TY - JOUR
T1 - Adaptive hyper reduction for additive manufacturing thermal fluid analysis
AU - Lu, Ye
AU - Jones, Kevontrez Kyvon
AU - Gan, Zhengtao
AU - Liu, Wing Kam
N1 - Funding Information:
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.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
KW - Adaptivity
KW - Additive manufacturing
KW - Gappy reduced basis learning
KW - Hyper reduction
KW - Thermal CFD
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U2 - 10.1016/j.cma.2020.113312
DO - 10.1016/j.cma.2020.113312
M3 - Article
AN - SCOPUS:85089484675
VL - 372
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
SN - 0374-2830
M1 - 113312
ER -