Deep learning methods for improved numerical simulations of pulverised biomass combustion

用于改进粉状生物质燃烧数值模拟的深度学习方法

• 批准号: 513858356
• 负责人: Professor Dr. Andreas Kronenburg
• 金额: --
• 依托单位: Institut für Technische Verbrennung (ITV)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/513858356?language=en
• 关键词: Deep; learning; methods; improved; numerical

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.

二氧化碳中性能源供应是我们社会面临的主要挑战之一。即使从中长期的角度来看，一种单一的技术也不可能取代所有化石燃料作为主要能源，需要寻求几种选择。生物质粉末燃烧（PBC）被认为是一个有吸引力的选择，因为它可以重新使用燃煤发电厂的现有基础设施。然而，生物质和煤的燃烧特性是不相同的，需要对燃烧器和燃烧室进行一些修改。这样的修改可以通过计算机模拟和大涡模拟（LES）的帮助下提出了自己作为一个有前途的工具，因为它捕捉固有的非线性瞬态湍流过程，是至关重要的固体燃料燃烧系统的性能。然而，LES不能解决所有与粉碎生物质燃烧相关的复杂小规模过程，例如加热、干燥、热解、释放的挥发性气体的均相燃烧和非均相炭反应。这些过程及其相互作用首先需要彻底理解，其次需要建模。应使用高保真直接数值模拟来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，这是彻底分析所有次网格尺度相互作用所必需的。然而，实际上也有必要为建设和平委员会获得一种计算上更负担得起、但相当准确的建模方法。我们打算实现一个显着的降低成本的国家的最先进的小火焰的方法类似于那些应用于煤粉燃烧的组合物空间可以参数化的混合物分数，进度变量和总焓。DNS使用完整的和减少的组成空间，然后将被用来开发LES子网格模型与机器学习方法的帮助。包含深层神经层的算法将学习底层的子网格相关性，概括这些相关性并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。在最后一步中，将通过生物质燃烧的独立LES测试封闭物，并与相应的DNS数据进行比较。