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LEAPS-MPS: Stochastic Particle-Continuum Hybrid Simulation Method for Model Heterogeneous Catalysts under Reaction Conditions

LEAPS-MPS：反应条件下多相催化剂模型的随机粒子连续混合模拟方法

基本信息

批准号：
2213368
负责人：
Changho Kim
金额：
$ 24.99万
依托单位：
University of California - Merced
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213368&HistoricalAwards=false
关键词：
LEAPS MPS Stochastic Particle Continuum

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). In this project, funded by the Mathematical and Physical Sciences Directorate and housed in the Chemistry Division, Professor Changho Kim and his students at the University of California, Merced (UCM) will develop an improved method for simulating complex chemical reactions on computers. Many important chemical processes, from synthesizing fuel to cleaning exhaust, involve so-called catalysts, added chemicals which make the reaction proceed faster, with fewer unwanted byproducts. Developing improved catalysts is very challenging, especially “heterogeneous catalysts” which are used in reactions combining gas, liquid, and/or solid chemicals. The outcome of the research will be computer models of heterogeneous catalysts that can help explain how they work and guide the design of improved versions. As they are developed, these computer simulations will be tested against experimental results on reactions involving heterogeneous catalysts. This project will provide research opportunities as well as extracurricular activities for undergraduate students at UCM, which is a Minority-Serving Institution. Additionally, Prof. Kim will create and lead educational activities for local K-12 teachers and students, demonstrating how math is used to understand chemistry. These efforts will contribute to improving science education in California’s ethnically and economically diverse Central Valley and help increase the regional STEM workforce.Professor Kim will develop a stochastic hybrid method that efficiently simulates gas-solid interfacial systems. To this end, this project will couple the kinetic Monte Carlo method (for an atomistic-resolution description of chemical reactions on the catalytic surface) with the fluctuating hydrodynamics method (for hydrodynamic transport at the mesoscopic scale). In this way, intrinsic thermal fluctuations present in the particle description of surface chemistry can be seamlessly coupled with the continuum description of the gas phase. Prof. Kim will develop a scalable implementation strategy that can be used efficiently on high-performance computing platforms, and he will perform theoretical and experimental validations using the example of carbon monoxide oxidation on metal surfaces.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.

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。在这个项目中，由数学和物理科学理事会资助，并设在化学部，Changho Kim教授和他的学生在加州大学，默塞德（UCM）将开发一种改进的方法，在计算机上模拟复杂的化学反应。许多重要的化学过程，从合成燃料到净化废气，都涉及所谓的催化剂，即添加化学物质，使反应进行得更快，副产物更少。开发改进的催化剂是非常具有挑战性的，特别是用于结合气体、液体和/或固体化学品的反应中的“非均相催化剂”。这项研究的成果将是多相催化剂的计算机模型，可以帮助解释它们是如何工作的，并指导改进版本的设计。随着它们的发展，这些计算机模拟将与涉及多相催化剂的反应的实验结果进行测试。该项目将为UCM的本科生提供研究机会和课外活动，UCM是一个少数民族服务机构。此外，金教授将为当地K-12教师和学生创建和领导教育活动，展示如何使用数学来理解化学。这些努力将有助于改善加州的种族和经济多样化的中央谷的科学教育，并有助于增加区域STEM劳动力。金教授将开发一种随机混合方法，有效地模拟气固界面系统。为此，该项目将把动力学蒙特卡罗方法（用于催化表面化学反应的原子分辨率描述）与波动流体力学方法（用于介观尺度的流体动力学输运）结合起来。通过这种方式，存在于表面化学的颗粒描述中的固有热波动可以与气相的连续描述无缝耦合。金教授将开发一个可扩展的实施策略，可以在高性能计算平台上有效地使用，他将使用金属表面上的一氧化碳氧化的例子进行理论和实验验证。该奖项反映了NSF的法定使命，并通过评估基金会的智力价值和更广泛的影响审查标准被认为值得支持。