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Flashback Propensity due to Hydrogen Blending in Natural Gas: Sensitivity to Operating and Geometrical Parameters

Abstract

Hydrogen has emerged as a promising option for promoting decarbonization in various sectors by serving as a replacement for natural gas while retaining the combustion-based conversion system. However, its higher reactivity compared to natural gas introduces a significant risk of flashback. This study investigates the impact of operating and geometry parameters on flashback phenomena in multi-slit burners fed with hydrogenmethane-air mixtures. For this purpose, transient numerical simulations, which take into account conjugate heat transfer between the fluid and the solid walls, are coupled with stochastic sensitivity analysis based on Generalized Polynomial Chaos. This allows deriving comprehensive maps of flashback velocities and burner temperatures within the parameter space of hydrogen content, equivalence ratio, and slit width, using a limited number of numerical simulations. Moreover, we assess the influence of different parameters and their interactions on flashback propensity. The ranges we investigate encompass highly H2 -enriched lean mixtures, ranging from 80% to 100% H2 by volume, with equivalence ratios ranging from 0.5 to 1.0. We also consider slit widths that are typically encountered in burners for end-user devices, ranging from 0.5 mm to 1.2 mm. The study highlights the dominant role of preferential diffusion in affecting flashback physics and propensity as parameters vary, including significant enrichment close to the burner plate due to the Soret effect. These findings hold promise for driving the design and optimization of perforated burners, enabling their safe and efficient operation in practical end-user applications.

Funding source: This research is funded by the Ministry of University and Research (MUR), Italy and Immergas S.p.A., Brescello, RE (Italy), as part of the PON 2014–2020 ‘‘Research and Innovation’’ resources - Green/Innovation Action - DM MUR 1061/2021 and DM MUR 1062/ 2021. We also thank Ing. Cristiana Bronzoni and Ing. Marco Folli from Immergas S.p.A. for the valuable discussions. This work is supported by PNRR M4C2 - HPC, Big data and Quantum Computing (Simulazioni, calcolo e analisi dei dati e altre prestazioni - CN1) - CUP I53C22000690001 SPOKE 6 Multiscale modelling & Engineering applications and by the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3 - Call for tender No. 1561 of 11.10.2022 of MUR - CUP B93C22001110006; funded by the European Union – NextGenerationEU, Project title ‘‘Network 4 Energy Sustainable Transition – NEST’’ with code PE0000021.
Related subjects: Hydrogen Blending
Countries: Italy
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2024-01-09
2024-02-22
http://instance.metastore.ingenta.com/content/journal5415
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