Data-Driven Dynamical System Models of Roughness-Induced Secondary Flows in Thermally Stratified Boundary Layers

TY - GEN

T1 - Data-Driven Dynamical System Models of Roughness-Induced Secondary Flows in Thermally Stratified Boundary Layers

AU - Hansen, Christoffer

AU - I. A. Yang, Xiang

AU - Abkar, Mahdi

PY - 2022/9

Y1 - 2022/9

N2 - The goal of this work is to investigate the feasibility of constructing data-driven dynamical system models of roughness-induced secondary flows in thermally stratified turbulent boundary layers. Considering the case of a surface roughness distribution which is homogeneous and heterogeneous in the streamwise and spanwise directions, respectively, we describe the streamwise averaged in-plane motions via a stream function formulation, thereby reducing the number of variables to the streamwise velocity component, an appropriately introduced stream function, and the temperature. Then, from the results of large-eddy simulations, we perform a modal decomposition of each variable with the proper orthogonal decomposition and further utilize the temporal dynamics of the modal coefficients to construct a datadriven dynamical system model by applying the sparse identification of nonlinear dynamics (SINDy). We also present a novel approach for enforcing spanwise reflection symmetry within the SINDy framework to incorporate a physical bias.

AB - The goal of this work is to investigate the feasibility of constructing data-driven dynamical system models of roughness-induced secondary flows in thermally stratified turbulent boundary layers. Considering the case of a surface roughness distribution which is homogeneous and heterogeneous in the streamwise and spanwise directions, respectively, we describe the streamwise averaged in-plane motions via a stream function formulation, thereby reducing the number of variables to the streamwise velocity component, an appropriately introduced stream function, and the temperature. Then, from the results of large-eddy simulations, we perform a modal decomposition of each variable with the proper orthogonal decomposition and further utilize the temporal dynamics of the modal coefficients to construct a datadriven dynamical system model by applying the sparse identification of nonlinear dynamics (SINDy). We also present a novel approach for enforcing spanwise reflection symmetry within the SINDy framework to incorporate a physical bias.

U2 - 10.1115/FEDSM2022-87630

DO - 10.1115/FEDSM2022-87630

M3 - Article in proceedings

T3 - American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FEDSM

BT - Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC)

PB - American Society of Mechanical Engineers (ASME)

T2 - ASME 2022 Fluids Engineering Division Summer Meeting

Y2 - 3 August 2022 through 5 August 2022

ER -