Assessment and predicting the axial power distribution effect on the thermal-mechanical parameters of the NuScale nuclear reactor core loaded with TVS-2 M fuel assemblies as well as axial Offset optimizing for load-following operation

• This study assesses thermal-mechanical behavior of a Small Modular Nuclear reactor with TVS-2M hexagonal fuel assemblies. • FRAPCON results were integrated with MCNP simulations and an Artificial Neural Network to predict fuel rod behavior. • Development of an ANN estimator for predicting fuel rod thermal-mechanical behavior at the beginning of the cycle. • Optimizing the AO for enhanced reactor performance during load-following operations. • The optimal design is validated using neutronic calculations. This study evaluates and examines the thermal–mechanical behavior of a NuScale reactor core which utilizes TVS-2 M hexagonal fuel assemblies. The efficiency of the fuel rods is validated using the FRAPCON code. Initially, the reactor’s core is modeled with the MCNP code to locate the control banks. The design phase ensures the capability to shut down the reactor in two scenarios. In the Hot Zero Power (HZP) scenario, MCNP simulation reveals a sub-critical state with a multiplication factor of 0.94481 ± 0.00023. In the Cold Zero Power (CZP) scenario, the multiplication factor of 0.9935 ± 0.00023 confirms the adequacy of control assemblies. Subsequently, a thermal–mechanical analysis is conducted on the fuel rod over 1330 days, confirming its acceptable design and operational effectiveness in the core. Also, one of the parameters that can be examined during reactor control and load-following operations is Axial Offset (AO). Therefore, the study investigates the impact of AO on fuel rod’s thermal–mechanical changes. The MCNP code was used to simulate control rod inputs and obtain power distribution data for each AO deviation. Based on assessments regarding the association between AO and the thermal–mechanical characteristics of fuel, it has been determined that the impact of power distribution increases significantly over time, particularly towards the end of the operational period. Afterward, based on FRAPCON results, an artificial neural network (ANN) estimator is developed to predict thermal–mechanical parameters at the beginning of the cycle (BOC). The ANN proves to be a powerful method for estimation. By employing the ANN estimator and exploring different cost functions based on thermal–mechanical parameters, the optimal AO is determined using a genetic algorithm, which enhances the reactor’s performance, particularly in load-following operations. The attained optimal AO value for various cost functions are as follows: −0.10316, −0.19635, and −0.25817. This approach allows for the selection of the most efficient AO, leading to improved performance of the NuScale reactor core loaded with TVS-2 M hexagonal fuel assemblies. Indeed, optimization of AO is very important and useful for load-following operation.