A Framework for Predictive Hybrid Models of Turbulence

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Design and operation of advanced technological systems rely on the ability to predict their behavior using reliable computer simulation. Many important systems including automotive, aeronautic, propulsion, power generation and wind energy systems involve complex fluid flows. However, their reliable simulation is hindered by the fact that the fluid flows in these systems are mostly turbulent. This means that the fluid motion is chaotic and unpredictable. There are currently no accurate and broadly applicable models to describe the effects of turbulence on such flows. Recent approaches to computer modeling of complex turbulent flows could address several fundamental limitations of the previous models and where they have failed to produce accurate simulations of complex turbulent flows. Improving turbulence models are therefore necessary for accurate description of the fluid motion in complex fluid systems. This requires addressing several outstanding challenges in this field of research. The new proposed modeling framework is aimed at addressing these challenges to enable accurate and reliable computer simulation of turbulent flows. This long- sought capability will allow the development of more capable and efficient fluid flow systems, like those in the areas listed above.It has long been recognized that Reynolds averaged Navier-Stokes (RANS) and large eddy simulation (LES) models of turbulence have complementary strengths and weaknesses suggesting their hybridization to produce a more capable model. However, previous hybridization techniques were found to have fundamental flaws. A new hybrid modeling approach eliminates these flaws and forms the basis for the research proposed here. This approach enables the consideration of three additional turbulence modeling challenges, which when addressed will result in highly reliable and broadly applicable hybrid RANS/LES models. These challenges are: the active exchange of energy between the resolved and unresolved turbulence to allow rapid development of resolved fluctuations; the elimination of large errors in LES that arise from the strongly inhomogeneous resolution that is usually necessary in complex fluid systems; and, the generalization of LES models to account for anisotropy of the unresolved turbulence which inevitably arises in hybrid simulations of complex turbulent flows. The result of these developments will be robust predictive hybrid RANS/LES models that will enable technological advances in many systems that involve turbulent fluid flow.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.