Tundish Optimization. Rodrigo Borges, Mech. Eng., Magnesita Brazil

Transcription

1 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

2 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.

3 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

4 Continuous Casting - Tundish Inlet Tundish (region of interest) Outlet Inlet Outlet Residence time is the key point: Separation of inclusions Cold spots (solidification)

5 Continuous Casting - Tundish 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

6 Continuous Casting - Tundish Residence Time Distribution methodology Ct [Kg/m 3 ] Outlet Inlet Average Residence Time Big enough Ideal Ct 3 [Kg/m ] Ct 3 [Kg/m ] t min Min. Residence Time Not too small t [s] C Average Residence Time Also Small Short Circuit Min. Residence Time Too small t [s] t [s] Ct t min 3 [Kg/m ] t 1 A passive scalar is Injected Average Residence Time Big Enough Min. Residence The Time Stagnated curve lasts Too small too (cold) long Regions t [s] t min

7 Continuous Casting - Tundish 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

8 Continuous Casting - Tundish To provide a better control over Residence Time Distribution, baffles are positioned along the domain. Baffles

9 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.

10 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

11 To simplify the problem, only two design parameters were considered: HB: Baffle Height DB: Baffle Distance (from the outlet) HB HB DB DB

12 First step: Automatize the Evaluation process. Geometry Generation 1E-5 (approx. Transient: 2500 seconds, 1E-5 Unstructured mesh with prisms6h) (370k(approx. nodes) 26h) CATIA V5 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

13 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)

14 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;

15 The DOE table was created uniformly along the Design Space: HB Design Space DB

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

17 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

18 Plugged Volume Outlet 1

19 Plugged Volume Outlet 2

20 Dead Volume Outlet 1

21 Dead Volume Outlet 2

22 Pluged Dead Outlet 2 Outlet 1

23 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)

24 Virtual Designs (the more red, the newer) Plugged Volume 1 Plugged Volume 2

25 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)

26 Best Designs x Original Design (%) h1 Outlet 1 Outlet 2 h2 (%) 31 Original Design h1 Original Design h2 h h1 Original Design Original Design h1 h3 h h h2 HB HB h3 Dead Volume Pluged Volume Dead Volume Pluged Volume

27 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.

28 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