This is cne of the r-are text books written in the discipline of Nuclear Reactor Analysis, where the author has considered the interests of both scientists and practising engineers, in addition to serving the needs of the academia. The most attractive feature of this book is a balanced treatment of theory and practice of the subject matter. The theoretical foundations of the reactor design methods are explained with simplified definitions and relevant practical illustrations. The author scans through quickly the traditional aspects of the so-called reactor physics and takes the reader through the details of the analytical aspects in a conventional manner. Hcwever, there is a definite departure from the classical method of approach in order to avoid lengthy and repetitive discussions, that are available in many standard text books on reactor physics. The chief departure fran tradition is the priority accorded to the treatment of the energy part of the problems as opposed to the spatial Dart normally devoted to by other authors . A similar unorthodox approach has been applied while dealing with the solution of the various equations by giving priority to computer oriented mrethods as opposed to the classical solutions.

Nuclear reactor analysis

Emerging technologies by hydrogen: A review

Neutron Physics By K H Beckurts and K Wirtz Pp x + 444 (Berlin: Springer-Verlag, 1964) 68 DM Introduction to Neutron Distribution Theory By L C Woods (Methuen's Monographs on Physical Subjects) Pp xii + 132 (London: Methuen and Co, Ltd; New York: John Wiley and Sons, Inc, 1964) 28s net

Nuclear Reactor Physics

Cascade-forward neural network is a class of neural network which is similar to feed-forward networks, but include a connection from the input and every previous layer to following layers. In a network which has three layers, the output layer is also connected directly with the input layer beside with hidden layer. As with feed-forward networks, a two-or more layer cascade-network can learn any finite input-output relationship arbitrarily well given enough hidden neurons. Cascade-forward neural network can be used for any kind of input to output mapping. The advantage of this method is that it accommodates the nonlinear relationship between input and output by not eliminating the linear relationship between the two. In this study, we apply the network in time series field. The optimal architecture was determined computationally by using incremental search method in both input and hidden units. The simple one was built first, and then the more complex is constructed by adding the units one by one. The optimal one is chosen then by using the mean square error criteria.

https://iopscience.iop.org/article/10.1088/1742-6596/1025/1/012097/pdf

Afshin Hedayat

Papers

A review on hydrogen generation, explosion, and mitigation during severe accidents in light water nuclear reactors

Estimation of research reactor core parameters using cascade feed forward artificial neural networks

Loss of coolant accident analyses on Tehran research reactor by RELAP5/MOD3.2 code

Optimization of the core configuration design using a hybrid artificial intelligence algorithm for research reactors

Developing a practical optimization of the refueling program for ordinary research reactors using a modified simulated annealing method