Reynolds-averaged Navier-Stokes (RANS) simulations are the most widespread approach to predict turbulent flows typical of industrial problems. Despite its success, the inherent simplifications and assumptions used to model the unknown Reynolds stresses are sources of inaccuracies. With this in mind, data assimilation techniques can be used to minim...
status: accepted
Terrestrial biosphere models (TBMs) are invaluable tools for studying plant-atmosphere interactions at multiple spatial and temporal scales, as well as how global change impacts ecosystems. Yet, TBM projections suffer from large uncertainties that limit their usefulness. Forest structure drives a significant part of TBM uncertainty as it regulates ...
status: published
status: published
Euronoise 2021, MADERE, PORTUGAL, 25-/10/2021 - 27/10/2021
A non-intrusive data assimilation methodology is developed to improve the statistical predictions of large-eddy simulations (LES). The ensemble-variational (EnVar) approach aims to minimize a cost function that is defined as the discrepancy between LES predictions and reference statistics from experiments or, in the present demonstration, independe...
This communication describes how datasets for the 1st challenge on Lagrangian Particle Tracking (LPT) and Data Assimilation (DA), held in 2020 and organized within the UE-funded H2020 project HOMER, have been generated. The physical situation, a turbulent wall-bounded flow in the wake of a cylinder, has been simulated using LES with the ONERA HPC s...
Reynolds-averaged Navier–Stokes (RANS)-based data assimilation has proven to be essential in many data-driven approaches, including the augmentation of experimental data and the identification of turbulence model corrections. As dense measurements of the whole mean flow are not always available when performing data assimilation, we here investigate...
status: published
