The combination of numerical methods from a novel linear transformation model and laser diagnostic methods for the characterisation of laminar flames

为了阻止大气中二氧化碳浓度的人为上升，不可避免地要用可再生的替代燃料来替代传统的化石燃料。为了确保使用这些新燃料的燃烧机械（如发电厂、汽车或飞机）的可靠和低污染运行，需要对这些机械进行改装或重新设计。因此，精确的化学动力学模型对于燃烧机械的数值设计至关重要。这个项目将有助于这种精确的化学动力学机制的发展。为此，激光诊断方法（拉曼光谱，激光诱导荧光）将应用于测量层流，预混甲烷和乙烷火焰中的物质浓度分布。这将为开发和验证化学动力学模型提供基础数据。首先，将采用一种新的线性变换模型，通过评估量化的实验不确定性和潜在地降低测量数据的不确定性，在数值上支持实验数据的解释。因此，激光诊断的实验数据质量——通常在浓度定量中具有很高的不确定性——将持续提高，并可能为进一步的测量提供新的校准可能性。此外，获得的实验数据将用于改进化学动力学模型。因此，一种优化方法——同样基于线性转换模型——将被应用于化学动力学模型，以提高实验数据的可重复性。在这方面，测量的物种概况将被整合到各种现有基础实验的目标数据数据库中，形成优化程序的基础。

To stop the anthropogenic rise of CO2 concentration in our atmosphere, the substitution of conventional fossil fuels by alternative, regenerative fuels is inevitable. To assure a reliable and low-pollutant operation of combustion machinery with these new fuels e.g. in power plants, motorcars or aeroplanes, the machinery needs to be adapted or newly designed. Thus, accurate chemical kinetic models are of utmost importance for the numerical design of combustion machinery. This project will contribute to the development of such accurate chemical kinetic mechanisms. For this purpose, laser diagnostic methods (Raman spectroscopy, laser-induced fluorescence) will be applied to measure species concentration profiles in laminar, premixed methane and ethane flames. This will create fundamental data to develop and validate chemical kinetic models. Firstly, a novel linear transformation model will be applied to numerically support the interpretation of the experimental data by evaluating the experimental uncertainty of the quantification and potentially reducing the uncertainty of the measured data. Thereby, the quality of the experimental data from laser diagnostic—often having high uncertainties in concentration quantification—will be sustainably increased and will potentially offer new calibration possibilities for further measurements. Additionally, the acquired experimental data will be utilised to improve a chemical kinetic model. Thus, an optimisation method — also based on the linear transformation model — will be applied to increase the reproducibility of experimental data by the chemical kinetic model. In this respect, the measured species profiles will be integrated into a database of target data of various, existing fundamental experiments, forming the basis for the optimisation procedure.