Deep learning methods for improved numerical simulations of pulverised biomass combustion

二氧化碳中性能源供应是我们社会面临的主要挑战之一。即使从中长期来看，单一技术也不太可能取代所有化石燃料作为主要能源，需要寻求多种选择。粉状生物质燃烧（PBC）被认为是一种有吸引力的选择，因为它允许对现有燃煤电厂的基础设施进行再利用。然而，生物质和煤的燃烧特性并不相同，需要对燃烧器和燃烧室进行一些改造。这种修改可以通过计算机模拟和大涡模拟（LES）来辅助，因为它捕获了对固体燃料燃烧系统性能至关重要的固有非线性瞬态湍流过程，因此它是一种很有前途的工具。然而，LES并不能解决与生物质燃烧相关的所有复杂的小规模过程，如加热、干燥、热解、释放的挥发性气体的均匀燃烧和非均相焦化反应。这些过程及其相互作用首先需要彻底理解，其次需要建模。高保真直接数值模拟应使用，以评估一些粉状生物质燃烧的基本原理，如点火和火焰特性，以及它们如何受到周围气相条件，颗粒特性和颗粒-气体相互作用的影响。模拟将包括相当详细的多步化学反应，这需要对所有亚网格尺度的相互作用进行彻底分析。然而，获得一种计算上更实惠、但相当准确的PBC建模方法具有实际意义。我们打算通过最先进的小火焰方法实现显著的成本降低，类似于那些应用于煤粉燃烧的方法，其中成分空间可以通过一组混合物馏分、进度变量和总焓来参数化。DNS使用完整和简化的组合空间，然后使用机器学习方法开发LES子网格模型。包含深层神经层的算法需要学习底层的子网格相关性，泛化这些相关性，生成与现实无法区分的合成数据，以便它们可以用作LES中的闭包。在最后一步，将通过生物质燃烧的独立LES进行测试，并与相应的DNS数据进行比较。

CO2 neutral energy provision is one of the major challenges that our society faces. Even in the mid to long term perspective, it is unlikely that one single technology will replace all fossil fuels as primary energy source and several options need to be pursued. Pulverized biomass combustion (PBC) is considered to be an attractive option as it allows to re-use the current infrastructure of coal-firing power plants. The combustion characteristics of biomass and coal are, however, not identical and some combustor and combustion chamber modifications will be needed. Such modifications can be aided by computer simulations and large-eddy simulation (LES) presents itself as a promising tool as it captures the inherently non-linear transient turbulent processes that are critical for the performance of solid fuel combustion systems. LES does not, however, resolve all the complex small scale processes that can be associated with pulverized biomass combustion such as heating, drying, pyrolysis, homogeneous combustion of released volatile gases and heterogeneous char reactions. These processes and their interactions need first to be thoroughly understood and second to be modelled. High fidelity direct numerical simulations shall be used to assess some of the pulverized biomass combustion fundamentals such as ignition and flame characteristics and how they are affected by the surrounding gas phase conditions, particle properties and particle-gas interactions. The simulations will include rather detailed multi-step chemistry that is needed for thorough analysis of all sub-grid scale interactions. It is of practical interest, however, to also obtain a computationally more affordable, yet reasonably accurate modelling approach for PBC. We intend to achieve a significant cost reduction by state-of-the-art flamelet approaches similar to those applied to pulverized coal combustion where the composition space could be parameterized by a set of mixture fractions, progress variable and total enthalpy. DNS using the complete and the reduced composition spaces shall then be used to develop LES sub-grid models with the aid of machine-learning methods. Algorithms that contain deep neural layers shall learn the underlying sub-grid correlations, generalize these correlations and generate synthetic data that are indistinguishable from reality such that they can be used as closures in LES. In a final step, the closures will be tested by stand-alone LES of biomass combustion and comparison with the corresponding DNS data.