🔬 research highlights
Turbulent fluid flow is a highly multi-scale, nonlinear physical system. Simulating turbulent flows requires memory- and cpu-intensive computations, and the resulting high-dimensional data is challenging to interpret. In my research, I develop methods for reduced-order modeling of near-wall and roughness sublayer turbulence. Day-to-day, my work includes running computational fluid dynamics simulations on high-performance computing clusters, developing meaningful data analyses and flow visualizations, and constructing reduced-order representations which capture key flow features using spectral and modal analysis techniques. I also use theory- and equation-driven methods, in particular resolvent analysis of the Navier-Stokes equations, to construct predictive low-order models.
Modeling roughness sublayer turbulence using resolvent analysis
Miles Chan, Zvi Hantsis, Ugo Piomelli, Beverley McKeon
Abstract:
Predictions of spatially-varying turbulence in the presence of surface roughness from resolvent analysis.
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Data-driven reduced-order modeling of near-wall turbulent fluctuations for wall-modeled LES
Miles Chan, Zvi Hantsis, Ugo Piomelli, Beverley McKeon
Abstract:
Developing a self-consistent low-order resolvent mode representation of near-wall turbulent eddies, scalable to engineering-relevant Reynolds numbers.
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