Regular readers of this blog will know that I occasionally feature publications from my research group.  The most recent was ‘Predicting release rates of hydrogen from stainless steel’ on September 13^th, 2023 and before that ‘Label-free real-tracking of individual bacterium’ on January 25^th 2023 and ‘A thermal emissions-based real-time monitoring system for in situ detection of cracks’ in ‘Seeing small changes is a big achievement’ on October 26^th 2023.  The subject of these publications might seem a long way apart but they are linked by my interest in trying to measure events in the real-world and use the data to develop and validate high-fidelity digital models.  Recently, I have stretched my research interests still further through supervising a clutch of PhD students with a relatively new collaborator working in the social sciences.  Two of the students have had their first papers published by the ASME (American Society of Mechanical Engineers) and the IEEE (Institute of Electrical and Electronics Engineers).  Their papers are not directly connected but they both explore the use of published information to gain new insights on a topic.  In the first one [1], we have explored the similarities and differences between safety cases for three nuclear reactors: a pair of research reactors – one fission and one fusion reactor; and a commercial fission reactor.  We have developed a graphical representation of the safety features in the reactors and their relationships to the fundamental safety principles set out by the nuclear regulators. This has allowed us to gain a better understanding of the hazard profiles of fission and fusion reactors that could be used to create the safety case for a commercial fusion reactor.  Fundamentally, this paper is about exploiting existing knowledge and looking at it in a new way to gain fresh insights, which we did manually rather than automating the process using digital technology.  In the second paper [2], we have explored the extent to which digital technologies are being used to create, collate and curate knowledge during and beyond the life-cycle of an engineering product.  We found that these processes were happening but generally not in a holistic manner.  Consequently, opportunities were being lost through not deploying digital technology in knowledge management to undertake multiple roles simultaneously, e.g., acting as repositories, transactive memory systems (group-level knowledge sharing), communication spaces, boundary objects (contact points between multiple disciplines, systems or worlds) and non-human actors.  There are significant challenges, as well as competitive advantages and organisational value to be gained, in deploying digital technology in holistic approaches to knowledge management.  However, despite the rapid advances in machine learning and artificial intelligence [see ‘Update on position of AI on hype curve: it cannot dream’ on July 26^th 2023] that will certainly accelerate and enhance knowledge management in a digital environment, a human is still required to realise the value of the knowledge and use it creatively.

References

1. Nguyen, T., Patterson, E.A., Taylor, R.J., Tseng, Y.S. and Waldon, C., 2023. Comparative maps of safety features for fission and fusion reactors. Journal of Nuclear Engineering and Radiation Science, pp.1-24
2. Yao, Y., Patterson, E.A. and Taylor, R.J., 2023. The Influence of Digital Technologies on Knowledge Management in Engineering: A Systematic Literature Review. IEEE Transactions on Knowledge and Data Engineering.