FLUID DYNAMIC ANALYSIS OF A SWIRL INJECTOR

Authors: H. Dabin

L. Armas

F. Bacchi

Company Overview

Computational Fluid Dynamic Group

(GFC-FI-UNLP)

Introduction

Types of Injectors

SWIRL Injectors

LOWER DROP PRESSURE

HOMOGENOUS ATOMIZATION

COMBUSTION STABILITY

LOWER CHAMBER LENGTH

Advantages:

Physics of Atomization

Continuos flow

Ligaments/

Droplets Droplets

1st Breakup

2nd Breakup

Φ: 1,4 mm

23

,9 m

m

Mesh

Mesh refinement

Lmín: 20 µm

Mesh Details

Methodology of VOF model

Simulation → Fluent 14

• Transient

• Pressure based model

• Incompressible

• Turbulence Models: k-ε Realizable y RSM

• Multiphase Model (VOF)→ Fuel & Air

• Adaptative time step → t < 1 𝑥 10−7 s

Goals: Determination of system variables

Fuel

Air

α

φ

α : Spray Angle

φ : Droplet diameter

ho : Sheet Thickness

Ve

Ve : Velocity Exit

Results: flow visualization

Results: flow visualization

Results: flow visualization

Results: Comparison with empiric eq.

0,E+00

5,E+05

1,E+06

2,E+06

2,E+06

3,E+06

3,E+06

4,E+06

0 0,005 0,01 0,015 0,02 0,025 0,03 0,035

PR

ES

ION

[B

AR

]

CAUDAL MÁSICO [KG/S]

Empírico numerico

0,00E+00

5,00E-05

1,00E-04

1,50E-04

2,00E-04

2,50E-04

3,00E-04

0 0,005 0,01 0,015 0,02 0,025 0,03 0,035

SM

D [

M]

CAUDAL MÁSICO [KG/S]

Empírico numerico empirico corregido

RSM

Results: Comparison with empiric eq.

0

10

20

30

40

50

60

0 0,005 0,01 0,015 0,02 0,025 0,03 0,035

SE

MI-

ÁN

GU

LO

[º]

CAUDAL MÁSICO [KG/S]

Series1 numerico semiempirico

0,00

10,00

20,00

30,00

40,00

50,00

60,00

0 0,005 0,01 0,015 0,02 0,025 0,03 0,035

VE

LO

CID

AD

DE

SA

LID

A

CAUDAL MÁSICO

Empírico Numérico Empírico corregido

RSM

RSM

Accuracy Vs Time

0

5

10

15

20

25

200.000 1.000.000 5.500.000

TIE

MP

O (

DÍA

S)

Nº DE ELEMENTOS

* Procesamiento en AMD A8: 3.6 GHz, 16 Gb RAM (4 núcleos en paralelo)

** Modelos K-ℰ

from VOF to DPM

Continuous Flow

Ligaments / droplets Droplets

VOF

DPM

3D Model with DPM

Application of DPM: Film Cooling

Conclusions

→ The process of atomization requires great CPU cost.

→ The goal of capture the process of atomization was reached succesfully.

→ Comparing different models, the RSM one was the most efficient.

→ DPM approach results in a very interesting way to model the second breakup. It also could be use for film cooling applications

Future Works

Experimental validation

Continue with Film cooling

Multiple injectors

Combustion Simulation

Thanks for your attention !