Physics-Informed Structure-Preserving Numerical Approximations of Thermodynamically Consistent Models for Non-equilibrium Phenomena

非平衡现象热力学一致模型的物理信息保结构数值近似

• 批准号: 2405605
• 负责人: Jia Zhao
• 金额: $ 20万
• 依托单位: SUNY at Binghamton
• 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: 2023-12-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2405605&HistoricalAwards=false
• 关键词: Physics; Informed; Structure; Preserving; Numerical

Non-equilibrium phenomena, including those described by non-isothermal and isothermal hydrodynamic models with applications to complex multiphase fluids, are ubiquitous in science. They require well-developed models to describe their dynamics but pose challenges to the algorithms for their approximations. This project aims to establish a computational framework for models of non-equilibrium phenomena that have the property of being thermodynamically consistent. The algorithms to be designed will preserve the desired properties at the discrete levels. Furthermore, these numerical schemes will be utilized to simulate and investigate the dynamics of several classes of non-equilibrium models in an accurate and efficient way. Software will be developed on high-performance computing platforms and made available to the public. Students will be involved and trained through research involvement in the project. Thermodynamically consistent (TC) partial differential equation (PDE) systems, derivable from the GENERIC formalism (General Equation for Non-Equilibrium Reversible-Irreversible Coupling), encompass a large class of models in science and engineering for non-equilibrium phenomena. The project will (1) establish a paradigm for designing structure-preserving, high order, energy stable, and efficient numerical approximations to solve TCPDE systems by exploiting the mathematical structure of the TC models and reformulating them using the GENERIC formalism; (2) design physics-informed deep neural network frameworks to solve TCPDE models while preserving their structures and properties; (3) apply the numerical framework to investigate several classes of TC models; and (4) develop an object-oriented, open-source, and high-performance software package for hybrid GPU-CPU architectures. The outcomes will advance modeling, analysis, and numerical simulations of non-equilibrium thermodynamic and hydrodynamic models, fostering a deeper understanding of non-equilibrium phenomena in a wide range of applications.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.

非平衡现象，包括非等温和等温流体动力学模型描述的复杂多相流体的应用程序，是无处不在的科学。它们需要完善的模型来描述其动态，但对近似算法提出了挑战。该项目旨在为具有热力学一致性的非平衡现象模型建立一个计算框架。要设计的算法将在离散水平上保持所需的属性。此外，这些数值格式将被用来模拟和研究几类非平衡模型的动力学在一个准确和有效的方式。将在高性能计算平台上开发软件，并向公众提供。学生将通过参与该项目的研究参与和培训。 热力学相容的偏微分方程（PDE）系统，可从通用形式主义（非平衡可逆-不可逆耦合的通用方程）推导出来，包含了科学和工程中用于非平衡现象的一大类模型。该项目将（1）通过利用TC模型的数学结构并使用GENERIC形式主义重新制定它们，建立一个设计结构保持，高阶，能量稳定和有效数值近似的范式来解决TCPDE系统;（2）设计物理信息深度神经网络框架来解决TCPDE模型，同时保持其结构和属性;（3）应用数值框架研究几类TC模型;（4）为混合GPU-CPU架构开发面向对象、开源和高性能的软件包。其成果将推进非平衡热力学和流体力学模型的建模、分析和数值模拟，促进对广泛应用中的非平衡现象的更深入理解。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。