Surrogate-based Shape Optimization of Immersion Nozzle in Continuous Casting

Abstract

In continuous casting, molten steel is fed from the tundish into the mold through the immersion nozzle. In the immersion nozzle, inclusions mainly composed of alumina present in the molten steel adhere and accumulate, it causes limitation of continuous castings. To prevent the nozzle clogging, Ar gas is blown into the immersion nozzle. However, Ar bubbles flow into the mold along with the molten steel and become trapped in the solidifying shell, causing bubbling defects of the slab. To suppress bubbling defects, it is effective to keep Ar bubbles away from the solidification interface or to use molten steel to wash away Ar bubbles that have adhered to the solidification interface. The molten steel flow in the mold is greatly affected by the shape of the immersion nozzle. In this paper, we consider the optimization of the shape of the immersion nozzle to reduce Ar bubbles trapped in the solidifying shell. A numerical model of molten steel flow and heat transfer solidification in the mold is combined with an optimization method. In the optimization process, Ar bubbles trapped in the solidifying shell are evaluated by a neural network to improve the calculation speed. The application of this method to the search for immersion nozzle shape is also reported, and the effectiveness of the obtained nozzle shape in reducing Ar bubbles is discussed.