~ 1.0.

29. 29 Measuring Discharge Coefficients Flow coefficients across valves, throttles, and orifices may be derived from measured data using the isentropic velocity equation for flow through an orifice. = Actual Mass Flow Rate C D = Discharge Coefficient A R = Reference Flow Area  o = Upstream Stagnation Density P R = Absolute Pressure Ratio (Static Outlet/ Total Inlet) R= Gas Constant T o = Upstream Fluid Temperature  = Specific Heat Ratio (1.4 for Air @ 300 K)

30. 30 Pipes

31. 31 Types of Flow Losses In approximate order of Priority…… –Expansion Losses –Contraction Losses (see flow connections) –Surface Friction Losses –Bends and tapered pipe losses

32. 32 Expansion and Contractions When modeling, always pay close attention to any point of area change –Large source of pressure drop in typical manifolds –Pressure waves reflect off of these area changes due to the change in velocity –Important to manifold wave dynamics—both for performance (breathing) AND acoustic results

33. 33 Flow Expansions Velocity becomes a turbulent eddy. Kinetic energy is turned into heat via internal and surface friction. Expansion Loss Velocity is expanded, Venturi-like. Kinetic energy is recovered as static pressure increase: Pressure Recovery

34. 34 Flow Expansions Expansion loss does not occur in the throat. Effective Area = Geometric Area (Cd = def = 1.0) Momentum Equation calculates pressure recovery and expansion loss from area ratio of boundary area to downstream area

35. 35 Surface Friction Surface friction –Depends of surface roughness and cross section Non-Circular cross-sections available (‘Pipe**Bend’) for increased friction and heat transfer –Manifold flow losses are often mistakenly attributed to surface friction Probably due to our familiarity with flow in liquids where friction is a dominant loss factor –Its influence on the heat transfer coefficient is typically more important than pressure drop Especially important for accurate prediction of turbine and catalyst inlet temperatures

36. 36 Steady Flow Simulation GT-POWER is sometimes used to model steady flow, such as steady pressure drop through a manifold or airbox FlowControl (Run Setup) has a Global Friction Multiplier and Global Heat Transfer Multiplier to account for –non-steady flow (oscillating) –non-fully developed flow Default settings are > 1.0 to account for typical engine characteristics For steady flow tests and liquids, set these values to 1.0

37. 37 Bent and Tapered Pipes Pressure Loss Coefficient –“def” option to automatically calculate K –dP = (0.5  V 2 )K –Most engines are carefully designed NOT to have sharp bends – therefore minor factor –Flow through pipes of increasing diameters do not “perfectly” convert dynamic pressure (i.e. kinetic energy) back into static pressure

38. 38 Accounts for: –Internal/External Heat Transfer –Heat Transfer to Neighbor (conductance) –Thermal Capacitance (transient option) –Initial Temperature (transient option) Recommended for most exhaust systems –Better to guess external heat transfer coefficient than to guess internal wall temp –Especially important for turbines –If wall temperature at a location is known, better to use it to calibrate external convection coefficient than to impose the wall temperature Pipe**** Thermal Wall Solver

39. 39 Exercise SteadyFlow.gtm

40. 40 Cylinder Ports

41. 41 Ports – Using Pipe*** Limitations when using Pipe*** templates: –Wall temperature is imposed as one value –Heat transfer from the back of the valve is not modeled (when using EngCylTWall – more later) –Valve guide heat transfer area is not modeled –Turbulence caused by valve guide is not modeled

42. 42 Ports – Using Pipe*** Recommended practices: –Friction multiplier* = 0 –Pressure loss coefficient* = 0 –Intake wall temperature ~= 450 K –Exhaust wall temperature ~= 550 K –Intake and Exhaust heat transfer multiplier = 1.5 to 2 * - Very important!

43. 43 Valves

44. 44 Valves - Types Valve lift based on rotational position of crankshaft –ValveCamConn, ValveCamPRConn, ValvePortConn, ValveCamUserConn Valve lift based on elapsed time since signal to open or close –ValveSolenoidConn Check valve - Effective area based on flow or valve dynamics –ValveCheckConn, ValveCheckSimpleConn

45. 45 Reference Area Reference area may be either constant (derived from the diameter) or variable (derived from the curtain area) for valves. –For constant reference area, the first discharge coefficient (at 0 lift) must be 0.0. –For curtain area, the first discharge coefficient (at 0.0 lift) must be greater than zero.

46. 46 Cam Lift Profile ValveCam*** Templates Lift profile input as function of cam angle or crank angle Profile shifted by “Cam Timing Angle”

47. 47 Exercise Save Ports.gtm as ValveTimingAdvance and change intake valve timing +2 degrees. Repeat as and ValveTimingRetard using -2 degrees Compare resulting volumetric efficiency curves.

48. 48 Injection

49. 49 INJECTION (InjProfileConn) Profile injection Typical use: direct injection Inputs: –Fuel mass per stroke –Vaporized fuel fraction –Injection rate vs. crank angle is specified dP, P, Mass flow rate Check nozzle discharge coefficient (between 0.65 and 0.75) Smoke-limits may be imposed

50. 50 INJECTION (InjAF-SeqConn) Sequential fuel injection Typically use: sequential port injection Inputs: –Injector delivery rate –Air Fuel Ratio –Number of injectors sharing the same mass flow sensor –Start of Injection –Vaporized fuel fraction Injection timing can be made RLT Dependent Ideal for developing fuel maps Vaporization effects on volumetric efficiency will be modeled more accurately

51. 51 SI Injection Delivery Rate Estimate Typically, the longest pulse duration in crank- angle degrees is at the highest RPM, WOT Typically, the longest pulse duration in crank- angle degrees is about equal to the duration of the intake valve opening: 180 to 210 degrees

52. 52 SI Injection Delivery Rate Estimate m Delivery = injector delivery rate (g/s)  V = volumetric efficiency (fractional)  ref = reference density for volef (kg/m 3 ) (typically 1.16 kg/m 3 for ambient) N RPM = engine speed (RPM) V D = engine displacement (liters) F/A= engine fuel-to-air ratio #CYL= number of cylinders PulseWidth= injection duration (crank degrees) Note: The 6 in the equation comes from unit conversions

53. 53 In-Cylinder Heat Transfer

54. 54 Cylinder Wall Temperatures Twall –Average temperature specified for head, piston, and cylinder walls only. –Does NOT transfer heat from back of valves to pipe TWallDetail –Piston and cylinder walls specified in zones –Head and valve face temperatures specified separately –Transfers heat from head only to PipePort parts TWallSoln –Wall temperature solved using finite element model –FE model built automatically from user specified dimensions –Transfers heat from head only to PipePort parts

55. 55 Cylinder Heat Transfer Woschni –Heat transfer calculated from correlative Woschni model Flow –More detailed, spatially resolved heat transfer model (Swirl data needed). User –User may write their own heat transfer model and incorporate it into GT-POWER. HG Profile –Heat transfer coefficients may be imposed as a function of crank angle (typically from CFD analysis).

56. 56 Combustion

57. 57 Combustion - Terms Burn Rate –Rate at which fuel and air is converted to combustion products –In GT-POWER this is the rate at which the fuel-air mixture is added to the equilibrium equations –Used for ALL combustion models (either imposed or predicted) Heat Release Rate –Chemical energy release rate –Differs from Burn Rate due to partial combustion –Heat release rates are NOT used in GT-POWER (available as a result from cylinder)

58. 58 Combustion - Terms

59. 59 Combustion - Terms Non-predictive combustion –The instantaneous burn rate is directly imposed as a simulation input –The total amount of energy released from the fuel depends only on the mass of fuel and air in the cylinder. Predictive Combustion –The instantaneous burn rate is predicted by a physical model within GT-POWER based on various simulation inputs and results Semi-predictive combustion –The instantaneous burn rate is imposed using non- predictive combustion models –The input parameters that define the burn rate respond to changes in model inputs/results via a lookup or other relation (non-physical model)

60. 60 Combustion - Terms Forward Run – Cylinder pressure is predicted based on an imposed (or predicted) combustion burn rate Reverse Run – Combustion burn rate is calculated from measured cylinder pressure FWD REV

61. 61 Combustion Available Forward Combustion Models:

62. 62 SI Wiebe Model –Non-predictive burn rate imposed according to the SI Wiebe function (50% burned, 10-90% duration, exponent) –Very fast execution Combustion - SI

63. 63 Combustion - SI SI Turbulent Flame Model –Predictive burn rate taking into account: cylinder temperature and pressure composition in the cylinder including fuel, fresh air, and egr/residuals Spark timing, position, and gap Fuel properties Flame area/Wall wetted area In-cylinder flow –Slow execution –Combustion chamber geometry (head and piston) may be read from an.STL file or generated automatically from dimensions entered by the user

64. 64 SI NOx and Knock Predictive NOx Model (extended Zeldovich) –Temperature tracked in many zones –Sensitive to heat release rate and composition (including EGR) –Sensitive to pressure, temperature, equivalence ratio, and dilution ratio Predictive Knock Model –Correlate predicted knock index to measurements of knock initiation and strength –Knock index predicts knock trends –Cylinder wall temperatures should be specified by either the detailed or solution reference objects

65. 65 Combustion - DI Diesel DI Wiebe Model (No default entries) –Non-predictive burn rate imposed according to the three-term DI Wiebe function –Very fast execution time

66. 66 Combustion - DI Diesel DI Wiebe model with default entries –“def” allowed for all parameters –Semi-predictive, includes effects of cylinder pressure, fuel injection rate, and injection timing. –Use only when an accurate injection profile and timing are available. –Not necessarily accurate for idle, high egr, or non-diesel fuels. –Very fast execution

67. 67 Combustion - DI Diesel DI Jet Model –Predictive burn rate model: Takes into account: Cylinder pressure and temperature Injection timing, rate, velocity, and plume shape Composition in the cylinder including fuel, fresh air, and EGR/residuals In-cylinder flow (Swirl and Tumble) –Slow execution –Predictive NOx Model –Predictive SOOT Model (trends only)

68. 68 Combustion – Any Type Multi-Wiebe Model –Non-predictive burn rate imposed based on sum of multiple SI Wiebe functions (up to 6) –Developed for DI with multiple pulses (but can be used anywhere) –Fast execution

69. 69 Combustion – Any Type Combustion Profile –Non-predictive burn rate imposed directly as function of crank angle –Can be used for any type of fuel or injection –Very fast execution –GT-POWER can perform a reverse run to calculate the burn rate directly from measured cylinder pressure

70. 70 Predictive or Non-predictive? The “real” combustion burn rate is dependent on a long list of variables (i.e. trapped cylinder conditions, fueling, spark/injection timing, in- cylinder flow, etc.) Predictive physical model is ideal but... –More difficult to build and calibrate model –Slower computation time (Use Master/Slave when approprate) Use predictive combustion when variables to be studied have a direct and significant effect on the combustion burn rate –DI Injection rate and profile –SI Combustion chamber shape

71. 71 Use non-predictive combustion whenever the influence on the burn rate of the variables to be studied can be: –Neglected (not the dominant factor) –Modeled using lookups, etc. (semi-predictive) Examples of non-predictive applications –Optimizing cam timing or runner length –Intake or exhaust back pressure/acoustics –Turbocharger matching Predictive or Non-predictive?

72. 72 Semi-predictive Example SI Wiebe model where 3 Wiebe parameters are predicted using neural networks as a function of: –Engine Speed –Trapped Mass –Trapped Burned Gas Fraction –Trapped Lambda –Trapped Temperature (IVC) –Turbulent Kinetic Energy (IVC) Neural networks trained using experimental data –Large DOE of operating points (rpm, load, VVT, lambda) –Burn rates determined from measured cylinder pressure –Wiebe parameters “fit” to apparent burn rate

73. 73 Exercise Use burn rate from DI-Injection.gx to determine DI-Wiebe coefficients using WiebeComb.xls

74. 74 Cylinder Pressure Analysis (Reverse Run)

75. 75 Reverse Combustion Two methods provided to calculate combustion burn rate from measured cylinder pressure –Pre-processing method –Simulation based method (Three Pressure Analysis) Both methods use same burn rate calculations Result of calculation is the burn rate required in GT-POWER to exactly reproduce the measured cylinder pressure in a forward run –Most other tools perform a classical heat release analysis

76. 76 Reverse Combustion Classical Heat Release Analysis (method used in most other cylinder pressure analysis tools): Result is “apparent” heat release (NOT burn rate) HRA requires assumptions and simplifications –Does not solve the same equations as forward analysis Rarely reproduces exactly the input pressure

77. 77 GT-POWER uses same methodology for both forward and reverse runs – Full Chemistry and Thermodynamics – Iterative method – fuel mass burned is varied within each timestep to match measured cylinder pressure Reverse Combustion

78. 78 Reverse Combustion

79. 79 Result is GT-POWER burn rate Capable of exactly reproducing the input pressure Reverse Combustion

80. 80 Reverse Combustion Difference between the two reverse run methods available in GT-POWER is how burn rate calculation inputs are acquired Pre-processing: –All calculation inputs are model inputs –‘EngBurnRate’ template –Very fast calculation (no convergence) TPA –Most calculation inputs are simulation results –‘EngCylCombPressure’ template –Normal multi-cycle simulation – requires convergence

81. 81 PRE-PROCESS (EngBurnRate) All Inputs defined in ‘EngBurnRate’ template: –Air, Fuel, Residuals in Cylinder –Heat Loss to Cylinder –Measured Cylinder Pressure

82. 82 Pre-process (EngBurnRate) Pre-process simulation run –Calculations done if ‘EngBurnRate’ in project tree Outputs of calculation: –Plots and tables modelname.gx Check integrity of input data Show resulting burn rate and other quantities –Combustion Profiles ready for use in forward run modelname_prof.dat Import into GT-ISE Contains ‘EngCylCombProfile’ objects ready to copy into any model

83. 83 Reverse Run Plots/Tables

84. 84 Reverse Run Combustion Profiles

85. 85 Three Pressure Analysis (TPA) Single cylinder model of an engine on test stand Three measured dynamic pressures are model inputs

86. 86 Measured cylinder pressure is entered in ‘EngCylCombPressure’ combustion object Three Pressure Analysis (TPA)

87. 87 Three Pressure Analysis (TPA) Normal simulation run (run until convergence) For each cycle of the simulation: – Apparent burn rate is calculated at IVC using the cylinder trapped quantities and heat transfer from simulation – Burn rate is imposed (exactly like ‘EngCylCombProfile’) Same outputs as ‘EngBurnRate’ plus folder of “Pressure Analysis” RLT’s in EngCylinder part

88. 88 Three Pressure Analysis (TPA)

89. 89 TPA vs. Pre-Process TPAEngBurnRate Cylinder contents at IVC PredictedModel Input Port pressures required? YesNo Simulation type NormalPre-processing Distributed processing? YesNo Simulation speed (1 case) SlowerFaster Heat transfer options AllWoschni only Wall temperature options AllTWall only

90. 90 Exercise Modify DI-Injection.gtm to save required data for TPA, then perform TPA analysis.

91. 91 Flow Splits

92. 92 Flowsplit Uses Flowsplits are used to create volumes which connect three or more pipes Used to discretize large volumes –Intake plenums –Airboxes –Muffler shells Used for other specialized volumes –Helmholtz resonators –Perforated pipes –Inlet/outlet to catalysts and heat exchangers

93. 93 Flowsplit Discretization Discretization Issues –Unlike Pipe components, flowpslits are always 1 subvolume –Choose flowsplit size to be close to target discretization –Always place flow connections at locations of area discontinuity (use centerline) –Be especially careful about maintaining discretization length in sensitive areas of the model and areas at higher velocities: Intake Plenum and Exhaust Manifold Collectors

94. 94 How To Discretize A Flow System Use target discretization length as rough FlowSplit size Place boundaries at very edge of pipes Flow must be normal to boundaries Considerations –FlowSplit is 1-D approximation of 3-D phenomena: may not be perfect –May require coupling with CFD to resolve certain 3-D effects

95. 95 Fundamental Theory of Flowsplits Mass, species and energy are calculated same as in pipes Momentum calculations are at OrificeConns Angles used to calculate the transfer of momentum to other openings (directional effects) Expansion diameters used to calculate momentum in FlowSplit after flow enters

96. 96 Flowsplit Attributes Volume Angles (X, Y and Z) –used to calculate pressure changes due to directional changes Expansion diameters –Used to calculate flow losses at the flowsplit openings Characteristic Lengths –Used to calculate wave response Others similar to Pipe* templates

97. 97 Flowsplit Angles Port angles relative to coordinate axes. Angles must be specified between -180 and 180 degrees. All vectors either all out of or all into the FlowSplit.

98. 98 Expansion Diameter Expansion diameter at each port –diameter to which fluid may expand upon entering the FlowSplit volume –used to calculate kinetic energy losses due to expansion when fluid enters the FlowSplit –also used to calculate discharge coefficients when the discharge coefficients are set to “def” Considerations –Apply short cone-discretization (e.g., catalytic converters and intercoolers) –May not be apparent (consider limits & study range between)

99. 99 Characteristic Lengths Characteristic length at each port –length that fluid entering the FlowSplit travels before it hits a wall or a port on the opposite side of the FlowSplit –used to calculate the propagation and/or reflection of traveling waves More info –Sometimes unclear (consider limits & study range between) –Overall system performance usually insensitive to input

100. 100 General Flowsplit Examples Example: Skewed junction of two pipes. Expansion Diameters: DIAC2=DIAC1, DIAC3 see figure Characteristic Lengths: DX2=DX1, DX3 see figure

101. 101 General Flowsplit Examples Pulsations out of phase from connections 2 & 3 Typical case Examples: Manifolds, Collectors, etc… Expansion Diameters: DIAC2=DIAC3=DIAC1=D1 Characteristic Lengths: DX2=DX3=DX1

102. 102 General Flowsplit Examples Pulsations in phase from connections 2 & 3 Special flow case Example: Bifurcated flow such as multiple intake/exhaust valves. Expansion Diameters: DIAC2=DIAC3 ~= Diameter for circle with ½ area of D1 DIAC2=DIAC3 < DIAC1 Characteristic Lengths: DX2=DX3=DX1 Flow from ports 2 & 3 expand into shared volume, DIAC2,3~=SQRT(0.5*D1^2)

103. 103 Predefined Flowsplits –Orifices placed at entry planes of any type of FlowSplit are defined by OrificeConn connections.

104. 104 Common Flowsplit Mistakes Flowsplit Angles –Incorrect: Flow into and out of the flowsplit in the same direction –Correct: Draw vectors pointing either all out of or all into the FlowSplit through each port (Vector directions depend only on geometry, not on flow direction) Expansion Diameters –Incorrect: Expansion diameter is same size as inlet pipe when there is an area discontinuity present –Correct: Expansion area should be determined by cross- section INSIDE the flowsplit boundary, not on the adjacent parts

105. 105 3-D Flowsplit Viewer Tool that displays graphically the input FlowSplit must be a part on map and connected to other parts. Use button of wireframe box on toolbar {Demonstration}

106. 106 Convergence

107. 107 Convergence Simulation may run up to the maximum defined simulation duration May be configured to stop when values do not change from cycle to cycle –Change in average flow rates for every part –Change in average pressure for every part If dP>1%, dF criteria is tightened by 4X –Change in average fluid temperature –Any RLT value

108. 108 Pipe Equations (Reference)

109. 109 Laminar region, Re D < 2000 Transition region Turbulent region, Re D > 4000 Larger of: where: Re D Reynolds number based on pipe diameter Dpipe diameter hroughness height Pipe**** Friction Losses

110. 110 Forward and Reverse Pressure Loss Coefficient where: p 2 total pressure at inlet p 1 total pressure at outlet  inlet density V 1 inlet velocity Calculated based on synthesis of various published data (e.g. Miller, D.S., Internal Flow Systems, Second Edition, BHR Group Ltd, 1990.) Pipe**** Pressure Losses

111. 111 Internal Heat Transfer Coefficient Colburn Analogy: where: C f = friction coefficient  = density U eff = effective velocity outside boundary layer C p = specific heat Pr = Prandtl number Heat Transfer to Neighbors (connections) HeatCFlange Pipe**** Heat Transfer

112. 112 Turbochargers

113. 113 Conventional Turbocharger Typical Applications

114. 114 Typical Applications Power Turbine Supercharger

115. 115 Turbine wastegate –Internal orifice which allows exhaust gas to bypass the turbine –Wastegate diameter can be constant or dynamically controlled (PID, etc.) Variable geometry turbine (or compressor) –Multiple maps may be entered at various rack positions –Interpolation between maps provides a continuously varying geometry between rack positions –Rack position can be constant or dynamically controlled (PID, etc.) Capabilities

116. 116 Twin-Scroll Turbines GT-POWER accepts more than one inlet to a turbine Internally, treated as 2 turbines Add “leak-path” upstream of turbine (see figure) Models same effect as leakage within the turbine Diameter in OrificeConn is calibrated to measurements First guess: use effective diameter of area between wheel and divider

117. 117 Turbines and Compressors operate based on performance maps Maps are based on steady state flow testing (generally performed by manufacturer) Data provided includes speed, mass flow, pressure ratio, and efficiency at a variety of test points Modeling in GT-POWER

118. 118 Modeling in GT-POWER Compressors and Turbines are components on model map Components do not include map data Map data is in reference objects: CompressorMap, TurbineMap, TurbineMapSAE Components point to reference objects or external files with data (*.cmp or *.trb) External files must be in specific format (consult GTI manual)

119. 119 Corrected data must be entered so GT-POWER will use a consistent set of map data Corrected Map Data

120. 120 CompressorMap Compressor map data can be read from an external SAE format text file or pasted directly into the Data folder

121. 121 TurbineMap Turbine map data can be pasted directly into the Data folder and must be “corrected”. The data correction is the same as in the compressor.

122. 122 Reduced data is an alternate method of generalizing map data Influence of inlet pressure and temperature is removed Easier to compare maps of different turbines, but similar size TurbineMapSAE – Reduced Data

123. 123 TurbineMapSAE Turbine map data can be read from an external SAE format text file or pasted directly into the Data folder, and must “reduced” data

124. 124 Performance is determined from lookup maps At each timestep: –The pressure ratio across the turbine or compressor is known from adjacent components –The reduced speed is known from the attached shaft part and the inlet temperature Operation in GT-POWER

125. 125 Mass flow and efficiency are determined from the map based on pressure ratio and speed, and imposed This affects the pressure ratio and the turbine and compressor power (and speed) for the next timestep Turbo speed is determined from the turbine and compressor power and the attached shaft inertia An imbalance between power generated by the turbine and consumed by the compressor will lead to an acceleration or deceleration of the shaft Operation in GT-POWER

126. 126 Turbocharger Map Pre-Processing (1) GT-POWER does not use the raw map data directly during the simulation Raw map data must be “pre-processed” During pre-processing of Compressor maps: –Data is interpolated between entered points. –Data is extrapolated to RPM=0.0 and PR=1.0 –Plots of extrapolated maps are made

127. 127 Turbocharger Map Pre-Processing (2) During pre-processing of Turbine maps: –In Turbines, curve shapes are fit to the raw data points –Data is extrapolated beyond the range of the raw data –Plots are produced which show the user how well the curve fitting and data extrapolation was done User may select to make external map files (*.cmp & *.trb ) to be used in the current or another simulation

128. 128 Pre-Processing Turbocharger Plots

129. 129 GT-POWER creates turbine maps by fitting the raw data to theoretically and empirically established shapes The fits should be compared with the raw data to judge the quality of the fit and the quality of the original data using the plots which are generated during pre-processing The ‘TurbineMap’ and ‘TurbineMapSAE’ templates have attributes which allow the user to improve the fit in some cases Turbine Map Analysis

130. 130 Turbine Map Analysis Pressure ratio fit and data curves should be close together and should be smooth Mass Ratio should be linear at low speeds, smooth throughout the curve, and have only one local maximum (at high speed) Max efficiency curve should be smooth

131. 131 All raw data points should collapse onto the theoretical fit lines Raw data should cover a wide range of normalized BSR – measured data bunched near 1.0 may not extrapolate well to off-design conditions Turbine Map Analysis

132. 132 Fit and data curves should be close together Most important set of plots to examine!! Turbine Map Analysis

133. 133 Full extrapolated maps Turbine Map Analysis

134. 134 Turbine Map Analysis Information messages written to *.out file Typical problem – maximum efficiency of speed line is first or last point

135. 135 Steady State Simulations Turbochargers have an inherent “feedback” mechanism (power -> boost -> power …) There is also a resistance to acceleration (Inertia) which slows turbo response These factors can increase significantly the number of cycles necessary to reach steady state convergence To minimize the number of cycles necessary, it is possible to manipulate the turbo shaft inertia during the simulation

136. 136 Steady State Simulations Increased for first few cycles: flow stabilizes Decreased to allow turbo to rapidly change towards steady speed Inertia returned to true value before the simulation finishes Use ‘ProfilePeriod’ for “Inertia Multiplier” in shaft part

137. 137 It is undesirable for the simulation to “converge” while the inertia is not at its true value Set the minimum simulation duration such that inertia mult will be 1.0 for converged final cycle Steady State Simulations

138. 138 Mass flow may converge long before turbo has reached a steady state speed Additional convergence criteria needed –Shaft torque imbalance is added automatically (dTqmx) Steady State Simulations

139. 139 Convergence can also be improved with good initial conditions (shaft speed, initial P and T) Steady State Simulations

140. 140 Typical transient simulation involves the variation of injection rate, wastegate diameter, rack position, speed, and load over time Before starting the transient event, the turbo should first be at a steady-state condition Turbo shaft inertia multiplier must be back at a value of 1 before beginning the transient (to model the turbo lag properly) Shaft Inertia and Transient Simulations

141. 141 It is possible to use controls to actuate dynamically the wastegate diameter or rack position to achieve a desired target value of a variable (for example boost pressure) If a PID controller is used, it is not necessary to lower artificially shaft inertia Shaft Inertia and TC Control

142. 142 Advanced Turbine Map Analysis What can be done about a poor fit? In the case of bad data or limited data, GT-POWER may not be able to improve fit In some cases, it is possible to improve the fit using the attributes in the Shape folder

143. 143 Slope of Optimal BSR Line Advanced Turbine Map Analysis

144. 144 Mass Flow Ratio at 0.0 BSR (intercept) Efficiency Shape Factor at Low BSR – Higher values increase curvature Efficiency Intercept at High BSR Advanced Turbine Map Analysis Mass Flow Ratio Exponent – Higher Values increase curvature

145. 145 Advanced Turbine Map Analysis “Max. Eff. Curve Fit” attribute can be used to indicate the speed lines have the same pressure ratios This changes fitting method and can improve fit of data

146. 146 Determining the cause of a correlation problem with a full engine model including a turbo can be extremely difficult The following procedure should be used to add a turbo to a model, and can be used to isolate a problem with a complete model: 1.Build the engine without the turbo –Impose the known compressor outlet P,T and turbine inlet P,T using EndEnvironment parts –Verify engine mass flow correlation Calibrating Model with a Turbo

147. 147 2.Add the compressor (no turbine) –Impose the known turbo speed –Verify engine mass flow correlation –Use the compressor mass multiplier if necessary 3.Add the turbine (no compressor) –Impose the same known turbo speed –Verify engine mass flow correlation –Use the turbine mass multiplier if necessary –Wastegate: perform steps when closed 4.Compare Turbine/Compressor powers from two above steps –Use either turbine or compressor efficiency multiplier to make the two values equal 5.Build the complete model Calibrating Model with a Turbo

148. 148 Heat Exchangers

149. 149 Heat Exchangers Three methods –Non-predictive: imposed outlet temperature –Semi-predictive: Effectiveness –Predictive: outlet temperature is calculated from heat exchanger correlations

150. 150 Non-Predictive Heat Exchanger Theory –Impose the wall temperature of a pipe as the Cooler outlet temperature –Increase the heat transfer between the pipe wall to a very large amount Method –Use attribute “No. of Identical Pipes” in Options folder of Pipe template - increases surface area –Impose small pipe diameter (1-5mm) –Impose large Heat Transfer Multiplier (~10-20)

151. 151 Non-Predictive Heat Exchanger Method (cont.) –Calibrate pressure drop by adjusting Friction Multiplier –Conserve length and volume of intercooler –Use flowsplits before and after pipe –Apply short-cone discretization to flowsplits at inlet and outlet Pros –Simple, requires very little data (most common) –Allows one to study effect of outlet temperature on engine performance Cons –Calibration requires outlet temperature from dyno –Interaction with other components not considered

152. 152 Semi-predictive Heat Exchanger Use of Effectiveness to describe Heat Exchanger performance is common Mix non-predictive method with Controls ‘Effectiveness’ Compound in library

153. 153 Predictive Heat Exchanger HxMaster and HxSlave –Component Templates –Allows modeling of multiple circuits: charge side and coolant side Requires Heat Exchanger performance data –Performance data reduced to Nusselt no. correlation –Interactions between charge and coolant side conditions will be modeled

154. 154 SI Fuel Puddling Model

155. 155 SI Port Puddling Model Advanced model for predicting fuel puddles that may form on the wall of the ports At steady state, the presence of a puddle does not significantly effect results During start-up and transients, puddle can have a big effect on performance and emissions –If the puddle is growing (absorbing fuel), cylinder gets lean mixture and vice versa Injection rates are typically programmed to compensate for puddling effects, especially for cold start

156. 156 SI Port Puddling Very difficult modeling – Use only when specifically of interest Many detailed inputs to the model are required: fraction of fuel hitting the wall, droplet sizes, fuel evaporation (distillation) data Very sensitive to small changes in port temperature – requires careful use of the predictive FE wall temperature solver for cylinder and ports Computationally expensive (many cycles)

157. 157 SI Port Puddling Model Very powerful tool for those interested in simulating cold-start at a research level Can give advance information for programming injection controller before engine is available

158. 158 EGR (Exhaust Gas Recirculation)

159. 159 EGR Modeling Pulsation mixing is easy to model in GT-POWER. Simply “plumb” pipes into the model to model the exhaust recirculation path EGR Coolers can be modeled in varying degrees of complexity just like heat exchangers (intercoolers, aftercoolers, etc.) Two types of mixing: Pulsation and 3-D. 3-D mixing must be done with CFD coupling.

160. 160 EGR Modeling – Best Practices A valve should be included to control the EGR fraction –A PID Controller is typically used to target the EGR fraction by controlling the valve position –Model based controller is under development Use good initialization to reduce computational expense Pipe discretization must be considered to avoid “numerical diffusion”.

161. 161 EGR Modeling – “Numerical Diffusion” Air Exhaust Air + Exhaust Intake Manifold and runners modeled using general performance discretization lengths

162. 162 EGR Modeling – “Numerical Diffusion” Air Exhaust Air + Exhaust Intake Manifold and runners modeled using a smaller discretization length (~1/4 the size)

163. 163 Predictive Spark-Ignition Turbulent Flame Combustion Model (SITurb Model)

164. 164 Why Use Predictive Combustion? Non-predictive combustion is useful when the subject of the study does not directly affect combustion rate in a significant way –Exhaust acoustics –Intake manifold geometry Predictive combustion is required when subject of study directly affects combustion rate –Combustion Chamber Geometry –EGR rates –In Cylinder Flow ( VVT? ) Predictive combustion also useful for running a variety of loads/speeds where it may not be practical to impose burn rates from cylinder pressure

165. 165 SITurb Combustion Model

166. 166 Calibration If high EGR or residual gases, adjust ‘Dilution Exponent Multiplier’ for laminar flame speed Ignition delay (up to 2% cum. burn rate) –‘Flame Kernel Growth Multiplier’ –‘Initial Spark Size’ 50% burn point and 10-90% burn duration –‘Turbulent Flame Speed Multiplier‘ (most significant on 10- 90% burn point) –‘Taylor Length Scale Multiplier’ might be necessary (most significant on 50% burn point) Unburned Hydrocarbons –‘HC Model Ref. Object or Crevice Vol. Fraction’ –Adjust the rate equation multipliers in the tab called "Kinetic" of the 'EngCylHC'

167. 167 Predictive Diesel Combustion (DI Jet Model)

168. 168 Why Use Predictive Combustion? Non-predictive combustion is useful when the subject of the study does not directly affect combustion rate in a significant way –Exhaust acoustics –Intake manifold geometry Predictive combustion is required when subject of study directly affects combustion rate –Injection timing and profiles –EGR rates –Swirl Predictive combustion also useful for running a variety of loads/speeds where it may not be practical to impose burn rates from cylinder pressure

169. 169 DI Jet Combustion Model Predicts: –combustion rate –NOx –Soot (trends only) Accounts for: –Cylinder pressure and temperature –Injection timing, rate, velocity, and plume shape –Composition in the cylinder including fuel, fresh air, and EGR/residuals –In-cylinder flow (Swirl and Tumble) Based on Hiroyasu model

170. 170 How it works Predicts injection of liquid fuel into cylinder –Divides fuel jet into 5 radial, 80-100 axial zones –Each zone has 3 subzones Liquid fuel Unburned vapor fuel and air Burned products

171. 171 How it works Injection of liquid fuel –Fuel jet velocity –Breakup time (jet into droplets) Penetration distance Effect of swirl (flow model) –Fuel heating due to friction Air Entrainment Evaporation of Droplets Ignition delay Combustion (after ignition is detected) NOx & Soot

172. 172 Important Input Accurate Injection profiles (pressure is best – check Cd) Other injector parameters (diameter, timing, etc.) Swirl Piston Cup shape Measured cylinder pressure curves (calibrate)

173. 173 Calibration Match measured pressure using ‘EngBurnRate’ or TPA Base model with imposed burn rates should be well correlated BEFORE attempting DIJet calibration Can use single cylinder model to save time Calibrate at several points in operating range –Speed –Load –EGR rates All calibrated multipliers should be constant for all speeds, loads and EGR rates

174. 174 Calibration Examine calculated nozzle Cd (0.65-0.8) Ignition delay (up to 2% cum. burn rate) –‘Overall Combustion Delay Multiplier –‘Dilution Effect Multiplier’ if EGR is present Evaporation –‘Droplet Drag Multiplier’ (pre-mix spike at low loads) Entrainment –‘Mult. Before Combustion’ –‘Breakup Length Multiplier’ –One influences other Increase one, decrease other Vice versa

175. 175 Emissions DIJet model includes emissions models for NOx and soot Extended Zeldovich mechanism for NOx –Requires calibration to measured data using constants provided Three soot models provided (Nagle and Strickland- Constable recommended) –Useful only for predicting trends. –Requires calibration to measured data using constants provided

176. 176 Using DIJet in Models DIJet is slower than non-predictive combustion models Can use Master/Slave –impose burn rate from master cylinder onto all slave cylinders Can use startup cycles –Use non-predictive model to allow simulation to come closer to steady state before starting DIJet calculations

177. 177 Exhaust Aftertreatment

178. 178 Exhaust Aftertreatment GT-SUITE Basics Catalyst Modeling Diesel Particulate Filter Modeling

179. 179 GT-SUITE Species Overview General composition of: C a H b O c N d Defined in ‘Fprop*’ Note: (‘FPropPredefined’) Reactions only occur when initiated by: –‘EngCylinder’ –'CatalystBrick' –‘Burner’ –‘*Reaction’ (Detailed chemistry objects) ‘FPropGas’ and ‘FPropLiqIncomp’ species comprise the set of “basic” species

180. 180 GT-SUITE Species Overview

181. 181 Exhaust Aftertreatment GT-SUITE Basics Catalyst Modeling Diesel Particulate Filter Modeling

182. 182 Catalyst Modeling without Reactions If concerned only with the affect of the catalyst on back-pressure and/or acoustics, usually sufficient to ignore the reaction altogether If concerned with downstream affects of temperature rise from reaction one can simply impose wall temperature based on measured data If concerned with time required to achieve “light- off”, then focus on incoming gas temperature and ignore the reaction (light-off temp is usually known and this will be a study of “upstream” parameters)

183. 183 Catalyst Modeling Overview Catalysts can be modeled using ‘CatalystBrick’ template In terms of flow, identical to ‘Pipe’ template using multiple pipes –Convenient inputs to let code calculate number of pipes Chemistry templates connect to ‘CatalystBrick’ to allow flexible user defined kinetics

184. 184 Catalyst Modeling with Reactions If a true prediction of chemical reaction is needed, modeling of reactions available through following templates: GaseousReaction GlobalReactions SurfaceReactions KinCatReaction

185. 185 GaseousReactions Template  Elementary gas-phase kinetic mechanisms  Two options for reactions specification 1. directly typed into the template 2. imported from a file using valid CHEMKIN format  Advanced feature set –Reversible and irreversible reactions –Generalized Arrhenius formulation (A*f(T)*g(P)*Exp(-E a /RT) –Pressure-Dependent reactions main Rate Specificaiton Reaction Specification

186. 186 GlobalReactions Template  Global kinetic mechanisms  Arbitrary reaction order and concentration expression  Generalized inhibition functions including Langmuir and Hinshelwood type  Support various types of rate specifications  Mass transfer from and to surface

187. 187 GlobalReactions Application: Three-Way Catalyst  CO oxidation: CO + 0.5O 2 => CO 2  HC (unburned and partially burned) oxidations: CH 4 + 2O 2 => CO 2 + 2H 2 O C 3 H 6 + 4.5O 2 => 3CO 2 + 3H 2 O  NO oxidation: CO + NO => CO 2 + 0.5N 2 NO + 0.5O 2 NO 2 Similar reactions occur in diesel oxidation catalysts (DOC).

188. 188 Three-Way Catalyst Application

189. 189 SurfaceReactions Template  All relevant features of GlobalReactions Template  Coverage calculation  Supports storage and arbitrary functions of coverage in the rate specification

190. 190  NOx storage forming nitrite: BaCO 3 + 2NO 2 +.5O 2 => Ba(NO 3 ) 2 + CO 2 BaCO 3 + 2NO + 1.5O 2 => Ba(NO 3 ) 2 + CO 2  NOx regeneration: NOx release: Ba(NO 3 ) 2 + 3CO => BaCO 3 +2NO + 2CO 2 Ba(NO 3 ) 2 + H 2 + CO2 => BaCO 3 +2NO 2 + H2O NOx reduction: CO + NO => 0.5N 2 + 2CO 2 NO 2 NO + 0.5O 2 SurfaceReactions Application: Lean Nox Trap (LNT)

191. 191 Lean Nox Trap (LNT) Ba(NO 3 ) 2 BaCO3 Picture taken from Oak Ridge National Laboratory

192. 192 Integrated DOC with LNT

193. 193 KinCatReactionsTemplate  Allows coupling with Reaction Design's KINetics API CHEMKIN  Requires valid CHEMKIN API license  References CHEMKIN files for input data

194. 194 Exhaust Aftertreatment GT-SUITE Basics Catalyst Modeling Diesel Particulate Filter Modeling

195. 195 Diesel Particulate Filter Modeling Overview  Clean and loaded filter pressure drop  Filtration  Regeneration Capable of modeling variety of aspects: Modeling options:  Lumped model: multi-layer filtration model  1D model: multi-layer filtration and axially discretized  Standalone (computationally efficient time steps)  As a subsystem

196. 196 Modeling Pressure Drop Where: ∆P1 = pressure drop due to contraction ∆P2 = pressure drop due to friction in inlet channel ∆P3 = pressure drop due to through soot cake layer ∆P4 = pressure drop due to through filter wall ∆P5 = pressure drop due to friction in outlet channel ∆P6 = pressure drop due to expansion

197. 197 ∆P 2 + ∆P 5 = (α + w) μQμQ 2V trap 4FL 2 3 w (α – 2w s ) 4 ln + 1 α4α4 Modeling Pressure Drop Channels: Contraction: Expansion: ∆P 1 = handled by GT-POWER orifice connection; not by DPF model directly ∆P 6 = handled by GT-POWER orifice connection; not by DPF model directly

198. 198 ∆P 4 = μ u w w(i) k s (i) +  ρ u w 2 w  Modeling Pressure Drop w 1 μ u w (α – 2w s ) ∆P 3 = 2k p α – 2w s ln +  ρ u w 2 w s Soot Layer: Wall Section: Delta P Through Porous Media = DP_Darcy + DP_ Forchheimer = “linear” + “quadratic” Note: u w = velocityks = wall permeability  = Forchheimer Constantkp = particulate permeability

199. 199 Modeling Pressure Drop References: (1) SAE No. 2005-01-0946; (2) SAE No. 2002-01-1015; (3) Env.Sci.Tech., 1979,13,466-470 Study deep bed filtration using theories of uniform spherical collectors and Brownian diffusion. Local permeability and collection efficiency at each discretized slab can be evaluated according to local information such as porosity and collector diameters. Substrate Wall Thickness Cell Length Deep Bed Filtration Model

200. 200 Calibrating Pressure Drop 1.Calibrate pressure drop for “clean” filter using: 'Clean Filter Wall Permeability' ∆P 4 = μ u w w(i) k s (i) +  ρ u w 2 w 

201. 201 Calibrating Pressure Drop 1. If Pressure Drop vs. Flow appears quadratic optionally use: 'Forchheimer Constant‘ ∆P 4 = μ u w w(i) k s (i) +  ρ u w 2 w 

202. 202 Calibrating DPF Pressure Drop DBF = Deep Bed Filtration SCL = Soot Cake Loading “SCL”“DBF” 2. Filter Loading

203. 203 Calibrating DPF Pressure Drop 2. Filter Loading - Percolation Constant A. dimensionless constant ψ (0<ψ<1) B. experimentally determined C. values typically 0.80~0.95 D. determines partition coefficient Ф Where: d c,1 = loaded filter “unit collector” diameter of slab 1 d c,0 = clean filter “unit collector” diameter b = clean filter unit cell diameter (b = dc0 / (1 - ε0)1/3) Partition coefficient Ф determines amount of soot that will build up as soot cake during initial loading.

204. 204 Calibrating DPF Pressure Drop 2. Filter Loading – Permeability A.Loaded Filter Wall and Soot Cake Layer B.User Specified or Predicted (recommended) C.Dynamic in nature Clean Filter Permeability Loaded Filter Permeability

205. 205 Calibrating DPF Pressure Drop 2. Filter Loading – Packing Density A.Particulate Packing Density Inside the Substrate (filter wall) B.Particulate Packing Density Inside the Soot Layer (soot cake) Filter Wall Packing DensitySoot Cake Packing Density

206. 206 Calibrating DPF Pressure Drop 2. Filter Loading - Collection Efficiency directly specified - supplied by manufacturer predicted using – porosity, pore diameter, soot diameter, fluid viscosity Substrate Wall Thickness

207. 207 Lumped model  Uniform temperature at gas and solid phases  Uniform wall flow distributions  Computational efficiency 1D quasi-steady model  Axially different temperature and pressure  Non-uniform wall flow distributions  Detailed information Modeling Soot Regeneration

208. 208 Lumped Model Soot Regeneration Soot Oxidation Reactions: R1: C(s) + (1 - f CO /2)O2 → f CO CO + (1 - f CO )CO2 (thermal rxn) R2: C(s) + (1 - f CO /2)O2 → f CO CO + (1 - f CO )CO2 (catalytic rxn) Where f CO = CO selectivity and rxn rates defined by:

209. 209 Lumped Model: Sample Outputs

210. 210 Schematic of a detailed 1D DPF model Side view of channel configuration and its enlarged wall section 1D Axially Discretized Model

211. 211 1D Model Soot Regeneration Soot Oxidation Reactions: R1: C(s) + (1 - f CO /2)O2 → f CO CO + (1 - f CO )CO2 (thermal rxn) R2: C(s) + (1 - f CO /2)O2 → f CO CO + (1 - f CO )CO2 (catalytic rxn) R3: C(s) + NO2 → CO + NO (catalytic rxn) R4: NO + 0.5O2 ↔ NO2 (catalytic rxn)

212. 212 1D Model: Sample Outputs

213. 213 1D Model: Sample Outputs

214. 214 Soot Regeneration Fundamentally Lumped and 1D soot regeneration models are solved using different approaches. For example if 1D model is used with a single volume, the results may be slightly different when compared to Lumped Model.

215. 215 Calibrating DPF Regeneration 3. Calibrate Regeneration: Activation Energy Energy required to start soot oxidation Both for thermal and catalytic rxn K = reaction rate E = activation energy A = frequency factor Y 02 = concentration of 02 T = substrate temperature

216. 216 Calibrating DPF Regeneration 3. Calibrate Regeneration: Frequency Factor how fast reaction takes place both for thermal and catalytic rxn K = reaction rate E = activation energy A = frequency factor Y 02 = concentration of 02 T = substrate temperature

217. 217 Calibrating DPF Regeneration 3. Calibrate Regeneration: CO Selectivity determines the concentration of CO and CO 2 as soot oxidizes C + (1 - f CO /2) O 2 => f CO CO + (1 - f CO ) CO 2 –Value f CO ranging from 0 – 1 –CO Selectivity = 1 (all CO, results in lower heat release) –CO Selectivity = 0 (all CO 2, results in higher heat release)

218. 218 Calibrating DPF Overview 1.Calibrate “clean” DPF pressure drop using measured pressure drop vs. flow: A.'Clean Filter Wall Permeability’ (linear effects) B.‘Forchheimer Constant' (quadratic effects - optional) 2.Calibrate loading of DPF: A.Permeability: (1) loaded filter wall, (2) soot cake B.Collection Efficiency: (1) specify, (2) predicted C.Packing Density: (1) filter wall, (2) soot cake 3.Calibrate Regeneration: A.Activation Energy B.Frequency Factor C.CO Selectivity

219. 219 Acoustics (Non-Linear)

220. 220 Acoustic Tools Numerous tools exist for predicting acoustic characteristics of a system in GT-POWER The Non-Linear tools make use of spectral analysis, which involves converting data from the time domain to the frequency domain Linear tools exist which allow quick analysis in the frequency domain (limited applications)

221. 221 ‘AcoustTransLoss’ Calculates a 4 microphone transmission loss (SAE standard) between 4 sets of pressure data. Transmission Loss is the ratio of incident (forward) waves upstream and downstream of the silencing element “White” noise from speaker provides excitation Anechoic termination prevents wave reflections (eliminates “tail-pipe” effects) Four-Microphone method used to decompose pressure into forward and backward components

222. 222 ‘AcoustTransLoss’ Each sensor should be located at the center of a sub-volume Distance in object should be same as distance between SensorConns If comparing to measurements, location of SensorConns in model must be same as in measurement hardware Transmission loss calculations are sensitive to the number of points used. –Set a maximum time step of 0.088 degrees to use the maximum number of points in an FFT (4096)

223. 223 ‘AcoustTransLoss’ The Transmission Loss component can be used during a simulation or for post-processing. Use “Data” object to point to data from simulation Care must be used to ensure that all data points are stored during the initial simulation.

224. 224 Filters Low-pass filter available in GT-SUITE to eliminate high- frequencies in the sampled data Eliminates contamination possible during interpolation 24 th order works well for most applications (TransLoss, Mic)

225. 225 ‘AcoustExtMicrophone’ Used to activate the external radiation model and predict the radiated acoustic characteristics at a microphone. Microphone Output –dB vs. Frequency –dB Order Tracking –Linear Order and Frequency Contours –dB Order and Frequency Contours The microphone object can be used to analyze data during a simulation or post-process simulation or experimental data.

226. 226 Microphone Analysis Computes radiated noise (generated by flow pulsation) at an external microphone located in a free-field Assumes flow pulsation noise radiates as a monopole Includes Doppler effect for moving source, by finding relative velocity of source wrt microphone (Dot product of source velocity w/ source-mic vector) Multiple sources may influence 1 microphone (dual exhaust) In v6.2, flow noise model will be available

227. 227 Microphone – How it works ‘SensorConn’ passes velocity to the Microphone at every timestep Microphone converts velocity data into Pressure

228. 228 Microphone – How it works GT-POWER interpolates pressure signal to array better suited for FFT The microphone pressure is transformed by FFT from a signal in time to a vector of coefficients and frequencies Amplitude of oscillation at each frequency is calculated from:

229. 229 Microphone - Recommendations Limit frequency analysis usually to a max of 1000 Hz or less Use 24 th order filter Transient Window? –steady state: no –transient simulation: probably (Hanning recommended) Coherence – always choose coherent Hemispherical or Spherical radiation? –real world usually between the two –difference of only 3 dB

230. 230 ‘AcoustToWAVFile’ Used to make a *.wav file (sound file) Steady-state: repeat last cycle to make file of length determined by the user Transient: will make file of entire length of transient event (e.g. vehicle accleration) Recommendations: –Bits: 16 –Sampling Rate: 44.1 kHz –Filter: 54 th order

231. 231 Sampling and Aliasing Aliasing is a phenomena which causes the data containing certain frequency content to appear as if it contains content at a different frequency According to the Nyquist Theorem, aliasing will occur if data is sampled at a frequency below twice the highest frequency content of the signal Digital filtering of the signal after sampling cannot eliminate aliasing, as it is a result of the sampling itself

232. 232 Sampling and Aliasing

233. 233 Sampling and Aliasing

234. 234 Sampling and Aliasing Simulations create output in a discrete form, not a continuum. Data output created by simulation cannot be contaminated with aliased data However, if every simulation step’s output is not stored, the storage sampling could generate aliasing Even this is unlikely unless the simulation predicts very high frequency content –In the previous example, 3046 data points in 0.12 sec. gives a 25,383 Hz sampling frequency. –Predictions should be valid below 12,691 Hz (well above the 1000 Hz max usually analyzed in engine simulations)

235. 235 Data Windows Side-lobe leakage results from an FFT of non- periodic data, where the start and end points of the sampling interval are not identical, and can severely distort the FFT output Data windows prevent side-lobe leakage by multiplying the function by some periodic function. This forces the two end-points to be the same. Windowing will alter the frequency response, but this result is outweighed by the elimination of the side-lobe leakage (Hanning recommended). Data windows should be used for transient simulations with a high rate of RPM change (when a windowing technique has been used in the measured data)

236. 236 Data Windows

237. 237 ‘AcoustTf2Mic’ Two microphone analysis performed on the true dynamic pressure at two internal locations The transfer function is defined as the ratio of the amplitude (from FFT) of signal 1 to signal 2 at each corresponding frequency. Provides a measure of pressure amplitude ratio between two locations Includes reflection and “tail-pipe” effects When output as dB, known as noise-reduction

238. 238 ‘AcoustInsLoss’ Involves measuring the radiated noise from a system twice, once with full silencing element installed, once with it replaced with a straight pipe of equal length Provides measure of radiated noise reduction due to the silencing element

239. 239 ‘AcoustInsLoss’ A separate simulation can be run without the silencing element and velocity data stored Then a ‘Data’ object can be used to provide the stored velocity data to the Insertion Loss object within the simulation containing the silencing element

240. 240 Acoustics (Linear)

241. 241 Performs rapid acoustic calculations Operates in the frequency domain Tool for acoustic specialists who already use some type of linear analysis “Speaks” in acoustics language: impedance, p+, p- Same analyses can be done with non-linear GT- POWER model Can use same components with linear and non-linear solver Reduces number of tools used Linear Acoustic Model

242. 242 Methodology Individual matrices are then combined into a large matrix for the whole system Uses transfer matrix method (4-pole) to link (p 1,u 1 ) at one location to (p 2,u 2 ) at another location Transfer matrix is written for each component separately; example for a component with 2 ends:

243. 243 Pipe Flowsplit Orifice CatalystBrick EndEnvironment EndFlowCap Same Basic Flow Components as Regular Model

244. 244 If the frequency of interest is small enough such that This translates to freq < 1000 Hz for D < 200 mm, which is generally satisfied for typical transverse dimensions of exhaust mufflers. Plane Wave, 1-D analysis, therefore holds good in this case. When is Linear Analysis Applicable?

245. 245 Pros: Fast Captures general behavior Cons: Is not a full simulation Does not capture finer details When to use: Only general behavior is required Acoustic analysis must be carried out quickly When to Use Linear Acoustics

246. 246 Comparison to Non-Linear Results Expansion Chamber

247. 247 Linear Analysis Tools

248. 248 Linear Analysis Templates TemplateDescriptionAnalog AcoustSourceLinear SourceEndFlowSpeaker EndFlowRadiationLinear boundaryEndEnvironment, conditionsEndFlowAnechoic AcoustLinEigenEigenfrequenciesNone (order/mode (natural frequencies)storage flag) AcoustLinTransLossTransmission lossAcoustTransLoss & transfer matrix AcoustLinExtMicSound pressure levelAcoustExtMicrophone & more AcoustLinInsLossInsertion lossAcoustInsLoss TMBlackBoxLinear transfer matrixData

249. 249 Linear Acoustic Source Source characteristics provided as a function of: Frequency Engine speed for each order

250. 250 Common Sources General "speaker" source provided for Case where source isn't important (transmission loss) General studies Values used from a Multiload model Characterization of particular sources (engines)

251. 251 Radiation Boundary Allows the impedance of the boundary to be modeled Impedance data can also be read in from a file Boundary can be specified as: Open end (spherical) Flange (hemispherical) Anechoic termination Data (specify impedance values)

252. 252 It is important to determine the natural frequencies (resonances) of the system These are the frequencies that are being excited by driving inputs The resonances are identified by a frequency- sweep at fixed excitation level The resonances are viewable in the same ways as orders (OLT) Natural (Eigen) Frequencies

253. 253 Resonance Frequencies of System Linear Eigenfrequency Analysis Object Eigenfrequency Analysis Helmholtz and Quarter-Wave Resonator

254. 254 Viewing the Natural Modes

255. 255 Linear Transmission Loss Analysis Object Transmission Loss Calculation Quarter Wave Resonator

256. 256 {Transmission loss example}

257. 257 Linear Microphone Analysis Object Linear Insertion Loss Analysis Object Sound Pressure Level Calculation Example Muffler

258. 258 Linear Insertion Loss Analysis Object Insertion Loss Analysis Expansion Chamber Replacing Pipe

259. 259 Black Box Transfer Matrix The 'TMBlackBox' template allows the input of a transfer matrix Can represent one part or a whole assembly The transfer matrix can come from: Regular GT-POWER run using 'TMatrixGenerator' Experimental measurements Other software Analytical equations User-model

260. 260 Linear Analysis Effects ViscoThermal Viscosity and thermal effects are included. Temperature is taken from the "Wall Temperature" attribute in each component part. Mean Flow Mach number effects are included. Mass flow rate is taken from the "Initial Mass Flow Rate" attribute in each orifice connection part.

261. 261 Bridging the Gap Between Linear and Non-Linear Acoustic Analysis

262. 262 'TMatrixGenerator' Produce a Transfer Matrix representing a geometrical configuration that is then used by the 'TMBlackBox' component. 'MultiLoad' Find equivalent source characteristics (p, Z) of a part of the system that can then be used by the 'AcoustSource' component. Non-Linear Components

263. 263 Standard non-linear solution must be done with boundary conditions reversed to get transfer matrix Transfer Matrix Generator

264. 264 The time-based flow/pressure driving functions must be translated into frequency-based pressure/impedance Indirect calculation of source characteristics Can be done internally or externally Multiload Method of Source Characterization A load is connected to the source The response is calculated at multiple loads The response is a function of the load and source characteristics The source characteristics are averaged from the response at all loads

265. 265 The internal method uses the volumetric flow rate and pressure inside of the system Variable Length Pipe Multiload Method Internal

266. 266 The external calculated method uses the volumetric flow rate at the system end This method requires a linear analysis object Multiload Method External Calculated Variable Length Pipe

267. 267 The external measured method uses the pressure from a microphone part This method requires a linear analysis object Multiload Method External Measured Variable Length Pipe

268. 268 {Internal Multiload example}

269. 269 {External microphone example}

270. 270 Model Correlation

271. 271 OVERVIEW Correlation is desirable before using the model Physical nature of engine used to find causes Use RLT Post-Processor to find typographical and unit errors –Use average Pressure and Temperature find problem part –Input and initial values (diameters, lengths, etc.) available Multipliers must be used within reason

272. 272 Recommended Sequence for Naturally Aspirated Engines Manifold Pressure –Calibrate only highest speed –Flow losses (CDs, expansion diameters) Volumetric Efficiency –Flow losses (highest speed only) –Run whole speed range: look for heat transfer & tuning effects (see plots) Back pressure Cylinder Pressure Profile –Correct cylinder pressure needed to predict the correct IMEP Engine Friction and Auxiliary Loads

273. 273 Recommended Sequence for Turbocharged Engines Calibrate Airflow by Running the Model without the Turbocharger Cylinder Pressure Profile –Correct cylinder pressure needed to predict the correct IMEP and exhaust temperature Build the Engine with No Turbine –calibrate mass flow rate with mass multiplier Build the Engine with No Compressor –calibrate mass flow rate with mass multiplier Calibrate the Power –Adjust the efficiency multipliers to match powers: use outlet temps of each to determine which one(s) needs to be adjusted Connect the Turbine to the Compressor Engine Friction and Auxiliary Loads

274. 274 Calibrating Volumetric Efficiency Compare the VE over the whole range of speed or load Causes of Offset Differences: –Valve Lifts and/or Discharge Coefficients –Discharge Coefficients between Plenum and Runner –Charge Air Coolers –Throttles Effects of Heat Transfer on VE –Intake Port Heat Transfer: Temps., Roughness, Multipliers –Plenum and Runner Heat Transfer: Temps., Wall Solver in Boosted Engines

275. 275 Calibrating Volumetric Efficiency (2) Comparison of Different Intake Valve CD’s

276. 276 Calibrating Volumetric Efficiency (3) Comparison of Different Runner CD’s

277. 277 Calibrating Volumetric Efficiency (4) Comparison of Different Intake Port Wall Temperatures

278. 278 Calibrating Volumetric Efficiency (5) Comparison of Different Intake Wall Temperatures

279. 279 Calibrating Volumetric Efficiency (6) Valve Events: Factors that “Tilt” the VE curves –Valve Timing –Valve Lash Tuning and Runner Lengths –Significantly changes the speed of max. VE –Significantly distorts the VE curve Helmholtz Resonators –Changes the VE only at certain speeds

280. 280 Calibrating Volumetric Efficiency (7) Comparison of Different Intake Valve Timings

281. 281 Calibrating Volumetric Efficiency (8) Comparison of Different Intake Valve Lashes

282. 282 Calibrating Volumetric Efficiency (9) Comparison of Different Intake Runner Lengths

283. 283 Calibrating Volumetric Efficiency (10) Comparison of Different Resonators

284. 284 Matching the Cylinder Pressure Match the Cylinder Pressure at IVC –This will determine the mass trapped in the cylinder Match the Pressure during Compression –Check straightness of the measured logP-logV plot Match the Combustion using ‘EngHeatRel’ Adjust In-cylinder Heat Transfer

285. 285 Matching the Cylinder Pressure (2) Incorrect Compression Ratio indicated by curvature near Start of Combustion

286. 286 Matching the Cylinder Pressure (3) Incorrect Gauge Pressure indicated by curvature near IVC

287. 287 Matching the Cylinder Pressure (4) Incorrect Phasing indicated by low pressure during compression, high pressure during expansion

288. 288 Matching the Cylinder Pressure (5) Matching the Pressure during Combustion –Use ‘EngHeatRel’ to calculate heat release rate from the pressure profile –Use heat release rate as a burn-rate in ‘EngCylCombProfile’ (automatically made by ‘EngHeatRel’) Adjusting the Heat Transfer –Woschni correlation can be low –Largest Effect during Expansion –Can also effect Vol. Eff. –Has more influence at low speeds

289. 289 Engine Friction and Auxiliary Loads Calculated from Measured Motoring Torque (recommended) –Motoring Torque includes Pumping Losses –PMEP must be removed to get FMEP –Technique described in the GT-POWER User’s Manual Calculated from Indicated Torque Measurement (not recommended) –Subtract Net IMEP (over 720 degrees) from BMEP –Small errors in IMEP can result in larger errors in FMEP –Cylinder-to-cylinder variations can have a large effect Auxiliary Loads can be Modeled using a ‘Torque’ component –Connect to ‘EngineCrankTrain’: Torque subtracted from Brake Torque

290. 290 Transient Simulations

291. 291 Transient Engine Simulation The same GT-POWER solver is used for steady- state simulations works for transient simulation –Steady State Simulations are really just transient simulations that shut off when the results repeat cycle- to-cycle Primary difficulty simulating transients –All transient input data must be known and specified, including effects of Engine Control Unit (ECU), sensor/actuator response, and mechanical or pneumatic devices

292. 292 Transient Simulation Topics EngineCrankTrain modeling: impose speed versus calculate speed Engine loading (dyno, vehicle, propeller, etc.) Transient inputs - Controls and dependencies Initialization (i.e. cold start vs. warm start) RunSetup and OutputSetup

293. 293 EngineCrankTrain Modeling Imposed Speed –Constant speed or imposed transient –Transient imposed by reference object or controls Calculated Speed –Speed calculated from the torque difference and the ‘EngineCrankTrain’ inertia

294. 294 Imposed Crankshaft Speed When speed is imposed, torque is calculated –Predicted brake torque is the “LOAD TORQUE” required to produce the specified speed at the specified throttle position / fueling rate If transient speed is imposed, then realistic inertia must be input into ‘EngineCrankTrain’ Imposed transient speed recommended when speed transient is the dominant input of interest and known Most Common Application: Acoustic tests over speed transients

295. 295 ‘EngineCrankTrain’ Load Mode (Calculated Speed) An external “Load Torque” must be applied to the ‘EngineCrankTrain’ –Torque may come from a ‘Torque’ component (which may be constant, imposed torque transient, or actuated from controls) or the vehicle model (Engine Torque) - (Load Torque) = Acceleration Torque Speed calculated every timestep using the Acceleration Torque and the crankshaft inertia

296. 296 Load-Mode Example Applications Instantaneous (fluctuating torque) prediction for vibration studies, max torque, etc. Transient response studies –Turbocharger lag and responsiveness –Fueling control (diesel smoke limit control) –Sensor/actuator response sensitivity –Engine warm-up (couple with GT-Cool) Full powertrain simulation of engine with –dynamometer –vehicle –external models via controls (propeller, military)

297. 297 ‘EngineCrankTrain’ Configuration Speed Mode Load Mode - Imposed Torque Load Mode - Torque from Vehicle

298. 298 Transient Inputs Impose an “Event” with reference object or actuator –Throttle / Fueling step change –Engine speed change (speed mode) –Load Torque change (Load mode)

299. 299 Transient Inputs All inputs that depend on operating conditions must be accounted for with: – ‘RLTDependence’ –Controls actuation –Predictive/adaptive model For example, consider combustion rate –Predictive DIJet or SITurb models can be used –BUT... then injection timing/spark timing, injection profile shape, etc. must be carefully imposed –OR... Impose combustion rate (i.e. a function of speed and load) from existing data

300. 300 Transient Inputs Other inputs that often require transient data –Turbocharger (Wastegate or VGT rack position) –Cam timing –Cam lift (for variable lift engines) –F/A ratio (for SI engines) –Injection smoke limit (for diesel engines) –EGR valve control –Varying upstream/downstream pressure and temperature if full intake/exhaust are not modeled –Coolant temperature (for warm-up studies)

301. 301 Other Input Considerations All inertia inputs become critical –Turbocharger inertia –Crankshaft Inertia All initial conditions become very important –For Steady-State simulations, initial conditions impact computation time only but not final results –For Transient simulations, initial conditions have a big influence on the transient results (hot-start vs. cold start; initial engine and turbo speed, etc.)

302. 302 Initial Conditions Typically the simulation should be run for some cycles to get to a steady operating condition before imposing a transient event

303. 303 Initialize Thermal Conditions For cold start –Impose wall temps and initial fluid temps to ambient conditions For warm start –Case 1: Steady-state at the initial condition (idle); Wall Temperature Solver = Steady –Case 2: Transient event; Wall Temperature Solver = Transient –Initialization State = “previous_case”

304. 304 Setup Issues for Transient Simulation Simulation duration (typically seconds instead of cycles) Initialization State “Automatic Shut-Off When Converged” = Off “Time RLT Storage Multiple” = 1 to store transient data

305. 305 3-D Diesel Combustion (KIVA)

306. 306 KIVA merged into GT-POWER I KIVA-3V code integrated/merged into GT-SUITE At this time focused on diesel applications Work done in cooperation with Profs. Reitz and Rutland, complementing KIVA with U. of Wisconsin combustion/emissions models: –Kelvin-Helmholtz model (fuel jet breakup) –Rayleigh-Taylor model (fuel droplet breakup) –Shell model (ignition) –Lam/Turb characteristic timescale models (combustion) –Extended Zel’dovich mechanism (NOx) –Hiroyasu model (soot formation) –Nagle-Strickland model (soot oxidation)

307. 307 The “difficulty-barrier” of CFD was lowered: –KIVA runs only for a specified period falling between IVC and EVO –No valve motion -- simple grid –Model building done completely by GT-ISE, in the same fashion as for other GT-SUITE software –Setup tasks fully automated –Mesh generation fully automated KIVA merged into GT-POWER II

308. 308 KIVA merged into GT-POWER III GT-SUITE input to KIVA –Initial conditions (pressure, temperature), flow field ( turbulence, swirl), and composition –Injection parameters (injection rate, velocity, temperature) –Cylinder wall temperatures KIVA output to GT-SUITE –Thermodynamic parameters (average pressure and temperature, composition) –Turbulence and momentum parameters –Heat flux

309. 309 KIVA Merged into GT-POWER IV Multiple cycle/ case KIVA simulation have been enabled: –Transient simulations now possible with KIVA Integrated tool can run KIVA intermittently in specified cycles –Always runs last Cycle –During non-KIVA cycles, regular combustion and heat transfer KIVA runs only for user-specified duration between IVC to EVO

310. 310 Conventional KIVA Architecture

311. 311 GT-KIVA STRUCTURE GT-POST otape9 (itape9) Optional As Desired KIVA physical / chemistry models Data files: in-cylinder average properties GMV plot files otape12 (info at each timestep) otape8 (for restarting) GT-ISE itape17 (not needed, but available itape5 Itape18 itapeERC Specific graphic converter for advanced post-processing e.g., Ensight, Fieldview ….

312. 312 FLOW SOLVER: KIVA Initialization Phase A Phase B Phase C Read Input Data Calculate gas viscosity Initialize time step, piston velocity Spray Modeling (injection, drop breakup, collision, evaporation) Combustion chemistry Emission modeling Fluid phase calculation Mass, momentum, velocity, temperature, pressure, turbulence properties (implicit solver, iterations) Snapping/Rezoning grids Remapping fluid properties to new grids Update cell properties

313. 313 IN-CYLINDER MODELS Fluid Phase –Continuity equations –Momentum equations –Internal energy equations –K-epsilon equations Boundary Conditions –Physical boundaries: inflow/outflow, rigid walls, periodic boundaries. –Numerical boundary conditions: Temperature: adiabatic, fixed T. Velocity: free slip, no slip, turbulent law-of-the-wall Turbulent parameters: Droplets: handled by drop-wall impingement model

314. 314 DIESEL SCHEMATIC (ERC) Fuel injection nozzle cavitation spray breakup Drop/wall impingement Drop distortion vaporization turbulent dispersion Autoignition/combustion soot/NOx formation Wall heat transfer

315. 315 FUEL INJECTION AND ATOMIZATION R/D L/D Breakup length Jet breakup relative V induces instability drops shearing off liquid surface fastest growing wave ,  create child droplets and change parent drops size KH Model RT Model drop breakup deceleration/deformation of drops induce instability fastest growing wave ,  sudden breakup into droplets

316. 316 ERC MODELS

317. 317 MOVING MESH TREATMENT

318. 318 KIVA INPUT: INJECTION INFORMATION

319. 319 NOZZLE GEOMETRY Solid cone spray Hollow cone spray z-axis x-axis spray cone angle = spray cone angle spray thickness angle spray thickness angle Rot. ang. of spray in x-z plane Rot. ang. of spray In x-z plane z-axis Top-view of the nozzle and spray x-axis y-axis Rot. ang. of spray in x-y plane

320. 320 KIVA INPUT: CALIBRATION

321. 321 KIVA INPUT: ADVANCED

322. 322 OUTPUT Temperature at 30 degree plane

323. 323 OUTPUT Fuel and CO mass fractions

324. 324 Integrated 1-D/3-D Simulation

325. 325 Side-View into the Combustion Chamber

326. 326 @ 23 ATDC @ 11 ATDC +2 +14 +20 +33 T & drops soot & drops soot formed during the first injection has time to move up to squish region to oxidize by surrounding fresh air Second fuel pulse ignites right away Integrated 1-D/3-D Simulation

327. 327 Selection of Modeling Approach User has several options within the integrated tool: –Separate injection and performance simulations –Integrated inj+perf simulation with Jet Model –Integrated inj+perf simulation with KIVA/CFD There is a trade-off between simulation time and model detail, and the user can choose depending on the specific application Since all of these options are resident in one tool, user can make smooth transition from one detail level to another

328. 328 Summary An integrated simulation tool has been developed for modeling the transient behavior of a fuel injection system, combustion and overall engine performance The new integrated tool is built on: –GT-SUITE (GT-POWER + GT-FUEL) –KIVA-3V –enhanced by well established, advanced diesel engine combustion and emissions models

329. 329 Summary Advantages of integrated tool: –Accounts for interactions between injection system and combustion –Enables transient simulations –Includes 3-D combustion chamber analysis –Eliminates laborious and error-prone data transfer between different tools –Offers time-efficiency of calculations –Higher user productivity

330. 330 Summary Tool brings together in one place all of the modeling components needed to optimize fuel injection in diesel engines It will be complemented by a new capability, integrating it with test cell measurements: –Cylinder pressure analysis - combustion rates –Automated calibration of combustion models –Use of these models in predictive mode

331. 331 Mean Value and Real Time Modeling

332. 332 Mean Value / RT Motivation for MeanV modeling What is a MeanV model? MeanV model creation process MeanV Results Running “Real Time”

333. 333 Motivation A representation of the engine is required for “system level” modeling –Engine/powertrain control system design –Full vehicle system models (vehicle transients) –Hardware in the Loop (HIL) simulation Primary requirement for system level engine model is speed –HIL testing requires “real time” simulation

334. 334 Motivation Typical system level engine model is non- physical (i.e. lookup map) –Poor accuracy for complex engines VVT? EGR? Lambda? Spark Timing? Injection Timing? Smoke Control? VGT/Wastegate? Turbo Lag? Temperature?...

335. Motivation SYSTEM MODELS GT-POWER models physics but... CPU time too high (~100xRT) GT-POWER Detailed Models

336. Motivation SYSTEM MODELS GT-POWER MeanV Models GT-POWER Detailed Models GT-POWER Mean Value models balance accuracy and CPU time

337. 337 Motivation GT-POWER MeanV models are physics based MeanV models can run at “real time” Controls/systems engineers can share models with powertrain simulation engineers

338. 338 Mean Value / RT Motivation for MeanV modeling What is a MeanV model? MeanV model creation process MeanV Results Running “Real Time”

339. 339 Mean Value Models Simplified intake/exhaust systems –Large “lumped” volumes –No detailed wave dynamics Simplified “mean value” cylinder –No breathing or combustion predictions –Air flow, piston work, and exhaust energy imposed via neural networks Larger calculation timesteps –Up to 15 deg CA

340. 340 Mean Value Models

341. 341 Mean Value Cylinder Inputs Volumetric Efficiency –Mass flow rate imposed at the cylinder boundaries IMEP (or indicated efficiency) –Indicated work done on pistons Exhaust temperature (or exhaust energy fraction) –Enthalpy of exhaust products

342. 342 Mean Value Models Leftover fuel energy

343. 343 Mean Value Models Neural Network is a non-linear function approximator –Functionally similar to a lookup table –Handles many inputs –Calculations are very fast

344. 344 Mean Value Models Feed Forward Neural Network

345. 345 Mean Value Models Neural Network weights and biases need to be “trained” using a set of input/output data NN Trainer minimizes the following function using the Levenberg-Marquardt algorithm: J = a(SSE) + b(SSW) a, b are weighting factors SSE = sum of squared error SSW = sum of squared neuron weights Weighting factors (a,b) can be fixed or automatically adjusted (Bayesian Regularization)

346. 346 Mean Value / RT Motivation for MeanV modeling What is a MeanV model? MeanV model creation process MeanV Results Running “Real Time”

347. 347 Run DOE: Generate NN training data Determine dependencies for Voleff, IMEP, and Exh Temp, considering –Dependencies of real engine (VVT, EGR, etc.) –Input variable sensitivities –Intended purpose of the model Select variables with a direct influence –RPM, Int. Man. P/T, Exh. Man. P, EGR frac, inj. fuel mass, A/F, valve timing, inj. timing, spark timing, etc. Do NOT select variables with indirect influence –VGT-position, EGR-diam, Throttle-angle, WG-diam

348. 348 Run DOE: Generate NN training data Create parameters in the detailed model to vary each selected NN input (or closely related input) No Turbos (impose comp-out and turb-in P) Determine ranges of each parameter for DOE considering: –Intended operating range of the model –Transient effects –NN’s do NOT extrapolate well outside of training data boundaries

349. 349 Run DOE: Generate NN training data Use Latin hypercube DOE type –Randomness of Latin hypercube will give best possible NN fit

350. 350 Run DOE: Generate NN training data Some points will be impractical –i.e. High boost P, low exhaust manifold P, EGR-valve wide open. These points can be effectively filtered out later when training NN’s Preferred over trying to run only “valid” points –Difficult to determine the exact thresholds and relationships (in multi-dimensional space) –NN’s do NOT extrapolate well outside of training bounds –Difficult to maintain random dispersion of data provided by Latin hypercube

351. 351 Determine number of DOE experiments Required points is a function of number of inputs, surface complexity, fitting method quality, desired accuracy Case Study using a DI Diesel with EGR/VGT: Run DOE: Generate NN training data

352. 352 Run DOE: Generate NN training data Use data suppression options to avoid massive result files and memory issues

353. 353 Use distributed computing Run DOE: Generate NN training data Solver Node Distribution Server Client Computer Solver Node

354. 354 Train the NN’s Using DOE results, train the NN’s (Tools / Train Neural Network …) “Automatic” training option available –Tries different variations of each method and selects “best” (typically feed-forward) –Can be overridden by “User” selected NN options Filter out “unwanted” data as needed, i.e. –Reverse flow through engine –Reverse flow in EGR circuit –Excessive EGR rates –Excessive F/A ratios

355. 355 Train the NN’s Output of NN Training: –filename.nno (the NN to be called by MeanV model) –filename_nn.gp (GT-POST file with NN training result plots) –filename_nn.gdt (database of NN training results) –filename.nnt (Ascii training output file, similar to *.out for standard simulation) –filename.nnr (all inputs, outputs, and NN predictions in Ascii column format) –filename.nns (NN training statistics, Ascii format)

356. 356 Train the NN’s Analyze NN fit quality (regression plot in.gp) –Look at “Testing” data to avoid over-fitting

357. 357 Train the NN’s In case of over-fitting: –Retrain NN in “User” mode –Use Feed-forward with same # of neurons selected by “automatic” mode 16 = 10 (1st layer) + 5 (2nd layer) + 1 (hidden layer) 21 = 15 (1st layer) + 5 (2nd layer) + 1 (hidden layer) 26 = 15 (1st layer) + 10 (2nd layer) + 1 (hidden layer) –Set Objective Coefficients to “fixed” SSE Coefficient = 0.99 SSW Coefficient = 0.01 –Iterate on Coefficients until training and testing regressions are similar Increase in SSW will increase training scatter and decrease testing scatter.

358. 358 Training NN’s Select Exh Temp at manifold inlet

359. 359 Simplify Flow System Combine flow elements into “lumped” volumes –Maximize sub-volume size –No discretization –Conserve system volume –Use “Scale View” for volumes

360. 360 Simplify Flow System Conserve heat transfer area –Use “Initial Heat Transfer Area” RLT from detailed model (in RLT-Viewer) –Add all and impose

361. 361 Simplify Flow System Set expansion diameters according to real geometry –Used for expansion pressure losses –Used to calculate Cd’s for adjacent “def” OrificeConn’s (can indirectly affect solver timestep) –Critical when the port is attached to turbine or compressor with a “total” pressure flag –If unclear, set to smallest value that will not add unwanted restriction in system Characteristic lengths unimportant

362. 362 Imposing Heat Exchanger Performance Impose heat exchanger outlet temp using ‘RTCoolerConn’ –Removes or adds energy as needed in a connection –Upstream/Downstream option to preserve pressure drop dependence on temperature –Avoids need for Pipe “bundle” and associated inlet/outlet flowsplits (less parts, bigger volumes) –Avoids solver stability problems that can result from high heat transfer rates and large timesteps

363. 363 Imposing Heat Exchanger Performance ‘RTCoolerConn’

364. 364 Calibrating Pressure Drops Pressure drops through intake/exhaust system will generally require calibration Impose restrictive orifice diameter between lumped volumes Impose Voleff, ExhT in MeanV cylinder at max airflow operating point Use direct optimizer to vary orifice diameter to target delta pressure (work ambient cyl) Restriction

365. 365 Calibrating Turbine Performance Real turbo performance is influenced by pulsing flow This influence is lost in quasi-steady mean value model Can correct for pulsed flow effects using mass and efficiency multipliers in the turbine

366. 366 Run a separate DOE to characterize these multipliers –Use full detailed engine model including turbo –Vary speed and load –Turbine RLT’s provide necessary multipliers Calibrating Turbine Performance

367. 367 Calibrating Turbine Performance Check DOE results to see if multipliers are needed –May not be needed for VGT engines –More significant for fixed geometry turbines If needed, create lookup (or train NN) to output the multipliers as f(speed, load) –May need extra input variable for engines with multiple turbines (mass flow through turbine)

368. 368 Calibrating Steady Heat Transfer Lumped volume heat transfer may not be the same as detailed model, even with same area –Quasi-steady flow –Different velocity Calibrate exhaust manifold steady state heat transfer –Run at highest speed/load (max heat transfer) –Use direct optimizer to vary heat transfer multiplier to target turbine inlet temperature

369. 369 Calibrating FMEP Friction (FMEP) requires calibration if detailed model included a dependence on peak cylinder pressure Train a neural network using original DOE results and same input variables as IMEP

370. 370 Add NN’s to Model

371. 371 Add NN’s to Model

372. 372 Calibrating Transient Heat Transfer Calibrate exhaust manifold transient heat transfer –Run at highest speed and load point –Initialize manifold walls “cold” (transient wall solver on) –Requires completed model (transient capable with NN’s) –Vary thermal capacitance of manifold walls to match transient warm-up behavior

373. 373 Filter NN Output NN response to step change in input will be instantaneous On real engine (or detailed model) cylinder firing is staggered Use 1 st Order Filter on NN output Tau = 30 / RPM

374. 374 Real Gas option “off” –No need for compressibility correction –Faster computations Maximum Timestep “def” –When MeanV cylinder in the model, “def” = 15 deg Misc. Final Model Setup

375. 375 Mean Value / RT Motivation for MeanV modeling What is a MeanV model? MeanV model creation process MeanV Results Running “Real Time”

376. 376 Results 4-cylinder DI diesel example –GTI\v6.2.0\examples\GTpower\meanv\detailed-FTP75.gtm –GTI\v6.2.0\examples\GTpower\meanv\meanv-FTP75.gtm Injection smoke control logic EGR with Cooler (up to 35% EGR) Variable geometry turbine VGT rack control (target boost pressure) Intercooler Full intake and exhaust systems –Airbox –Diesel Oxidation Catalyst and Particulate Filter –Muffler

377. 377 Results Transient U.S. EPA FTP 75 Drive Cycle (1 st 300 sec. shown) Imposed Engine Speed Profile (from GT-DRIVE kinematic simulation with representative vehicle) Imposed Pedal Position (to match required BMEP from GT- DRIVE simulation)

378. 378 Results

379. 379 Results

380. 380 Results

381. 381 Results

382. 382 Results

383. 383 Results

384. 384 Results

385. 385 Results

386. 386 Results

387. 387 Results Mean Value model runs ~150x faster than detailed model, and runs faster than real time –Detailed Model = 73299 sec. (20+ hours) –MeanV Model (gtsuite.exe) = 502 sec. (~8 min.) –MeanV Model (gtsuite_RT.exe) = 237 sec. (~4 min.) –Real Time = 1372 sec. (~23 min.) On a desktop PC, 2.13 GHz Intel Core2 processor

388. 388 Mean Value / RT Motivation for MeanV modeling What is a MeanV model? MeanV model creation process MeanV Results Running “Real Time”

389. 389 Running Real Time Requires RT hardware/software solution –Simulation speed must be faster than RT –RT machine delays data exchange to clock time –Typical target ~50% CPU utilization

390. 390 Running Real Time Use special “RT” version of GT-SUITE solver –Optimized for speed –Extra “bulk” in standard solver removed –No RLT’s, plots or other output stored Coupled run with Simulink model –Results sensed, passed to Simulink via harness Executable that runs on RT machine is compiled by user –Includes GT-SUITE RT solver –Exact steps depend on supplier of RT solution

391. 391 Steps to Run Real Time 1.Create mean value model 2.Change project type to “GT-SUITE-RT” 3.Run “stand-alone” with the RT solver executable (optional) Check for errors and CPU runtime

392. 392 Steps to Run Real Time 4.Run coupled with Simulink model using RT solver library (optional) Check for errors and CPU runtime Check operation of Simulink controls/vehicle Check results passed via Simulink 5.Compile to target RT machine and run Steps depend on supplier of RT solution

393. 393 Steps to Run Real Time STEP 1. Create MeanV model 3. Test RT solver (stand-alone) 4. Test RT solver (w/ Simulink) 5. Run on RT hardware Proj. Type GT-POWER (or GT-SUITE) GT-SUITE-RT SolverStandard exeRT exeRT library Compiled to RT machine* Run from:GT-ISE SimulinkRT Software* License POWER (or SUITE) RT RT (create.dat) None (runtime) RT (create.dat) None (runtime) ResultsAllNone Sensed via Simulink PlatformAny Windows or Linux_x86 Windows or Linux_x86 Windows or Linux_x86* * Depends on RT supplier

394. 394 What You Need to Run Real Time GT-SUITE V6.2, Build 6+ –Includes RT solver executable RT files from GTI (not in installation) –Includes RT solver library –Includes all files required to compile to RT machine GT-SUITE-RT license from GTI GT-SUITE compatible RT Solution

395. 395 Running Real Time GT-SUITE compatible RT solutions available from: AandD Cybermetrics dSpace ETAS RTI

396. 396 CFD Coupling

397. 397 Available CFD Codes Coupling may be performed between GT-SUITE and the following 3D CFD codes: –STAR-CD –FLUENT Check current manual for version compatibility

398. 398 Typical Applications in which GT- SUITE and a CFD code are Coupled Study Cylinder-to-Cylinder EGR Distribution –Two causes: pulsations and mixing –GT-SUITE can be used to study pulsation induced distribution (use smaller discretization lengths, ~10mm) –CFD is needed to study mixing effects In-cylinder 3-D modeling with dynamic boundary conditions Other 3-D effects that are not modeled in GT- SUITE

399. 399 Methodology Domains –CFD domain is represented in GT-SUITE model using ‘CFDComponent’ –Zones (or regions) are selected in CFD model to communicate with GT-SUITE Transferring Boundary Conditions –Pressure, velocity, temperature, density and mass species are averaged over a zone (or region) in the CFD domain. This zone should be equal to roughly 1 discretization length (DX) from the GT-SUITE model. –These averages are calculated at each time step –Inlet or pressure boundary conditions are allowed in the CFD domain, but inlet is strongly recommended.

400. 400 Methodology (2) Pre-cycles –GT-SUITE runs pre-cycles, so CFD code will begin simulation with well developed boundary conditions –CFD Component in GT-SUITE is modeled as a ‘FSplitSphere’ during pre-cycles Time Steps –CFD code is the “master” program, GT-SUITE is a library of it –CFD code takes imposed step –GT-SUITE steps are dynamic, not constant. GT-SUITE takes as many steps as needed to meet CFD time step exactly (last one may be shortened, as needed)

401. Methodology (3) Restarts –GT-SUITE saves the latest boundary conditions from CFD in a *.cfd file –When restarting, GT-SUITE reruns the pre-cycles using the boundary conditions from the pre-cycles –Coupled cycles are started using the GT-SUITE boundary conditions Parallel Processing –GT-SUITE runs on 1 processor –No changes needed in GT-SUITE model –Use “Standard” Domain Decomposition in STAR-CD

402. 402 Recommendations Selection of regions in the CFD domain –Ensure the flow in the boundary region in the CFD domain is one-dimensional –Flow in the region that is not one-dimensional may cause numerical instabilities, divergence or incorrect average quantities (which will cause incorrect results). Time Step Sizes –Do not make the Time step in the CFD code larger than 2 crank angle degrees in an engine simulation. –GT-SUITE should take about 2 or 3 time steps per CFD time step. The benefit of coupling is reduced when the boundary conditions do not change for more than 3 time steps.

403. 403 CFD Dump The boundary conditions provided by calculations using the 0-D Flowsplit during the precycle may not work well for some coupled simulations – many coupled cycles must be run to reach convergence. CFD Dump allows one to run a separate model using GT- SUITE only in which the CFD domain is modeled using many 1-D components. This provides better boundary conditions for the coupled cycles. Use SensorConn parts to sense the B.C.s (using the sensed quantity ‘CFD Dump’) and send the signal to a CFDDump part (which is in the Controls folder of the template library) Boundary conditions are written to the *.cfd file of the non- coupled GT-SUITE model Use the *.cfd (restart) file of the non-coupled model to restart the coupled model

404. 404 In the GT-SUITE Model Replace the components to be simulated by the CFD code with a single ‘CFDComponent’. Select the tracking method. Between the ‘CFDComponent’ and the adjacent ‘Pipe*’ component, place a ‘CFDConn’ connection. Must be placed where flow is nearly one dimensional. (Very Important!) In each ‘CFDConn’ part the boundary type must be specified. “inlet” is strongly recommended. When linking to the ‘CFDComponent’, the port numbers must correspond to the region/zone numbers defined at each boundary in CFD model.

405. 405 In the GT-SUITE Model (cont.) Complete Run Setup –determine number of cycles to be run in the CFD code –take this number + the number of startup or pre-cycles listed in the ‘CFDComponent’ part and enter this for simulation duration –set the Fluid Steady State Multiplier to 0 so GT-SUITE will not converge before coupling begins –Restart data saved automatically via *.cfd file –Only one case may be run in coupled simulations Complete Plot Setup When the GT-SUITE model is complete, save it and select “Create.dat file” from the run menu in GT-ISE Copy the *.dat file to the CFD code’s working directory

406. 406 In the STAR-CD Model For each boundary described as “inlet”, the boundary condition must be specified with respect to the global coordinate system. GT-POWER flag must be turned on inside of the StarGUIde/Analysis Controls/Other Controls/Miscellaneous Controls folder. The SCALARS in the STAR-CD model must be defined to match the fluid properties in the GT- SUITE model. –The number of SCALARS in the STAR-CD model must be the same as the number of ‘FPropGas*’ and ‘FPropLiq*’ objects in the GT-SUITE model.

407. 407 In the STAR-CD Model (cont.) In addition, if combustion exists in the model, additional scalars must be defined that give the properties of the products of combustion. Two options exist for the products of combustion: track each species or lump all species into one. If the lumped option is used, then one additional SCALAR must be defined to represent the lumped products of combustion. If the track option is chosen, then eleven additional scalars must be defined. The SCALARS must be defined in the order corresponding to the GT-SUITE objects. The order is FPropGas* and then FPropLiq* objects. Within each group of objects, the order of scalars must correspond to the alphabetical order of the GT-SUITE object names.

408. 408 In the STAR-CD Model (cont.) The cell type of each cell in the boundary region must be set to the region number + 100.

409. 409 In the STAR-CD Model (cont.) Enter the time step size and number of time steps. Choose the time step size and other numerical controls in STAR-CD such that the simulation is stable and that the STAR-CD simulation runs longer than the GT-SUITE simulation. In the STAR-CD working directory, create a file called star.gtp. On the first line enter the name of the GT-SUITE product (e.g. GTpower, GTcool), on the second line enter the input data file (without.dat extension) and on the third line enter the license type (GTpowerX or GTsuite). Check that the environment variable that points to the GT- SUITE dynamic or shared libraries has been set. Run the GTI supplied starlink script with the -G option (not required in STAR-CD v3.24). Invoke STAR-CD as usual.

410. 410 In the FLUENT Model Ensure that the name of the species in GT-SUITE matches the Chemical Name of the corresponding species in FLUENT. Naming can originate in either code but must be consistent. The order of the species in FLUENT is important, FLUENT expects the last species to be the bulk species. Specify an operating pressure which typically is the absolute mean pressure in the CFD domain.

411. 411 In the FLUENT Model (cont.) Specify location of shared library and name of GT-SUITE data file in FLUENT’S 1-D Simulation Library window. Follow FLUENT guidelines for location of libraries. Select Start to read in GT- SUITE’s data file and automatically create User Defined Functions (UDF’s) for each boundary zone. UDFs created based on the Part name of the CFDConn objects. Specify boundary conditions for flow inlets, flow outlets, and walls. Flow boundary conditions can be: mass flow inlet, velocity inlet, pressure, inlet, and pressure outlet.

412. 412 In the FLUENT Model (cont.) If mass flow inlet or pressure inlet is specified, select Normal to Boundary as the Direction Specification Method. If velocity inlet is specified, select Magnitude, Normal to Boundary as the Velocity Specification Method. Set numerical controls such as under-relaxation factors, pressure-velocity coupling method, and discretization accuracy (first order, second order, etc.).

413. 413 In the FLUENT Model (cont.) Initialize the CFD domain. Note could first run GT-SUITE alone to provide better initial conditions for the CFD domain. Enter a time step size. Also enter the number of iteration per time step such that convergence is obtained on each time step. If convergence is poor, consider reducing time step size or increasing under relaxation factors. Enter the total number of time steps. Note the total number of time steps should include 10 extra time steps for every coupled cycle run to ensure that GT-SUITE completes writing to the output file, *.out at the end of the simulation.