Ammonia combustion; Bayesian inference; Defossilization; Gas turbine; Model error; Optimisation; Polynomial chaos expansion; Uncertainty quantification; Chaos expansions; Model errors; Optimisations; Polynomial chaos; Reactor network; Uncertainty quantifications; Renewable Energy, Sustainability and the Environment; Fuel Technology; Condensed Matter Physics; Energy Engineering and Power Technology
Abstract :
[en] Low-fidelity, cost-effective, physics-based models are useful for assessing the environmental performance of novel combustion systems, especially those utilizing alternative fuels, like hydrogen and ammonia. However, these models require calibration and quantification of their limitations to be reliable predictive tools. This paper presents a framework for calibrating a simplified Chemical Reactor Network model using higher-fidelity Computational Fluid Dynamics data from a micro-gas-turbine-like combustor fuelled with pure ammonia. A Bayesian inference strategy that explicitly accounts for model error is used to calibrate the most relevant CRN parameters based on NO emissions data from CFD simulations and to estimate the model's structural uncertainty. The calibrated CRN model accurately predicts NO emissions within the design space and can extrapolate reasonably well to conditions outside the calibration range. By utilizing this framework, low-fidelity models can be employed to explore various operating conditions during the preliminary design of innovative combustion systems.
Disciplines :
Energy
Author, co-author :
Savarese, Matteo ; Université de Mons - UMONS > Faculté Polytechniqu > Service de Thermique et Combustion ; Aero-Thermo-Mechanics Department, Université Libre de Bruxelles, Belgium ; Brussels Institute for Thermal-Fluid Systems and Clean Energy (BRITE), Université Libre de Bruxelles and Vrije Universiteit Brussel, Brussels, Belgium
Giuntini, Lorenzo; Department of Civil and Industrial Engineering, Università di Pisa, Pisa, Italy
Malpica Galassi, Riccardo ; Aero-Thermo-Mechanics Department, Université Libre de Bruxelles, Belgium ; Brussels Institute for Thermal-Fluid Systems and Clean Energy (BRITE), Université Libre de Bruxelles and Vrije Universiteit Brussel, Brussels, Belgium
Iavarone, Salvatore ; Aero-Thermo-Mechanics Department, Université Libre de Bruxelles, Belgium ; Brussels Institute for Thermal-Fluid Systems and Clean Energy (BRITE), Université Libre de Bruxelles and Vrije Universiteit Brussel, Brussels, Belgium
Galletti, Chiara ; Department of Civil and Industrial Engineering, Università di Pisa, Pisa, Italy
De paepe, Ward ; Université de Mons - UMONS > Faculté Polytechniqu > Service de Thermique et Combustion
Parente, Alessandro; Aero-Thermo-Mechanics Department, Université Libre de Bruxelles, Belgium ; Brussels Institute for Thermal-Fluid Systems and Clean Energy (BRITE), Université Libre de Bruxelles and Vrije Universiteit Brussel, Brussels, Belgium
Language :
English
Title :
Model-to-model Bayesian calibration of a Chemical Reactor Network for pollutant emission predictions of an ammonia-fuelled multistage combustor
Fonds De La Recherche Scientifique - FNRS H2020 Marie Skłodowska-Curie Actions Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture Horizon 2020 Horizon 2020 European Research Council
Funding text :
This work was funded by the Fonds National de la Recherche Scientifique (FNRS) of Belgium through an individual FRIA fellowship. The project was carried out under the framework of the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 714605 (funded by the European Research Council). RMG acknowledges Marie Sklodowska-Curie funding under Grant Agreement No. 801505 . SI acknowledges the financial support of the Fonds National de la Recherche Scientifique (FRS-FNRS). We also acknowledge the Energy Transition Fund of Belgium that contributed to this work via the BEST project.
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