35 citations · 116 across the 16 of their papers we have counts for
16 papers
Towards Streaming Prediction of Oscillatory Flows: A Data Assimilation and Machine Learning Approach
Miguel M. Valero, Marcello Meldi
Data-driven methods have demonstrated strong predictive capabilities in fluid mechanics, yet most current applications still focus on simplified configurations, often characterised…
Physics-based localization methodology for Data Assimilation by Ensemble Kalman Filter
Sarp Er, Marcello Meldi
A physics-based methodology for the determination of the localization function for the Ensemble Kalman Filter (EnKF) is proposed. The spatial features of such function evolve dynam…
Multi-fidelity Ensemble Kalman Filter algorithms enhanced by Convolutional Neural Networks
Tom Moussie, Paolo Errante, Marcello Meldi
The present research work proposes advancement for Data Assimilation strategies using Convolutional Neural Networks (CNN). More precisely, multi-fidelity and multi-level algorithms…
Enhancement of Large Eddy Simulation for the prediction of an intake flow rig using sequential Data Assimilation
Lucas Villanueva, Karine Truffin, Jacques Borée +1
A Data Assimilation (DA) strategy based on an ensemble Kalman filter (EnKF) is used to enhance the predictive capabilities of scale resolving numerical tools for the analysis of fl…
Enhanced State Estimation for turbulent flows combining Ensemble Data Assimilation and Machine Learning
Miguel M. Valero, Marcello Meldi
A novel strategy is proposed to improve the accuracy of state estimation and reconstruction from low-fidelity models and sparse data from sensors. This strategy combines ensemble D…
Statistical inference of upstream turbulence intensity for the flow around a bluff body with massive separation
Tom Moussie, Paolo Errante, Marcello Meldi
The Benchmarck on the Aerodynamics of a Rectangular 5:1 Cylinder is studied using a data-driven technique which bridges numerical simulation and available experimental results. Bec…
Synchronization and optimization of Large Eddy Simulation using an online Ensemble Kalman Filter
Lucas Villanueva, Karine Truffin, Marcello Meldi
An online Data Assimilation strategy based on the Ensemble Kalman Filter (EnKF) is used to improve the predictive capabilities of Large Eddy Simulation (LES) for the analysis of th…
A physics-infused Immersed Boundary Method using online sequential Data Assimilation
Miguel M. Valero, Marcello Meldi
A physics-infused strategy relying on the Ensemble Kalman Filter (EnKF) is here used to augment the accuracy of a continuous Immersed Boundary Method (IBM). The latter is a classic…
Augmented state estimation of urban settings using intrusive sequential Data Assimilation
Lucas Villanueva, Miguel Martinez Valero, Anina Sarkic Glumac +1
A data-driven investigation of the flow around a high-rise building is performed combining heterogeneous experimental samples and RANS CFD. The coupling is performed using techniqu…
Multigrid sequential data assimilation for the large-eddy simulation of a massively separated bluff-body flow
Gabriel Moldovan, Alessandro Mariotti, Laurent Cordier +3
The potential for data-driven applications to scale-resolving simulations of turbulent flows is assessed herein. Multigrid sequential data assimilation algorithms have been used to…
Analysis of Lundgren's matched asymptotic expansion approach to the Kármán-Howarth equation using the EDQNM turbulence closure
Marcello Meldi, John Christos Vassilicos
In this paper we investigate whether the features of the non-equilibrium cascade, which have been identified in recent studies using high-fidelity tools, can be captured in the cas…
A multigrid/ensemble Kalman Filter strategy for assimilation of unsteady flows
Gabriel Moldovan, Guillame Lehnasch, Laurent Cordier +1
A sequential estimator based on the Ensemble Kalman Filter for Data Assimilation of fluid flows is presented in this research work. The main feature of this estimator is that the K…
A reduced order Kalman Filter model for sequential Data Assimilation of turbulent flows
Marcello Meldi, Alexandre Poux
A Kalman filter based sequential estimator is presented in the present work. The estimator is integrated in the structure of segregated solvers for the analysis of incompressible f…
Implementation of a discrete Immersed Boundary Method in OpenFOAM
E. Constant, C. Li, J. Favier +3
In this paper, the Immersed Boundary Method (IBM) proposed by Pinelli is implemented for finite volume approximations of incompressible Navier-Stokes equations solutions in the ope…
Quantification of errors in large-eddy simulations of a spatially-evolving mixing layer
Marcello Meldi, Maria Vittoria Salvetti, Pierre Sagaut
A stochastic approach based on generalized Polynomial Chaos (gPC) is used to quantify the error in Large-Eddy Simulation (LES) of a spatially-evolving mixing layer flow and its sen…
A stochastic view of isotropic turbulence decay
Marcello Meldi, Pierre Sagaut, Didier Lucor
A stochastic EDQNM approach is used to investigate self-similar decaying isotropic turbulence at high Reynolds number (). The realistic energy spectrum func…