Modeling flame acceleration in H2 turbulent combustion: Strategies to reduce computational cost

Reducing the computational cost of fluid dynamics simulations is one of the main challenges in many complex industrial flows’ problems. This challenge becomes more crucial in nuclear safety applications, such as the simulation of accident sequences in large volumes. The present study summarizes the...

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Main Authors: A. Dahmani, R.A. Otón-Martínez, F. Nicolás-Pérez, F.J.S. Velasco, O. de Francisco
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Results in Engineering
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Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025002105
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author A. Dahmani
R.A. Otón-Martínez
F. Nicolás-Pérez
F.J.S. Velasco
O. de Francisco
author_facet A. Dahmani
R.A. Otón-Martínez
F. Nicolás-Pérez
F.J.S. Velasco
O. de Francisco
author_sort A. Dahmani
collection DOAJ
description Reducing the computational cost of fluid dynamics simulations is one of the main challenges in many complex industrial flows’ problems. This challenge becomes more crucial in nuclear safety applications, such as the simulation of accident sequences in large volumes. The present study summarizes the investigations conducted to identify and assess reliable modeling strategies for reducing computational cost for the simulations of hydrogen turbulent combustion in nuclear accident scenarios which consider the flame acceleration, with a primary focus on the use of LES coupled with Artificially Thickened Flame Model (TFM) using a detailed chemical reaction mechanism. For this purpose, the LES-TFM model was benchmarked against experimental data of flame acceleration in a tube for lean mixtures. The obtained results show that LES-TFM model with a detailed 12-reaction chemical kinetics can predict flame acceleration. Moreover, the use of a dynamic grid refinement with an initial coarser mesh with LES-TFM is found to overestimate the time lapse of the initial low-velocity stage of the sequence. However, it permits to simulate the flame acceleration dynamics. Finally, using an effective Lewis of the mixture, adaptive mesh refinement and ISAT are found to be efficient strategies to reduce computational cost with TFM-LES.
format Article
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institution Kabale University
issn 2590-1230
language English
publishDate 2025-03-01
publisher Elsevier
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series Results in Engineering
spelling doaj-art-75ffd6807f2f4133b5bf67596d06188f2025-01-27T04:22:11ZengElsevierResults in Engineering2590-12302025-03-0125104122Modeling flame acceleration in H2 turbulent combustion: Strategies to reduce computational costA. Dahmani0R.A. Otón-Martínez1F. Nicolás-Pérez2F.J.S. Velasco3O. de Francisco4Universidad Politécnica de Cartagena, Dep. Ingeniería Térmica y de Fluidos, Campus Muralla del Mar (30202) Cartagena, Spain; Corresponding author.Centro Universitario de la Defensa (CUD), Academia General del Aire. Cl. Coronel Lopez Peña s/n 30720, San Javier, SpainLynx Simulations S.L., Calle del Metal 4, 30009 Murcia, SpainUniversidad Politécnica de Cartagena, Dep. Ingeniería Térmica y de Fluidos, Campus Muralla del Mar (30202) Cartagena, SpainUniversidad Politécnica de Cartagena, Dep. Ingeniería Térmica y de Fluidos, Campus Muralla del Mar (30202) Cartagena, SpainReducing the computational cost of fluid dynamics simulations is one of the main challenges in many complex industrial flows’ problems. This challenge becomes more crucial in nuclear safety applications, such as the simulation of accident sequences in large volumes. The present study summarizes the investigations conducted to identify and assess reliable modeling strategies for reducing computational cost for the simulations of hydrogen turbulent combustion in nuclear accident scenarios which consider the flame acceleration, with a primary focus on the use of LES coupled with Artificially Thickened Flame Model (TFM) using a detailed chemical reaction mechanism. For this purpose, the LES-TFM model was benchmarked against experimental data of flame acceleration in a tube for lean mixtures. The obtained results show that LES-TFM model with a detailed 12-reaction chemical kinetics can predict flame acceleration. Moreover, the use of a dynamic grid refinement with an initial coarser mesh with LES-TFM is found to overestimate the time lapse of the initial low-velocity stage of the sequence. However, it permits to simulate the flame acceleration dynamics. Finally, using an effective Lewis of the mixture, adaptive mesh refinement and ISAT are found to be efficient strategies to reduce computational cost with TFM-LES.http://www.sciencedirect.com/science/article/pii/S2590123025002105Turbulent hydrogen combustionLarge eddy simulation (LES)Thickened flame model (TFM), Flame accelerationNuclear safety
spellingShingle A. Dahmani
R.A. Otón-Martínez
F. Nicolás-Pérez
F.J.S. Velasco
O. de Francisco
Modeling flame acceleration in H2 turbulent combustion: Strategies to reduce computational cost
Results in Engineering
Turbulent hydrogen combustion
Large eddy simulation (LES)
Thickened flame model (TFM), Flame acceleration
Nuclear safety
title Modeling flame acceleration in H2 turbulent combustion: Strategies to reduce computational cost
title_full Modeling flame acceleration in H2 turbulent combustion: Strategies to reduce computational cost
title_fullStr Modeling flame acceleration in H2 turbulent combustion: Strategies to reduce computational cost
title_full_unstemmed Modeling flame acceleration in H2 turbulent combustion: Strategies to reduce computational cost
title_short Modeling flame acceleration in H2 turbulent combustion: Strategies to reduce computational cost
title_sort modeling flame acceleration in h2 turbulent combustion strategies to reduce computational cost
topic Turbulent hydrogen combustion
Large eddy simulation (LES)
Thickened flame model (TFM), Flame acceleration
Nuclear safety
url http://www.sciencedirect.com/science/article/pii/S2590123025002105
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AT fnicolasperez modelingflameaccelerationinh2turbulentcombustionstrategiestoreducecomputationalcost
AT fjsvelasco modelingflameaccelerationinh2turbulentcombustionstrategiestoreducecomputationalcost
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