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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Additive Manufacturing Modeling, Simulation and Artificial Intelligence
Presentation Title F-24: Convolution Tensor Decomposition Method for Efficient High-Resolution Solutions to the Allen-Cahn Equation: Application to Grain Growth Simulations in Additive Manufacturing
Author(s) Chaoqian Yuan, Ye Lu
On-Site Speaker (Planned) Ye Lu
Abstract Scope The Allen-Cahn equation is widely used to characterize phase separation or the motion of anti-phase boundaries in materials, under the framework of phase field modeling. Its solution is known to be time-consuming when high-resolution meshes and large time scale integration are involved. To overcome this challenge, we propose a convolution tensor decomposition based model reduction method [1] for efficiently solving the Allen-Cahn equation. Numerical examples using both 2D and 3D Allen-Cahn type problems will be presented for demonstrating the performance of the method. The proposed computational framework opens numerous opportunities for simulating the complex microstructure formation in metal additive manufacturing at a deeply reduced computational cost. Reference: [1] Lu, Ye, Chaoqian Yuan, and Han Guo. "Convolution tensor decomposition for efficient high-resolution solutions to the Allen–Cahn equation." Computer Methods in Applied Mechanics and Engineering (2025).
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Computational Materials Science & Engineering, Solidification

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F-22: An Artificial Neural Network Modeling Approach to Electroforming of Micro-Tubes in the Presence of Surfactants
F-23: Computational Thermodynamics-Based Optimization of Ti-6Al-4V via Al and V Substitution for Additive Manufacturing
F-24: Convolution Tensor Decomposition Method for Efficient High-Resolution Solutions to the Allen-Cahn Equation: Application to Grain Growth Simulations in Additive Manufacturing
F-25: Creep Property of SS316L Lattices Fabricated by Laser Powder Bed Fusion
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F-28: Experimental‑CFD-Based Observation of Thermo‑Fluid Mechanisms in LPBF with Nanoparticle‑Coated Powders
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