The data enclosed in this repository is associated with the manuscript for an article "On the numerical modelling of frozen walls in a molten salt fast reactor" submitted to the Nuclear Engineering and Design Journal at the end of March 2019. The article was selected for the CFD4NRS-7 Special Issue of the Journal. The data in the article was presented at the CFD4NRS-7 Workshop in Shanghai, September 2018 and the NUTHOS-12 topical meeting in Qingdao, October 2018. The data is in the form of figures and tables. The figures in the corresponding directory were prepared using bash scripts, python version 2.7, gnuplot version 4.6 and latex to extract and analyse simulated data. The tables in the corresponding directory were prepared using bash scripts and python version 2.7 to extract and analyse simulated data. The python scripts can be found in the repository. Note that numpy is a requirement. The raw data was prepared using the SCARF (scarf.rl.ac.uk), CIRRUS (cirrus.ac.uk), University of Liverpool (https://www.liverpool.ac.uk/csd/advanced-research-computing/facilities/high-performance-computing/) and SCAFELLPIKE (http://community.hartree.stfc.ac.uk/wiki/site/admin/home.html) clusters. There is inexcess of 10Gb of data generated by the solvers Code_Saturne (https://www.code-saturne.org/cms/), DYN3D-MG (https://www.hzdr.de/db/Cms?pOid=11771&pNid=542) and SERPENT (http://montecarlo.vtt.fi/). Code_Saturne (version 5.0) was used to perform simulations of thermal fluid dynamic and conjugate heat transfer of a molten salt fast reactor. The models studied the formation of frozen salt films on cooled reactor vessel walls. DYN3D-MG modelled the nodal diffusion neutronic behaviour of the molten salt fast reactor. SERPENT (version 2.1.29) modelled the neutronic behaviour of the molten salt fast reactor using the Monte Carlo method. Both Code_Saturne and DYN3D-MG were coupled to one another in 3-D simulations of the reactor. The coupling procedures were implemented with the Multiscale Universal Interface, MUI (https://github.com/MxUI/MUI).EPSRC: Grant No. EP/R001618/1; H2020-EU.1.2.2. - FET Proactive Grant No. 671564