5 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…