Collaborative Research: Efficient, Stable and Accurate Numerical Algorithms for a class of Gradient Flow Systems and their Applications

合作研究：一类梯度流系统高效、稳定、准确的数值算法及其应用

• 批准号: 1720440
• 负责人: Jie Shen
• 金额: $ 13万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1720440&HistoricalAwards=false
• 关键词: Collaborative; Research; Efficient; Stable; Accurate

This project focuses on the development of efficient, stable and accurate numerical algorithms for gradient flow systems which are ubiquitous in modeling of real-world phenomena. It is expected that the research will not only lead to efficient numerical algorithms for a class of problems of current interests, but also contribute to better understandings of some fundamental issues in materials science, biotechnology, and other related fields through numerical simulations. This project will also provide opportunities for the involved graduate and undergraduate students to learn critical skills of computational and applied mathematics and to develop state-of-the-art numerical algorithms for science and engineering applications. The free energies of gradient flow systems usually consist of various nonlinear potentials formulated in diverse complex formats which present a major challenge in the construction of efficient and accurate time discretization schemes. The project aims at overcoming this challenge by using a flexible and robust IEQ approach that enables one to develop time discretization schemes for a large class of gradient flow systems. The goals of this proposal are three folds: (i) to develop a unified numerical framework of time-marching schemes for solving general gradient flow models with high nonlinearity; (ii) to develop efficient numerical schemes for a number of challenging gradient flow models of current interests (e.g., nonlinear coupled multivariable models, nonlocal models, anisotropic models, nonlinear coupled systems that follow various physical principles, tensor based liquid crystal models); (iii) to investigate some fundamental issues of viscoelastic drops on substrates and active liquid crystal droplets using the developed predictive numerical tools. The proposed schemes will lead to numerical predictive tools that extend the capability of mathematical and experimental analysis, and contribute to better understanding of some pressing science and engineering issues related to multi-phase complex fluids.

该项目的重点是开发高效，稳定和精确的数值算法，用于梯度流系统，这些系统在现实世界现象的建模中无处不在。预计该研究不仅将导致当前感兴趣的一类问题的有效数值算法，而且还有助于通过数值模拟更好地理解材料科学，生物技术和其他相关领域的一些基本问题。该项目还将为参与的研究生和本科生提供机会，学习计算和应用数学的关键技能，并为科学和工程应用开发最先进的数值算法。梯度流系统的自由能通常由各种复杂格式的非线性势组成，这对构建高效准确的时间离散格式提出了重大挑战。该项目旨在克服这一挑战，通过使用灵活和强大的IEQ方法，使人们能够开发一个大类的梯度流系统的时间离散化计划。该提案的目标有三个方面：（i）开发一个统一的时间推进格式的数值框架，用于求解具有高非线性的一般梯度流模型;（ii）为当前感兴趣的一些具有挑战性的梯度流模型（例如，非线性耦合多变量模型，非局部模型，各向异性模型，非线性耦合系统，遵循各种物理原理，张量为基础的液晶模型）;（iii）调查一些基本问题的粘弹性液滴在基板上和主动液晶液滴使用开发的预测数值工具。所提出的方案将导致数值预测工具，扩展的数学和实验分析的能力，并有助于更好地理解一些紧迫的科学和工程问题有关的多相复杂流体。

项目成果

Bound preserving and energy dissipative schemes for porous medium equation

• DOI: 10.1016/j.jcp.2020.109378
• 发表时间: 2020-06
• 期刊: J. Comput. Phys.
• 作者: Yiqi Gu;Jie Shen

ENERGY STABILITY AND CONVERGENCE OF SAV BLOCK-CENTERED FINITE DIFFERENCE METHOD FOR GRADIENT FLOWS

梯度流SAV块心有限差分法的能量稳定性和收敛性

• DOI: 10.1090/mcom/3428
• 发表时间: 2019-09-01
• 期刊: MATHEMATICS OF COMPUTATION
• 影响因子: 2
• 作者: Li, Xiaoli;Shen, Jie;Rui, Hongxing
• 通讯作者: Rui, Hongxing

Fundamental gaps of the fractional Schrödinger operator

• DOI: 10.4310/cms.2019.v17.n2.a7
• 发表时间: 2018-01
• 期刊: Communications in Mathematical Sciences
• 影响因子: 1
• 作者: W. Bao;Xinran Ruan;Jie Shen;Changtao Sheng

Unconditionally Stable Pressure-Correction Schemes for a Nonlinear Fluid-Structure Interaction Model

非线性流固耦合模型的无条件稳定压力修正方案

• DOI: 10.1007/s42967-019-0004-0
• 发表时间: 2019
• 期刊: Communications on Applied Mathematics and Computation
• 影响因子: 1.6
• 作者: He, Ying;Shen, Jie
• 通讯作者: Shen, Jie

A spectrally accurate approximation to subdiffusion equations using the log orthogonal functions

使用对数正交函数对次扩散方程进行光谱精确逼近

• DOI: 10.1137/19m1281927
• 发表时间: 2020
• 期刊: SIAM Journal on Scientific Computing
• 影响因子: 3.1
• 作者: Sheng Chen;Jie shen;Zhimin Zhang;Zhi Zhou
• 通讯作者: Zhi Zhou