This study aimed to optimize the design of a runner for high-pressure die casting (HPDC) using computational fluid dynamics (CFD) simulations, and to verify the effectiveness of the runner with water-model experiments. A runner is a part of the flow path through which molten metal enters a product part. As a design problem, we sought to optimize the shape of the runner to minimize air entrainment in the runner and align the flow of molten metal after it passed through the runner. The problem was solved using our proposed nonparametric shape optimization method. The method is based on a genetic algorithm (GA), and directly treats a geometric shape that is comprised of several curves as an individual of a GA in the form of a set of mathematical functions. In addition, the crossover, which is one of the genetic operations, is defined as a weighted summation of two parent curves. Thus, the optimization method can generate optimized shapes with a lot of flexibility. The effectiveness of the optimized shape of the runner was demonstrated with both CFD simulations and water-model experiments using a visualization device for HPDC.

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