DNS-driven development of predictive LES models for gas turbine emissions

DNS 驱动的燃气轮机排放预测 LES 模型的开发

• 批准号: 434872808
• 负责人: Professor Dr.-Ing. Heinz Pitsch
• 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/434872808?language=en
• 关键词: DNS; driven; development; predictive; LES

The reduction of pollutant emissions is one of the main challenges in the development of gas turbine combustors. In this context, large-eddy simulations (LES), can contribute to reducing development cost and accelerating development processes. Therefore, the aim of this project is the improvement of emission models for LES of both aeroengines and stationary gas turbines. The present proposal is for the second phase of a 2+2-year project. For aeroengines, the focus is on modeling of soot. After soot formation and oxidation have been investigated in the first project phase, a better understanding for the breakthrough of soot from rich to fuel-lean regions is intended in the second phase. Soot breakthrough occurs when, due to the high turbulence levels in aeroengines, turbulent eddies transport soot pockets from fuel rich regions through the flame to fuel lean regions fast enough that a complete oxidation of soot particles cannot take place. Current LES models cannot describe these events with sufficient accuracy, thus significantly underpredicting soot emissions. In this project, state of the art LES models available in the literature and developed in previous project phases will be extended to consider soot breakthrough phenomena. This will be done in a three-step process: 1) generation of a DNS data base for three different levels of soot breakthrough; 2) assessment of current models based on the DNS and development of model extensions considering soot breakthrough; 3) model validation via LES of a lab-scale jet flame configuration and of a full model combustor. In stationary gas turbines, carbon monoxide (CO) emissions will be investigated. The aim in this phase is to study the effect of the hydrogen (H2) addition to methane (CH4) on CO emissions and their modeling. Starting point is the importance of H2 as future energy carrier. Here, we will consider the addition of small amounts of H2 to natural gas as fuel in stationary gas turbines. Previous work has shown the ability of even small amounts of H2 to significantly reduce CO emissions as a result of the increased flame speeds. A series of DNS of turbulent combustion of CH4/H2 mixtures will be performed. The DNS data will be analyzed to extend the present CO model to adequately consider the effects of H2 addition. The full model will be used in the LES of two model combustion chambers and validated with experimental data. Funding for this project is requested jointly from the DFG and the FVV. While the two proposed work packages address pollutant formation in different environments, strong connections are given by the embedding of the work packages in the project support provided by industrial partners of the FVV. Similar modeling approaches and research methodologies will be used for the prediction of CO and soot, so that the joint model development can lead to a generic description of pollutant formation.

减少污染物排放是发展燃气涡轮机燃烧室的主要挑战之一。在这种情况下，大涡模拟（LES），可以有助于降低开发成本和加快开发过程。因此，本项目的目标是改进航空发动机和固定式燃气轮机的LES排放模型。本提案是2+2年项目的第二阶段。对于航空发动机，重点是对碳烟进行建模。在项目第一阶段研究了碳烟的形成和氧化之后，打算在第二阶段更好地理解碳烟从富燃区到贫燃区的突破。当由于航空发动机中的高湍流水平，湍流涡流将烟灰袋从富燃料区域通过火焰输送到贫燃料区域时，烟灰突破发生，其速度足够快，以至于烟灰颗粒的完全氧化不能发生。目前的LES模型不能以足够的精度描述这些事件，因此显著低估了烟尘排放。在这个项目中，最先进的LES模型在文献中，并在以前的项目阶段开发的国家将扩展到考虑碳烟突破现象。这将在一个三步的过程中完成：1）生成一个DNS数据库的三个不同级别的碳烟突破; 2）评估当前的模型的基础上的DNS和发展的模型扩展考虑碳烟突破; 3）模型验证通过LES的实验室规模的射流火焰配置和一个完整的模型燃烧室。在固定式燃气轮机中，将研究一氧化碳（CO）排放。该阶段的目的是研究向甲烷（CH 4）中添加氢气（H2）对CO排放的影响及其建模。出发点是H2作为未来能源载体的重要性。在这里，我们将考虑在天然气中添加少量H2作为固定式燃气轮机的燃料。先前的工作已经表明，即使少量的H2也能够显著减少CO排放，这是由于火焰速度增加。本文对CH_4/H_2湍流燃烧进行了一系列的数值模拟。DNS数据将进行分析，以扩展目前的CO模型，充分考虑H2加成的影响。将该模型应用于两个模型燃烧室的大涡模拟中，并用实验数据进行验证。该项目的资金由DFG和FVV共同申请。虽然这两个拟议的工作包涉及不同环境中的污染物形成，但通过将工作包嵌入FVV工业合作伙伴提供的项目支持中，提供了强有力的联系。类似的建模方法和研究方法将用于CO和烟尘的预测，以便联合模型开发可以导致对污染物形成的通用描述。