Building-Block-Flow Model for Large-Eddy Simulation

用于大涡模拟的积木流模型

• 批准号: 2317254
• 负责人: Adrian Lozano-Duran
• 金额: $ 32万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2317254&HistoricalAwards=false
• 关键词: Building; Block; Flow; Model; Large

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.

计算流体动力学是设计和优化空气动力/流体动力车辆的重要工具。据估计，将运输阻力减少5％的影响相当于使美国风能产量增加一倍。然而，由于复杂流体物理的无处不在，包括不良压力梯度效应，流动分离和椎板到扰动的跃迁，围绕逼真的车辆流动的计算预测构成了独特的挑战。尽管某些计算模型预测了一个或两个方案，但在所有流动现象中，没有任何模型可以准确地执行。该项目将寻求为计算流体动力学的统一闭合模型，以计算丰富的流动物理收集。该项目的目标是将新计算流体模型的基本物理学和机器学习建模融入。该项目还利用现有计划来促进多样性和包括在工程学中的多样性和包容性，包括参与年度夏季研究计划和本科研究的机会，使妇女和人为不足的少数群体吸引妇女。提出的封闭模型的核心假设是，一组有限的简单规范流动包含一个必要的物理学，以预测更复杂的风景。 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按比例缩放以捕获其他流动物理，例如（例如，冲击波），（4）该模型通过补偿数值错误来确保与数值离散化和网格策略保持一致性，并且（5）模型的输出伴随着置信度得分，在预测中，用于不确定的量化性量化和网格细化。研究案例范围从规范平板湍流到复杂的流量，例如逼真的飞机配置。这项工作中建立的基础将使新的场所能够在计算流体动力学中对多个流动制度进行建模。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估评估标准来通过评估来支持的。