Parametric and model uncertainties induced by reduced order chemical mechanisms for biogas combustion
| Autor: | Rodolfo S. M. Freitas, Xi Jiang, Fernando A. Rochinha, Daniel Mira |
|---|---|
| Přispěvatelé: | Barcelona Supercomputing Center |
| Rok vydání: | 2020 |
| Předmět: |
Surrogate modeling
General Chemical Engineering Combustibles gasosos 02 engineering and technology Combustion Industrial and Manufacturing Engineering Methane symbols.namesake chemistry.chemical_compound Surrogate model 020401 chemical engineering Biogas Simulació per ordinador Machine learning 0204 chemical engineering Uncertainty quantification Process engineering Parametric statistics Arrhenius equation Model error business.industry Applied Mathematics Biogas combustion General Chemistry Renewable fuels 021001 nanoscience & nanotechnology Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria [Àrees temàtiques de la UPC] chemistry symbols Environmental science 0210 nano-technology business |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| ISSN: | 0009-2509 |
| DOI: | 10.1016/j.ces.2020.115949 |
| Popis: | This study investigates the impact of chemical kinetic uncertainties on biogas combustion using a Uncertainty Quantification (UQ)-based methodology. The results indicate that the variation of physicochemical properties introduced by composition variability introduces smaller uncertainties in the resulting flame properties than the Arrhenius parameters involved in the kinetics used to describe the oxidation process. We demonstrate that the use of reduced mechanisms for methane-air oxidation could be a starting point to develop optimized schemes for biogas combustion. In that regard, we adopted an embedded discrepancy approach to understanding the limits of the use of a reduced mechanism for methane/air in this renewable fuel. This strategy provides a way to reduce systematically the cost of reaction kinetics in simulations, while quantifying the accuracy of predictions of important target quantities. Finally, we develop a surrogate model for biogas flame propagation using machine learning techniques to make feasible a broader UQ analysis. The research leading to these results had received funding from the European Union’s Horizon2020 Programme (2014-2020) and from Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (www.hpc4e.eu), grant agreement number 689772 |
| Databáze: | OpenAIRE |
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