Estimating buildup factor of alloys based on combination of Monte Carlo method and multilayer feed-forward neural network

摘要

Up to now, different methods have been developed for estimation of buildup factor (BF). However, either expensive estimation or time-consuming estimation are major restrictions/challenges of these methods. In this study a new technique utilizing combination of Monte Carlo method and the Bayesian regularization (BR) learning algorithm of multilayer feed-forward neural network (FFNN) is employed for estimation of BFs. First, the BFs of the different elements (i.e. Al, Cu, and Fe) at different energies and different mean free paths (MFPs) are modeled by the MCNP code. The results show that the calculated BFs by MCNP code are in good agreement with the reported values of American nuclear society (ANS). Afterwards, the appropriate architecture of FFNN (i.e. the appropriate number of hidden neurons and hidden layers) and the appropriate input patterns features are investigated. The resulted FFNN is trained using the modeled BFs and the selected category of features. In the test process, the BFs of the master alloys (i.e. Fe-Al%50, Cu-Fe50%, Al-Cu50%) are estimated. To evaluate the performance of the proposed FFNN for training/estimation of the new elements/alloys, Si is added to the training process and the BFs of the Al-Si35% is estimated. Average mean relative error (AMRE) and cumulative distribution function (CDF) of the errors show the acceptable accuracy of the estimating the BFs of the alloys. The noticeable advantages of the proposed technique are: 1- The BFs of the different alloys are estimated only by using the BFs of the constituent elements of the alloys. 2- The time needed to estimate the new BFs by the proposed technique can be neglected versus the time needed to model the new BFs by Monte Carlo. 3- The proposed technique can generalize its ability for estimating the BFs of the new alloys. 4- Monte Carlo codes need the trained person to model the BFs of the alloys while the FFNN generates the new BFs easily. (C) 2020 Elsevier Ltd. All rights reserved.