Benchmarking of Massively Parallel Phase-Field Codes for Directional Solidification

TL;DR

This paper compares two advanced phase-field simulation codes for alloy solidification, evaluating their accuracy, performance, and suitability for realistic experimental conditions in materials engineering.

Contribution

It provides a comprehensive benchmark of GPU-accelerated and CPU-parallelized phase-field codes under realistic solidification scenarios, highlighting their capabilities and challenges.

Findings

Both codes accurately predict dendritic morphology and tip dynamics.

GPU-accelerated code shows faster computation times for large-scale simulations.

Benchmark results guide code validation and application in materials engineering workflows.

Abstract

We present a detailed benchmark comparing two state-of-the-art phase-field implementations for simulating alloy solidification under experimentally relevant conditions. The study investigates the directional solidification of Al-3wt%Cu under high-velocity solidification conditions and SCN-0.46wt% camphor under microgravity conditions from National Aeronautics and Space Administration (NASA) DECLIC-DSI-R experiments. Both codes, one employing finite-difference discretization with uniform mesh and GPU-acceleration (GPU-PF) and the other one employing finite-element discretization with adaptive-mesh and CPU-parallelization (PRISMS-PF), solve the same quantitative phase-field formulation that incorporates an anti-trapping current for the solidification of dilute alloys. We evaluate the predictions of each code for dendritic morphology, primary spacing, and tip dynamics in both 2D and 3D, as well as their numerical convergence and computational performance. While existing benchmark problems have primarily focused on simplified or small-scale simulations, they do not reflect the computational and modeling challenges posed by employing experimentally relevant time and length scales. Our results provide a practical framework for assessing phase-field code performance as well as validating and facilitating their application in integrated computational materials engineering (ICME) workflows that require integration with realistic experimental data.