Mft Crankshaft Proe Ansys Avl

7/29/2019 Mft Crankshaft Proe Ansys Avl

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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



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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



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Comparison between original and optimized oil film pressure

original best



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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



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Similar conclusions for roughness vs power loss



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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

Mft Crankshaft Proe Ansys Avl

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