Computational Modeling of the Soot Size Distribution in Turbulent Reacting Flows: Leveraging Data Science Tools to Rapidly Accelerate Physics-Based Model Development and Validation

烟灰是在烃燃料的不完全富燃料燃烧期间形成的不期望的燃烧副产物，并且对人类健康和环境具有不利影响。这些不利影响随颗粒尺寸而变化，因此控制从实际燃烧系统排放的碳烟颗粒尺寸的分布越来越受到关注。不幸的是，甚至湍流燃烧中碳烟尺寸分布的基本定性特征也是未知的。因此，目前不存在有效预测湍流燃烧中的碳烟尺寸分布的计算能力。该项目将使用详细的全保真度计算模拟，并使用深度学习加速，对湍流燃烧中烟尘尺寸分布的演变进行基本了解。然后，利用这些知识来开发一种新的计算效率高的建模框架，用于预测适用于实际燃烧系统的湍流燃烧中的碳烟尺寸分布的演变。新的建模框架将包括一个新的统计模型的烟尘粒径分布，一个从根本上新的方法来捕捉相对缓慢的化学气体烟尘前体，和一个新的湍流传输模型。新的建模框架将最终验证实验测量。新的建模框架的软件实现也将公开提供给其他研究人员和工业界使用。用于预测湍流反应流中碳烟尺寸分布的计算方法是极其有限的，通常限于过于简化和不准确的碳烟模型，但计算仍然非常昂贵。虽然一些有限的证据表明，烟尘尺寸分布有时可能是单峰的湍流火焰，并可能优先产生非常大的烟尘颗粒，湍流对烟尘尺寸分布的影响是根本上知之甚少。本计画将发展一个计算效率高的模型架构，以预测紊流燃烧中碳烟粒径分布的演化。将开发自适应截面矩（ASM）模型，该模型将一个内部坐标中的粗截面法与每个截面内两个内部坐标中的高级矩法相结合。ASM将用于进行直接数值模拟（DNS），使用深度学习和数据衍生流形的组合进行加速，以提供对湍流对烟尘尺寸分布影响的前所未有的见解。这些数据库将进一步用于解决大涡模拟（LES）中与烟尘粒度分布相关的两个关键建模挑战。多环芳烃（PAH）的缓慢化学反应将通过一种新的部分非平衡流形模型以准确而一致的方式捕获，并将利用基于物理和基于数据的方法开发用于烟尘小尺度湍流传输的新模型。最终，新的LES建模框架与ASM将验证对DNS数据库和实验测量的烟尘粒径分布在turbulentflames.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

Soot is an undesirable by-product of combustion formed during incomplete fuel-rich combustion of hydrocarbon fuels and has adverse effects on human health and the environment. These adverse effects vary with particle size, so controlling the distribution of soot particle sizes emitted from practical combustion systems is of increasing interest. Unfortunately, even the basic qualitative features of the soot size distribution in turbulent combustion are unknown. As a result, computational capabilities do not currently exist for efficiently predicting the soot size distribution in turbulent combustion. This project will develop fundamental understanding of the evolution of the soot size distribution in turbulent combustion using detailed, full-fidelity computational simulations, accelerated using deep learning. This knowledge will then be leveraged to develop a new computationally efficient modeling framework for predicting the evolution of the soot size distribution in turbulent combustion applicable to practical combustion systems. The new modeling framework will include a novel statistical model for the soot size distribution, a fundamentally new approach to capture the relatively slow chemistry of gaseous soot precursors, and a new turbulent transport model. The new modeling framework will be ultimately validated against experimental measurements. The software implementation of the new modeling framework will also be made publicly available for use by other researchers and in industry.Computational approaches for predicting the soot size distribution in turbulent reacting flows are extremely limited, generally restricted to oversimplified and inaccurate soot models yet still computationally very expensive. While some limited evidence has suggested that the soot size distribution may sometimes be unimodal in turbulent flames and may preferentially generate very large soot particles, the influence of turbulence on the soot size distribution is fundamentally poorly understood. This project will develop a computationally efficient modeling framework for predicting the evolution of the soot size distribution in turbulent combustion. An Adaptive Sectional-Moment (ASM) model will be developed that combines a coarse sectional method in one internal coordinate with an advanced moment method in two internal coordinates within each section. ASM will be used to conduct Direct Numerical Simulations (DNS), accelerated using a combination of deep learning and data-derived manifolds, to provide unprecedented insights into the influence of turbulence on the soot size distribution. These databases will be further used to address two key modeling challenges in Large Eddy Simulation (LES) relevant to the soot size distribution. The slow chemistry of Polycyclic Aromatic Hydrocarbons (PAH) will be captured in an accurate yet consistent manner though a new partially non-equilibrium manifold model, and new models for the small-scale turbulent transport of soot will be developed utilizing physics-based and data-based approaches. Ultimately, the new LES modeling framework with ASM will be validated against the DNS databases and experimental measurements of the soot size distribution in turbulent flames.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.