EAGER: Establishing a novel computational framework to investigate the role of chemical kinetics in chemical looping combustion

1638837-为了限制化石燃料使用对环境的影响，特别是在温室气体排放方面，迫切需要迅速开发和部署更清洁的燃烧技术。化学循环燃烧(CLC)被认为是一种很有前途的发电和碳捕获相结合的技术，是限制并最终减少包括煤炭和生物质在内的各种燃料的温室气体排放的关键概念。计算模拟在许多与燃烧相关的领域得到了广泛而成功的应用。然而，由于严重缺乏描述CLC中发生的复杂流体动力学和化学过程的可靠模型，CLC技术的研究和开发在性质上不成比例地是经验性的。该项目的主要活动是开发一种为《中图法》应用程序量身定做的新型计算框架，它将为燃烧过程中发生的控制物理和化学现象提供前所未有的洞察力。这项工作最终将使高保真计算流体动力学(CFD)模型和软件的形成成为可能，以帮助工业设计高效的CLC反应器。该项目将提供教育项目机会，结合先进的计算技术和明显的社会影响。这些项目有可能吸引不同的本科生去学习计算科学和工程，在这个领域，女性和其他URM的代表性严重不足。拟议的EIGER项目旨在建立一个新的拉格朗日-欧拉计算框架，以在中观和实验室尺度上研究CLC的关键过程。将考虑CLC系统中通常涉及的两个反应器，即空气反应器和燃料反应器，在空气反应器中，氧气载体通过空气流氧化再生；在燃料反应器中，实际燃烧过程通过氧气载体氧化燃料来发生。该项目的具体目标是：(1)展示拉格朗日-欧拉方法模拟与CLC系统相关的特定气固流体动力学的潜力，包括高速提升管和鼓泡床；(2)在这一框架内，开发新的算法来捕获与CLC相关的高温环境中金属氧化物与周围气体之间的特定和复杂的相互作用；以及(3)展示需要包括详细的化学动力学和相关的小规模传热和传质过程，以充分捕获CLC系统中氧载体的特性和性能。这将通过评估《中图法》对包括在CFD中的化学描述的详细程度的敏感性来实现。初步模拟将考虑由常见的、特征良好的金属氧化物进行简单的气态燃料氧化。通过这项工作获得的史无前例的初步数据和观察将使我们能够自信地确定未来的研究方向，以显着改进用于CLC设计和优化的CFD工具。

1638837 - PepiotTo limit the environmental impact of fossil fuel usage, especially in terms of greenhouse gas emissions, a rapid development and deployment of cleaner combustion technologies is urgently needed. Chemical-Looping Combustion (CLC) has been identified as a promising technology for combined power generation and carbon capture, a key concept in limiting, and eventually decreasing green-house gas emissions from a wide variety of fuels, including coal or biomass. Computational simulations are widely and successfully used in many combustion-related areas. Yet, research and development for CLC technologies is disproportionately empirical in nature, due to a critical lack of reliable models to describe the complex hydrodynamics and chemical processes occurring in CLC. The main activity undertaken in this project is the development of a novel computational framework tailored for CLC applications, which will provide unprecedented insight into the controlling physical and chemical phenomena occurring during combustion. This work will eventually enable the formulation of high-fidelity Computational Fluid Dynamics (CFD) models and software to assist industry with the design of efficient CLC reactors. The project will provide educational project opportunities combining advanced computational techniques and a clear societal impact. Such projects have the potential to attract a diverse undergraduate student population toward Computational Science and Engineering, an area in which women and other URM are woefully under-representedThe proposed EAGER project aims at establishing a novel Lagrange-Euler computational framework to investigate key processes in CLC at the meso- and lab-scale. Two reactors typically involved in a CLC system will be considered, namely, an air-reactor, where the oxygen carrier is regenerated through oxidation with an air stream, and a fuel reactor, where the actual combustion process takes place through oxidation of the fuel by the oxygen carrier. The specific objectives of this project are: (1) Demonstrating the potential of a Lagrange-Euler approach to simulate the specific gas-solid hydrodynamics relevant for CLC systems, including high-velocity risers and bubbling fluidized beds; (2) Within this framework, developing novel algorithms to capture the specific and complex interactions between metal oxides and surrounding gases in the high temperature environments relevant to CLC; and (3) Demonstrating the need to include detailed chemical kinetics and associated small-scale heat and mass transfer processes to adequately capture the specificities and performance of oxygen carriers in CLC systems. This will be accomplished by assessing the sensitivities of CLC to the level of details of the chemical description included in CFD. Preliminary simulations will consider simple gaseous fuel oxidation by common, well-characterized metal oxides. The unprecedented preliminary data and observations obtained through this work will allow to identify with confidence future research directions to significantly improve CFD tools for CLC design and optimization.