A dual-mesh method for efficient thermal stress analysis of large-scale welded structures
Abstract
Transient thermo-mechanical analysis of welding problem requires tremendous computation cost. To accelerate the thermal analysis of large-scale welded structures, an efficient computation scheme based on heat transfer localization and dual meshes was proposed. The computation accuracy is guaranteed by a local fine mesh model with size determined by a theoretical solution and a global coarse mesh model with equivalent heat input. The validity and accuracy of the dual-mesh method were verified using an experimental bead-on-plate model. By extending the weld length, the computation time of the proposed method was proved to be almost linearly dependent on the model scale. The thermal analysis of fillet welding of a large panel structure with 6-m-long weld was accelerated by 10 times over conventional finite element analysis and 2.2 times over adaptive mesh method. Meanwhile, the physical memory consumption was also greatly reduced by the dual-mesh method. Such efficient computation method enables fast evaluation of welding stress and distortion which are vital for manufacturing process and structure performance.
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1567007
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- International Journal of Advanced Manufacturing Technology
- Additional Journal Information:
- Journal Volume: 103; Journal Issue: 1-4; Journal ID: ISSN 0268-3768
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; Welding; FEM; Thermal analysis; Dual-mesh method; Heat localization
Citation Formats
Huang, Hui, Ma, Ninshu, Murakawa, Hidekazu, and Feng, Zhili. A dual-mesh method for efficient thermal stress analysis of large-scale welded structures. United States: N. p., 2019.
Web. doi:10.1007/s00170-019-03606-4.
Huang, Hui, Ma, Ninshu, Murakawa, Hidekazu, & Feng, Zhili. A dual-mesh method for efficient thermal stress analysis of large-scale welded structures. United States. https://doi.org/10.1007/s00170-019-03606-4
Huang, Hui, Ma, Ninshu, Murakawa, Hidekazu, and Feng, Zhili. Tue .
"A dual-mesh method for efficient thermal stress analysis of large-scale welded structures". United States. https://doi.org/10.1007/s00170-019-03606-4. https://www.osti.gov/servlets/purl/1567007.
@article{osti_1567007,
title = {A dual-mesh method for efficient thermal stress analysis of large-scale welded structures},
author = {Huang, Hui and Ma, Ninshu and Murakawa, Hidekazu and Feng, Zhili},
abstractNote = {Transient thermo-mechanical analysis of welding problem requires tremendous computation cost. To accelerate the thermal analysis of large-scale welded structures, an efficient computation scheme based on heat transfer localization and dual meshes was proposed. The computation accuracy is guaranteed by a local fine mesh model with size determined by a theoretical solution and a global coarse mesh model with equivalent heat input. The validity and accuracy of the dual-mesh method were verified using an experimental bead-on-plate model. By extending the weld length, the computation time of the proposed method was proved to be almost linearly dependent on the model scale. The thermal analysis of fillet welding of a large panel structure with 6-m-long weld was accelerated by 10 times over conventional finite element analysis and 2.2 times over adaptive mesh method. Meanwhile, the physical memory consumption was also greatly reduced by the dual-mesh method. Such efficient computation method enables fast evaluation of welding stress and distortion which are vital for manufacturing process and structure performance.},
doi = {10.1007/s00170-019-03606-4},
journal = {International Journal of Advanced Manufacturing Technology},
number = 1-4,
volume = 103,
place = {United States},
year = {Tue Mar 26 00:00:00 EDT 2019},
month = {Tue Mar 26 00:00:00 EDT 2019}
}
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