A multi-Gaussian quadrature method of moments for simulating high Stokes number turbulent two-phase flows

With the great increase in computational resources, Large Eddy Simulation (LES) of industrial configurations is now an efficient and tractable tool. Numerous applications involve a liquid or solid disperse phase carried by a gaseous flow field (eg, fuel injection in automotive or aeronautical engines, fluidized beds, and alumina particles in rocket boosters). To simulate this kind of flow, one may resort to a Number Density Function (NDF), which satisfies a kinetic equation

A hierarchy of Eulerian models for trajectory crossing in particle-laden turbulent flows over a wide range of Stokes numbers

With the large increase in available computational resources, large-eddy simulation (LES) of industrial configurations has become an efficient and tractable alternative to traditional multiphase turbulence models. Many applications involve a liquid or solid disperse phase carried by a gas phase (eg, fuel injection in automotive or aeronautical engines, fluidized beds, and alumina particles in rocket boosters)

Insights into the accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender–career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data

Simulation aux grandes échelles d'écoulements diphasiques turbulents à phase liquide dispersée

Les écoulements diphasiques turbulents sont présents dans de nombreux systèmes industriels (moteur à piston, turbines à gaz, moteurs fusée...). La compréhension fine de telles configurations s'avèrent de nos jours nécessaire pour limiter notamment les émissions de polluants et de gaz à effet de serre, et la consommation des énergies fossiles. Nous nous intéressons ici à la simulation aux grandes échelles des écoulements diphasiques turbulents, permettant de capturer une large partie du spectre de la turbulence, et ainsi être capable de prédire des phénomènes instables ou transitoires. La phase dispersée est ici modélisée par une approche eulérienne, en raison de ses avantages dans le contexte du calcul haute performance. Le travail de cette thèse a consisté à étendre le formalisme eulérien existant dans le code AVBP à la simulation de sprays polydisperses dans des écoulements turbulents. Pour cela, le Formalisme Eulérien Mésoscopique (FEM) a été couplé à une approche Multi-fluide. Cette nouvelle approche, intitulée Formalisme Eulérien Mésoscopique Multi-fluide (FEMM), a été évaluée sur des cas simples canoniques, permettant de bien caractériser le comportement autant en terme de dynamique turbulente que d'effets polydisperses. Les stratégies numériques disponibles dans le code de calcul AVBP sont aussi analysées, afin d'en cerner les limites pour la simulation eulérienne d'une phase liquide. Ce nouveau formalisme est finalement appliqué à la configuration aéronautique MERCATO, pour laquelle on dispose de résultats numériques obtenus avec d'autres approches (FEM et approche lagrangienne), et de résultats expérimentaux. Un accord satisfaisant avec l'expérience est montré pour toutes les approches, même si le FEM, monodisperse, obtient de moins bon résultats en terme de fluctuations. D'autres résultats expérimentaux s'avèrent nécessaires pour évaluer les approches et déterminer quelle est la plus prédictive pour cette configuration, notamment concernant la fraction massique de kerosene, autant en phase liquide qu'en phase gazeuse.Turbulent two-phase flows are encountered in several industrial devices (piston engine, gas turbine, rocket engine...). A fine understanding of such configurations is mandatory to face problems of pollutant emissions, greenhouse gas, and fossil fuel rarefaction. The Large Eddy Simulation seems to be a good candidate. This kind of simulation captures a wide part of turbulence spectrum, and thus allows to predict instabilities and transient phenomena. The dispersed phase is simulated using an Eulerian approach, which seems to be more suitable than lagrangian methods for High Performance Computing. The present work consists in the extension to polydisperse flows of the existing eulerian formalism in the AVBP code. The Mesoscopic Eulerian Formalism (MEF) is coupled with the Multifluid approach. This new formalism, called Multifluid Mesoscopic Eulerian Formalism, is evaluated on simple test cases, showing the ability of such approach to capture turbulent and polydisperse effects. Numerical strategies available in AVBP are also evaluated, in order to emphasize on their limiting aspects for the eulerian simulation of a dispersed phase. The new formalism is finally applied to the simulation of the aeronautical configuration called MERCATO. Several experimental results are available, as well as numerical results using FEM and lagrangian approach. Results show a good agreement between experiments and numerical results, even if FEM results are worse concerning the fluctuations. New experimental results are necessary to determine which is the best approach, especially in terms of liquid and gas kerosene mass fraction.TOULOUSE-INP (315552154) / SudocSudocFranceF

A multi-Gaussian quadrature method of moments for simulating high Stokes number turbulent two-phase flows

With the great increase in computational resources, Large Eddy Simulation (LES) of industrial configurations is now an efficient and tractable tool. Numerous applications involve a liquid or solid disperse phase carried by a gaseous flow field (eg, fuel injection in automotive or aeronautical engines, fluidized beds, and alumina particles in rocket boosters). To simulate this kind of flow, one may resort to a Number Density Function (NDF), which satisfies a kinetic equation.This article is from Center for Turbulence Research Annual Research Briefs 2011: 309-320. Posted with permission.</p

A hierarchy of Eulerian models for trajectory crossing in particle-laden turbulent flows over a wide range of Stokes numbers

With the large increase in available computational resources, large-eddy simulation (LES) of industrial configurations has become an efficient and tractable alternative to traditional multiphase turbulence models. Many applications involve a liquid or solid disperse phase carried by a gas phase (eg, fuel injection in automotive or aeronautical engines, fluidized beds, and alumina particles in rocket boosters).This article is from Center for Turbulence Research Annual Research Briefs 2012: 193-204. Posted with permission.</p

Insights into accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists? forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data