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Turbulent complex flows reconstruction via data assimilation in large eddy models

Pranav Chandramouli 1, 2
2 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
Inria Rennes – Bretagne Atlantique , IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture, IRMAR - Institut de Recherche Mathématique de Rennes
Abstract : Data assimilation as a tool for fluid mechanics has grown exponentially over the last few decades. The ability to combine accurate but partial measurements with a complete dynamical model is invaluable and has numerous applications to fields ranging from aerodynamics, geophysics, and internal ventilation. However, its utility remains limited due to the restrictive requirements for performing data assimilation in the form of computing power, memory, and prior information. This thesis attempts at redressing various limitations of the assimilation procedure in order to facilitate its wider use in fluid mechanics. A major roadblock for data assimilation is the computational cost which is restrictive for all but the simplest of flows. Following along the lines of Joseph Smagorinsky, turbulence modelling through large-eddy simulation is incorporated in to the assimilation procedure to significantly reduce computing power and time required. The requirement for prior volumetric information for assimilation is tackled using a novel reconstruction methodology developed and assessed in this thesis. The snapshot optimisation algorithm reconstructs 3D fields from 2D cross- planar observations by exploiting directional homogeneity. The method and its variants work well with synthetic and experimental data-sets providing accurate reconstructions. The reconstruction methodology also provides the means to estimate the background covariance matrix which is essential for an efficient assimilation algorithm. All the ingredients are combined to perform variational data assimilation of a turbulent wake flow around a cylinder successfully at a transitional Reynolds number. The assimilation algorithm is validated with synthetic volumetric observation and assessed on 2D cross-planar observations emulating experimental data.
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  • HAL Id : tel-01951540, version 1
  • IRSTEA : PUB00059795

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Pranav Chandramouli. Turbulent complex flows reconstruction via data assimilation in large eddy models. Fluids mechanics [physics.class-ph]. Université Rennes 1, 2018. English. ⟨NNT : 2018REN1S035⟩. ⟨tel-01951540⟩

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