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Collaborative Research: Computational Methods for Optimal Transport via Fluid Flows

合作研究：流体流动优化传输的计算方法

基本信息

批准号：
2111315
负责人：
Yangwen Zhang
金额：
$ 8.65万
依托单位：
University of Delaware
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2111315&HistoricalAwards=false
关键词：
Collaborative Research Computational Methods Optimal

项目摘要

Transport and mixing in fluids is a topic of fundamental interest in engineering and natural sciences, with broad applications ranging from industrial and chemical mixing on small and large scales, to preventing the spreading of pollutants in geophysical flows. This project focuses on computational methods for control of optimal transport and mixing of some quantity of interest in fluid flows. The question of what fluid flow maximizes mixing rate, slows it down, or even steers a quantity of interest toward a desired target distribution draws great attention from a broad range of scientists and engineers in the area of complex dynamical systems. The goal of this project is to place these problems within a flexible computational framework, and to develop a solution strategy based on optimal control tools, data compression strategies, and methods to reduce the complexity of the mathematical models. This project will also help the training and development of graduate students across different disciplines to conduct collaborative research in optimal transport and mixing, flow control, and computational methods for solving these problems.The project is concerned with the development and analysis of numerical methods for optimal control for mixing in fluid flows. More precisely, the transport equation is used to describe the non-dissipative scalar field advected by the incompressible Stokes and Navier-Stokes flows. The research aims at achieving optimal mixing via an active control of the flow velocity and constructing efficient numerical schemes for solving this problem. Various control designs will be investigated to steer the fluid flows. Sparsity of the optimal boundary control will be promoted via a non-smooth penalty term in the objective functional. This essentially leads to a highly challenging nonlinear non-smooth control problem for a coupled parabolic and hyperbolic system, or a semi-dissipative system. The project will establish a novel and rigorous mathematical framework and also new accurate and efficient computational techniques for these difficult optimal control problems. Compatible discretization methods for coupled flow and transport will be employed to discretize the controlled system and implement the optimal control designs numerically. Numerical schemes for the highly complicated optimality system will be constructed and analyzed in a systematic fashion. New incremental data compression techniques will be utilized to avoid storing extremely large solution data sets in the iterative solvers, and new model order reduction techniques specifically designed for the optimal mixing problem will be developed to increase efficiency. The synthesis of optimal control and numerical approximation will enable the study of similar phenomena arising in many other complex and real-world flow 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.

流体中的输运和混合是工程和自然科学中的一个基本感兴趣的主题，具有广泛的应用范围，从小规模和大规模的工业和化学混合，到防止地球物理流动中污染物的扩散。该项目的重点是控制流体流动中某些感兴趣的量的最佳传输和混合的计算方法。什么样的流体流动使混合速率最大化、使混合速率减慢、甚至使感兴趣的量转向期望的目标分布的问题引起了复杂动力系统领域的广泛科学家和工程师的极大关注。该项目的目标是将这些问题放在一个灵活的计算框架内，并开发基于最优控制工具，数据压缩策略和方法的解决方案策略，以降低数学模型的复杂性。本项目还将帮助培养和发展不同学科的研究生，以便在最佳输送和混合、流动控制以及解决这些问题的计算方法方面进行合作研究。本项目涉及流体流动混合最佳控制的数值方法的开发和分析。更准确地说，输运方程被用来描述由不可压缩的Stokes和Navier-Stokes流平流的非耗散标量场。该研究旨在通过主动控制流速实现最佳混合，并构建有效的数值方案来解决这个问题。将研究各种控制设计以操纵流体流动。通过在目标泛函中引入非光滑惩罚项，提高了最优边界控制的稀疏性。这本质上导致了一个高度具有挑战性的非线性非光滑控制问题的耦合抛物和双曲系统，或半耗散系统。该项目将为这些困难的最优控制问题建立一个新颖而严格的数学框架和新的精确而有效的计算技术。耦合流动和传输的兼容离散化方法将被用来离散控制系统，并实现最佳控制设计的数字。高度复杂的最优性系统的数值方案将被构造和系统地分析。将利用新的增量数据压缩技术，以避免在迭代求解器中存储非常大的解数据集，并将开发专门为最佳混合问题设计的新模型降阶技术，以提高效率。最优控制和数值逼近的综合将使许多其他复杂和现实世界的流动动力学中出现的类似现象的研究成为可能。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。