CAREER: Towards Understanding and Modeling Turbulent Reacting Particle-Laden Flows

多相反应堆是几乎所有能源过程的核心。 例子包括生产生物燃料、燃烧后碳捕获和基于颗粒的太阳能接收器。 此外，许多自然过程涉及湍流中的多相传热和传质。 例如，破碎的海浪喷射出的海洋喷雾会影响海气通量，从而影响飓风的强度。 然而，有许多挑战与建模湍流混合如何相互作用的反应动力学，传质，传热跨越长度和时间尺度。 由于这些挑战，大多数现有的模型都是基于经验相关性，在预测感兴趣的应用中的测量方面做得很差。 该项目旨在提高我们对这些复杂多相流的基本理解，并实现在不同制度下工作的预测模型。 首席研究员将利用他在多相流和湍流建模方面的专业知识来实现三个目标。 他将1）建立两相流动力学与传热传质速率之间的联系，2）利用数据科学和机器学习的最新进展将跨尺度的物理过程联系起来，3）创建一个博物馆展览，突出这些复杂流动的艺术和科学及其在能源领域的作用。对于相间传热和传质的了解要少得多。工业界和学术界使用的最先进的代码依赖于平均反应速率以及传热和传质系数的简单模型，这些模型已知在两相流状态下会发生数量级的变化，从而导致巨大的预测不确定性。主要的限制是缺乏有意义的数据，描述多相相互作用，从单个粒子的水平，以更大的规模的利益。在这个项目中，独特的，高保真的数值模拟将隔离相之间的双向耦合，并建立多相相互作用和热/质传递之间的关系。逆建模和机器学习的出现使新的方法能够用这些数据系统地通知模型，并将在这里用于量化和减少模型的不确定性。这些努力的结合将导致在数据驱动的多相湍流建模的新范式。 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。

Multiphase reactors are central to nearly all energy processes. Examples include the production of biofuels, post-combustion carbon capture, and particle-based solar receivers. In addition, many natural processes involve multiphase heat and mass transfer in a turbulent flow. For example, ocean spray ejected from breaking waves affects air-sea fluxes that influence the intensity of hurricanes. However, there are many challenges associated with modeling how turbulent mixing interacts with the reaction kinetics, mass transfer, and heat transfer across length and time scales. Because of these challenges, most existing models are based on empirical correlations that do a poor job at predicting measurements in applications of interest. This project aims to improve our fundamental understanding of these complex multiphase flows and enable predictive models that work across varying regimes. The Principal Investigator will use his expertise in multiphase flows and turbulence modeling to accomplish three goals. He will 1) establish a connection between two-phase flow dynamics and rates of heat and mass transfer, 2) use recent advances in data science and machine learning to link physical processes across scales, and 3) create a museum exhibit that highlights the art and science of these complex flows and its role in the energy sector.While there has been significant progress characterizing hydrodynamic interactions in particle-laden flows, much less is known about interphase heat and mass transfer. State-of-the-art codes used in industry and academia rely on simplistic models for average reaction rates as well as heat and mass transfer coefficients that are known to vary by orders of magnitude across two-phase flow regimes, resulting in enormous predictive uncertainty. The main limitation is the lack of meaningful data that describe multiphase interactions from the level of individual particles to larger scales of interest. In this project, unique, high fidelity numerical simulations will isolate two-way coupling between the phases and establish a relationship between multiphase interactions and heat/mass transfer. The emergence of inverse modeling and machine learning has enabled new approaches to systemically inform models with such data, and will be used here to quantify and reduce model uncertainty. The combination of these efforts will result in a new paradigm in data-driven multiphase turbulence modeling. Results are anticipated to have far-reaching impact on energy and environmental applications.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.