Pages that link to "Item:Q1692004"
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The following pages link to A data-driven adaptive Reynolds-averaged Navier-Stokes \(k\)-\(\omega\) model for turbulent flow (Q1692004):
Displaying 14 items.
- Data-driven POD-Galerkin reduced order model for turbulent flows (Q781977) (← links)
- Retrospective cost adaptive Reynolds-averaged Navier-Stokes \(k\)-\(\omega\) model for data-driven unsteady turbulent simulations (Q1699486) (← links)
- Efficient assimilation of sparse data into RANS-based turbulent flow simulations using a discrete adjoint method (Q2088382) (← links)
- Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations (Q2176735) (← links)
- Sharp interface approaches and deep learning techniques for multiphase flows (Q2214553) (← links)
- A framework to develop data-driven turbulence models for flows with organised unsteadiness (Q2214628) (← links)
- Data-driven RANS closures for three-dimensional flows around bluff bodies (Q2245405) (← links)
- RANS closures for non-neutral microscale CFD simulations sustained with inflow conditions acquired from mesoscale simulations (Q2294973) (← links)
- Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures (Q3388856) (← links)
- Small-scale reconstruction in three-dimensional Kolmogorov flows using four-dimensional variational data assimilation (Q5207673) (← links)
- Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned (Q5235562) (← links)
- Turbulence Modeling in the Age of Data (Q5377509) (← links)
- Comparison of different data-assimilation approaches to augment RANS turbulence models (Q6060768) (← links)
- On the origin of counter-gradient transport in turbulent scalar flux: physics interpretation and adjoint data assimilation (Q6648312) (← links)
