About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
A Computationally Scalable Time-parallel Approach for Melt Pool Resolved Simulations of Additive Manufacturing |
Author(s) |
John Coleman, Matt Bement, Alex Plotkowski, Benjamin Stump |
On-Site Speaker (Planned) |
John Coleman |
Abstract Scope |
A major challenge in simulating the thermal behavior of an entire part made by additive manufacturing processes is the disparate length and time scales between transport phenomena occurring in the melt pool and the component. A common approach for the concurrent solution of partial differential equations on multiple computational resources is spatial parallelization by means on mesh decomposition. Ideally, the speed up from spatial parallelization would be linear, however, communication between resources eventually limits the speed up to a constant value, known as the strong scaling limit. Once spatial parallelization becomes saturated, additional parallelism by means of time-domain decomposition is needed to take advantage of high performance computing (HPC) resources. In this talk, a time-parallel method is proposed to improve the computational scalability of additive manufacturing simulations on HPC systems. Example results are shown for the NIST AM benchmark, and the tradeoff between accuracy and speedup is investigated. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Computational Materials Science & Engineering, Modeling and Simulation |