Instantaneous turbulent kinetic energy modelling based on Lagrangian stochastic approach in CFD and application to wind energy

We present the construction of an original stochastic model for the instantaneous turbulent kinetic energy at a given point of a flow, and we validate estimator methods on this model with observational data examples. Motivated by the need for wind energy industry of acquiring relevant statistical information of air motion at a local place, we adopt the Lagrangian description of fluid flows to derive, from the $3$D+time equations of the physics, a $0$D+time-stochastic model for the time series of the instantaneous turbulent kinetic energy at a given position. Specifically, based on the Lagrangian stochastic description of a generic fluid-particles, we derive a family of mean-field dynamics featuring the square norm of the turbulent velocity. By approximating at equilibrium the characteristic nonlinear terms of the dynamics, we recover the so called Cox-Ingersoll-Ross process, which was previously suggested in the literature for modelling wind speed. We then propose a calibration procedure for the parameters employing both direct methods and Bayesian inference. In particular, we show the consistency of the estimators and validate the model through the quantification of uncertainty, with respect to the range of values given in the literature for some physical constants of turbulence modelling

ANALYSE DE SENSIBILITÉ DE LA DISPERSION DE GOUTTELETTES AUX CONDITIONS D'ÉMISSION ET A L'AIR AMBIENT

National audienceThis work presents a methodology to analyse the sensitivity of numerical simulations related to the dispersion of droplets in the air. The methodology is based on existing tools for sensitivity analysis (e.g. Sobol sensitivity index). This methodology is illustrated by analysing a large number of numerical results obtained in two situations: first a simple toy model (without underlying flow) and then a more realistic case (with underlying flow). The preliminary results allow to identify the parameters affecting the results but show a significant impact of the observable chosen for the analysis.Nous présentons une méthodologie pour analyser la sensibilité et quantifier l'incertitude des résultats de simulation numérique obtenus dans le contexte de la dispersion de gouttelettes dans l'air. La méthodologie se fonde sur les outils existants d'analyse de sensibilité (notamment la méthode de Sobol). L'intérêt de recourir à ces outils d'analyse de grands nombres de résultats est illustré à travers deux situations: un cas simplifié sans écoulement fluide environnant et un cas réaliste avec écoulement fluide. Les résultats préliminaires permettent d'identifier les paramètres influençant les résultats numériques mais montrent une forte sensibilité à l'observable choisie pour l'analyse

ANALYSE DE SENSIBILITÉ DE LA DISPERSION DE GOUTTELETTES AUX CONDITIONS D'ÉMISSION ET A L'AIR AMBIENT

National audienceThis work presents a methodology to analyse the sensitivity of numerical simulations related to the dispersion of droplets in the air. The methodology is based on existing tools for sensitivity analysis (e.g. Sobol sensitivity index). This methodology is illustrated by analysing a large number of numerical results obtained in two situations: first a simple toy model (without underlying flow) and then a more realistic case (with underlying flow). The preliminary results allow to identify the parameters affecting the results but show a significant impact of the observable chosen for the analysis.Nous présentons une méthodologie pour analyser la sensibilité et quantifier l'incertitude des résultats de simulation numérique obtenus dans le contexte de la dispersion de gouttelettes dans l'air. La méthodologie se fonde sur les outils existants d'analyse de sensibilité (notamment la méthode de Sobol). L'intérêt de recourir à ces outils d'analyse de grands nombres de résultats est illustré à travers deux situations: un cas simplifié sans écoulement fluide environnant et un cas réaliste avec écoulement fluide. Les résultats préliminaires permettent d'identifier les paramètres influençant les résultats numériques mais montrent une forte sensibilité à l'observable choisie pour l'analyse

Social Distancing: The Sensitivity of Numerical Simulations

International audienceResearchers from Inria and the French Aerospace Lab ONERA are collaborating on a joint project. The goal is to assess the variability in the advice for social distancing precautions that can be drawn from numerical simulations of airborne dispersion. This variability depends on a number of factors, including: physical variables (e.g. droplet size, ejection velocity), modelling methods used (e.g. turbulence model) and numerical aspects (mesh). We use sensitivity analysis tools to quantify and order the role these factors play in influencing the numerical results