Abstract
Thermal fluid coupled analysis is essential to enable an accurate temperature prediction in additive manufacturing. However, numerical simulations of this type are time-consuming, due to the high non-linearity, the underlying large mesh size and the small time step constraints. This paper presents a novel adaptive hyper reduction method for speeding up these simulations. The difficulties associated with non-linear terms for model reduction are tackled by designing an adaptive reduced integration domain. The proposed online basis adaptation strategy is based on a combination of a basis mapping, enrichment by local residuals and a gappy basis reconstruction technique. The efficiency of the proposed method will be demonstrated by representative 3D examples of additive manufacturing models, including single-track and multi-track cases.
| Original language | English (US) |
|---|---|
| Article number | 113312 |
| Journal | Computer Methods in Applied Mechanics and Engineering |
| Volume | 372 |
| DOIs | |
| State | Published - Dec 1 2020 |
Funding
The authors would like to acknowledge the support of the National Science Foundation under Grant No. CMMI-1934367 . Kevontrez K. Jones would like to thank the Murphy Fellowship provided by Northwestern University. Ye Lu would like to thank Zhiyong Li for helpful discussion.
Keywords
- Adaptivity
- Additive manufacturing
- Gappy reduced basis learning
- Hyper reduction
- Thermal CFD
ASJC Scopus subject areas
- Computational Mechanics
- Mechanics of Materials
- Mechanical Engineering
- General Physics and Astronomy
- Computer Science Applications
