Using field inversion to quantify functional errors in turbulence closures

By Anand Pratap Singh, Karthik Duraisamy. 2016

217 citations recorded by the source corpus.

Subfields: Computational Mechanics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/456e089a145c82e2ef4fa29c792e4ed432562a71

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In Computational Mechanics

In Environmental Engineering

Citations in the corpus

Machine learning methods for turbulence modeling in subsonic flows around airfoils (2018, unknown): “[12-14] combined the inversion model and machine learning to infer and reconstruct better functional forms in turbulence and transitions modeling; Xiao et al.”
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework (2017, methodology): “Dow and Wang [5] chose the discrepancy field ∆νt in turbulent viscosity as the response, while Duraisamy and co-workers [7, 11, 12] introduced a full-field multiplicative discrepancy factor β into the production term of the transport equation as the learning target.”
Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned (2018, unknown): “…challenges in data-driven Reynolds stress closures Recently, data-driven turbulence modeling has emerged as a promising field of research with a number of approaches been proposed in the past few years (e.g., Ling et al. 2016; Singh & Duraisamy 2016; Wang et al. 2017; Weatheritt & Sandberg 2016).”
Quantification of model uncertainty in RANS simulations: A review (2018, methodology): “Taking the k – ω equation for example, a multiplicative discrepancy field x ( ) is introduced to the source terms of the ω transport equation by Singh and Duraisamy [43]: where ω is the turbulent frequency; P , D , and T indicate production, dissipation, and transport, respectively, of ω .”
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data (2016, background): “Duraisamy and co-workers [11, 16] introduced a full-ﬁeld multiplicative discrepancy term β into the production term of the transport equations of turbulent quantities (e.g., ˜ ν t in the SA model and ω in the k – ω models).”
Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils (2016, methodology): “Results can be improved by considering more information from the experiment [29], but this exercise offers further evidence that for separated ﬂows, pressure data is valuable in inferring model discrepancy.”