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10 May 2019 Structural field analysis of a large eddy turbulent flow simulation using probabilistic graphical modeling
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Environmental engineering remote sensing platforms using hyperspectral imagery are often responsible for monitoring coastal regions in order to safeguard national waters. This objective requires determining subsurface turbulent structure from surface water spatial measurements for flow state assessment and decision-making. The inability of remote sensing platforms to penetrate the water column at depth because of turbulence-induced sediment-concentration modulation necessitates using models that dynamically link surface and subsurface structures. A hidden Markov model is applied to large-eddy simulated three-dimensional turbulent flow for the purpose of exploring the feasibility of constructing a system model possessing diagnostic/prognostic statistical power for turbulent state evolution. The data-driven model is based on machine-learning techniques that rely on data statistical covariance structure. Initial results suggest strong nonlinear coupling between the mean flow directed vorticity, cross mean flow velocity, and sediment concentration. In addition, a Bayesian-based state-action estimation algorithm is employed that demonstrates which turbulent feature variables should be focused on at specific times, given the desire to reach a known goal state, and given only a limited number of observations. Such a model gives experimentalists time- and resource-saving guidance for determining what turbulent variables to measure at different times in order to reach a known turbulent goal state.
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Nicholas V. Scott and Zhen Cheng "Structural field analysis of a large eddy turbulent flow simulation using probabilistic graphical modeling ", Proc. SPIE 11014, Ocean Sensing and Monitoring XI, 110140S (10 May 2019);

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