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An iterative data-driven turbulence modeling framework based on Reynolds stress representation 被引量:3
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作者 Yuhui Yin Zhi Shen +2 位作者 Yufei Zhang Haixin Chena Song Fu 《Theoretical & Applied Mechanics Letters》 CAS CSCD 2022年第5期371-387,共17页
Data-driven turbulence modeling studies have reached such a stage that the basic framework is settled,but several essential issues remain that strongly affect the performance.Two problems are studied in the current re... Data-driven turbulence modeling studies have reached such a stage that the basic framework is settled,but several essential issues remain that strongly affect the performance.Two problems are studied in the current research:(1)the processing of the Reynolds stress tensor and(2)the coupling method between the machine learning model and flow solver.For the Reynolds stress processing issue,we perform the theoretical derivation to extend the relevant tensor arguments of Reynolds stress.Then,the tensor representation theorem is employed to give the complete irreducible invariants and integrity basis.An adaptive regularization term is employed to enhance the representation performance.For the coupling issue,an iterative coupling framework with consistent convergence is proposed and then applied to a canonical separated flow.The results have high consistency with the direct numerical simulation true values,which proves the validity of the current approach. 展开更多
关键词 Turbulence modeling Reynolds-averaged Navier-Stokes equations Reynolds stress representation Machine learning
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