Adaptive hyper reduction for additive manufacturing thermal fluid analysis

Ye Lu, Kevontrez Kyvon Jones, Zhengtao Gan, Wing Kam Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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 languageEnglish (US)
Article number113312
JournalComputer Methods in Applied Mechanics and Engineering
StatePublished - Dec 1 2020


  • 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


Dive into the research topics of 'Adaptive hyper reduction for additive manufacturing thermal fluid analysis'. Together they form a unique fingerprint.

Cite this