Fundamentals of turbulent swirl-stabilized combustion of ammonia/hydrogen blends for carbon-free energy applications

University Of Texas At Austin, Austin TX

Investigators

Abstract

Due to the negative impact of carbon dioxide on climate, decarbonization of electricity production is a societal imperative. Replacement of fossil fuels with carbon-free alternatives is an expedient and practical solution because it would leverage existing gas turbine technology with limited modifications. While ammonia and hydrogen are two most viable carbon-free fuels, their combustion behavior and pollutants emissions in regimes relevant to applications are unexplored, hindering progress. This project will provide a comprehensive theory of the combustion of such blends, thereby supporting their deployment for carbon-free combustion-based electricity production. Despite their importance, key aspects of power generation, such as its impacts on economy, health, and climate change, are largely outside of the public discourse. Proposed work will lead to more awareness and understanding of key issues in combustion-based electricity generation by leveraging podcast content production. The proposed work will accelerate the deployment of ammonia/hydrogen fuel blends for combustion-based electricity production by meeting two specific objectives. The first research objective is to generate a comprehensive database of key turbulence-chemistry interactions in turbulent flames of ammonia/hydrogen blends in practical configurations via large-scale high-fidelity Direct Numerical Simulations and experimental measurements with laser-based optical diagnostics. This objective will be achieved by considering rich premixed swirl-stabilized turbulent flames relevant to Rich-Quick-Lean staged combustion and isolate the effects of turbulence on flame propagation and formation of oxides of nitrogen (NOx). The second research objective is to leverage numerical and experimental data to formulate generalizable dimensionless scaling laws for turbulent flames of ammonia/hydrogen blends by applying an innovative statistical learning approach based on similarity theory. The approach combines classical error-in-variables statistical models with the theory of similarity in asymptotic parameters. Hypotheses on regimes of similarity will be evaluated via Bayesian model selection. If successful, proposed research will lead to the systematic understanding of critical turbulence-chemistry interactions in ammonia/hydrogen mixtures and demonstrate an entirely new approach to statistical learning, whereby the objective is to discover limit forms of dimensionless scaling laws in asymptotic parameters. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.