http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/6731 Post graduate dissertations (Theses) of Institute of Nuclear Power Engineering (INPE) Thu, 23 Apr 2026 03:15:36 GMT 2026-04-23T03:15:36Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7045 Evaluation of safety for VVER-1200-based nuclear power plants during main steam-line break accidents Khan, Abid Hossain; Sharif, Iffat; 0421322004; 621.48/IFF/2023 In this research project, main steam-line break accidents (MSLBs) in a VVER-1200 nuclear power plant were investigated. Three different break sizes, 100, 200 and 400 cm2, were considered to observe the effect of break size on the transient response of the safety systems. It was assumed that the break was located inside the containment building and thus there was no emission of radioactive substances unless there was a containment failure. The simulations were performed for two cases, one in which the SCRAM system was initiated depending on the trip conditions specified for VVER-1200, and another in which there was no SCRAM i.e., anticipated transient without SCRAM (ATWS) situation. The simulations were performed for 12 hours to observe the performance of the safety systems to control the situation when emergency response is unavailable. The accident scenarios were simulated in PCTRAN (Personal Computer Transient Analyzer) VVER-1200 module. Results revealed that SCRAM was triggered due to low SG level signal for 100 and 200 cm2 MSLBs while it was triggered by low sub-cooling margin (SCM) for 400 cm2 MSLB. In absence of SCRAM, however, fuel temperature coefficient (FTC) and negative moderator temperature coefficient played a crucial role in keeping reactor thermal power under control. Nevertheless, sharp spikes in reactor power were observed for ATWS cases between 400-600 seconds due to the simultaneous effect of control rods and moderator temperature on reactivity. For both scenarios considered, fuel and clad temperatures did not rise above 1875 and 625oC respectively, indicating that both fuel meltdown and cladding failure is extremely unlikely. The containment pressure was significantly below 5 bar for all the scenarios, eliminating the possibility of containment failure. However, MSLBs with ATWS cases recorded bubble condenser membrane rupture due to overpressure. Finally, DNBR was always greater than unity for MSLBs with SCRAM. They were well above 4.5 and 3.0 for 100 and 200 cm2 MSLBs with ATWS, respectively. However, DNBR went below unity for 400 cm2 MSLB with ATWS after 2705 seconds, which is an indication of boiling crisis. Wed, 30 Aug 2023 00:00:00 GMT http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7045 2023-08-30T00:00:00Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/6987 Neutronic and thermal-hydraulic performance analysis of nanofluids in pressurized water reactor fuel subchannel. Mamun, Dr. Mohammad Arif Hasan; Taimoor, Imtiaz; 0421322047 This research focuses on the utilization of nanofluids as coolants in Pressurized Water Reactors (PWRs). By focusing on two distinct reactor designs, the AP-1000 with its characteristic square array fuel subchannel and the VVER-1000 with a triangular array, a comprehensive analysis is conducted to evaluate the neutronic and thermal-hydraulic performance of various nanofluids. A diverse range of nanofluids, including Al2O3, TiO2, SiO2, CNT, Cu, and their hybrid combinations, are assessed across multiple volume fractions ranging from 0% to 2%. The study meticulously examined key parameters such as the Effective Multiplication Factor (Keff) using OpenMC and Nusselt Number, Surface Heat Transfer Co-efficient, Pressure Drop, and Pumping Power using ANSYS Fluent solver. Among the findings, particularly noteworthy is the performance of hybrid nanofluids compared to homogenous ones, especially the one incorporating CNT and Cu, which demonstrates significant heat transfer enhancements. This is quantified by an increase in the Nusselt Number by up to 15.68% for the AP-1000 and 18.35% for the VVER-1000 reactor, relative to the best-performing homogenous nanofluid, CNT-based nanofluid. These improvements, however, are coupled with an incremental rise in pressure drop, by 9.81% for the AP-1000 and 5.48% for the VVER-1000, suggesting a nuanced balance between thermal performance and hydraulic resistance. On the other hand, SiO2, in both reactors, showcases the highest Keff, indicating its potential to maintain a stable and efficient nuclear reaction. Conversely, Cu-based nanofluids, particularly at higher concentrations, are found to be less optimal, due to their lower Keff, which is 0.92% and 0.97% lower for AP-1000 and VVER-1000 respectively than the highest performer SiO2, combined with increased pumping power, which highlights challenges that need addressing for their effective use in PWRs. By judiciously selecting the type and concentration of nanofluids, significant enhancements in reactor performance can be achieved. However, the study also emphasizes the need for further research, especially in understanding the long-term stability, and interaction of nanofluids with reactor internals in real-world reactor environments. Tue, 29 Aug 2023 00:00:00 GMT http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/6987 2023-08-29T00:00:00Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/6982 Efficient computational analyses of boiling water reactor fuel assemblies. Khan, Abid Hossain; SHAMIM HASSAN, MD.; 0421322031; 621.48/SHA/2023 This research is focused on the development of computationally efficient artificial neural network (ANN) models for the neutronic analysis of accident-tolerant fuel (ATF) candidates in BWR fuel assemblies. Two ATF options: U3Si2 fuel with SiC cladding, and Al2O3 and Cr2O3 doped UO2 fuel (ADOPT fuel) with FeCrAl cladding were considered for 8X8, 9X9 and 10X10 BWR reactor assemblies. Some U3Si2 fuels in the assemblies contained burnable poison gadolinium (Gd) and some ADOPT fuels contained gadolinium oxide (Gd2O3) to account for the excess reactivity at the beginning of life (BOL). The proposed research needed an extensive dataset for training the artificial neural networks (ANNs), which was generated using the Monte Carlo neutronic code, OpenMC. Fuel enrichment, burnable poison concentration, boron concentration, pitch and number of burnable poison rod were considered as the input parameters for the ANN models. 3000 sets of data (k∞) were provided for training the ANN models for each fuel type and each assembly geometry. A three-layer ANN architecture was considered in the study where the number of neurons in the hidden layer were varies in the range 5-20. Results revealed that the average predictive errors of the ANN models developed for U3Si2 fuel with SiC/SiC cladding were in the range 104.71 to 580.17 pcm, respectively. For the ANN models developed for ADOPT fuel with FeCrAl cladding, the average predictive errors were in the range 102.91-567.81 pcm, respectively. A genetic algorithm (GA)-based optimization process was then employed to identify the assembly pitch that corresponds to the maximum value of infinite multiplication factor (k∞) for a specific type of BWR assembly geometry and composition. The obtained results were utilized to derive expressions that defines the boundary separating the under and over-moderated regions. Considering the decrease in U-235 enrichment and burnable poison inventory at the end of cycle (EOL) scenario, it was recommended that pitch should be around 1.40cm to 1.46cm, depending on the fuel assembly type, to ensure design safety from neutronic point of view. Wed, 09 Aug 2023 00:00:00 GMT http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/6982 2023-08-09T00:00:00Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/6785 New data-driven model for estimating the weather-dependent thermodynamic performance of nuclear power plants Khan, Abid Hossain; Azmaeen-Bin-Amir, SK.; 0421322001; 621.48/AZM/2023 This research developed a new data-driven model to estimate the impact of condenser pressure, a function of the temperature of the tertiary coolant and thus the surrounding weather, on the thermodynamic performance of a nuclear power plant (NPP). Artificial Neural Network (ANN) was utilized to construct the data-driven model. Considering the wide variety of thermodynamic cycles and plants components involved in the NPPs, two simplified thermodynamic models were developed, one applicable for water-cooled nuclear reactor-based power plants and the other one for metal and gas-cooled reactor-based plants. These thermodynamic cycles were employed to generaterandom data for training two separate ANN models, each trained with 20,000 datasets. To make the simplified thermodynamic models and ANN models generalized i.e., applicable for all the nuclear power plant options available throughout the world, a self-calibrating feature was added to the models through global optimization algorithms where the isentropic efficiencies of the turbines would be calibrated to match the rated output power of the NPP. Two evolutionary algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were considered for the purpose. To observe which algorithm has superior calibration performance, a simplified thermodynamic model for VVER-1200 from the available literature was calibrated using both the algorithms. Results revealed that GA can ensure better calibration of the model compared to PSO. To evaluate the sensitivity of the calibration process to the selection of rated condenser pressure of a NPP, calibration of the model for VVER-1200 was done for two condenser pressures, 4kPa and 7kPa. Results indicated that the calibrated models for both cases have almost identical predictive accuracies. A comparative analysis between the simplified thermodynamic models and the ANN models was performed to realize the justification for constructing the data-driven models. It was observed that all these models had similar predictive performances but the self-calibration time for the ANN models are around 10-20 times less than the simplified thermodynamic models, indicating a significant reduction in computational costs. The developed models were utilized to observe the changes of efficiencies, output powers and condenser thermal loads of different Gen III, III+ and IV reactor based NPPs with the change of condenser pressure after calibrating them with data from Advanced Reactors Information System (ARIS). The condenser pressure was varied in the range 4-15kPa. It was observed that the metal and gas-cooled reactor-based NPPs are comparatively less affected by increased condenser pressure than the water-cooled ones. Sun, 30 Apr 2023 00:00:00 GMT http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/6785 2023-04-30T00:00:00Z