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ISBN : 978-1-56700-478-6

Proceedings of the 24th National and 2nd International ISHMT-ASTFE Heat and Mass Transfer Conference (IHMTC-2017)
2017, December 27-30 , BITS Pilani, Hyderabad, India


Get access DOI: 10.1615/IHMTC-2017.1210
pages 859-863


During the postulated severe accident conditions of water cooled nuclear reactors, release of large quantities of hydrogen into the containment building can induce combustible hydrogen-air mixtures. These combustible mixtures may be global or local clouds depending on the prevailing thermal-hydraulic conditions, multi-compartment interconnected containment geometry and the presence of various containment internals. Global combustion phenomena could be averted due to the presence of hydrogen mitigation systems such as Passive Autocatalytic Recombiners (PARs). However, possible ignition of a locally formed hydrogen-air cloud, depending on its size and concentration, may also generate large pressure pulses on the structural walls and safety components. These pulse loads may also jeopardize the structural integrity of the containment walls and functioning of safety components which may lead to release of radioactivity in the public domain. Therefore, as part of reactor safety assessment, it is important to have an estimate of peak pressure loads which may get generated under different postulated hydrogen-air cloud combustion scenarios.
In this context, CFD modeling has become popular for estimation of combustion pressure loads in confined geometries. Generally, CFD analysis of a reactor scale combustion problem is done using an appropriate single step reaction rate model for faster computations. Therefore, in this work, several single-step reaction rate models have been tested for simulating localized hydrogen-air cloud combustion experiment conducted at FZK, Germany in a test cell. Performance of these models, in terms of the flame arrival time and the peak pressure values at various spatial locations, has been discussed by comparing the predicted results against the experimental data.
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