activity
20172022
most citedLeveraging reduced-order models for state estimation using deep learning

61 citations · 82 across the 4 of their papers we have counts for

collaborators

5 papers

physics.flu-dyn2022★ 19 cited

Bio-inspired variable-stiffness flaps for hybrid flow control, tuned via reinforcement learning

Nirmal J. Nair, Andres Goza

A bio-inspired, passively deployable flap attached to an airfoil by a torsional spring of fixed stiffness can provide significant lift improvements at post-stall angles of attack.…

physics.flu-dyn2022★ 2 cited

Fluid-structure interaction of a bio-inspired passively deployable flap for lift enhancement

Nirmal J. Nair, Andres Goza

Birds have a remarkable ability to perform complex maneuvers at post-stall angles of attack. The passive deployment of self-actuating covert feathers in response to unsteady flow s…

physics.flu-dyn2021

A strongly coupled immersed boundary method for fluid-structure interaction that mimics the efficiency of stationary body methods

Nirmal Jayaprasad Nair, Andres Goza

Strongly coupled immersed boundary (IB) methods solve the nonlinear fluid and structural equations of motion simultaneously for strongly enforcing the no-slip constraint on the bod…

physics.flu-dyn2019★ 61 cited

Leveraging reduced-order models for state estimation using deep learning

Nirmal J. Nair, Andres Goza

State estimation is key to both analyzing physical mechanisms and enabling real-time control of fluid flows. A common estimation approach is to relate sensor measurements to a redu…

physics.flu-dyn2017

Transported snapshot model order reduction approach for parametric, steady-state fluid flows containing parameter dependent shocks

Nirmal J. Nair, Maciej Balajewicz

A new model order reduction approach is proposed for parametric steady-state nonlinear fluid flows characterized by shocks and discontinuities whose spatial locations and orientati…