Abstract
In this paper, a feed forward artificial neural network (ANN) is used to predict the effective multiplication factor (k eff ), an indication of the reactivity of a nuclear reactor, given a fuel Loading Pattern (LP). In nuclear engineering, the k eff is normally calculated by running computer models, e.g. Monte Carlo model and finite element model, which can be very computationally expensive. In case that a large number of reactor simulations is required, e.g. searching for the optimal LP that maximizes the k eff in a solution space of 1010 to 10100, the computational time may not be practical. A feed forward ANN is then trained to perform fast and accurate k eff prediction, by using the known LPs and corresponding k eff s. The experiments results show that the proposed ANN provides accurate, fast and robust k eff predictions.
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Jiang, S. et al. (2008). Nuclear Reactor Reactivity Prediction Using Feed Forward Artificial Neural Networks. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_45
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DOI: https://doi.org/10.1007/978-3-540-87732-5_45
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