LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Development and validation of an agglomeration model for CFD simulations of aerosol dispersion in the frame of Fukushima fuel debris retrieval

Photo from wikipedia

ABSTRACT This article is devoted to demonstrate the feasibility of laser cutting technique for the fuel debris retrieval into the Fukushima Daiichi damaged reactors. IRSN is involved in a project… Click to show full abstract

ABSTRACT This article is devoted to demonstrate the feasibility of laser cutting technique for the fuel debris retrieval into the Fukushima Daiichi damaged reactors. IRSN is involved in a project led by ONET Technologies, in collaboration with CEA, to evaluate the risk occurred by the dispersion of aerosols emitted by the dismantling operations [4]. During the laser cutting operations of fuel debris in air condition, particles will be produced, inducing a potential risk of dispersion into the environment. Hence, evaluating the fate of these particles after their emission is one of the safety key issues for these dismantling operations. In this objective, IRSN performed CFD simulations of dispersion of particles representative of inactive fuel debris simulants (1)(2)(3)(4). The first numerical results (5) showed a quite good agreement but some improvements were needed to take into account complex particle size distributions (PSD). Consequently, this article proposes an alternative method to better initialize the agglomeration calculation, in order to take account for different kinds of particle size distribution in the CFD simulations and to better evaluate the fate of aerosols produced by the debris cutting in the damaged reactors of Fukushima-Daiichi pedestal.

Keywords: fuel debris; dispersion; cfd simulations; debris retrieval; debris

Journal Title: Journal of Nuclear Science and Technology
Year Published: 2021

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.