tundish optimization - ansys · tundish optimization rodrigo borges, mech. eng., magnesita brazil...

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Tundish OptimizationTundish Optimization

Rodrigo Borges, Mech. Eng., Magnesita Brazil

Marcelo Kruger, Mech. Eng., ESSS Brazil

Regis Ataídes, Mech. Eng., ESSS Brazil

Rodrigo Ferraz, Mech. Eng., ESSS Brazil

Leonardo Trindade, Mech. Eng., STE Brazil


Magnesita – The company

– Brazilian company providing solutions for the metallurgic market

– Technological development in:

• Raw materials for refractories

• Metallurgical fluxes

• Refractory products

• Mechanisms

• Numerical simulation

– Also providing technical and laboratory support for subsidiaries, production, marketing and quality control.


Magnesita and ESSS

• Numerical Simulation:

– Good interaction with ESSS (ANSYS distributor in South America)

– Some Analyses Performed:

• Open nozzle continuous casting

• Submerged entry nozzle

• Structural analyses of a steel ladle

• Tundish flow analyses


Continuous Casting - Tundish

Tundish (region of interest)

Inlet

Residence time is the key point:•Separation of inclusions

•Cold spots (solidification)

Outlet

Inlet

Outlet



• There are different ways to obtain a Tundish’sResidence Time Distribution (RTD):

– Using a Tracer

• Injecting tracer as a step function and monitoring its concentration as a function of time at the outlets

– Lagrangean models

• Monitoring particles traveling time at the outlets

– Solving for Age

• Additional variable that represents residence time

Largely compared with experimental data


[Kg/m ]3

tmin

t [s]

Ct


• Residence Time Distribution methodology

[Kg/m ]3

t1

t [s]

Ct

C

Inlet

Outlet

Ideal

Short Circuit

[Kg/m ]3

tmin

t [s]

Ct

[Kg/m ]3

tmin

t [s]

Ct

Stagnated (cold) Regions

Min. Residence TimeNot too small

Average Residence TimeBig enough

Min. Residence TimeToo small

Average Residence TimeAlso Small

Min. Residence TimeToo small

Average Residence TimeBig Enough

The curve laststoo long

A passive scalar is Injected



• Characteristic Volumes:– Plugged Volume

– Dead Volume

avettmin

[Kg/m ]3

tmin

t [s]

Ct

Average RT

2*Average RT

Area = Dead Volume

To be Maximized

To be Minimized



• To provide a better control over Residence Time Distribution, baffles are positioned along the domain.

Baffles


Tundish Optimization

• Baffle Designs are well suitable for optimization tools:

– Finding the optimum point is a hard process due to non-linearity of the Navier-Stokes Equations and the two conflicting objectives;

– CFD analyses provide an easy way to inspect different baffle configurations;

– Automatic mesh generation can be easily overcome with ANSYS ICEM CFD.



• The case illustrated here represents a real problem solved by Magnesita and ESSS:– The problem is only ½ symmetric, decreasing the

number of DOFand elements in the mesh

– Two baffles need to be analyzed

– RTD must be inspected at both outlets



• To simplify the problem, only two design parameters were considered:– HB: Baffle Height

– DB: Baffle Distance (from the outlet)

DB DB

HB HB


Unstructured mesh with prisms (370k nodes)Transient: 2500 seconds, 1E-5 (approx. 26h)1E-5 (approx. 6h)


• First step: Automatize the Evaluation process.

Geometry GenerationCATIA V5

Mesh generationANSYS ICEM CFD

Solve for FlowANSYS CFX

Solve TracerANSYS CFX

RTD AnalysesMS Excel

One evaluation: approx. 30h

modeFrontier• Process Integration• Optimization algorithms



• modeFrontier setup:

Input Parameters

Step 1: Geometry GenerationCatia Direct Node

Step 2: Mesh GenerationANSYS ICEM CFD

Step 3: Solve for FlowANSYS CFX

Step 4: Solve TracerANSYS CFX

Output Parameters(extracted from MS-Excel)

Step 5: DTR AnalysesMS-Excel Direct Node



• Since CFD analyses are computational demanding, a DOE and RSM (Response Surface) approach was adopted, saving computational time.

– DOE table with 25 designs:

• A Response Surface is created;

– The Optimization process runs in the RSM

• Virtual designs are founded;

– Virtual designs are evaluated;



• The DOE table was created uniformly along the Design Space:

HB

DB

Design Space



• After solving the DOE table statistical information may be extracted within modeFrontier, such as the Correlation Matrix:



• With the results of the DOE table a Response

Surface is created for each outlet and objective,

using Kriging’s algorithm:

– RSM for Plugged volume at Outlet 1

– RSM for Plugged volume at Outlet 2

– RSM for Dead volume at Outlet 1

– RSM for Dead volume at Outlet 2



• Plugged Volume – Outlet 1



• Plugged Volume – Outlet 2



• Dead Volume – Outlet 1



• Dead Volume – Outlet 2



Out

let1

Out

let2

Pluged Dead



• Response Surface inspection indicate that the best designs:– Are closer to the outlets

– Have higher baffles

• A Multi-Objective algorithm (MOGA II) is used to extract the best results from the Response Surface– Only two Objectives: Maximize Plugged volume at

both outlets (since Plugged and Dead volume are strongly correlated)



• Virtual Designs (the more red, the newer)

Plugged Volume 1

Plu

gged

Vol

ume

2



• Best Virtual Designs were located at the minimum distance from the outlets

• Three Virtual Designs were chosen to be validated

• The results were then compared to the initially suggested configuration (next Slide)



• Best Designs x Original Design

h1

h2

h3

h1

h2

h3

Original Design

Original Design

22

23

24

25

26

27

28

29

30

31

HB

(%)

Dead Volume Pluged Volume

h1

h2

h3

h1

h2

h3

Original Design

Original Design

22

23

24

25

26

27

28

29

30

31

HB

(%)

Dead Volume Pluged Volume

Outlet 1 Outlet 2


Conclusions

• Baffles:

– Better Residence Time Distribution were found with higher baffles and closer to the outlet

– Increasing height will only improve results to a certain value

• Characteristic Volumes:

– Plugged and Dead volumes are strongly correlated.


Future Studies

– Validate different methodologies for Residence Time Distribution calculation:

• Lagragean Models• Solving Residence Time as a Scalar

– Apply optimization techniques in other metallurgic components

tundish optimization - ansys · tundish optimization rodrigo borges, mech. eng., magnesita brazil...

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