Novel Computational Methods for Imperfectly-Mixed Chemical Reactions

不完全混合化学反应的新计算方法

• 批准号: 1911145
• 负责人: Stephen Pankavich
• 金额: $ 33.69万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911145&HistoricalAwards=false
• 关键词: Novel; Computational; Methods; Imperfectly; Mixed

Many chemicals that move within fluids undergo reactions. Often these convert toxic compounds into harmless byproducts. One such example is the cleanup of gasoline that has leaked into a groundwater aquifer. Unfortunately, current mathematical models and computational methods that assume the reactants are well mixed fail to accurately predict the duration or rate of these reactions, primarily due to the poor mixing of associated reactants. Recent studies show that mixing-limited reactions play a dominant role in most Earth-bound systems across a wide range of scales, including reactions in atmospheric plumes, granular and fractured aquifers, sedimentary (e.g., petroleum-generating and carbon-dioxide-sequestering) basins, hydrothermal areas, and ore bodies. Imperfect mixing and modified dynamics also occur at the nanometer to micron scale in space and picosecond to microsecond scale in time, revealing the ubiquity of mixing-limited reaction, from molecular to global scales. Hence imperfect mixing poses a significant theoretical and practical problem because most current models of reactive transport in hydrological systems are based on empirical adjustments to classical laws, which are built upon the flawed well-mixed assumption. In order to make reliable predictions in such systems, improved methods are critical for scientists and engineers, and ultimately decision makers, stakeholders, and policy developers working in fields such as environmental contamination and remediation. This project develops new computational methods to simulate chemical transport and reaction dynamics, with applications to a variety of fields, including climate-change related atmospheric reactions as well as ecological and micro-biochemical systems. Graduate students participate in the research.The investigators recently developed new computational models that demonstrate the need for new methods to simulate reactions in imperfectly-mixed chemical systems. These stochastic Lagrangian methods directly track particle positions and calculate reactions based on the probability that particles are co-located. The methods correspond to perturbation expansions of the classical diffusion-reaction equation and also match several benchmark experiments. In this project, new algorithms are developed that account for random particle migration time and the statistical structure of initial conditions to properly simulate subgrid fluctuations. Preliminary studies show that the methods work well for bimolecular reactions in simple systems, but it remains to be proven that such techniques can be extended to more complicated reactions, geometries, and flow fields. Additionally, the investigators build upon a continuum approach to simulate reactions and track the growth of concentration perturbations. Finally, they construct and extend Lagrangian numerical methods that are known to be accurate and efficient, to benchmark theoretical results and facilitate large-scale reactive simulations. The approaches are unified through detailed mathematical analyses and applications to well-studied laboratory and field experiments. Graduate students participate in the research.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.

许多化学物质在流体中移动时会发生反应。 通常这些将有毒化合物转化为无害的副产品。 其中一个例子是清理泄漏到地下蓄水层的汽油。 不幸的是，假设反应物充分混合的当前数学模型和计算方法不能准确地预测这些反应的持续时间或速率，这主要是由于相关反应物的不良混合。 最近的研究表明，混合限制反应在大多数地球系统中起着主导作用，范围很广，包括大气羽流，颗粒和断裂含水层，沉积（例如，产油和二氧化碳封存）盆地、热液区和矿体。 不完全混合和修改动力学也发生在纳米到微米尺度的空间和皮秒到微秒尺度的时间，揭示了无处不在的混合限制反应，从分子到全球尺度。 因此，不完美的混合提出了一个重要的理论和实践问题，因为目前大多数模型的反应运输水文系统是基于经验调整的经典法律，这是建立在有缺陷的良好混合的假设。 为了在这些系统中进行可靠的预测，改进的方法对于科学家和工程师，以及最终在环境污染和修复等领域工作的决策者，利益相关者和政策制定者至关重要。 该项目开发新的计算方法来模拟化学传输和反应动力学，并应用于各种领域，包括与气候变化有关的大气反应以及生态和微生物化学系统。 研究人员最近开发了新的计算模型，证明了需要新的方法来模拟混合化学系统中的反应。 这些随机拉格朗日方法直接跟踪粒子位置，并根据粒子共存的概率计算反应。 该方法对应于经典扩散反应方程的微扰展开，也符合几个基准实验。 在这个项目中，开发了新的算法，考虑随机粒子迁移时间和初始条件的统计结构，以正确模拟亚网格波动。 初步研究表明，该方法在简单系统中的双分子反应中工作良好，但仍有待证明，这种技术可以扩展到更复杂的反应，几何形状和流场。 此外，研究人员建立在一个连续的方法来模拟反应和跟踪浓度扰动的增长。 最后，他们构建和扩展拉格朗日数值方法，已知是准确和有效的，基准的理论结果，并促进大规模的反应模拟。 通过详细的数学分析和应用程序，以及研究实验室和现场实验的方法是统一的。 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。

项目成果

Reactive particle-tracking solutions to a benchmark problem on heavy metal cycling in lake sediments

湖泊沉积物中重金属循环基准问题的反应粒子追踪解决方案

• DOI: 10.1016/j.jconhyd.2020.103642
• 发表时间: 2020
• 期刊: Journal of Contaminant Hydrology
• 影响因子: 3.6
• 作者: Schmidt, Michael J.;Pankavich, Stephen D.;Navarre-Sitchler, Alexis;Engdahl, Nicholas B.;Bolster, Diogo;Benson, David A.
• 通讯作者: Benson, David A.

Parallelized domain decomposition for multi-dimensional Lagrangian random walk mass-transfer particle tracking schemes

多维拉格朗日随机游走传质粒子跟踪方案的并行域分解

• DOI: 10.5194/gmd-16-833-2023
• 发表时间: 2023
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: Schauer, Lucas;Schmidt, Michael J.;Engdahl, Nicholas B.;Pankavich, Stephen D.;Benson, David A.;Bolster, Diogo
• 通讯作者: Bolster, Diogo

Asymptotic growth and decay of two-dimensional symmetric plasmas

二维对称等离子体的渐近生长和衰变

• DOI: 10.3934/krm.2023015
• 发表时间: 2023
• 期刊: Kinetic and Related Models
• 影响因子: 1
• 作者: Ben-Artzi, Jonathan;Morisse, Baptiste;Pankavich, Stephen
• 通讯作者: Pankavich, Stephen

Asymptotic Dynamics of Dispersive, Collisionless Plasmas

• DOI: 10.1007/s00220-022-04317-w
• 发表时间: 2021-06
• 期刊: Communications in Mathematical Physics
• 影响因子: 2.4
• 作者: S. Pankavich

A Computational Information Criterion for Particle-Tracking with Sparse or Noisy Data

稀疏或噪声数据粒子追踪的计算信息准则

• DOI: 10.1016/j.advwatres.2021.103893
• 发表时间: 2021
• 期刊: Advances in Water Resources
• 影响因子: 4.7
• 作者: Tran, Nhat Thanh;Benson, David A.;Schmidt, Michael J.;Pankavich, Stephen D.
• 通讯作者: Pankavich, Stephen D.