Calculation of 14–15 MeV (n, d) Reaction Cross Sections Using Newly Evaluated Empirical and Semi-empirical Systematics

Simple parametrization of (n,d) cross sections using Flerov and Talyzin's formula

This article aims at providing new cross section data from empirical formulas for the (n,d) reactions of which experimental measurements at 14-15 MeV energy are not available or limited. If the experimental data for a nuclear reaction at a given energy are scarce, theoretical calculations and also developing empirical formulas have a critical importance. Here, we propose a new empirical formula of (n,d) reactions for analysis of the relationship between the experimental data and the parameters of empirical formula. The present formula was obtained by using the non-elastic Flerov and Talyzin expression to calculate (n,d) cross sections at 14-15 MeV. Due to the good overall agreement with the measured cross sections, our empirical formula with polynomial fitting model including asymmetry parameter can be useful in planning new experiments of (n,d) reactions for energies around 14 MeV.

Estimations for (n,α) reaction cross sections at around 14.5MeV using Levenberg-Marquardt algorithm-based artificial neural network

Prediction of neutron-induced reaction cross-sections at around the 14.5 MeV neutron energy is crucial to calculate nuclear transmutation rates, nuclear heating, and radiation damage from gas formation in fusion reactor technology In this research, the new approach of (n,α) reaction cross-section is presented. It has been assessed by utilizing the artificial neural network (ANN) when compared to more advanced algorithms, the Levenberg-Marquardt algorithm-based ANN can be exceedingly fast. The correlation coefficients for a training R-value of 0.99283, a validation R-value of 0.991190, a testing R-value of 0.97337, and an overall R-value of 0.98515 demonstrate that Levenberg-Marquardt algorithm-based ANN is well suited for this purpose. . The obtained results were compared to theoretical calculations of TALYS 1.95 nuclear code. As a consequence, it has been demonstrated that the ANN model can be used to determine the systemic study for (n, α) reaction cross-sections.

Investigation of (d, 3n) reaction cross section using theoretical nuclear codes calculations on some nuclear materials

It is important to examine the effects of the nuclear reaction, which is used as a building material in nuclear reactors. Nuclear reactions occur as a result of the interaction between incident particles with the target nuclei. The charged particle-induced reactions have prime importance in understanding the reaction mechanism which can be applicable to understand the particles resulting from the reaction. It is useful to develop shielding the particle accelerators and fusion reactors. The present study contributes to providing the theoretical prediction of excitation functions for ¹¹²Cd (d, 3n)¹¹¹In, ¹⁴¹Pr (d, 3n)¹⁴⁰Nd, ¹⁶⁷Er (d, 3n)¹⁶⁶Tm, ¹⁹⁷Au (d, 3n)¹⁹⁶Hg and ²⁰⁹Bi (d, 3n)²⁰⁸Po reactions using theoretical model codes such as TALYS-1.95, EMPIRE-3.2.3, and ALICE-2014 within the incident deuteron energy range of threshold energy to 50 MeV. Also, newly developed (d, 3n) cross-section formula (Kavun, 2020) calculations have been performed for these reactions at 20 MeV of deuteron energy. Lastly, all calculated results have been compared with one another and with the previously published experimental data of the EXFOR database.

New semi-empirical formulae for (n,d) cross sections at 14-15 MeV

The aim of this letter is to suggest new semi-empirical formulae for the prediction of (n,d) cross sections with a neutron energy of 14-15 MeV. In this context, the cross sections are calculated using systematic that is function only of the reaction Q-value and mass number A. The (n,d) reactions are analyzed by using the systematics based on the statistical theory. Thus, the present semi-empirical formulae compared with the cross section data in literature give a good fit for the prediction of the (n,d) cross sections at 14-15 MeV.

Estimation of (n,p) reaction cross sections at 14.5 ∓0.5 MeV neutron energy by using artificial neural network

The aim of this study is to develop an accurate artificial neural network algorithm for the cross-section of (n,p) reactions at 14.5 ∓0.5 MeV neutron energy which is important to developing materials for fusion reactor design. The experimental data used at artificial Neural network calculations have been taken from the Experimental Nuclear Reaction Data (EXFOR) database. Bayesian algorithm has been used at training section of artificial neural network. Regression (R) values of artificial neural network calculations have been found as 0.99363, 0.98574 and 0.99257 for training, testing and all process respectively. In addition to artificial neural network calculations, TALYS 1.95 nuclear reaction code has been used to reproduce (n,p) reactions at 14.5 ∓0.5 MeV. Two-component exciton model and Constant Temperature Fermi Gas Model have been used as pre-equilibrium and level density models respectively. Mean square errors of our calculations have been found 48.51 and 495.06 for artificial neural network and TALYS 1.95 respectively. Artificial Neural network estimations have been compared and analyzed with the TALYS 1.95 calculations and the experimental data taken from EXFOR database.