A Data-centric Approach to Turbulence Simulation

湍流或湍流在许多日常现象中都很常见，如车辆上方的气流、滚滚的云、上升的烟雾或湍急的水流。了解湍流并对其进行精确建模对于交通运输(飞机、汽车等)、能源生产(风力涡轮机或更传统的发电厂)和制造等各种应用中的设计都很重要。湍流通过广泛的长度和时间尺度的相互作用而演变，这给使用计算机模拟来模拟湍流流动的工程师们带来了巨大的挑战。解决湍流运动的高保真计算模型在最大的超级计算机上可能需要几个月的时间来模拟即使是相对简单的流动。相比之下，试图仅预测湍流净影响的低保真模型可以在几秒钟内给出复杂流动的结果，但对许多重要流动的预测精度较低。为了有效地将湍流模拟用于设计应用，工程师通常需要数千个定义问题的参数组合的流动解决方案。由于需要大量的案例，通常需要使用较低的保真度模型来保持成本和时间的可行性。这一建议涉及使用来自精选的少量高保真模拟的更详细的信息，以及全套廉价的低保真模拟，以共同实现对所需的数千个参数变化的显著更准确的预测。所提出的比例分辨率模拟适用于流体结构的动画。虽然K-12水平的学生无法完全理解，但显示宏观和小规模流体结构的模拟有望对科学、技术、工程和数学(STEM)领域产生极大的激励作用。因此，这项研究的可视化结果正在与科罗拉多博尔德大学的领导力与多样性(BOLD)中心合作，用于直接扩展活动。该研究项目涉及三个主要目标：1)使用来自尺度解析模拟的数据驱动的机器学习来改进尺度建模闭包；2)开发多保真模型，融合来自O(1000)低成本模拟和O(20)高成本模拟的数据，以显著降低的成本实现接近所有O(1000)参数组合的高成本模拟的精度；以及3)结合这两种方法。这些发展在目前已知超出低成本模型预测能力的流动上得到了证明。重点是不利的压力梯度流动，包括那些导致流动分离的流动。本项目所进行的模拟将推动逆压梯度流动模拟的发展。具体地说，直接数值模拟的动量雷诺数为6,000，壁面分辨大涡模拟为30,000，壁面模型大涡模拟为150,000。所有模拟的数据都将提供给科学和工程界，以便进行进一步的分析和基本的洞察。

Turbulence or turbulent flow is commonly observed in many everyday phenomena, such as airflow over a vehicle, billowing clouds, rising smoke or fast-flowing waters. Understanding and accurately modeling turbulent flow is important for design in applications as diverse as transportation (planes, cars, etc.), energy generation (wind turbines or more traditional power plants), and manufacturing. Turbulence evolves through interaction of a broad range of length and time scales, posing significant challenges to engineers who model turbulent fluid flow using computer simulations. High fidelity computational models, which resolve turbulent motions, can require several months of time on the largest supercomputer to simulate even a relatively simple flow. By contrast, low fidelity models that attempt to predict only the net effects of the turbulence can give results on complicated flows in seconds but the prediction has low accuracy for many important flows.  To effectively use simulations of turbulence for design applications, engineers typically require flow solutions for thousands of problem-defining parameter combinations. The need for a large number of cases typically necessitates the use of lower fidelity models to keep cost and time feasible. This proposal involves using the more detailed information from a select, small number of higher fidelity simulations together with a full set of inexpensive low fidelity simulations to collectively achieve a substantially more accurate prediction for the thousands of parameter variations needed. The scale-resolving simulations proposed lend themselves to animations of fluid structures. While a full understanding is beyond students at the K-12 levels, simulations that show macro- and small-scale fluid structures are expected to be highly motivating to science, technology, engineering, and mathematics (STEM) areas. Therefore, visualization results from this research are being packaged for direct outreach activities in coordination with the Broadening Opportunity through Leadership and Diversity(BOLD) Center at the University of Colorado Boulder.This research project involves three main objectives: 1) using data-driven machine learning from scale-resolving simulations to improve scale-modeling closures, 2) developing multi-fidelity models that fuse data from O(1000) low-cost simulations and O(20) higher-cost simulations to achieve an accuracy approaching that of the higher-cost simulations for all O(1000) parameter combinations at substantially reduced cost, and 3) combining these two approaches. These developments are being demonstrated on flows that are currently known to be beyond the predictive capacity of the low-cost models. The focus is on adverse pressure gradient flows, including those that lead to flow separation. The simulations performed in this project will advance the state-of-the-art in adverse pressure gradient flow simulation. Specifically, simulations are being executed at momentum Reynolds numbers of 6,000 for direct numerical simulation, 30,000 for wall-resolved large eddy simulation, and 150,000 for wall-modeled large eddy simulation. The data from all simulations is being be made available to the science and engineering community to enable further analysis and fundamental insight.