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DC Field | Value | Language |
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dc.citation.startPage | 108014 | - |
dc.citation.title | AEROSPACE SCIENCE AND TECHNOLOGY | - |
dc.citation.volume | 132 | - |
dc.contributor.author | Lee, Young Mo | - |
dc.contributor.author | Lee, Jae Hwa | - |
dc.contributor.author | Lee, Jungil | - |
dc.date.accessioned | 2023-12-21T13:08:36Z | - |
dc.date.available | 2023-12-21T13:08:36Z | - |
dc.date.created | 2023-02-10 | - |
dc.date.issued | 2023-01 | - |
dc.description.abstract | Wall-models in a large-eddy simulation (LES) are essential to alleviate the large near-wall resolution requirements for high-Reynolds-number turbulent flow simulations. Among the existing wall-models for a LES, an equilibrium wall-stress model has the highest computational efficiency. Because this model has limitations, such as a lack of non-equilibrium effects and the assumption of a particular law of the wall in the mean velocity, we propose artificial neural network-based wall-stress models (AWMs). The input variables for the AWMs are extracted from the decomposition of the skin-friction coefficient proposed by Fukagata et al. [1], and the AWMs are shown to be able to predict the wall-shear stress in complex flows accurately. The performance of the AWMs is tested for two types of flows, a fully developed turbulent channel flow and a separated turbulent boundary layer flow. A direct comparison of the turbulence statistics with those obtained by previous wall-models (i.e., a log-law-based wall-stress model and a non-equilibrium wall-stress model) shows that better predictions are achieved using the AWMs for both flows, even with untrained Reynolds numbers. When using a coarse grid along the wall-normal direction in wall-modeled LESs (WMLESs) with the AWMs, an upward shift of the mean velocity profile (positive log-layer mismatch, LLM) compared to direct numerical simulation data is found, consistent with previous studies. However, this LLM problem can be overcome by imposing a filtered wall-normal velocity at the wall that is dynamically determined based on the continuity equation and the Taylor series expansion within wall-adjacent cells. | - |
dc.identifier.bibliographicCitation | AEROSPACE SCIENCE AND TECHNOLOGY, v.132, pp.108014 | - |
dc.identifier.doi | 10.1016/j.ast.2022.108014 | - |
dc.identifier.issn | 1270-9638 | - |
dc.identifier.scopusid | 2-s2.0-85142803430 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/62011 | - |
dc.identifier.wosid | 000914893600005 | - |
dc.language | 영어 | - |
dc.publisher | Elsevier BV | - |
dc.title | Artificial neural network-based wall-modeled large-eddy simulations of turbulent channel and separated boundary layer flows | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Engineering, Aerospace | - |
dc.relation.journalResearchArea | Engineering | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Large-eddy simulation | - |
dc.subject.keywordAuthor | Wall-modeling | - |
dc.subject.keywordAuthor | Turbulent channel flow | - |
dc.subject.keywordAuthor | Separated turbulent boundary layer flow | - |
dc.subject.keywordPlus | Turbulent channel flow | - |
dc.subject.keywordPlus | Wall-modeling | - |
dc.subject.keywordPlus | Large-eddy simulation | - |
dc.subject.keywordPlus | Separated turbulent boundary layer flow | - |
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