Accurate Determination of Branching Fractions in Ammonia Combustion

氨燃烧中支化分数的准确测定

• 批准号: 2329341
• 负责人: Claude Goldsmith
• 金额: $ 35.34万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329341&HistoricalAwards=false
• 关键词: Accurate; Determination; Branching; Fractions; Ammonia

Ammonia is a potential bridge fuel for carbon-neutral combustion. However, the combustion of ammonia can lead to various nitrogen oxides (NOx), which are major pollutants, and which can exacerbate climate change. To mitigate the impact of NOx on human health and the environment, government regulations increasingly require reduced emissions of nitrogen oxides from combustion sources. The ability to make accurate predictions of NOx formation levels will benefit engineers and scientists who need to quantify the impact of a new fuel or a new engine design on NOx emissions. These NOx submechanisms also will benefit the broader atmospheric science community, both at the local level (e.g. smog) and at the global level (e.g. climate change). The goal of this project is to measure the branching fractions for several reactions that are important in the kinetics of ammonia combustion, particularly those related to various nitrogen oxides. The investigators hypothesize that by measuring the transient concentration of two or more species simultaneously, the uncertainty in the branching fractions can be reduced relative to the current state of the art in NOx literature. To test this hypothesis, the investigators will use tunable diode laser absorption spectroscopy (TDLAS) to measure the transient concentration of NO, NO2, N2O, H2O, and/or temperature behind the reflected shock in a shock tube. These profiles will serve as validation targets for a kinetic model.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.

氨是一种潜在的碳中和燃烧的桥梁燃料。然而，氨的燃烧会导致各种氮氧化物（NOx），这是主要的污染物，并可能加剧气候变化。为了减轻NOx对人类健康和环境的影响，政府法规越来越多地要求减少来自燃烧源的氮氧化物的排放。准确预测氮氧化物形成水平的能力将使需要量化新燃料或新发动机设计对氮氧化物排放影响的工程师和科学家受益。这些氮氧化物的子机制也将有利于更广泛的大气科学界，无论是在地方一级（如烟雾）和全球一级（如气候变化）。该项目的目标是测量氨燃烧动力学中重要的几个反应的分支分数，特别是与各种氮氧化物相关的反应。研究人员假设，通过同时测量两种或更多种物质的瞬态浓度，相对于NOx文献中的现有技术，可以降低分支分数的不确定性。为了验证这一假设，研究人员将使用可调谐二极管激光吸收光谱（TDLAS）来测量激波管中反射激波后面的NO，NO2，N2 O，H2O和/或温度的瞬态浓度。该奖项反映了NSF的法定使命，并被认为是值得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的支持。