Building-Block-Flow Model for Large-Eddy Simulation

计算流体力学是气动/流体动力飞行器设计和优化的重要工具。据估计，减少运输阻力5%的影响将相当于美国风能发电量翻一番。然而，由于复杂流动物理的普遍存在，对现实飞行器周围流体流动的计算预测提出了独特的挑战，包括不利的压力梯度效应、流动分离和层流到湍流的转变。虽然一些计算模型可以预测一到两种情况，但没有一种模型能够在所有流动现象中准确执行。这个项目将寻求设计一个计算流体力学的统一闭合模型，能够解释丰富的流动物理集合。该项目的目标是将基础物理和机器学习建模结合起来，以建立一种新的计算流体模型。该项目还利用现有的计划来促进工程领域的多样性和包容性，包括参与年度暑期研究计划和本科生研究机会，以吸引女性和代表性不足的少数群体。所提出的闭合模型的核心假设是，有限组简单的正则流包含预测更复杂场景的基本物理。该方法是用具有大涡模拟的人工神经网络实现的，并结合了五个独特的优点：(1)该模型直接适用于任意复杂的几何形状，(2)它被构建用于预测不同的流型(零/有利/不利平均压力梯度壁面湍流、分离、具有平均流三维的统计非定常湍流和层流)，(3)如果需要，该模型可以放大以捕捉额外的流动物理(例如，冲击波)，(4)该模型通过补偿数值误差来保证与数值离散和网格策略的一致性，(5)模型的输出伴随着用于不确定性量化和网格细化的预测的置信度分数。研究的范围从典型的平板湍流到复杂的流动，如真实的飞机外形。这项工作中建立的基金会将使新的场所能够模拟计算流体动力学中的多种流动状态。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Computational fluid dynamics stands as an essential tool for the design and optimization of aerodynamic/hydrodynamic vehicles. It is estimated that the impact of reducing transportation drag by 5% would be equivalent to that of doubling the US wind energy production. However, computational predictions of fluid flows around realistic vehicles poses a unique challenge due to the ubiquity of complex flow physics, including adverse pressure-gradient effects, flow separation, and laminar-to-turbulent transition. While some computational models predict one or two scenarios, no model performs accurately across all flow phenomena. This project will seek to devise a unified closure model for computational fluid dynamics capable of accounting for a rich collection of flow physics. The goals of this project are to couple fundamental physics and machine-learning modeling for a new computational fluids model. The project also leverages existing programs to promote diversity and inclusion in engineering, including participation in annual summer research programs and undergraduate research opportunities to engage women and underrepresented minorities.The core assumption of the closure model proposed is that a finite set of simple canonical flows contains the essential physics to predict more complex scenarios. The approach is implemented using artificial neural networks with large-eddy simulation and brings together five unique advances: (1) the model is directly applicable to arbitrary complex geometries, (2) it is constructed to predict different flow regimes (zero/favorable/adverse mean-pressure-gradient wall turbulence, separation, statistically unsteady turbulence with mean-flow three-dimensionality, and laminar flow), (3) the model can be scaled-up to capture additional flow physics if needed (e.g., shock waves), (4) the model guarantees consistency with the numerical discretization and the gridding strategy by compensating for numerical errors, and (5) the output of the model is accompanied by a confidence score in the prediction used for uncertainty quantification and grid refinement. The cases of study range from canonical flat plate turbulence to complex flows such as realistic aircraft configurations. The foundations established in this work will enable new venues to model multiple flow regimes in computational fluid dynamics.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.