دکتر غلامرضا انصاری فر

استاد

گروه مهندسی هسته‌ای
دانشکده فیزیک

پست الکترونیکی

ghr.ansarifar@ast.ui.ac.ir

آدرس

دانشگاه اصفهان میدان آزادی اصفهان، ایران
کد پستی : 8174673441

معرفی کلی

برونداد‌های پژوهشی

همه موارد

مقالات

مقالات لاتین

2025

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

( Article . )

Zahedi yeganeh m.h., , Ansarifar, G.R., Rishehri, H.Z.

Publication Date: 2025

Nuclear Engineering and Design (00295493)437

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. © 2025 Elsevier B.V.

Assessment and predicting the effect of accident tolerant fuel composition and geometry on neutronic and safety parameters in small modular reactors via artificial neural network and adaptive neuro-fuzzy inference system

( Article . )

Hajipour m., , Ansarifar, G.R., Yeganeh, M.Z.

Publication Date: 2025

Nuclear Engineering and Design (00295493)433

This study investigates the application of Artificial Intelligence in nuclear reactors, focusing on the impact of Accident Tolerant Fuel (ATF) composition and geometry on Small Modular Reactors (SMRs) parameters. Leveraging Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), the research comprehensively examines the effects of cladding material (FeCrAl) modifications and burnable absorber concentration variations on key characteristics of the NuScale reactor. Neutronic calculations were meticulously conducted using MCNP6, a state-of-the-art Monte Carlo particle transport code, to assess reactivity, radial power peaking factor, feedback coefficients, and delayed neutron fraction. The results demonstrate that cladding thickness, chromium content, aluminum content, and gadolinia concentration significantly influence neutronic parameters. Furthermore, the study reveals intricate relationships between these parameters and reactor performance, providing valuable insights for reactor design and optimization. In addition to the aforementioned case studies and simulations, ANNs, and ANFIS were developed to predict key neutronic and safety parameters in the NuScale SMR loaded with ATF. The models, trained on extensive neutronic data, accurately predicted these parameters. The model's inputs included gadolinium concentration, cladding material weight percentage, and cladding thickness, while outputs encompassed excess reactivity, hot full power reactivity, effective delayed neutron fraction, radial power peaking factor, and fuel and coolant reactivity feedback coefficients. Both ANN and ANFIS models demonstrated exceptional accuracy and generalizability, offering a valuable tool for predicting the influence of ATF variations on reactor behavior. However, the ANN model consistently outperformed the ANFIS model, exhibiting lower prediction errors and demonstrating superior suitability for the intended application. © 2025 Elsevier B.V.

Dynamic analysis and performance evaluation of CPS-AR insertion into VVER-1000 fuel assemblies under normal and accident reactor conditions

( Article . Green • Gold )

Kianpour r., , Ansarifar, G.R.

Publication Date: 2025

Results in Engineering (25901230)28

The objective of this research is to investigate the performance and insertion time of Control and Protection System - Absorber Rods (CPS-ARs) into the fuel assembly under normal reactor operating conditions, as well as under reactor accident conditions (seismic events), considering both dynamic and static forces acting upon them. In this study, a MATLAB Simulink model was first developed to simulate the control rod movement of a VVER-1000 reactor. This model generated several outputs, including the control rod's velocity profile, which was limited to 1.7 m/s, as well as the buoyancy force (constant at 1.63 N), shear stress, and pressure stress. These outputs were then used as input parameters for ABAQUS software. Then, a single control rod from a VVER-1000 reactor control rod cluster, along with its corresponding guide tube, is modeled and simulated using the ABAQUS software. This simulation is then subjected to both dynamic forces (from the coolant fluid and seismic loads) and static forces (including the control rod’s weight, fluid buoyant force, and the weight of components above the fuel assembly). The resulting bending of the guide tube is subsequently calculated based on the application of these forces. Following the dynamic and static analyses, and determination of guide tube displacement and deflection, the insertion conditions of the CPS-ARs into the fuel assembly are investigated under distinct scenarios. In normal reactor conditions, the forces acting on the control rod encompass its weight, the buoyant force from the fluid, the shear stress force from the fluid, the pressure stress force from the fluid, and the friction between the control rod and the guide channel walls. And the fall time is 2.192 s. During a seismic event, the reactor's geometry is subjected to vibrational forces from the earthquake, which act in addition to the normal operational forces. This comprehensive assessment effectively evaluates the feasibility of control rod insertion into the reactor core across various operational and accident conditions. Results show greater falling time for earthquake condition (falling time for Bam earthquake = 2.26 s) than that of normal condition (2.192 s). Also, spacer grids cause to smaller falling time for earthquake condition (time for Bam earthquake with spacer grid = 2.15 s) than that of normal condition. © 2025 The Author(s).

Linear stability analysis of a HALEU-Fueled space nuclear reactor considering fuel composition and Xenon reactivity feedback effects

( Article . )

Raouf, A.H., Ansarifar, G.R., Rafiei, M.

Publication Date: 2025

Nuclear Engineering and Design (00295493)445

Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment

( Article . Gold )

Koraniany a., , Ansarifar, G.R.

Publication Date: 2025

Results in Engineering (25901230)26

The reactor core of a functioning power plant usually contains hundreds of fuel assemblies. When various fuel assembly designs coexist in the core—often due to changes in fuel suppliers, the introduction of improved designs, or other factors—it is known as a mixed core. To maintain nuclear safety, it is essential to carefully assess the interactions between the various fuel assemblies to prevent any adverse effects within the reactor core. This article analyzes the neutronic and thermal-hydraulic modeling of a VVER-1000 reactor core, which includes TVS-2 M fuel assemblies. It also investigates a mixed core configuration created by adding a UTVS fuel assembly. The study aimed to identify the optimal fuel composition and loading location for the UTVS assembly based on various neutronic and thermal-hydraulic parameters. Finally, fuel burnup calculations were conducted on the optimized mixed core, with results compared to those of the original core. This study comprehensively ensures that the UTVS fuel assembly is placed in the reactor core with minimal stress regarding neutronic and thermal-hydraulic parameters while maintaining core performance. The VVER-1000 reactor core was initially modeled using the DRAGON and PARCS codes. Next, the new UTVS fuel assembly was placed in each fuel assembly position with varying levels of UTVS fuel enrichment. Neutronic parameters were then calculated for each configuration using computational tools. In the next stage, the hot channel equivalent cell of the reactor core and the hot channel equivalent cell of the UTVS fuel assembly were modeled using Fluent software, and the thermal-hydraulic parameters were calculated for all the created configurations. The obtained results were employed to develop a machine learning-based artificial neural network in MATLAB. By integrating this neural network with a genetic algorithm, optimization was carried out to identify the optimal fuel placement and enrichment for loading the UTVS fuel assembly into the targeted reactor core. Finally, fuel burnup calculations were performed using the PARCS code for a complete operational cycle, comparing the optimized mixed core and the original reactor core results. © 2025 The Author(s)

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