1 citations · 1 across the 5 of their papers we have counts for
5 papers
Active Flow Control for Bluff Body under High Reynolds Number Turbulent Flow Conditions Using Deep Reinforcement Learning
Jingbo Chen, Enrico Ballini, Stefano Micheletti
This study employs Deep Reinforcement Learning (DRL) for active flow control in a turbulent flow field of high Reynolds numbers at . That is, an agent is trained to obta…
Level set-fitted polytopal meshes with application to structural topology optimization
Nicola Ferro, Stefano Micheletti, Nicola Parolini +3
We propose a method to modify a polygonal mesh in order to fit the zero-isoline of a level set function by extending a standard body-fitted strategy to a tessellation with arbitrar…
Reducing the Drag of a Bluff Body by Deep Reinforcement Learning
Enrico Ballini, Alberto Silvio Chiappa, Stefano Micheletti
We present a deep reinforcement learning approach to a classical problem in fluid dynamics, i.e., the reduction of the drag of a bluff body. We cast the problem as a discrete-time…
Advanced Modeling of Rectangular Waveguide Devices with Smooth Profiles by Hierarchical Model Reduction
Gines Garcia-Contreras, Juan Corcoles, Jorge A. Ruiz-Cruz +4
We present a new method for the analysis of smoothly varying tapers, transitions and filters in rectangular waveguides. With this aim, we apply a Hierarchical Model (HiMod) reducti…
Enhancing level set-based topology optimization with anisotropic graded meshes
Davide Cortellessa, Nicola Ferro, Simona Perotto +1
We propose a new algorithm for the design of topologically optimized lightweight structures, under a minimum compliance requirement. The new process enhances a standard level set f…