Alternative approaches for enhancing thermal radiation modeling in combustion systems via Machine Learning Methods

• Grant number: 25/01579-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: January 01, 2026
• End date: December 31, 2028
• Field of knowledge: Engineering - Mechanical Engineering - Thermal Engineering
• Principal Investigator: Rogério Gonçalves dos Santos
• Grantee: Roberta Juliana Collet da Fonseca
• Host Institution: Faculdade de Engenharia Mecânica (FEM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

The growing concern about reducing CO2 emissions from the burning of fossil fuels has led to the search for clean and renewable energy sources, such as solar, wind, biomass etc. While they offer benefits, these sources also present challenges, such as intermittency, high initial installation costs and the need for infrastructure to store energy. Green combustion represents an important step toward decarbonizing the energy matrix, as it reduces the dependence on polluting energy sources. Another alternative that aids in the energy transition is the use of supercriticalCO2 (s-CO2) for carbon capture and storage, where CO2 is captured directly from the emissionsources and compressed until it becomes supercritical. This CO2 is safely stored underground, preventing it from being released into the atmosphere and contributing to global warming. In this regard, the accurate modeling of the radiative properties of participating species, such as CO2, is essential to improve the efficiency of the combustion processes and energy storage systems. Thermal radiation is an important and often dominant heat transfer mechanism in combustion problems due to the high temperatures involved. For gases produced by combustion processes, the radiative energy absorbed and emitted at a given wavenumber varies strongly with the radiative properties of the chemical species, which depend not only on the local thermodynamic state, but also on the radiation spectrum, typically following a complex dependence on the latter. Therefore, it is expected that this project will develop methodologies to properly model the radiative transfer of green fuels (such as H2 and NH3) and s-CO2, contributing to a more sustainable future and the mitigation of climate change. (AU)