mft crankshaft proe ansys avl
TRANSCRIPT
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Robust Design Crankshaft OptimizationRobust Design Crankshaft Optimization
A multi-disciplinary real application case
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Model definition and parameterisationModel definition and parameterisation
Engine crankshaft, dynamically coupled with 5 radial bearings and 1axial bearing
The geometry of the crankshaft (web shape) is defined andparameterised by ProEngineer
10 geometric variables, that are assigned to the web side (Beziercurves) and to fillet radius
Rup
Bezup
RmidRdown
Bezdown
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Modal Analysis by ANSYS WorkbenchModal Analysis by ANSYS Workbench
The first natural frequency is computed byANSYS Workbench (~150 Hz with free-free BC)
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EquivalentEquivalent discretizeddiscretized model by AVLmodel by AVL AutoshaftAutoshaft
AVL-Autoshaft AVL-Excite
The crankshaft is discretized by finite volumes: the equivalent model of masspoint is computed
AVL-Excite uses this model to calculate the forces (inertial, active and
reactive) acting on the crankshaft and in particular in correspondence of thebearings
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Dynamical coupling with bearings by AVLDynamical coupling with bearings by AVL--
ExciteExcite
The equivalent model of crankshaft is coupled with the bearings
The general data (bearing type, friction coefficients, oil pressure andviscosity, roughness and clearance, etc.) are specified
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Hydrodynamic analysis of bearings by AVLHydrodynamic analysis of bearings by AVL
ExciteExcite
The fitness of the bearings isquantified by the evaluation of threefunctions, calculated by a not-
stationary hydrodynamic analysisof the bearings in a complete cycle
The complete cycle analysis isrequired since the contact point (and
thus risk of damage) in the bearing isnot fixed during a cycle
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Definition of stochastic variables andDefinition of stochastic variables and
optimisation objectivesoptimisation objectives
Oil film pressure is considered as deterministic variable [1-10bar]
10 geometric deterministic variables (web shape) Bearing clearance and roughness are stochastic variables
(due to manifacturing tolerances): defined by a fixed mean
value and a standard deviation
D fi i i f i i i bj i
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Definition of optimisation objectivesDefinition of optimisation objectives
(deterministic and stochastic)(deterministic and stochastic)
Obj.1: minimize crankshaft mass (DETERMINISTIC)
Obj.2: maximize first natural mode frequency (DETERMINISTIC)
Obj.3: minimize mean of maximumoil filmpressure (STOCHASTIC)
Obj.4 minimize mean of oil filmpowerloss (STOCHASTIC)
Obj.5 maximize mean of oil film thickness (STOCHASTIC)
Constr. on standard deviation of oil film pressure, loss andthickness to be lower than original (STOCHASTIC)
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Design parameters
Input variables
10 geometric var for crankshaft, oil pressure, 10stochastic var for bearings
Output variables
Mass, frequency, oil film
power loss, pressure and thickness
Design goals
Minimize mass, mean of power loss and pressure
Maximize frequency and mean of thickness
Optimizations set-up data
Sampling phase:
Original configuration nr initial individuals: 1
Exploration phase:
MOGT scheduler 6 Simplex iterations for 4 players steps
Constraints
Standard deviation of oil filmoutput variables less than
original
modeFRONTIER workflow setupmodeFRONTIER workflow setup
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Color=Powerloss
Diameter=Max pressure
46 designs of the total 180 computed by MOGT belong toPareto front (not-dominated)
The numbers indicatePareto front designs
modeFRONTIER postmodeFRONTIER post--processingprocessing
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Pareto front designs are compared relatively to the 5 objectives
Original and best design 156 (arbirarychosen) are indicated in green
modeFRONTIER postmodeFRONTIER post--processingprocessing
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objective constraint
Mass
[mm3
]
Frequency
[Hz]Mean
pressure
[bar]
Mean film
thickness
[mm]
Mean loss
[W]
pressure
[bar]
film
thickness
[mm]
loss
[W]
original 3.77 106 150 79.3 1.174E-5 628 6.77 1.48E-7 64.3
best 3.54 106 164 77.0 1.175E-5 612 6.77 1.48E-7 64.3
original best
modeFRONTIER postmodeFRONTIER post--processingprocessing
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Comparison between original and optimized oil film pressure
original best
modeFRONTIER postmodeFRONTIER post--processingprocessing
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Values of clearance for 3 bearings vs power loss:only one bearing indicates a inverse linearity
The distribution of clearancevalues is normal such as defined
modeFRONTIER postmodeFRONTIER post--processingprocessing
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Similar conclusions for roughness vs power loss
modeFRONTIER postmodeFRONTIER post--processingprocessing
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ConclusionConclusion
The basic definition ofRobust Design has beenformulated
The necessity of Robust Design has been
clarified by two examples in different fields
The application ofMulti Objective Game Theoryalgorithmhas revealed particularly efficient forthe solution of these kind of applications