Speaker
Description
Keywords: Monte-Carlo simulation, image reconstruction, transport and storage casks
Abstract
Muon imaging is a highly promising and rapidly evolving technique for non-invasive monitoring or investigation of various structures across a wide range of applications. This is particularly advantageous for objects that cannot be examined using traditional methods such as X-ray radiography or CT due to their high density and/or large geometries.
A frequently discussed area of application is the monitoring of transport and storage casks to determine the condition of the radioactive inventory. In addition to Monte Carlo simulations of such casks and the development of suitable image reconstruction algorithms, we are preparing a series of large-scale experiments. A robust and modular muon detection system, consisting of scintillation detectors and stacked drift chambers, has been developed for use in combination with novel image reconstruction algorithms. To precisely measure the cask geometry and test these reconstruction algorithms, a large-scale phantom was constructed, replicating a cross-section of a CASTOR V/19 cask. The vertically mounted steel disc, simulating a horizontally positioned cask, matches the material composition of the actual cask wall and ensures realistic muon scattering behavior. Steel rods, held by rotatable perforated plates, represent the fuel rods and allow for the simulation of various cask orientations. The phantom’s frame supports the integration of muon detectors above and below the disc, providing a realistic testing environment for muon scattering tomography. This experiment enables the acquisition of experimental benchmark data for muon imaging of transport and storage cask geometries. Once the detection system and image reconstruction algorithms are proven to deliver accurate images, the next step will involve examining an actual cask in an interim storage facility.
Based on the current state of the art, there is growing interest in utilizing muon imaging for other applications. The detection system and reconstruction algorithms can also be applied to the monitoring of industrial infrastructure. Currently, small samples of reinforced concrete are being examined with our muon detectors to gain initial insights into the condition of objects such as bridges and to identify areas with reduced density or corrosion. Furthermore, the algorithms are being applied to simulations of industrial furnace geometries to evaluate the wear of lining materials. The accuracy and required measurement time to detect these effects are also being investigated.