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Monday, February 22, 2016
10:00 AM - 11:30 AM
CNLS Conference Room (TA-3, Bldg 1690)

Seminar

Machine Learning and Optimization for Modeling and Control of Turbulent Flows

Ryan King
University of Colorado, Boulder

Turbulence is pervasive throughout most flows of scientific and engineering interest, yet the modeling of turbulence and its effect on engineering designs remains a persistent challenge. This talk introduces a data-driven machine learning and optimization framework that enables new insights into turbulence physics, modeling, and applied engineering design. In the first part of the talk, a new autonomic closure for coarse-grained turbulence simulations is introduced and examined in a priori tests of large eddy simulations (LES). The autonomic closure is a nonparametric approach that learns a flow-specific closure on-the-fly, and is fundamentally different from previous approaches rooted in continuum mechanics. The closure is found without external training data by solving a supervised learning problem in the inertial range by using powerful kernel-based machine learning techniques. The autonomic kernel closure is remarkably accurate in a priori tests, and shows promise as a tool for data-enabled model discovery. In the second part of the talk, high-dimensional optimization is applied to the design and control of practical thermal-fluid systems. A canonical example of turbulent flow in engineering practice is found in utility-scale wind farms. We demonstrate a novel optimization of the turbine layout and control settings using powerful adjoint techniques. Adjoint simulations are used to efficiently find gradients of high-dimensional, nonlinear wind farm flow simulations. This enables a groundbreaking increase in the fidelity of the fluid flow simulations used in wind farm layout and control optimizations. The talk is concluded with a discussion of how data-driven machine learning and optimization can enable insights into new physics and aid in reduced-order model development.

Host: Misha Chertkov