Predicting Valve Train Dynamics using Simulation with Model Validation Brice Willis, Engineer Kevin Ireland, Engineer Computational Engine Research Honda R&D Americas, Inc.
Overview 2 Objectives Validation Test Setup Model Build Model Validation Other Systems Achievements Q & A
Objective 3 Develop and validate 2D valve train modeling methodology using GT-SUITE and motoring bench testing Develop a modeling methodology that can be used consistently for different valve train configurations and engine operating conditions Minimize model calibration and maintain accuracy A validated model can help; Reduce overall development cost Lead design Reduce required testing Predict, evaluate, and improve valve train system performance Design of experiments Evaluate cam profiles Evaluate valve spring stiffness Capture dynamic valve lift
Testing-Simulation: One Approach 4 X The test plan does not always consider what measurements are needed to validate the simulation. This leads to high levels of model calibration and non-repeatable model correlation.
Testing-Simulation: Another Approach 5 Planning test measurements for model validation will reduce model calibration and increase the repeatability of model correlation.
Validation Test Setup 6 Validation testing was completed using motoring bench testing Constant engine temperature (25 C to maintain build condition) Speed sweeps (0-6800 RPM) High and low valve timing/lift configurations Crank Encoder Wheel Painted Valves with Camera Gap Sensor Rocker Arm Strain Gauges Valve Spring Strain Gauges Motoring RPM Valve Lift via calibrated voltage Valve Lift 1mm Measured strain tappet force Measured strain spring stress *Exhaust rocker shown Validation testing was intended to capture tappet force and valve lift over a range of engine speeds while maintaining the build condition.
Test Instrumentation Layout 7 Total of 40 channels R = Rocker Arm strain gauge S = Spring Strain Gauge G = Gap sensor V = Painted Valve T = Thermocouple T Due to the engine configuration, instrumentation of multiple cylinders allows six different valve train configurations to be evaluated (Front & Rear / Intake & Exhaust / High & Low).
Model Build Overview 8 μμ PP = μμ oo ee αααα Material Properties Part/Assy. Geometry (3D CAD) Valve Train Design & Layout 2D Valve Train Model Raw Information Intermediate Model Direct GT Input Optional GT Input
Model Build Overview 9 μμ PP = μμ oo ee αααα Material Properties Part/Assy. Geometry (3D CAD) Valve Train Design & Layout Mass Properties 2D Valve Train Model Kinematic VT Layout Raw Information Intermediate Model Direct GT Input Optional GT Input
Model Build Overview 10 μμ PP = μμ oo ee αααα Material Properties Part/Assy. Geometry (3D CAD) Measured Valve Train Design & Layout Mass Properties 2D Valve Train Model Kinematic VT Layout Raw Information Intermediate Model Direct GT Input Optional GT Input
Model Build Overview 11 μμ PP = μμ oo ee αααα Material Properties Part/Assy. Geometry (3D CAD) Measured Valve Train Design & Layout Mass Properties 2D Valve Train Model Kinematic VT Layout Raw Information Intermediate Model Direct GT Input Optional GT Input 3D Finite Element Analysis Part Stiffness
FEA Rocker Stiffness Prediction 12 Rocker shaft and cam are defined as rigid bodies Frictionless contact between roller, cam, rocker shaft, and rocker Fixed condition applied to rigid bodies Deflection measured from nodes at the center of the contact radius FF FF Force applied to tappet through valve stem contact along valve axis The rocker stiffness was predicted independent from the valve train assembly and supporting structure. Using 3D finite element analysis (FEA) the rocker was defined as a flexible body while the cam and shaft were defined as rigid bodies.
FEA Rocker Stiffness Prediction 13 KK rrrrrr = FF δδ cos φφ The red geometry annotations represent the 2D rocker element (as defined by GT) roller rocker shaft contact center FF φφ δδ Actual rocker geometry is represented by the shaded green image. The rocker shaft is a fixed joint free to rotate and the roller is free to rotate and slide. KK rrrrrr = rocker stiffness FF = force along valve axis (load axis) δδ = deflection along valve axis φφ = angle between valve axis and rocker perpendicular The deflection values predicted by FEA are used to calculate the stiffness The method used to predict rocker stiffness is consistent with GT s rocker element definition.
FEA Rocker Support Stiffness Prediction 14 All applicable contacts are enforced FF FF Forces are applied to rigid body reference nodes in space Rocker components are modeled as rigid bodies. All relevant boundary conditions are applied The rocker support stiffness was predicted independent of the rocker arm and includes all relevant supporting structure and boundary conditions. Rigid body rockers were used to load the assembly.
FEA Rocker Support Stiffness Prediction Symmetry boundary condition to cut faces 15 Apply pretension loads to all relevant bolts Apply clearance contact to rocker shaft, cam, and journals Fixed boundary condition to faces of head bolt sleeves The boundary conditions include; bolt pretension, shaft clearances, all contact, fixed conditions at head bolts, and symmetry definitions.
FEA Rocker Support Stiffness Prediction 16 KK ii = FF ii δδ ii FF xx δδ xx Rigid body reference nodes δδ yy FF yy Loading direction must be consistent with X and Y directions in the respective GT model. Predict stiffness in each direction separately. Apply load along an axis that is coincident with the rocker shaft (pivot) center. FEA Vertical GT Y-axis FEA Horizontal GT X-axis KK ii = pivot stiffness FF ii = force along local axis δδ ii = deflection along local axis ii = xx, yy Using the rocker to load the support structure captures the contact footprint The support stiffness prediction considered the contact footprint between the rocker to the shaft and was calculated in X & Y directions consistent with GT s coordinate system.
Calculating Stiffness for GT 17 FF ii mm = KK ii = 2 ii = NN yy = mmmm + bb (linear fit) δδ ii = deflection predicted by FEA δδ ii = shifted deflection predicted by FEA FF ii = interval force FF ii = shifted interval force FF mmmmmm = maximum force ii = interval number KK = calculated stiffness linear fit slope NN = number ofloading intervals integer ii = 1 δδ ii FF ii = ii FF mmmmmm NN ii = 1,2,3,, NN δδ ii = δδ ii δδ 1 FEA Linear fit KK FF ii = FF ii FF 1 An average stiffness for the rocker and its support was calculated by measuring respective deflections at predetermined force intervals.
Key Model Input Requirements 18 Basic (w/o VTEC) High Valve Timing Low Valve Timing Requirement VT Configuration Dependencies Must Have Rocker Stiffness Basic (w/o VTEC) Rocker Stiffness High Valve Timing Rocker Stiffness Low Valve Timing Support Stiffness (X&Y) Base (w/o VTEC) Support Stiffness (X&Y) High Valve Timing Support Stiffness (X&Y) Low Valve Timing Cam Journal Stiffness (X&Y) Independent Valve Spring Stiffness Independent Should Have Could Have Valve Stiffness Independent Valve Seat Stiffness Independent Spring Retainer Stiffness Independent Cam Journal Clearance Independent Valve Guide Clearance Independent Valve Lash Independent The rocker and support stiffness are dependent on operating valve train configuration and are key model inputs. Additional key model inputs include; clearances, journal stiffness, valve lash, and component mass.
Intake High Tappet Force 19 Intake High Tappet Force at 4000 RPM Intake High Tappet Force at 5000 RPM Force (N) CH27:CYL4-A Force (N) CH28:CYL4-B CH29:CYL6-A GT Intake High Tappet Force at 6000 RPM Intake High Tappet Force at 6800 RPM Force (N) Force (N) Good correlation of magnitude and frequency was observed for the tappet force of the intake high rocker.
Intake High Valve Lift 20 Intake High Valve Lift at 4000 RPM Intake High Valve Lift at 5000 RPM Lift (mm) CH99:CYL4-B CH101:CYL6-A Lift (mm) GT Intake High Valve Lift at 6000 RPM Intake High Valve Lift at 6800 RPM Lift (mm) Lift (mm) Good correlation of magnitude and closing response was observed for the valve lift of the intake high rocker.
Intake High Frequency Map 21 Frequency spectrum shows resonance correlation for the intake high rocker.
Intake Low Tappet Force 22 Intake Low Tappet Force at 4000 RPM Intake Low Tappet Force at 5000 RPM Force (N) CH27:CYL4-A Force (N) CH28:CYL4-B CH29:CYL6-A GT Intake Low Tappet Force at 5500 RPM Intake Low Tappet Force at 6000 RPM Force (N) Force (N) Good correlation of magnitude and frequency was observed for the tappet force of the intake low rocker.
Intake Low Valve Lift 23 Intake Low Valve Lift at 4000 RPM Intake Low Valve Lift at 5000 RPM Lift (mm) CH99:CYL4-B CH101:CYL6-A Lift (mm) GT Intake Low Valve Lift at 5500 RPM Intake Low Valve Lift at 6000 RPM Lift (mm) Lift (mm) Good correlation of magnitude and closing response was observed for the valve lift of the intake low rocker.
Intake Low Frequency Map 24 Frequency spectrum shows resonance correlation for the intake low rocker.
Valve Train with Hydraulic Lash Adjuster 25 Fixed Rocker Pivot HLA Rocker Pivot The methodology used for the previously explain fixed pivot system was expanded to include a system using a hydraulic lash adjuster (HLA) at the pivot.
Engine Oil Viscosity 26 Barus viscosity-pressure formula: Barus formula was used to calculate dynamic μμ PP = μμ oo ee αααα μμ PP = dynamic viscosity (kg/m-s) μμ 0 = dynamic viscosity at atmospheric pressure (kg/m-s) αα = viscosity-pressure coefficient (Pa -1 ) PP = pressure (Pa) viscosity of oil under high pressure Barus formula is valid for pressures under 0.5 GPa α is unknown and can be a function of pressure and temperature Pressure effects on oil viscosity must be considered. For this study, the Barus formula, with a calibrated viscosity-pressure coefficient, was applied. Along with small calibration of oil aeration and leak down pressure.
Key Model Inputs for HLA 27 Requirement Oil Temperature Oil Bulk Modulus as a function of pressure and temperature Oil Viscosity as a function of pressure and temperature HLA Part Leak Down Time (per spec.) HLA Part Leak Down Time as a function of applied load/chamber pressure HLA Starting Height Must Have Should Have Could Have *All applicable inputs from a fixed pivot system must also be applied Oil Bulk Modulus as a function of pressure and temperature is known Oil Viscosity is known for temperatures between 40 and 150 C at atmospheric pressure Applied Barus Formula αα is unknown Calibration of αα provides HLA model accuracy for all engine speeds and operating temperatures Oil properties become a key model input for valve train systems that include a HLA. Other key inputs include; the leak down time and starting height of the HLA
HLA Cyclic Test: LDT & Temp. at 1000 RPM (Cam) 28 HLA Hysteresis Overlay at 60C HLA Hysteresis Overlay at 120C LDT = 2.20s Measured GT HLA Hysteresis Overlay at 60C HLA Hysteresis Overlay at 120C LDT = 0.45s HLA Hysteresis Overlay at 60C HLA Hysteresis Overlay at 120C LDT = 0.20s Cyclic test results were used calibrate oil pressure-viscosity coefficient. HLA behavior correlates well for multiple leak down times (LDT) and oil temperatures (60 and 120 C). Verifying the model for LDT and temperature.
HLA Cyclic Test: 1000-8000 RPM (Cam) 29 Simulation and test results diverge as engine speed increases, but correlation remains good between 1000 and 4000 RPM. This verifies the HLA model over a range of engine speeds.
Tappet Force Rocker Strain w/hla Pivot 30 Tappet Force - Rocker Strain 5000 RPM Force (N) Microstrain GT Measured Oil Temperature = 120 C Using a limited data set, the frequency of the predicted tappet force was correlated to measured strain.
Valve Lift w/hla Pivot 31 Valve Lift at 5000 RPM Valve Lift at 5500 RPM Lift (mm) Lift (mm) Measured GT Valve Lift at 6000 RPM Oil Temperature = 120 C Valve Lift at 6500 RPM Lift (mm) Lift (mm) Good correlation was observed for valve lift magnitude and closing response.
Achievement 32 GT-SUITE accurately predicts dynamic responses for modeled valve train systems Modeling methodology validated via correlation of predicted and measured valve train system responses Tappet force Valve lift Valve spring response Modeling methods are valid for multiple valve train configurations and engine operating conditions High and low valve timing With and without HLA High and low temperature High and low engine speed
Questions 33
Appendix 34
Calculating Tappet Force 35 Measured strain from rocker arms was used to calculate tappet force Slope was used to scale measured strain to calculate force Intake Slope = 0.002361969 Exhaust Slope = 0.001843369 Component level testing of rocker arms was used to determine a calibration factor to convert measured strain to tappet force.
Model Build Overview 36 μμ PP = μμ oo ee αααα Material Properties Part/Assy. Geometry (3D CAD) Measured Valve Train Design & Layout Mass Properties 2D Valve Train Model Kinematic VT Layout Raw Information Intermediate Model Direct GT Input Optional GT Input 3D Finite Element Analysis Part Stiffness
Exhaust Spring Stress 37 The frequency of measured spring strain and predicted spring shear stress shows good correlation in the exhaust springs for multiple engine speeds.
Intake Spring Stress 38 The frequency of measured spring strain and predicted spring shear stress shows good correlation in the intake springs for multiple engine speeds.
Exhaust Tappet Force 39 Exhaust Tappet Force at 4000 RPM Exhaust Tappet Force at 5000 RPM Force (N) CH20:CYL4-A Force (N) CH21:CYL6-A CH22:CYL6-B GT Exhaust Tappet Force at 6000 RPM Exhaust Tappet Force at 6800 RPM Force (N) Force (N) Good correlation of magnitude and frequency was observed for the tappet force of the basic exhaust rocker.
Exhaust Valve Lift 40 Exhaust Valve Lift at 4000 RPM Exhaust Valve Lift at 5000 RPM Lift (mm) CH97:CYL4-A CH100:CYL6-B Lift (mm) GT Exhaust Valve Lift at 6000 RPM Exhaust Valve Lift at 6800 RPM Lift (mm) Lift (mm) Good correlation of magnitude and closing response was observed for the valve lift of the basic exhaust rocker.
Exhaust Frequency Map 41 Frequency spectrum shows resonance correlation for the basic exhaust rocker.
Next Steps 42 Use current models to support and drive design Increase development support Build confidence in model predictions Expand modeling capabilities Preform further validation