This paper describes the latest developments at the Institute for Energy Technology (IFE) in Norway, in the field of real-time 3D (three-dimensional) radiation risk assessment for the support of work simulation in nuclear environments. 3D computer simulation can greatly facilitate efficient work planning, briefing, and training of workers. It can also support communication within and between work teams, and with advisors, regulators, the media and public, at all the stages of a nuclear installation's lifecycle. Furthermore, it is also a beneficial tool for reviewing current work practices in order to identify possible gaps in procedures, as well as to support the updating of international recommendations, dissemination of experience, and education of the current and future generation of workers.IFE has been involved in research and development into the application of 3D computer simulation and virtual reality (VR) technology to support work in radiological environments in the nuclear sector since the mid 1990s. During this process, two significant software tools have been developed, the VRdose system and the Halden Planner, and a number of publications have been produced to contribute to improving the safety culture in the nuclear industry.This paper describes the radiation risk assessment techniques applied in earlier versions of the VRdose system and the Halden Planner, for visualising radiation fields and calculating dose, and presents new developments towards implementing a flexible and up-to-date dosimetric package in these 3D software tools, based on new developments in the field of radiation protection. The latest versions of these 3D tools are capable of more accurate risk estimation, permit more flexibility via a range of user choices, and are applicable to a wider range of irradiation situations than their predecessors.

There is an increasing international focus on the need to optimise decommissioning strategies, driven by the anticipation of high costs and major effort for the decommissioning of nuclear facilities in the coming decades. The goals are to control and mitigate costs and negative impacts on workers, the general public, and the environment. The methods presently employed for many decommissioning tasks do not apply the latest advancements of science and technology. Therefore, there is growing interest in research and development into the adoption of novel techniques for improving safety, reducing costs, and increasing transparency. This paper provides a comprehensive overview of the authors' results from investigating how current and emerging technologies can be applied to enhance the international decommissioning strategy, focussing in particular on three-dimensional simulation, virtual reality, advanced user interfaces, mobile and wearable devices, and geographical information systems. Our results demonstrate that emerging technologies have great potential for supporting adoption of new instrumentation, improving data and knowledge management, optimising project plans, briefing and training field operators, and for communication, surveillance, and education in general.

Objective To investigate speech features, human–machine alarms, and operator–system interaction for the estimation of cognitive workload in full-scale realistic simulated scenarios. Background Theories and models of cognitive workload are critical for the design and evaluation of human–machine systems. Unfortunately, there are very few nonintrusive cognitive workload measures available for realistic dynamic human–machine interaction. Method The study was conducted in a full-scope control room research simulator of an advanced nuclear reactor. Six crews, each consisting of three operators, participated in 12 scenarios. The operators rated their workload every second minute. Machine learning algorithms were trained to estimate operators’ workload based on crew communication, operator–system interaction, and system alarms. Results Random Forest (RF) utilizing speech and system features achieved an accuracy of 67% on test data. Utilizing speech features only, the accuracy achieved was 63%. The most important speech features were pitch, amplitude, and articulation rate. A 61% accuracy was achieved when alarms and operator–system interaction features were used. The most important features were the number of alarms and amount of operator–system interaction. Accuracy for algorithms trained for each operator ranged from 39% to 98%, with an average of 72%. For a majority of analyses performed, RF and extreme gradient boosting (XGB) outperformed other algorithms. Conclusion The results demonstrate that the features investigated and machine learning models developed provide a potential for the dynamic nonintrusive measurement of cognitive workload. Application The approach presented can be developed for nonintrusive workload measurement in real-world human–machine applications, simulator-based training, and research.

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