课题基金 / 基金详情

Polynomial Optimization and Finite Element Methods for Nonlinear Mechanics

Polynomial Optimization and Finite Element Methods for Nonlinear Mechanics
非线性力学的多项式优化和有限元方法
批准号:
2012658
负责人:
Federico Fuentes
金额:
$11.39万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-06-15 至 2021-08-31

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中文摘要
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英文摘要
The purpose of this interdisciplinary project is to devise a new generation of computational methods based on the combination of finite element methods and polynomial optimization to analyze problems in nonlinear mechanics, which often exhibit a complex evolution over time and space. Some examples include fluid flows, convection, and nonlinear elasticity. Computing these systems’ equilibria and producing a detailed diagnosis of their stability can be of notorious difficulty, but are of profound importance for elucidating the underlying physical mechanisms involved. These novel algorithms and rigorous analysis will allow the improvement of longstanding results in fluid mechanics related to the stability and dynamics of canonical shear flows. The results may lead to a deeper knowledge of turbulent losses in fluid systems, which could play a critical role in engineering problems within the transport and energy sectors.The project has two parts. The first part focuses on rigorously establishing the nonlinear stability of fluid flows with the goal of sharpening the lower bounds on the global stability threshold of shear flows (the largest Reynolds number under which any initial velocity field eventually converges to the laminar flow), such as plane Couette and plane Poiseuille flows. This will be achieved by carefully constructing Lyapunov functionals with the computer using polynomial sum-of-squares constraints and a special framework to pose the incompressible Navier-Stokes equations. The second part proposes the first connection between finite element analysis and sparse polynomial optimization. The combination has some nice theoretical implications, since finite element error analysis is deeply rooted in functional analysis and approximation theory, while the nascent field of polynomial optimization is closely tied to results in real algebraic geometry. From the practical standpoint, the computational methods developed will form a general framework to directly solve nonlinear partial differential equations (PDEs) in general domains while concurrently globally optimizing relevant quantities of interest (e.g. energy, heat transport, etc.). The resulting numerical methods provide a systematic pathway to compute exact coherent states of physical systems without using homotopic continuation. This is essential in problems where non-unique solutions do not bifurcate from a trivial state, like in plane Couette and pipe flows.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.
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
供应链管理中的稳健型(Robust)策略分析和稳健型优化(Robust Optimization )方法研究
  • 批准号:
    70601028
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    7.0万元
  • 批准年份:
    2006
  • 负责人:
    王明征
  • 依托单位: