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Sum Over Histories Representation of Chemical Kinetics: An Interpretive and Predictive Method for Modeling Chemical Kinetics Using Time-dependent Pathways

机译：化学动力学的历史记录总和：一种使用时间相关途径对化学动力学建模的解释性和预测性方法

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摘要

Chemical kinetics can be viewed as an intricate network of inter-related chemical reactions that work cooperatively to convert reagent species into product species. The network is in general time dependent reacting the non-steady state nature of the chemistry. When it comes to interpreting and predicting chemical kinetics, the history of chemical moieties can play vital roles. In order to study the histories of chemical substances using time-dependent chemical network, this thesis focuses on developing a Sum Over Histories Representation (SOHR for short) of chemical kinetics. The description of time-dependent chemistry of a reaction network is provided by chemical pathways defined at a molecular level. Using this methodology, the quantitative time evolution of the kinetics is described by enumerating the most important pathways followed by a chemical moiety such as a tagged atom. An explicit formula for the pathway probabilities is derived which takes the form of an integral over a time-ordered product. This expression has a simple and computationally efficient Monte Carlo representation which permits the method to be applied to a wide range of problems.;In SOHR, the history of the chemical moiety can be described by time-dependent pathways. Unlike the static flux methods for path analysis, SOHR includes the explicit time-dependence of pathway probabilities. Using SOHR, the sensitivity of an observable with respect to a kinetic parameter such as a rate coefficient is then analyzed in terms of how that parameter affects the chemical pathway probabilities. This thesis demonstrates that large sensitivities are often associated with rate limiting steps along important chemical pathways or by reactions that control the branching of reactive flux, though they vary with time.;In addition to interpreting chemical kinetics, this thesis studies the practical approach to modeling chemical kinetics without solving conventional mass-action ODEs. An iterative framework was introduced that allows the time-dependent pathway probabilities to be generated from a knowledge of elementary rate coefficients. To avoid the pitfall of integrating over the histories of long paths, we proposed a sector-by-sector strategy that shortens the candidate path without losing numerical accuracy. This method was successfully applied to the model Lotka-Volterra system and to a realistic H2 combustion system.;This thesis culminates with a discussion of the interpretative and predictive applicability of SOHR.
机译：化学动力学可以看作是相互关联的化学反应的复杂网络，这些网络协同工作以将试剂种类转化为产物种类。该网络通常是时间相关的，对化学物质的非稳态性质有反应。在解释和预测化学动力学方面，化学部分的历史可以发挥至关重要的作用。为了使用时变化学网络研究化学物质的历史，本论文着重于发展化学动力学的历史总和表示法。反应网络的时间依赖性化学的描述由在分子水平上定义的化学途径提供。使用这种方法，通过列举最重要的途径以及随后的化学部分（例如标记的原子）来描述动力学的定量时间演化。导出了路径概率的显式公式，该公式采用时间顺序积上的积分形式。该表达式具有简单且计算效率高的蒙特卡洛表示法，该方法使该方法可应用于广泛的问题。在SOHR中，​​化学部分的历史可以通过时变路径来描述。与用于路径分析的静态通量方法不同，SOHR包括路径概率的显式时间依赖性。使用SOHR，然后根据该参数如何影响化学途径概率来分析相对于动力学参数（例如速率系数）的可观察性灵敏度。本论文表明，大的灵敏度通常与重要化学路径上的速率限制步骤或控制反应性通量分支的反应有关，尽管它们随时间而变化。除了解释化学动力学外，本论文还研究了实用的建模方法化学动力学，而无需解决传统的质量作用ODE。引入了一个迭代框架，该框架允许从基本速率系数的知识中生成与时间有关的路径概率。为了避免在长路径的历史上进行整合的陷阱，我们提出了逐个扇区的策略，该策略可以缩短候选路径而不丢失数值精度。该方法已成功地应用于Lotka-Volterra模型系统和现实的H2燃烧系统。;本文最后对SOHR的解释性和预测性适用性进行了讨论。

著录项

  • 作者

    Bai, Shirong.;

  • 作者单位

    University of Colorado at Boulder.;

  • 授予单位 University of Colorado at Boulder.;
  • 学科 Physical chemistry.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 103 p.
  • 总页数 103
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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