Adaptive LES Methodology for Turbulent Flow Simulations

Abstract

Although turbulent flows are common in the world around us, a solution to the fundamental equations that govern turbulence still eludes the scientific community. Turbulence has often been called one of the last unsolved problem in classical physics, yet it is clear that the need to accurately predict the effect of turbulent flows impacts virtually every field of science and engineering. As an example, a critical step in making modern computational tools useful in designing aircraft is to be able to accurately predict the lift, drag, and other aerodynamic characteristics in numerical simulations in a reasonable amount of time. Simulations that take months to years to complete are much less useful to the design cycle. Much work has been done toward this goal (Lee-Rausch et al. 2003, Jameson 2003) and as cost effective accurate tools for simulating turbulent flows evolve, we will all benefit from new scientific and engineering breakthroughs. The problem of simulating high Reynolds number (Re) turbulent flows of engineering and scientific interest would have been solved with the advent of Direct Numerical Simulation (DNS) techniques if unlimited computing power, memory, and time could be applied to each particular problem. Yet, given the current and near future computational resources that exist and a reasonable limit on the amount of time an engineer or scientist can wait for a result, the DNS technique will not be useful for more than 'unit' problems for the foreseeable future (Moin & Kim 1997, Jimenez & Moin 1991). The high computational cost for the DNS of three dimensional turbulent flows results from the fact that they have eddies of significant energy in a range of scales from the characteristic length scale of the flow all the way down to the Kolmogorov length scale. The actual cost of doing a three dimensional DNS scales as Re{sup 9/4} due to the large disparity in scales that need to be fully resolved. State-of-the-art DNS calculations of isotropic turbulence have recently been completed at the Japanese Earth Simulator (Yokokawa et al. 2002, Kaneda et al. 2003) using a resolution of 40963 (approximately 10{sup 11}) grid points with a Taylor-scale Reynolds number of 1217 (Re {approx} 10{sup 6}). Impressive as these calculations are, performed on one of the world's fastest super computers, more brute computational power would be needed to simulate the flow over the fuselage of a commercial aircraft at cruising speed. Such a calculation would require on the order of 10{sup 16} grid points and would have a Reynolds number in the range of 108. Such a calculation would take several thousand years to simulate one minute of flight time on today's fastest super computers (Moin & Kim 1997). Even using state-of-the-art zonal approaches, which allow DNS calculations that resolve the necessary range of scales within predefined 'zones' in the flow domain, this calculation would take far too long for the result to be of engineering interest when it is finally obtained. Since computing power, memory, and time are all scarce resources, the problem of simulating turbulent flows has become one of how to abstract or simplify the complexity of the physics represented in the full Navier-Stokes (NS) equations in such a way that the 'important' physics of the problem is captured at a lower cost. To do this, a portion of the modes of the turbulent flow field needs to be approximated by a low order model that is cheaper than the full NS calculation. This model can then be used along with a numerical simulation of the 'important' modes of the problem that cannot be well represented by the model. The decision of what part of the physics to model and what kind of model to use has to be based on what physical properties are considered 'important' for the problem. It should be noted that 'nothing is free', so any use of a low order model will by definition lose some information about the original flow.