Tundish Optimization - Ansys

Tundish 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 

© 2006 ANSYS, Inc. 

ANSYS, Inc. Proprietary

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 

Inlet 

Tundish (region of interest) Outlet 


Inlet 

Outlet 

Residence time is the key point: 

•Separation of inclusions 

•Cold spots (solidification) 



• There are different ways to obtain a Tundish’s 

Residence 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 

Largely compared with 

experimental data 

• Additional variable that represents residence time 




• Residence Time Distribution methodology 

Ct[Kg/m ] 

Outlet 

3 

t min 

t [s] 

Ct 

3 

[Kg/m ] 

Inlet 

Ct 

3 

[Kg/m ] 

Average Residence Time 

Big enough Ideal 

Min. Residence Time 

Not too small 

C 

t 1 

A passive scalar is Injected 

t [s] 

Ct 

t min 

3 

[Kg/m ] 


Also Small 

Short Circuit 

Min. Residence Time 

Too small 

t [s] 


Big Enough 

Min. Residence The Time Stagnated curve lasts 

Too small 

too (cold) long Regions 

t [s] 

t min 




• Characteristic Volumes: 

– Plugged Volume 

– Dead Volume 

Ct 

3 

[Kg/m ] 

t min 

t ave 

Average RT 

2*Average RT 

Area = Dead Volume 

t [s] 

To be 

Maximized 

To be 

Minimized 

t min 




• To provide a better control over Residence Time 

Distribution, baffles are positioned along the 

domain. 

Baffles 




• 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 DOF 

and 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) 

HB 

HB 

DB 

DB 




• First step: Automatize the Evaluation process. 

Geometry Generation 

CATIA V5 

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

Mesh generation 

ANSYS ICEM CFD 

Solve for Flow 

ANSYS CFX 

modeFrontier 

• Process Integration 

• Optimization algorithms 

Solve Tracer 

ANSYS CFX 


RTD Analyses 

MS Excel 

One evaluation: approx. 30h 



• modeFrontier setup: 

Input Parameters 

Step 1: Geometry Step 2: Mesh Generation Step Generation 3: Solve Step for 4: Flow Solve Step Tracer 5: DTR Analyses 

Catia Direct ANSYS NodeICEM ANSYS CFDCFXANSYS CFX MS-Excel Direct Node 

Output Parameters 

(extracted from MS-Excel) 




• 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 

Design Space 


DB 



• 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 




Pluged 

Dead 

Outlet 2 

Outlet 1 




• 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 

Plugged Volume 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 

31 

30 

29 

(%) 

h1 

Outlet 1 Outlet 2 

h2 

(%) 

31 

Original Design 

30 

29 

28 

28 

h1 


h2 

h3 

27 

27 

h1 


26 

25 

24 


h1 

h3 

h3 

26 

25 

24 

h2 

23 

22 

h2 

HB 

23 

22 

HB 

h3 

Dead Volume 

Pluged Volume 

Dead Volume 

Pluged Volume 



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

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