Description du projet
S’affranchir des restrictions dans la recherche des causes de la turbulence
La modélisation des comportements des systèmes en fonction des comportements antérieurs s’est avérée extrêmement utile pour notre compréhension de processus allant du changement climatique et de la pollution environnementale aux activités pharmaceutiques ou aux effets toxiques d’un nanomatériau. Toutefois, ces algorithmes, tout comme les êtres humains eux-mêmes, peuvent converger vers des solutions et des «suggestions» qui ont été influencées par des connaissances et des hypothèses antérieures. Le projet CausT, financé par l’UE, tire parti de la puissance de calcul et des méthodes de simulation de Monte-Carlo les plus récentes pour inverser ce processus et identifier de nouvelles structures d’écoulement turbulent. Au lieu de définir ou de restreindre les propriétés importantes des structures d’écoulement turbulent, les scientifiques rechercheront les configurations d’écoulement les plus sensibles aux perturbations et dévoileront leurs propriétés.
Objectif
Simulations have driven many recent scientific advances. In the case of the physics of fluid turbulence, they have involved some of the most expensive computations at any time, but faster computers now permit meaningful simulations to run in minutes in a modest machine. This proposal centres on exploring the role of simulations in this limit of ‘zero’ computing cost, and on the analysis of the resulting data. What ‘free’ simulations allow is ‘Monte-Carlo’ research, in which ideas are ‘randomly’ tested and only evaluated afterwards, in the hope that some of them be fruitful. Their main advantage is to alleviate ‘paradigm lock’, in which radically new ideas are unlikely to get tested and knowledge gets stuck in a local optimum. But ensembles of cheap simulations also provide causal information about what the effect of a particular ‘random’ initial condition is. The main result in turbulence is expected to be the identification of novel flow structures, with definitions grounded in the underlying physics. Up to now, structures have mostly been described in terms of properties assumed to be important (e.g. intensity), with their effect on the flow being tested a-posteriori, but Monte-Carlo search allows us to reverse the process, identifying structures from their effects. In particular, we will search for flow configurations that are ‘causally most sensitive’ to perturbations, in the sense that the perturbations are most effective when applied to them. Both the probing perturbations and the receptive flow states constitute ‘causes’. The implied definition of causality only applies over times of the order of a turnover, and is connected with control: changing the cause modifies the effect, with obvious applications. The flows examined will mainly be wall-bounded ones, including effects such as rotation and rheology, but we will also examine the general inverse energy and momentum cascades towards larger scales. Some preliminary experiments are described.
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(s’ouvre dans une nouvelle fenêtre) ERC-2020-ADG
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ERC-ADG - Advanced GrantInstitution d’accueil
28040 Madrid
Espagne