A Framework for Predictive Hybrid Models of Turbulence

先进技术系统的设计和操作依赖于使用可靠的计算机模拟来预测其行为的能力。包括汽车、航空、推进、发电和风能系统在内的许多重要系统都涉及复杂的流体流动。然而，它们的可靠模拟受到这些系统中的流体流动大多是湍流的事实的阻碍。这意味着流体运动是混乱和不可预测的。目前还没有准确和广泛适用的模型来描述湍流对这种流动的影响。最近的复杂湍流的计算机建模方法可以解决以前模型的几个基本限制，以及它们未能产生复杂湍流的精确模拟的地方。因此，改进湍流模型对于精确描述复杂流体系统中的流体运动是必要的。这需要解决这一研究领域的几个突出挑战。新提出的建模框架旨在解决这些挑战，以实现湍流的准确和可靠的计算机模拟。这种长期追求的能力将允许开发更有能力和更有效的流体流动系统，如上面列出的领域中的那些。长期以来，人们已经认识到，湍流的雷诺平均纳维尔-斯托克斯（RANS）和大涡模拟（LES）模型具有互补的优点和缺点，这表明它们的混合可以产生更有能力的模型。然而，以前的杂交技术被发现有根本性的缺陷。一种新的混合建模方法消除了这些缺陷，并形成了这里提出的研究的基础。这种方法可以考虑三个额外的湍流建模挑战，解决后将导致高度可靠和广泛适用的混合RANS/LES模型。这些挑战是：的解决和未解决的湍流之间的能量的积极交换，以允许快速发展的解决波动;消除大的错误，在LES中所产生的强烈不均匀的决议，这通常是必要的，在复杂的流体系统;和，一般化的LES模型，以占各向异性的未解决的湍流不可避免地出现在混合模拟复杂的湍流。这些发展的结果将是强大的预测混合RANS/LES模型，这将使技术进步，在许多系统，涉及湍流流体flow.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

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.