• Non ci sono risultati.

CFD Analysis and Multi-Objective Optimization of a Diesel Engine for Automotive Applications

N/A
N/A
Protected

Academic year: 2022

Condividi "CFD Analysis and Multi-Objective Optimization of a Diesel Engine for Automotive Applications"

Copied!
29
0
0

Testo completo

(1)

CFD Analysis and Multi-Objective Optimization of a Diesel Engine

for Automotive Applications

Fabio Bozza

DIME - Università di Napoli “Federico II”, Napoli, ITALY

M. Costa, D. Siano

Istituto Motori CNR, Napoli, ITALY

(2)

Introduction

 New combustion concepts for Diesel Engines

yHCCI (Homogeneous Charge Compression Ignition)

yPCCI (Premixed Charge Compression Ignition)

yPLTC (Premixed Low Temperature Combustion)

yCAI (Controlled Auto-Ignition)

y

 Objective: further reduction of Soot - NOx

 Additional Constraints: IMEP - BSFC, HC - CO, Noise levels, reliability, driveability, costs, …

(3)

Introduction

 Each combustion mode best behaves in its own (and limited) engine operating range

 A switch between standard mode (Premixed- Diffusive) and advanced ones is required

 Available control parameters:

yInjection Pressure, Modulation and Phase (CR)

yBOOST Level (LP/HP)

yEGR (Short/Long route, VVA systems)

ySwirl Ratio (Dedicated control valves)

yFuel properties (Dual-Fuel supplied engines)

(4)

Introduction

 How to realize the control of a so large number of parameters?

 Employment of advanced and integrated numerical (and experimental) tools

1D modeling (intake-exhaust system & turbo-matching)

3D modeling (spray behavior, combustion, emissions)

Additional models (prediction of radiated noise)

 Definition of proper optimization techniques

Identification of optimal values of control parameters for Emission, Fuel Consumption and Noise minimization

(5)

Objective

 Light-Duty Common-Rail Engine Development

 1st Section: Experimental Analyses

 Performance tests, Acoustic tests on a BASE engine

 Characterization of the CR - Fuel Injection System

 2nd Section: Models Setting & Validation

 1D model, 3D model, Radiated Noise model

 3rd Section: Optimization (Base Engine + CR)

 Injection strategy parameterization (triple-injection)

 Definition of control parameters and objectives

 Results analysis: selection of optimal solutions

(6)

1

st

Section: Experimental Activity

 Performance tests on a BASE, Mechanical Injection Engine

4t–1 cyl. (505 cm3), Full load

 Radiated Noise is measured by

a free-field microphone at 1m from the source

 Spray characterization of the CR injection system on dedicated test-benches

Injector: micro-sac, 5 holes, D/L: 0.13mm/1.0mm

Rail Pressure: 28-140 MPa, Density: 12-20 kg/m3

Injection Rate Measurements and Spray Images

(7)

1

st

Section: Inj. Rate Measurements

 Different injection strategies are analyzed:

 Low Load (3.4 mg): Triple injection, 28 MPa

 Medium Load (11.87 mg): Triple injection, 71 MPa

 High Load (26.35 mg): Single injection, 140 MPa

 Injection Gauge Rate System (AVL) based on a Bosch tube principle

0 500 1000 1500 2000 2500 3000 3500 Time, µs

0 0.04 0.08 0.12 0.16

Injection Rate, mg/s

Low Load (Mf=3.40 mg, Pinj=28 MPa) Medium Load (Mf=11.87 mg, Pinj=71 MPa) High Load (Mf=26.35 mg, Pinj=140 MPa)

High Load

Low Load Medium Load

(8)

1

st

Section: Spray characterization

 Image analysis procedure (imaging technique):

 Spray images are collected by a CCD camera, 1280x1024 pixels, 8 bit, 0.5 µs exposure time

 Liquid spray tip penetration and the spray cone angle are reconstructed on 10 single shot images

0 50 100 150 200 250 300 350

Time, µs 0

5 10 15 20 25 30

Tip Penetration, mm

ρ=12.5 kg/m3

First Pulse Main Pulse

ρ=20.6 kg/m3

Medium Load

0 100 200 300 400 500 600 700 800 900 1000 Time, µs

0 10 20 30 40 50 60

Tip Penetration, mm

ρ=12.5 kg/m3

High Load

Medium Load (Main Pulse)

(9)

2

nd

Section: 1D Analysis, Base Engine

 GT-Power code:

 Provides initial conditions for the 3D model

1200 1600 2000 2400 2800 3200

Engine Speed [rpm]

20 25 30 35 40 45

Air Flow Rate [kg/h]

Numerical Experimental

1D Engine layout

(10)

 Numerical and experimental tip penetration:

 High Load, high density case: Single shot, 140 MPa

 Experimental fuel flow rate is employed

 Hug-Gosman and Wave models are tested

 Wave model gives good agreement with an adjusted C1 constant

2

nd

Section: 3D Spray analysis

0 100 200 300 400 500 600 700 800 900 1000 Time, µs

0 10 20 30 40 50 60

Tip Penetration, mm

Experimental

Numerical (Wave C1=60)

(11)

-60 -30 0 30 60 Crank Angle, deg

0 20 40 60 80 100

Pressure, bar

Num.

Exp.

2

nd

Section: 3D Analysis, Base Engine

 Fire code (AVL)

 Spray model employs the measured pinj

 Break-up: Wave model

 Combustion: ECFM-3Z

 NO: Zeldovich

 Soot: Nagle et al.

 Validation with

experimental data

3D domain:

40000 cells

2200 rpm Full Load

(12)

2

nd

Section: Noise Emission Model

 Torregrosa’s Approach:

 Decomposition of the

in-cylinder pressure cycle

 High-pass filter (4.5kHz) on the total pressure FFT

res comb

mot

tot p p p

p = + +

mot tot

excess res

comb p p p p

p + = =

101 102 103 104

Frequency [Hz]

80 120 160 200 240

Pressure Amplitude [dB]

4.5 kHz

-90 -60 -30 0 30 60 90

Crank Angle, [deg]

0 25 50 75 100

Pressure [bar]

Total Pressure Motored Combustion Resonance

-20 -10 0 10 20

Crank Angle [deg]

-0.4 -0.2 0 0.2 0.4 0.6

Pressure [bar]

Resonance Pressure

(13)

2

nd

Section: Noise Emission Model

 Proper indices are defined:

 In (accounts for mechanical noise):

 I1 (accounts for pressure gradients):

 or

 I2 (accounts for resonance phenomena):

=

mot comb idle

dt dp

dt dp n

I n

max max 1

=

idle

n n

I log10 n

+

=

mot

comb comb

idle

dt dp

dt dp dt

dp n

I n

max

2 max 1

max

1





=

dt p

dt p I

mot res 2

2 6

10

2 log 10

(14)

2

nd

Section: Noise Model Validation

 Overall Noise Correlation [dB]:

 Ci: adjustable tuning constants

 Validation on acoustic measurements on

the BASE engine:

 Good agreement !

1200 1600 2000 2400 2800 3200

Engine Speed [rpm]

90 95 100 105 110 115

Overall Noise [dB]

Exp Calc

2 2 1

1

0 C I C I C I

C

ON = + n n + +

(15)

2

nd

Section: Remarks

 Proper models are available for Base Engine and spray evolution realized by the CR system

 A prototype Engine is “Numerically” analyzed, equipped with the previously tested CR system

 Medium Load operating point: 2200rpm @ 11.87mg/cyc

 A optimization procedure detects the best

injection profile to minimize FC-NO-Soot-Noise

(16)

3

rd

Section: Optimization

 Parameterization of the injection profile:

 5 degrees of freedom:

 SOIP, pilot%1, pilot%2, dwell_1 and dwell_2

 3D Run window [-123°,110°]: pdV

IMEP V

HP +

=

123110

123

1

0 500 1000 1500 2000 2500

Time, µs 0

0.02 0.04 0.06 0.08

Injection Rate, mg/s

Experimental Profile Parameterized Profile

1 2

3 4

8

10 9

pilot%_1

soip

dwell_1 pilot%_2

5

6 7

dwell_2

-60 -40 -20 0 20 40 60

Crank Angle, deg 0

20 40 60 80 100

Pressure, bar

10-9 10-8 10-7 10-6 10-5 10-4 10-3 10-2 10-1

NO Mass Fraction

Experimental Profile Parameterized Profile

10-5 10-4 10-3

Soot

NO

Soot Pressure

(17)

 Optimization Layout (ModeFRONTIERTM code)

Pilot%_2 Dwell_2

3

rd

Section: Optimization

SOIP

Dwell_1

Pilot%_1

Injection Profile coordinates

3D run

Noise Model

Noise

IMEP Soot

NO

Objectives Parameters

3D input file

(18)

3

rd

Section: Optimization Results

 Optimization Method (Scheduler): MOGA II

 Multi-Objective Problem:

 Infinite solutions located on Pareto Frontiers

 How to select a single Solution? MCDM tool

 Set of preferences are defined to identify compromise solutions:

 1st set: High IMEP (same as Low Fuel Consumption)

 2nd set: Low NO

 3rd set: Low Noise

(19)

 NO-Soot trade-off (Pareto-Frontier) ∼500 points

 Bubble-size:

 HP-IMEP

3

rd

Section: Optimization Results

 1st sol: #297

High IMEP

1st sol.

2nd sol.

3rd sol.

3rd sol.

0 5 10 15 20 25 30 35 40 45 50

NO, g/kgFUEL 0

5 10 15 20 25 30

SOOT, g/kgFUEL

0 1 2 3 4 5

NO g/kgFUEL 0

5 10 15 20 25 30

SOOT, g/kgFUEL

Zoom

Pareto-Frontier

HP-IMEP 3.5 bar 5.5 bar

 2nd sol: #428 Low NO

 3rd sol: #115

Low Noise

(20)

0 5 10 15 20 25 30 35 40 45 50 NO, g/kgFUEL

0 5 10 15 20 25 30

SOOT, g/kgFUEL

0 1 2 3 4 5

NO g/kgFUEL 0

5 10 15 20 25 30

SOOT, g/kgFUEL

Zoom

Noise Level

Pareto-Frontier

98 dB 110 dB

3

rd

Section: Optimization Results

 NO-Soot trade-off (Pareto-Frontier)

 Bubble-size:

 NOISE

 1st sol: #297

High IMEP

2nd sol: #428 Low NO

3rd sol: #115

Low Noise

1st sol.

2nd sol.

3rd sol.

3rd sol.

(21)

3

rd

Section: Optimization Results

0 1 2 3 4 5

HP-IMEP, bar 96

100 104 108 112

Overall Noise, dB

428

115 297

0 1 2 3 4 5

HP-IMEP, bar 0.0x100

2.0x10-4 4.0x10-4 6.0x10-4 8.0x10-4 1.0x10-3 1.2x10-3

NO Mass Fraction

428 297

115

1st sol.

2nd sol.

3rd sol.

1st sol.

2nd sol.

3rd sol.

 NO-IMEP  Noise-IMEP

 Alternative Representations:

IMEP Penalty 1.2 bar

Noise Reduction8 dB

(22)

3

rd

Section: Optimal Modulation

 1st Sol: High IMEP (#297):

 Quasi-Homogeneous combustion mode (HCCI)

 2nd Sol: Low NO (#428):

 Near-TDC injection

 PCCI comb. mode

 3rd Sol: Low Noise

(#115):

 Delayed Main &

greater dwell

 Standard premixed-

diffusive mode -120 -100 -80 -60 -40 -20 0 20

Crank Angle, deg 0

0.02 0.04 0.06 0.08

Injection Rate, mg/s

# 297 (High IMEP & Low Soot)

# 428 (Intermediate Case)

# 115 (Reduced NO & Noise)

High IMEP (HCCI)

Low NO (PCCI)

Low Noise (Standard)

#297

#428

#115 Heat Release Starts

(23)

3

rd

Section: Final Results

 Injection Strategy effects on pressure cycle and Rate of Heat Release (ROHR)

 #297 High IMEP

 #428 Low NO

 #115 Low Noise

-60 -40 -20 0 20 40 60

Crank Angle, deg 0

20 40 60 80 100

Pressure, bar

0 40 80 120

Rate of Heat Release, J/deg

# 297

# 428

# 115

Noise, HP-IMEP = 106.4 dB, 5.16 bar 101.1 dB, 4.89 bar

98.7 dB, 4.0 bar

Pressure

ROHR

(24)

 Injection Strategy effects on SOOT and NO

 #297 High IMEP

 #428 Low NO

 #115 Low Noise

3

rd

Section: Final Results

-20 0 20 40 60

Crank Angle, deg 10-7

10-6 10-5 10-4 10-3

NO Mass Fraction

10-7 10-6 10-5 10-4 10-3 10-2 10-1

Soot Mass Fraction

# 297

# 428

# 115 NO

Soot

BEST COMPROMISE

(25)

Conclusions

 Optimal inj. strategies are identified through:

 Coupled 1D, 3D and Noise emission tools, plus a multi-objective optimization procedure

 Code validation is realized with reference to BASE Engine and CR Fuel Injection System

 Results:

 High IMEP: Advanced start of both Pilot & Main

 Low NO: Delayed Pilot & reduced dwells

 Low Noise: Delayed Main & greater dwells

(26)

 Besides the practical results, main interest consists in the developed overall procedure

 Different objective importance, depending on the load level

 High IMEP & Low Noise at high load

 Low NO & Low Soot at low load

 Great Potential of extension & validation

 Inclusion of Boost, EGR, Swirl, Pinj, and so on

 Different combustion modes established

Conclusions

(27)

Work in progress

-360 -270 -180 -90 0 90 180 270 360

Crank Angle, deg 0

10 20 30 40 50 60 70

Pressure, bar

Exp.

1D Results

1500 rpm, BMEP=1.5 bar

C1 C3 C5 C2 C4 C6

 6 Cyl. Turbocharged Engine

 VGT – EGR – Common Rail

 1D Scheme

 1D Model Validation

(28)

Work in progress

0 1000 2000 3000 4000

Time, microsec 0

0.01 0.02 0.03 0.04

Total Injection Rate, mg/microsec

0 5 10 15 20 25

Total Injected Fuel, mg

Caso 2 700

 3D Model Validation

 Injection Parameterization

(29)

Work in progress

 Optimization !!

 …to be continued

THANKS FOR THE ATTENTION

Riferimenti

Documenti correlati

In 11 out 43 joints (25%) effusion within the sheath of the biceps tendon was associated with su- praspinatus tear (no association with subscapularis or infraspinatus tendon

Focus of the research is on the performance analy- sis of high-speed serial interfaces (Intersymbol Interference and Jitter) with the statistical simulation approach, the design of

is calculated after running the max-sum algorithm and depends on the particular MRF (structure and parameters) and therefore provide no guarantees on the quality of

Questo capitolo è dedicato alla costruzione dell'algebra di Dyer-Lashof, che uti- lizzeremo estensivamente per descrivere la struttura algebrica della coomologia dei gruppi

Instead of fixing a control structure and tuning the parameters by some optimization method, a combination of a recursive nonlinear system identification and a nonlinear

These studies demonstrated the feasibility of the method and the correlation of fluoride uptake with cardiovascular risk factors; moreover, they also demonstrated that such

Other important result was that in the Tomsk studies online group-exercising (social condition) did not result in higher adherence when compared with individual training