Researchers Develop Data-Driven Framework to Optimize Aviation Fuel Formulations for Lower Emissions
Scientists have created the Fuel Optimizer, an inverse design framework that uses genetic algorithms and surrogate modeling to identify optimal combinations of chemical species for sustainable aviation fuels. The framework addresses the trade-off between nitrogen oxides and carbon monoxide emissions while meeting fuel property standards. The approach could help the aviation industry reduce pollutant emissions during landing, takeoff, and cruise operations.
The Fuel Optimizer is an inverse design framework that begins with user-defined performance targets and works backward to identify the best chemical compositions for aviation turbine fuel. Researchers built a database of fuel blends meeting selected property standards and simulated them in a reactor model to predict pollutant emissions at cruise conditions. To reduce computational costs, they developed a surrogate model that takes fuel composition as input and predicts emissions as output. A genetic algorithm then optimized fuel formulations according to merit functions designed to minimize emissions and break the typical trade-off between nitrogen oxides and carbon monoxide. The optimal fuel candidates identified by the framework outperformed existing blends in the training database across all merit functions, and their performance was validated through additional reactor simulations.
What different sources said
- arXiv physicsCenter
The Fuel Optimizer: A Data-Driven Numerical Framework for Formulation of Aviation Turbine Fuel
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