An analysis workflow has been developed to assess energy deposition and material damage for ITER vertical displacement events (VDEs) and major disruptions (MD). This paper describes the use of this workflow to assess the melt damage to be expected during unmitigated current quench (CQ) phases of VDEs and MDs at different points in the ITER research plan. The plasma scenarios are modeled using the DINA code with variations in plasma current I p, disruption direction (upwards or downwards), Be impurity density n Be, and diffusion coefficient χ. Magnetic field line tracing using SMITER calculates time-dependent, 3D maps of surface power density q ⊥ on the Be-armored first wall panels (FWPs) throughout the CQ. MEMOS-U determines the temperature response, macroscopic melt motion, and final surface topology of each FWP. Effects of Be vapor shielding are included. Scenarios at the baseline combination of I p and toroidal field (15 MA/5.3 T) show the most extreme melt damage, with the assumed n Be having a strong impact on the disruption duration, peak q ⊥ and total energy deposition to the first wall. The worst-cases are upward 15 MA VDEs and MDs at lower values of n Be, with q ⊥,max = 307 MW m−2 and maximum erosion losses of ∼2 mm after timespans of ∼400–500 ms. All scenarios at 5 MA avoided melt damage, and only one 7.5 MA scenario yields a notable erosion depth of 0.25 mm. These results imply that disruptions during 5 MA, and some 7.5 MA, operating scenarios will be acceptable during the pre-fusion power operation phases of ITER. Preliminary analysis shows that localized melt damage for the worst-case disruption should have a limited impact on subsequent stationary power handling capability.

Background Sustainable energy transition of a country is complex and long-term process, which requires decision-making in all stages and at all levels, including a large number of different factors, with different causality. The main objective of this paper is the development of a probabilistic model for decision-making in sustainable energy transition in developing countries of SE Europe. The model will be developed according to the specificities of the countries for which it is intended—SE Europe. These are countries where energy transition is slower and more difficult due to many factors: high degree of uncertainty, low transparency, corruption, investment problems, insufficiently reliable data, lower level of economic development, high level of corruption and untrained human resources. All these factors are making decision-making more challenging and demanding. Methods Research was done by using content analysis, artificial intelligence methods, software development method and testing. The model was developed by using MSBNx— Microsoft Research’s Bayesian Network Authoring and Evaluation Tool . Results Due to the large number of insufficiently clear, but interdependent factors, the model is developed on the principle of probabilistic (Bayesian) networks of factors of interest. The paper presents the first model for supporting decision-making in the field of energy sustainability for the region of Southeastern Europe, which is based on the application of Bayesian Networks. Conclusion Testing of the developed model showed certain characteristics, discussed in paper. The application of developed model will make it possible to predict the short-term and long-term consequences that may occur during energy transition by varying these factors. Recommendations are given for further development of the model, based on Bayesian networks.