Ensemble/Variational Estimation (EnVE) and its application to turbulent flows in complex geometries

ORAL

Abstract

A new algorithm, Ensemble/Variational Estimation (EnVE), has been developed as a consistent hybrid data assimilation method that combines the nonlinear statistical propagation properties of the Ensemble Kalman Filter (EnKF) and the retrospective analysis capabilities of 4DVar/Moving Horizon Estimation (MHE). A sophisticated C++ object-oriented framework has been developed that implements the EnVE algorithm to facilitate its application to any complex (multiscale/multiphysics) flow code of interest in a highly parallel fashion with minimal changes to the existing flow solver. In the present work, this framework has been applied to the flagship unstructured LES code (CDP) developed at the Center for Integrated Turbulence Simulations (CITS) at Stanford University.

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Authors

  • Joe Cessna

    UC San Diego, University of California-San Diego, Flow Control and Coordinated Robotics Labs, UC San Diego

  • Christopher Colburn

    UC San Diego, University of California-San Diego, Flow Control and Coordinated Robotics Labs, UC San Diego

  • Thomas Bewley

    UC San Diego, University of California-San Diego, University of California, San Diego, Flow Control and Coordinated Robotics Labs, UC San Diego, MAE, UCSD

  • Frank Ham

    Stanford University, Center for Turbulence Research

  • Qiqi Wang

    Stanford University

  • Gianluca Iaccarino

    Stanford University