CFD Simulations for Containment of Low Level Nuclear Waste
Characterisation of Material and Computational Fluid Dynamics
FIBERSTONE™ is a commercially-available metallic fibre reinforced ceramic composite with high toughness, excellent high temperature strength and an economically attractive cost. It is currently employed in industrial environments that are (thermally, mechanically and chemically) aggressive, but there is potential for penetration into the civil nuclear market, particularly for containment of low level nuclear waste. However, one major obstacle to the long-term containment of nuclear waste is the relatively high porosity of FIBERSTONE™ which, while useful for many applications, is a problem in this instance.
In this project, the permeability of FIBERSTONE™ was measured as a function of applied pressure during processing. Pore volume, pore type and pore architecture were characterised using various techniques including densitometry, porosimetry and pycnometry. However, most prominent amongst these characetrisation techniques was X-ray computed microtomography which, with suitable software, can be used to reconstruct 3D pore architectures. In this project, we used ScanIP, which is the image visualisation and processing software from Simpleware. The software was used to generate the images shown which include a 3D-rendered image of the complete scanned volume, a reconstructed sub-volume of the pore architecture after application of ScanIP thresholding and segmentation algorithms (then meshed using the ScanIP+FE meshing tools), and predicted flow streamlines (coloured according to velocity) that were obtained using the ScanIP+FLOW module and its in-built Stokes solver. Importantly for this project, the ScanIP+FLOW module was used for quick and easy calculation of the matrix permeability.
Our CFD simulations were used to calculate the permeability of FIBERSTONE™ variants. The data correlated very closely with experimental measurements. Unlike the experiments though, our tomographically-captured architectural data were used, along with our predictions, to inform process route decisions at an early stage in the manufacturing design process.