Development and Experimental Validation of Novel Evaluation Criteria for Turbulent Two-Phase VOF Simulations in High-Pressure Die Casting

TL;DR

This paper develops and validates new evaluation criteria for simulating turbulent two-phase flows in high-pressure die casting, demonstrating that turbulence modeling improves accuracy in predicting gas entrapment and cavity pressurization.

Contribution

It introduces novel quantitative criteria for assessing mold filling quality and validates the simulation approach with experimental data, advancing predictive capabilities in die casting.

Findings

Turbulence modeling accelerates cavity pressurization.

Turbulence reduces the persistence of entrapped gas.

Simulation results align with experimental porosity and surface data.

Abstract

Air entrapment during mold filling critically affects porosity and overall casting quality in High Pressure Die Casting. This study assesses the feasibility of applying the vof method within OpenFOAM to simulate compressible, turbulent mold filling in a thin-walled geometry. Three-dimensional simulations with the "compressibleInterFoam" solver were carried out under ambient initial cavity conditions, using both laminar flow and the k-e turbulence model. The free surface dynamics were examined across a range of inlet velocities to evaluate their influence on interface morphology, cavity pressurization, and gas entrapment. To quantify these effects, three evaluation criteria were introduced: the TIFSA as a measure of oxidation risk, the TMVF as an indicator of filling continuity and air entrapment, and the TIVF as a proxy for surface loading. Results show that turbulence modeling accelerates pressurization and limits the persistence of entrapped gas, with velocity governing the balance between smooth filling, turbulent breakup, and exposure duration. Comparison with experimental casting trials, including CT based porosity analysis and photogrammetric surface evaluation, validated that the model captures key defect mechanisms and provides quantitative guidance for process optimization.