CFD Benchmark for a Single Strand Tundish (Part II)

Odenthal, Hans-Jürgen; Javurek, Mirko; Kirschen, Marcus; Vogl, N.

doi:10.1002/srin.201000079

Cited by 28 publications

(39 citation statements)

References 4 publications

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“…Three parameters, ε sl , ε t , and β, are determined in advance from engineers' experiences, and the remaining ten parameters are estimated through the least squares method using real process data. In the firstprinciple model, the tundish is described as a compartment model; the number of baths in the tundish model is set at three on the basis of the CFD study performed by Odenthal et al (2010). The input variables of the first-principle model are the number of ladle uses, the weight and the initial temperature of the molten steel in the ladle, the transportation time, the casting flow rate, and the initial temperature of each bath composing the tundish.…”

Section: First-principle Modelmentioning

confidence: 99%

Prediction of Molten Steel Temperature in Steel Making Process with Uncertainty by Integrating Gray-Box Model and Bootstrap Filter

Ahmad

Kano

Hasebe

et al. 2014

J. Chem. Eng. Japan

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Stable operation of a continuous casting process requires precise control of molten steel temperature in a tundish (TD temp), which is a container used to feed molten steel into an ingot mold. Since TD temp is implicitly controlled by adjusting molten steel temperature in the preceding secondary re ning process (RH temp), a model relating TD temp with RH temp is required. This research proposes a procedure to predict the probability distribution of TD temp by integrating a gray-box model and a bootstrap lter to cope with uncertainties of the process. The derived probability distribution is used not only to predict TD temp but also to evaluate the reliability of prediction. The proposed method was validated through its application to real operation data at a steel work, and it was con rmed that the developed model satis ed the requirements for its industrial application.

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Section: First-principle Modelmentioning

confidence: 99%

Prediction of Molten Steel Temperature in Steel Making Process with Uncertainty by Integrating Gray-Box Model and Bootstrap Filter

Ahmad

Kano

Hasebe

et al. 2014

J. Chem. Eng. Japan

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“…The CFD simulations have indicated that TD temp is distributed in the flow direction. 10) Thus, in this work, the tundish is modeled as a compartment model consisting of N t isothermal baths connected in series as shown in Fig. 4.…”

Section: Molten Steel In Tundishmentioning

confidence: 99%

“…4. The heat balance of the k-th bath is ... (9) ...... (10) where W, H, and L denote the width, the height, and the length of molten steel in the tundish, respectively.…”

Section: Molten Steel In Tundishmentioning

confidence: 99%

High-Performance Prediction of Molten Steel Temperature in Tundish through Gray-Box Model

et al. 2013

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A novel gray-box model is proposed to estimate molten steel temperature in a continuous casting process at a steel making plant by combining a first-principle model and a statistical model. The first-principle model was developed on the basis of computational fluid dynamics (CFD) simulations to simplify the model and to improve estimation accuracy. Since the derived first-principle model was not able to estimate the molten steel temperature in the tundish with sufficient accuracy, statistical models were developed to estimate the estimation errors of the first-principle model through partial least squares (PLS) and random forest (RF). As a result of comparing the three models, i.e., the first-principle model, the PLS-based graybox model, and the RF-based gray-box model, the RF-based gray-box model achieved the best estimation performance. Thus, the molten steel temperature in the tundish can be estimated with accuracy by adding estimates of the first-principle model and those of the statistical RF model. The proposed gray-box model was applied to the real process data and the results demonstrated its advantage over other models.

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“…The motion of inclusions is mainly controlled by the flow of the molten steel, and the heat transfer is critical to the flow which induces the natural convection. 6,22) The inclusion removal efficiency was underestimated, because the adhesion to the lining solid surface as well as the collision and coalescence of inclusions was ignored. 21) Takahashi and Taniguchi conducted an experiment and numerical simulation to study the separation of inclusions from liquid metal in an induction furnace.…”

Section: Introductionmentioning

confidence: 99%

Behavior of Non-metallic Inclusions in a Continuous Casting Tundish with Channel Type Induction Heating

Wang

et al. 2014

ISIJ Int.

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A generalized three dimensional mathematical model, which adopted the Euler-Lagrange approach, has been developed for the motion of inclusions in a continuous casting tundish with channel type induction heating. The inclusion trajectories were obtained by numerical solution of the motion equation including gravity, buoyancy, drag, lift, added mass, Brownian, electromagnetic pressure and thermophoretic forces. Besides, the collision and coalescence of inclusions and adhesion to the lining solid surfaces were also taken into account. The Brownian, Stokes and turbulent collision were clarified, separately. Then the effect of induction heating power on the inclusion behavior was demonstrated. A reasonable agreement between the experimental observation and numerical results was obtained. The results indicate that the electromagnetic pressure force significantly promotes the removal of inclusions, especially for the bigger inclusion. Although the thermophoretic force goes against the removal of inclusions, its influence is negligibly small. The removal ratio of inclusions in the tundish with induction heating increases from 67.45% to 96.43%, while the power varies from 800 kW to 1 200 kW. The collision and coalescence should be included when model the inclusion motion, because it can promote the removal of inclusion. The turbulent and Brownian collision becomes more active with the increasing power, while the Stokes collision is just opposite.

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CFD Benchmark for a Single Strand Tundish (Part II)

Cited by 28 publications

References 4 publications

Prediction of Molten Steel Temperature in Steel Making Process with Uncertainty by Integrating Gray-Box Model and Bootstrap Filter

Prediction of Molten Steel Temperature in Steel Making Process with Uncertainty by Integrating Gray-Box Model and Bootstrap Filter

High-Performance Prediction of Molten Steel Temperature in Tundish through Gray-Box Model

Behavior of Non-metallic Inclusions in a Continuous Casting Tundish with Channel Type Induction Heating

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