MODIFICATION OF TALYS-1.95 TO FIND THE FORM OF POLYNOMIAL EQUATIONS BASED ON THE MASS NUMBER OF THE COMPOUND NUCLIDE IN THE RUPTURE PROBABILITY OF THE TEMPERATURE-DEPENDANT BROSA MODEL

• Main Author: Fajri Assalam, Awla
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/57457
• Institution: Institut Teknologi Bandung

TALYS-1.95 is one of the front lines of nuclear reaction codes. Among the models available in TALYS-1.95 and used to find the cumulative yield is the temperature-dependent Brosa model. This model analyzes the macroscopic-microscopic fission reaction using a combination of the concept of multichannel evolution to scission and multimodal random neck-rupture model. This study discusses the search for mass number-based polynomials that can be added to the rupture probability temperature-dependent Brosa model and can improve the accuracy of this model against benchmark data. The benchmark data used in this study comes from the JENDL-4.0 library with the nuclei included in the scope of this study are 6 Uranium nuclei, 5 Plutonium nuclei, and 5 Curium nuclei with varying mass numbers in the neutron-induced reaction which has an incident energy of 0.5 MeV. This study provides the final result in the form of general equations for the three elements tested (Uranium, Plutonium, and Curium) along with the parameters for each element and their evaluation. This study begins with the modification of TALYS-1.95 in the form of changes to its 4 subroutines, namely talys.cmb, neck, input2, and checkkeyword. This modification aims to add a dummy variable ???????????????? in the equation for the probability of rupture in TALYS-1.95. The modified program was then used to generate simulation data for each nuclei in the scope with the ???????????????? value varied for each nuclei ranged from 0.01 to 4.00. This simulation aims to obtain the ???????????????? value which gives the cumulative yield with the highest accuracy against the benchmark data (defined as the cumulative yield data with the lowest root-mean-square-error and the highest correlation coefficient against the benchmark data). ???????????????? like this is hereinafter referred to as ????????????????????. The ???????????????????? are then regressed against the mass number to obtain the form of the equation. There are two types of regression carried out at this stage, namely linear regression and second-order polynomial regression. This regression is carried out separately for the nuclei of Uranium, Plutonium, and Curium so as to produce 6 different forms of the ???????????????????? equation. The six forms of this equation are substituted for the dummy ???????????????? variable in the rupture probability equation, simulated, and evaluated to find the best rupture probability form for each element. From all these steps, finally we get the probability equation for rupture for each element that can be combined into one general equation for rupture probability ????(????)?exp(?(????????1????????????2+????????2????????????+????????3)2????????0[????2(????????)?????2(????)]????) with ????????1,????????2, and ????????3 defined as parameters that depend on the elements and is provided in chapter IV of this study. This new rupture probability equation succeeded in increasing the accuracy of 15 of the 16 tested nuclei. This equation also increases the average cumulative yield correlation coefficient of the simulation against the benchmark from 0.7945 to 0.8428 and lowers the root-mean-square-error from 6.60001 to 5.8088. Thus, it can be concluded that this study succeeded in finding a mass number-based polynomial that can be added to the rupture probability of the temperature-dependent Brosa model and can improve the accuracy of this model against benchmark data.