Cerberus Receives ARC Funding to Develop Criticality Safety VR Training Software
Cerberus Nuclear is a hub for innovation in criticality safety and radiation shielding and we are pleased to announce that we have made a successful application for funding from the UK’s Alpha Resilience and Capability (ARC) programme. ARC was created by BEIS (Department for Business, Energy & Industrial Strategy) to ensure that the UK retains its world-leading alpha capabilities: from operations and maintenance, to high-end R&D and design. This cross-industry consortium includes the National Nuclear Laboratory, Sellafield Limited, AWE and ONR.
Over the last two years Cerberus Nuclear has developed CARTA, a concept for criticality safety VR (Virtual Reality) training software, which we successfully presented at ICNC2019. Uniquely, CARTA uses a machine learning algorithm to predict k-effective 'on the fly' for a given system, such as an alpha facility glovebox. When coupled to a VR headset, CARTA gives users an immersive experience of the facility environment and the effect of their actions on the system’s reactivity.
The ARC funding will support the next phase of development, to refine the concept into a software package for members of the ARC consortium to use. CARTA will deliver tangible benefits directly to operators on plant, criticality safety specialists and other stakeholders in criticality safety. The software package will use a variety of scenarios in desktop and VR environments, to provide intuitive user interfaces. The underpinning data will be based on accurate modelling of the neutron physics, providing a realistic environment for trainees to improve their understanding of the complexities associated with criticality safety.
The specifics of the training scenarios will be guided by a Technical Steering Committee, comprising stakeholders from the various ARC member organisations. This will ensure that the training scenarios are relevant and can be effectively integrated into their existing training programmes.
We are now actively seeking organisations that would benefit from bespoke criticality safety training scenarios. If you would like to discuss your idea, please get in touch using nuclear@cerberusnuclear.com.
Eddy - MCNP & SCALE Html Generator
Cerberus Nuclear has created Eddy, an open-source Html output generator for MCNP and SCALE. The function of Eddy is to parse MCNP and SCALE output files into an easy-to-read and user-friendly format. Eddy has been written to work for both radiation transport and criticality calculations.
Eddy collates key information from an output file so that it can be quickly reviewed. Normalisation factors can also be specified to simplify interpretation of tally outputs.
Eddy Html outputs include:
Normalised Tally Results with Error
Highlighted Statistical Checks
K-effective and Error
Comments and Warnings
Cell Mass and Volumes
Particle Populations
Full MCNP Input
Eddy is simple to use from the command line or via its built-in interface. Hyperlinks within the Html enable the user to navigate to the required part of the output with ease.
The Html output from Eddy assists in the preparation of technical reports supports QA processes and improves workflow efficiency. The contained nature of the Html output and its small file size also facilitates the sharing of calculation outputs for independent review purposes.
If you would like to provide any feedback or would like to request additional features please get in touch by emailing nuclear@cerberusnuclear.com
Object Recognition - Computer Vision
We have developed a custom computer vision object identification algorithm. As a demonstration, we trained a neural network to identify cars and lorries on a motorway using R-CNN object detection.
The algorithm identifies a number of objects, object type (car/lorry etc.), object colour, object speed and confidence in match. In addition, a report is generated that summarises the information gathered over time.
Cerberus Nuclear has access to the necessary computing power for near real-time, high-accuracy identification. The example was produced to demonstrate validity for stopped car identification as well as traffic monitoring.
In addition to this example, computer vision has a number of nuclear applications that we will be looking to build upon in the coming weeks.
If you would like to know more or if you have a specific challenge, which you think computer vision may solve, don't hesitate to get in touch.
