Search

During operation of the HTTR, hundreds of technical signals and operating conditions must be observed and evaluated to ensure safe operation of the reactor. The accumulated experiment data of the HTTR is not only important for the HTTR operation, but also for the basic development of the HTGR in general. Artificial intelligence (AI) and particularly machine learning (ML) could give the ability to make predictions as well as allow the extraction of key information about physical process from large datasets. Hence, there is a lot of potential to apply AI and ML to predict the operating and safety parameters of the HTTR. In this study, the control rod position of the HTTR is predicted based on ML without using the conventional neutronic codes. The ML with a linear regression algorithm finds a functional relationship between the input dataset and a reference dataset, constructing a function that predicts control rod position from the other operation conditions. As result, the ML gives a good prediction of the HTTR control rod position with less than 5% difference compared to that in the experiment. With increasingly complicated experiments that create a large amount of data, ML is also expected to improve the design and safety analysis of the HTTR in the future.

This study investigated a postulated criticality event that occurred in the fuel debris produced after the loss of the cooling function in a spent fuel pool. The 1/fβ spectrum model was introduced to overcome this situation considering the difficulties of predicting the random fuel debris distribution. The number of fissions in the first peak power was analyzed using the Nordheim – Fuchs model. A total of 100 replicas were considered to investigate the fluctuation of the number of fissions. The results show that the number of fissions per volume varied from 3.78 × 1019 to 1.49 × 1020 (fissions/m3). Moreover, the distribution of the number of fissions was positively skewed.