analysis and simulation of lap time: innovative tools for design and structural behavior evaluation...

ANALYSIS AND SIMULATION OF LAP TIME: INNOVATIVE TOOLS

FOR DESIGN AND STRUCTURAL BEHAVIOR EVALUATION OF A

COMPETITION GO-KART CHASSIS

University of Rome “Tor Vergata”

PH.D in Mechanical Systems Design

XXIV Cycle

Ing. Marco Urbinati

Tutor : Prof. Marco E. Biancolini

Coordinator: Prof. Carlo Brutti

University of Rome“Tor Vergata”

Go-Kart... In

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Go-Kart... In

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Design processes state of art In

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Telemetry experimental

data

Complex kinematic model

Simplified model

Wo

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Chassis characterization

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From the vehicle to PC

Moving

vehicle Sensors Electrical

signal

Anlog to digital

converter

Data stored

into ECU

1

Data stored

into ECU

Data stored

into PC

Base 2 to real

data conversion Graphical representation

and analysis

2

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Go-Kart telemetry system

Dashboard

GPS

Brake& Accelerator

potentiometer

Expansion with

lateral&longitudinal

acceleration sensor

Water, exhoust and tire

temperature sensoors

Exhoust valve sensor

Simplification of the real model to a

material point constrained on a

trajectory.

GPS data: objective

information of the

vehicle position along

the track Sim

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Sim

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(GPS data)

Trajectory sampling

Acceleration calculation

Speed profile determination during braking

and acceleration

OUTPUT:

-velocità

-accelerazioni

-laptime

Lap time definition

Comparison with experimental data

Curvature determination along the

trajectory

Chassis data input:

• sproket

• Weight

• tire

Dynamic data input:

• Lateral&longitudinal

acceleration

• Frontal area

• Cd

Engine data input:

• Power

• Torque

• ηtr

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Trajectory sampling and curvature determination


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Coordinate transformation


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Minimum speed point ≡ max lateral acceleration




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Four “models” for curvature determination

are tested:

1. Resampling constant model

2. Interpolation of experimental data

model

3. Curvature direct formula model

4. Osculating circle model



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[m]

Curvature

Input data…

Acceleration diagram


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Power & Torque

of the engine

5 103

1 104

1.5 104

2 104

0

10

20

30

40

0

10

20

30

Motore_rawid_m otore 2 Motore_rawid_m otore 3

Motore_rawid_m otore 1

Cx & Frontal

Area

Tire data

Defining point of minimum speed ad the speed

profile for braking and acceleration


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Defining point of minimum speed ad the speed

profile for braking and acceleration

Envelope of in and out speed for each corner


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Acceleration Braking

Tuning of the lumped model


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Min

speed

fitting

Brake

fitting

Exit

fitting

Tuning of the lumped model


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Simulated Lap time: 47.172 sec VS Real data lap time: 47.110

Speed

Longitudinal acceleration Lateral acceleration


Sim

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rFactor: what and why…

1. Advanced car racing simulation

2. Ability to play any kind of four-wheeled vehicle

3. Includes complex simulation models of the wheels and aerodynamics and a

physics engine with 15 degrees of freedom

4. Open architecture for simulator implementations by external users (cars,

circuits, championship, general add-on)


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rFactor: what and why…

1. Advanced car racing simulation

2. Ability to play any kind of four-wheeled vehicle

3. Includes complex simulation models of the wheels and aerodynamics and a

physics engine with 15 degrees of freedom

4. Open architecture for simulator implementations by external users (cars,

circuits, championship, general add-on)

5. Can be used with most complex systems of driving simulators


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rFactor…inside


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rFactor…inside

Constraint: a suspension

system for the front and rear

had to exist

Kart model implementation…


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1 Engine & sproket

KZ Category

KF Category



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1 Engine & sproket

KZ Category

KF Category

// KF1 25.0 N a 11.500

// Engine data generated by PhysicsEditor

RPMTorque=( 0.0, -0.7, 0.0)

RPMTorque=( 500.0, -1.1, 1.71)

RPMTorque=( 1000.0, -1.6, 3.40)

RPMTorque=( 1500.0, -2.2, 5.09)

RPMTorque=( 2000.0, -2.8, 6.74)

RPMTorque=( 2500.0, -3.4, 8.37)

RPMTorque=( 3000.0, -4.0, 9.96)

RPMTorque=( 3500.0, -4.7, 11.50)

RPMTorque=( 4000.0, -5.3, 12.99)

RPMTorque=( 4500.0, -6.0, 14.42)

RPMTorque=( 5000.0, -6.7, 15.78)

RPMTorque=( 5500.0, -7.4, 17.06)

RPMTorque=( 6000.0, -8.1, 18.27)

RPMTorque=( 6500.0, -8.8, 19.39)

RPMTorque=( 7000.0, -9.5, 20.42)

RPMTorque=( 7500.0, -10.1, 21.36)

RPMTorque=( 8000.0, -10.8, 22.20)

RPMTorque=( 8500.0, -11.4, 22.93)

RPMTorque=( 9000.0, -12.0, 23.56)

RPMTorque=( 9500.0, -12.5, 24.07)

RPMTorque=( 10000.0, -13.0, 24.48)

RPMTorque=( 10500.0, -13.5, 24.77)

RPMTorque=( 11000.0, -13.9, 24.94)

RPMTorque=( 11500.0, -14.2, 25.00)

RPMTorque=( 12000.0, -14.5, 24.75)

RPMTorque=( 12500.0, -14.8, 23.99)

[…]

Code implementation



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2 Tires



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2 Tires

//

[SLIPCURVE]

Name="Default"

Step=0.009000 // Slip step

DropoffFunction=0.00 // See explanation above

Data: 0.000000 0.174836 0.349483 0.518060 0.668882 0.790665 0.878928 0.936783 0.971287 0.989751

0.997978 1.000000 0.999865 0.999478 0.998839 0.997952 0.996820 0.995447 0.993838 0.992000

0.989937 0.987659 0.985172 0.982486 0.979609 0.976552 0.973322 0.969932 0.966391 0.962709

0.958897 0.954965 0.950924 0.946785 0.942557 0.938251 0.933876 0.929442 0.924958 0.920432

0.915874 0.911292 0.906693 0.902084 0.897473 0.892866 0.888269 0.883688 0.879128 0.874595

0.870092 0.865624 0.861195 0.856808 0.852466 0.848173 0.843931 0.839741 0.835607 0.831529

0.827510 0.823550 0.819651 0.815813 0.812037 0.808324 0.804674 0.801087 0.797563 0.794103

0.790705 0.787371 0.784098 0.780888 0.777739 0.774651 0.771624 0.768656 0.765747 0.762895

0.760102 0.757364 0.754682 0.752055 0.749482 0.746961 0.744492 0.742074 0.739707 0.737388

0.735117 0.732894 0.730717 0.728585 0.726497 0.724453 0.722452 0.720492 0.718572 0.716693

0.714852 0.713050 0.711285 0.709556 0.707863 0.706205 0.704581 0.702990 0.701431 0.699904

0.698409 0.696943 0.695508 0.694101 0.692722 0.691371 0.690047 0.688750 0.687478 0.686231

0.685009 0.683811 0.682636 0.681484 0.680355 0.679247 0.678161 0.677095 0.676050 0.675025

0.674020 0.673033 0.672065 0.671115 0.670183 0.669268 0.668370 0.667489 0.666624 0.665775

0.664941 0.664123 0.663319 0.662530 0.661754 0.660993 0.660245 0.659511 0.658789 0.658080

0.657383 0.656699 0.656026 0.655365 0.654716 0.654077 0.653449 0.652832 0.652226 0.651629

0.651043 0.650466 0.649899 0.649341 0.648793 0.648253 0.647722 0.647200 0.646686 0.646181

0.645683 0.645194 0.644712 0.644238 0.643771 0.643312 0.642860 0.642415 0.641977 0.641545

0.641120 0.640702 0.640290 0.639884 0.639484 0.639091 0.638703 0.638321 0.637945 0.637574

0.637209 0.636849 0.636494 0.636145 0.635800 0.635461 0.635126 0.634796 0.634471 0.634151

0.633835 0.633523 0.633216 0.632913 0.632614 0.632320 0.632029 0.631742 0.631460 0.631181

0.630906 0.630635 0.630367 0.630103 0.629842 0.629585 0.629332 0.629081 0.628834 0.628590

0.628350 0.628112 0.627878 0.627646 0.627418 0.627192 0.626970 0.626750 0.626533 0.626319

0.626107 0.625898 0.625692 0.625488 0.625286 0.625088 0.625000

Code implementation



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3 Chassis

Hummer

Stock car

Touring car

Street car



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3 Chassis



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3 Chassis

Structural data

CarFactory software: for rfactor vehicle suspension editing KPChassis software: for

chassis structural analysis



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3 Chassis

Driveline definition for Artifical Inteligence

GPS data line

AI path line

Fitting the line manualy



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3 Chassis

Driveline definition for Artifical Inteligence



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3 Chassis

The choice…

Detailed cornering analysis

Best A

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Data comparison between the chosen model (red) and real data (blue)


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Model optimization…


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1 Tire model

New set values:

SpringBase=10000

SpringkPa=850

Damper=500

DryLatLong=(2.6, 2.85)

WetLatLong=(1.85, 2.15)

Data comparison between the chosen model (red) and real data (blue)


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1 Tire model

New set values:

SpringBase=10000

SpringkPa=850

Damper=500

DryLatLong=(2.6, 2.85)

WetLatLong=(1.85, 2.15)

2 Chassis inertia

Software CarFactory:

Used for inertia conditions

optimization for vehicle

models implemented in

rFactor


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Final model results


Des

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Standard design process

New chassis

design Production

Track test and

race

Historical

experience

Evaluation tests and

driver feedback


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Standard design process

New chassis

design Production

Track test and

race

Historical

experience


driver feedback

Proposed design process

New Chassis

design Production

Track test and

race

Historical

experience


driver feedback

Simulation

Compare and

upgrade data


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Proposed design process

KP-Chassis implementation

Updating rFactor code with ChassisMod

Simulation and testing in rFactor

Develop of

the geometry

of the frame

Optimized

shape of

the frame


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Sensitivity Test

Chassis1 Road Rebel:

chrome molybdenum steel tubing

32 mm diameter tubes

Chassis2 Black Star:

chrome molybdenum steel tubing

30 mm diameter tubes for rails

32 mm diameter tubes for crossbars

ChassisMod: software developed for quikly implement chassis data


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For each type of frame were compared to the actual data with those obtained during the

simulation, in order to identify the differences and verify that the recorded response had

remained sufficiently reliable

TEST A

Real (blue) VS

Virtual (red)

Chassis 1


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TEST A

Real (blue) VS

Virtual (red)

Chassis 1

Real (blue) VS

Virtual (red)

Chassis 2


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TEST A


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TEST B

The real data of the test of the two frames were compared between them by determining

what were the differences due to the two different geometries, then the same analysis

was carried out on simulated data to verify that the sensitivity of the simulator was able to

actually declare the same result.


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TEST B





Real data of

Chassis 1(blue) VS

Chassis2 (red)


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TEST B





Real data of

Chassis 1(blue) VS

Chassis2 (red)

Virtual data of

Chassis 1(blue) VS

Chassis2 (red)


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TEST B





1. The simplified model shows a good sensitivity to the variations of

the input parameters, allowing the use to a level of analysis mainly

limited to them.

2. The adoption of a more complete and complex model allowed us to

identify the kinematic configuration that provides the best

correspondence between the behavior of the frame present in the

simulator and the real one.

1. It was created a simplified and quick to use go-kart simulation

model;

2. The representative model of a go kart frame was implemented into

rFactor, approximating a three-body model with stiffness obtained

from structural analysis of the same.

3. It was presented and trained a innovative design process and a

wide margin of development.


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1. Extension of the work implementing all the tracks of a

championship to broaden the spectrum of response of the model.

2. Promote the design process presented for the design of go kart

frames in which the dynamic simulation can become an essential

tool as much as track testing.

3. Extend the possibilities of using the model associating it with a

physical simulator (under construction) for driver training and

chassis optimization on individual tracks.


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Thanks for your attention ...


analysis and simulation of lap time: innovative tools for design and structural behavior evaluation...

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