132 citations · 329 across the 25 of their papers we have counts for
27 papers
Ergodicity breaking transition beyond qubits
Tanay Pathak, Abhik Kumar Saha
The local Hilbert space dimension is a fundamental parameter in quantum many-body systems, but its role in controlling the ergodicity-breaking transition remains less explored. We…
Feature-based manifold model of actuated wakes
Alicia Rodríguez-Asensio, Guy Y. Cornejo Maceda, Bernd R. Noack +2
We propose a feature-based reduced-order model to predict the transient dynamics of bluff-body wakes under arbitrary time-varying actuation. Starting point is a control-oriented PO…
Information decomposition for disentangled and interpretable manifold learning of fluid flows via variational autoencoders
Zhiyuan Wang, Iacopo Tirelli, Stefano Discetti +1
We introduce an information-theoretic framework that uses variational autoencoders (VAEs) to extract compact, physically interpretable manifolds from high-dimensional flow-field da…
Divide and conquer complex flows. Part I: cluster and manifold-based local analysis
Qihong L. Li-Hu, Guy Y. Cornejo Maceda, Andrea Ianiro +1
This work is a two-part study on the description and prediction of complex fluid flows through the partitioning of the flow domain. In this first part, we propose a framework for a…
Drag reduction via separation control using plasma actuators on a truck cabin side
Lucas Schneeberger, Stefano Discetti, Andrea Ianiro
We investigate the drag reduction on a heavy-duty vehicle using dielectric-barrier discharge plasma actuators located on the A-pillars. An experimental campaign is carried out on a…
Sensor optimization for urban wind estimation with cluster-based probabilistic framework
Yutong Liang, Chang Hou, Guy Y. Cornejo Maceda +8
We propose a physics-informed machine-learned framework for sensor-based flow estimation for drone trajectories in complex urban terrain. The input is a rich set of flow simulation…
Meshless Super-Resolution of Scattered Data via constrained RBFs and KNN-Driven Densification
Iacopo Tirelli, Miguel Alfonso Mendez, Andrea Ianiro +1
We propose a novel meshless method to achieve super resolution from scattered data obtained from sparse, randomly positioned sensors such as the particle tracers of particle tracki…
Assessment of non-intrusive sensing in wall-bounded turbulence through explainable deep learning
A. Cremades, R. Freibergs, S. Hoyas +3
In this work we present a framework to explain the prediction of the velocity fluctuation at a certain wall-normal distance from wall measurements with a deep-learning model. For t…
Instantaneous convective heat transfer at the wall: a depiction of turbulent boundary layer structures
Firoozeh Foroozan, Andrea Ianiro, Stefano Discetti +1
We demonstrate the ability to experimentally measure fluctuations of the convective heat transfer coefficient at the wall in a turbulent boundary layer. For this, we measure two-di…
Model-based time super-sampling of turbulent flow field sequences
Qihong Lorena Li-Hu, Patricia García-Caspueñas, Andrea Ianiro +1
We propose a novel method for model-based time super-sampling of turbulent flow fields. The key enabler is the identification of an empirical Galerkin model from the projection of…
Full-domain POD modes from PIV asynchronous patches
Iacopo Tirelli, Adrian Grille Guerra, Andrea Ianiro +3
A method is proposed to obtain full-domain spatial modes based on Proper Orthogonal Decomposition (POD) of Particle Image Velocimetry (PIV) measurements performed at different (ove…
Machine-learned flow estimation with sparse data -- exemplified for the rooftop of a UAV vertiport
Chang Hou, Luigi Marra, Guy Y. Cornejo Maceda +9
We propose a physics-informed data-driven framework for urban wind estimation. This framework validates and incorporates the Reynolds number independence for flows under various wo…
Measuring time-resolved heat transfer fluctuations on a heated-thin foil in a turbulent channel airflow
Antonio Cuéllar, Enrico Amico, Jacopo Serpieri +4
We present an experimental setup to perform time-resolved convective heat transfer measurements in a turbulent channel flow with air as the working fluid. We employ a heated thin f…
Some effects of limited wall-sensor availability on flow estimation with 3D-GANs
Antonio Cuéllar, Andrea Ianiro, Stefano Discetti
In this work we assess the impact of the limited availability of wall-embedded sensors on the full 3D estimation of the flow field in a turbulent channel with Reτ = 200. The estima…
Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements
Antonio Cuéllar, Alejandro Güemes, Andrea Ianiro +3
Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements.…
A meshless method to compute the proper orthogonal decomposition and its variants from scattered data
Iacopo Tirelli, Miguel Alfonso Mendez, Andrea Ianiro +1
Complex phenomena can be better understood when broken down into a limited number of simpler "components". Linear statistical methods such as the principal component analysis and i…
Actuation manifold from snapshot data
Luigi Marra, Guy Y. Cornejo Maceda, Andrea Meilán-Vila +5
We propose a data-driven methodology to learn a low-dimensional manifold of controlled flows. The starting point is resolving snapshot flow data for a representative ensemble of ac…
Genetically-inspired convective heat transfer enhancement in a turbulent boundary layer
Rodrigo Castellanos, Andrea Ianiro, Stefano Discetti
The convective heat transfer in a turbulent boundary layer (TBL) on a flat plate is enhanced using an artificial intelligence approach based on linear genetic algorithms control (L…
A simple trick to improve the accuracy of PIV/PTV data
Iacopo Tirelli, Andrea Ianiro, Stefano Discetti
Particle Image Velocimetry (PIV) estimates velocities through correlations of particle images within interrogation windows, leading to a spatial modulation of the velocity field. A…
Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers
L. Guastoni, A. G. Balasubramanian, F. Foroozan +6
Fully-convolutional neural networks (FCN) were proven to be effective for predicting the instantaneous state of a fully-developed turbulent flow at different wall-normal locations…
An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification
Iacopo Tirelli, Andrea Ianiro, Stefano Discetti
We introduce a novel end-to-end approach to improving the resolution of PIV measurements. The method blends information from different snapshots without the need for time-resolved…
Heat transfer enhancement in turbulent boundary layers with a pulsed slot jet in crossflow
Rodrigo Castellanos, Gianfranco Salih, Marco Raiola +2
The convective heat transfer enhancement in a turbulent boundary layer (TBL) employing a pulsed, slot jet in crossflow is investigated experimentally. A parametric study on actuati…
From snapshots to manifolds - A tale of shear flows
Ehsan Farzamnik, Andrea Ianiro, Stefano Discetti +4
We propose a novel non-linear manifold learning from snapshot data and demonstrate its superiority over Proper Orthogonal Decomposition (POD) for shedding-dominated shear flows. Ke…
Machine learning flow control with few sensor feedback and measurement noise
R. Castellanos, G. Y. Cornejo Maceda, I. de la Fuente +3
A comparative assessment of machine learning (ML) methods for active flow control is performed. The chosen benchmark problem is the drag reduction of a two-dimensional Kármán vorte…