Capturing the impact of realistic multicomponent fuels in high-pressure spray combustion simulations

在高压喷雾燃烧模拟中捕捉真实多组分燃料的影响

• 批准号: 2140141
• 负责人: Simcha Singer
• 金额: $ 31.9万
• 依托单位: Marquette University
• 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: 2023-01-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140141&HistoricalAwards=false
• 关键词: Capturing; impact; realistic; multicomponent; fuels

Soot is one of the most harmful pollutants generated during combustion, as it is associated with cancer, lung disease, and vascular disease, and contributes to climate change. Understanding the fundamental processes that lead to soot formation in gasoline, diesel, and jet engines is necessary for reducing soot emissions. Liquid transportation fuels are composed of hundreds of chemical species, and it is known that certain species have an outsized effect on soot formation. By creating a state-of-the-art combustion simulation that accounts for the complexities of multicomponent fuel droplets in high-pressure engine environments, this project will generate fundamental knowledge of the impact of realistic fuel composition on soot formation. This will advance the field beyond current understanding, which is based on oversimplified representations of fuel composition, and will benefit society by contributing to the design of cleaner engines that improve public health and reduce environmental damage. The project will educate and train young engineers and researchers by incorporating the modeling tools, concepts, and findings into undergraduate and graduate courses and by supporting mentoring for undergraduate and graduate students.The goal of this project is to understand the mechanism by which liquid transportation fuels, which are composed of hundreds of species, generate soot in high-pressure engine environments. The approach is to create a state-of-the-art simulation tool for spray combustion that incorporates a novel, accurate, efficient, and flexible model for multicomponent droplet vaporization. In contrast to the prevailing surrogate approach, which represents a complex fuel using several discrete species, the proposed method employs a continuous thermodynamic framework for the liquid fuel droplets and then computes an adaptive surrogate vaporization flux composed of a few discrete species. The open-source solver will also include advanced models for turbulence-chemistry interactions and turbulence-radiation interactions. Following validation, the simulation will be used to evaluate the hypothesis that spatial variations in aromatic concentrations caused by preferential vaporization play a key role in soot inception. The combination of high-fidelity models will be used to improve fundamental understanding of the impact of real multicomponent fuels on soot formation in high-pressure spray combustion. In the future, the simulation tool can be used to study the impacts of conventional and alternative fuels on phenomena like lean blowout, auto-ignition, and the emission of other pollutants.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.

烟灰是燃烧过程中产生的最有害的污染物之一，因为它与癌症、肺部疾病和血管疾病有关，并导致气候变化。了解导致汽油、柴油和喷气发动机烟灰形成的基本过程对于减少烟灰排放至关重要。液体运输燃料由数百种化学物质组成，众所周知，某些化学物质对烟灰的形成具有巨大影响。通过创建最先进的燃烧模拟来解释高压发动机环境中多组分燃料液滴的复杂性，该项目将产生有关实际燃料成分对烟灰形成影响的基础知识。这将推动该领域超越目前基于燃料成分过于简单化表示的理解，并将通过促进设计更清洁的发动机来造福社会，从而改善公众健康并减少环境破坏。该项目将通过将建模工具、概念和研究结果纳入本科生和研究生课程，并支持对本科生和研究生的指导，教育和培训年轻的工程师和研究人员。该项目的目标是了解液体运输燃料的机制由数百种组成，在高压发动机环境中产生烟灰。该方法旨在创建最先进的喷雾燃烧模拟工具，该工具结合了新颖、准确、高效且灵活的多组分液滴汽化模型。与使用几种离散物质代表复杂燃料的流行替代方法相比，所提出的方法对液体燃料液滴采用连续热力学框架，然后计算由一些离散物质组成的自适应替代汽化通量。开源求解器还将包括湍流化学相互作用和湍流辐射相互作用的高级模型。验证后，模拟将用于评估以下假设：优先汽化引起的芳香族浓度的空间变化在烟灰起始过程中发挥关键作用。高保真模型的组合将用于提高对真实多组分燃料对高压喷雾燃烧中烟灰形成的影响的基本了解。未来，该模拟工具可用于研究传统燃料和替代燃料对稀薄井喷、自燃和其他污染物排放等现象的影响。该奖项反映了 NSF 的法定使命，并被认为值得支持：使用基金会的智力价值和更广泛的影响审查标准进行评估。