IMT: Development of LES and RANS models for H2 turbulentcombustion leveraging DNS data

与经典流体力学和动力学相比，计算流体力学（CFD）在科学界是一个相对较新的工具。现代硬件的计算能力不断增强，使得用迭代求解器求解像纳维尔·斯托克斯方程这样的方程，即使是一台普通的笔记本电脑，也很常见。然而，许多流动现象仍然很难精确地建模，湍流就是其中之一。目前，使用雷诺平均纳维·斯托克斯（RANS）和大涡模拟（LES）来模拟湍流的模型有许多相对成功的模型，这就是为什么许多这些技术被用来显著加快从飞机到汽车等工业环境的设计过程。燃烧和火焰传播是另一个CFD有望成为未来发展基石的领域。随着向可持续发展的明确转变，氢燃烧由于其不产生二氧化碳的优势而处于能源生产和推进的前沿。不幸的是，尽管H2是最简单的分子，但它的燃烧过程却截然不同。它受到热扩散不稳定性的强烈影响，当与湍流[3]，[4]耦合时，火焰锋面传播产生一系列非线性。了解这些非线性至关重要，这样才能尽可能高效地在燃气轮机（GT）和内燃机（ICE）中使用氢气，从而最大限度地提高其能量输出并确保其安全运行。这是必要的，因为在一定体积的氢气中储存的能量比化石燃料少得多，这使得效率成为设计师的首要任务。复杂的三维热扩散不稳定性现象及其与湍流的耦合导致火焰速度增加和火焰起皱，这在很大程度上取决于局部反应速率的变化。这意味着，在湍流状态下，火焰是高度不规则的，这是由于湍流和氢的热扩散不稳定性的共同作用。这产生了舌状结构，穿透未燃烧的气体区域，这在传统燃料如甲烷的湍流燃烧中是不存在的。这种高度不规则的流动很难成功地建模，这导致了这个项目的目标。因此，设想创建这些现象的准确模型，以协助工业设计过程。直接数值模拟（DNS）模拟在计算上非常昂贵，除了学术环境之外，在其他任何地方都不可行。因此，通过使用现有的DNS数据并执行进一步的模拟，计划生成可用于LES甚至RANS的低阶模型。此外，除了基于显式方程的经典建模方法b[5]之外，机器学习（ML）也可以使用b[6]。ML是一个强大的工具，它允许在数据集中产生的参数之间绘制关系。因此，可以通过训练基于DNS数据的算法来创建上述模型。这将利用现有的高性能计算硬件（HPC）提供给大学，然后可以使用它来针对初始数据集或其他现有的LES和RANS模型重新测试生成的模型。因此，旨在在项目结束时生成更新甚至新的氢湍流燃烧模型，将为将其应用于更复杂的环境和模拟以及根据实验数据验证其性能铺平道路，从而迈出在工业应用中实施氢燃烧的第一步。

Computational Fluid Dynamics (CFD) is a relatively new tool in the scientific community compared to classical fluid mechanics and dynamics. The continuously increasing computational capabilities of modern hardware, make solving equations such as the Navier Stokes, with iterative solvers, a common day occurrence for even a mere laptop. However, many flow phenomena remain very hard to model accurately, turbulence being one of them [1]. Many relatively successful models exist today for modelling turbulence using Reynolds Averaged Navier Stokes (RANS) and Large Eddy Simulation (LES),which is why many of these techniques are used to significantly speed up the design process in industrial settings from aeroplanes to automobiles. Combustion and flame propagation is another area where CFD is expected to be the cornerstone for future development. With a clear shift towards sustainability, Hydrogen combustion is at the forefront of energy generation and propulsion due to its advantage of not producing CO2 [2]. Unfortunately, despite H2 being the simplest molecule, its combustion process is anything but that. It is strongly affected by thermodiffusive instabilities that produce a range of non-linearities in flame front propagation when coupled with turbulence [3],[4]. These non-linearities are critical to understand so that the use of Hydrogen in Gas Turbine (GT) and Internal Combustion Engines (ICE) is performed as efficiently as possible to maximise its energy output as well as ensure their safe operation. This is necessary since the energy stored in a given volume of hydrogen is much less than that of fossil fuels, making efficiency, the designer's number one priority. The complex 3D phenomena of thermodiffusive instability, and its coupling with turbulence, produce an increased flame speed and flame wrinkling, heavily dependent on the variation of local reaction rates. This means that, in the turbulent regime, the flame is highly irregular which is due to the combined contributions of turbulence and hydrogen's thermodiffusive instabilities. This produces tongue-like structures which penetrate in the unburned gas area which do not exist in turbulent combustion of traditional fuels such as methane. This highly irregular flow is very hard to model successfully which leads to the aim of this project. It is therefore envisioned to create accurate models of these phenomena to assist industrial design processes. Direct Numerical Simulations (DNS) simulations are extremely computationally expensive and cannot deemed feasible anywhere other than an academic setting. Therefore, by using existing DNS data and performing further simulations, it is planned to produce low order models that can be used in LES or even RANS. Furthermore, in addition to classical modelling approaches which are based on explicit equations [5], Machine Learning (ML) could be used [6]. ML is a powerful tool that allows relationships to be drawn between parameters arising from a dataset. Therefore, it could be possible to create the aforementioned models by training an algorithm based on the DNS data. This wouldmake use of the existing hardware of High-Performance Computing (HPC) available to the university which could then be used to retest the produced models against the initial dataset or other existing LES and RANS models. Thus, aiming to produce updated or even new models for Hydrogen turbulent combustion by the end of the project would pave the way for applying them in more complex settings and simulations and validate their performance against experimental data, in making the first step of implementing Hydrogen combustion in industrial applications.