Hydrogen is enjoying a renewed and rapidly growing attention in Europe and around the world. The most important advantage of hydrogen usage is that it does emit greenhouse gases. The EU's priority is to develop renewable hydrogen where the H2 is produced from the electrolysis of water, with the electricity stemming from renewable energy. This meets the goal of net-zero greenhouse gas emissions by 2050. There are two options for hydrogen usage. One is the drop-in approach, where only a limited amount of H2 can be added to the fossil fuel to reuse the existing chambers, due to the very different properties of H2. However, this option still emits a large amount of greenhouse gases. The other one is to redesign the existing chambers to burn substantial H2. The most challenging issues for burning substantial H2 are the strong differential diffusion and its induced instabilities. The state-of-the-art combustion model cannot capture these phenomena with accuracy, particularly when they further interact with turbulence. The aim of this proposal is to develop and validate such a model to close this gap. The model will be based on a flamelet approach, and a novel machine learning technology will be introduced to consider the differential diffusion (objective 1). To model the positive and negative curvatures in the unstable premixed H2 flame, a novel flamelet model will be developed based on the detailed a priori and a posteriori analyses of the state-of-the-art DNS datasets (objective 2). Finally, the developed flamelet model will be extended to LES with the differential diffusion and curvature related sub-grid scale (SGS) effects being considered with the artificially thickened flame (ATF) model. Particularly, the SGS effects will be considered by modifying the efficiency function. LES will be conducted for the DNS configuration and a turbulent methane flame with substantial H2 addition using the developed flamelet model coupled with the modified ATF approach (objective 3).
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