Band 89, Heft 1

The electrical system of a nuclear reactor facility (NRF) must incorporate a grounding grid in order to ensure it functions safely and reliably. Any disturbance in the electrical system affects the nuclear process, so it is, therefore, crucial to estimate the safety performance of the research nuclear reactor facility grounding system. The paper discusses CYMGrd 6.3, which is based on IEEE Standard 80-2013, and the optimization approaches for finding the best grounding grid design for a nuclear reactor facility connected substation of 500/11 kV in the case of a ground fault and lightning strikes. The result shows that the surface, step and touch potentials along the diagonal coordinates of the proposed grounding grid are below the safety limits in case of a ground fault but also results indicate that 100 kA lightning stroke poses a considerable threat to reactor equipment and personnel safety because the measured grid surface potential exceeds the safe ground potential rise of 3.2 kV standard at the striking point in conjunction with the proposed ground grid, so it must be modified. In this paper, three different optimization algorithms are investigated in order to determine the optimal design of the grounding grid with respect to mesh size: gradient method (GM), the genetic algorithm (GA), and simulated annealing (SA). These methods are used to achieve the purpose of obtaining an effective ground grid design. Comparing these techniques of GM and SA is good for quickly locating local minima, but they may fail to identify global solutions. In contrast, a genetic algorithm is often excellent at achieving a global minimum. Also, the utilization of GA, SA, and GM achieve the reduction of the surface potential of a proposed grounding grid by 16 %, 10 %, and 6 % respectively.

In thermal engineering implementations, heat exchangers need to have improved thermal capabilities and be smaller to save energy. Surface adjustments on tube heat exchanger walls may improve heat transfer using new manufacturing technologies. Since quantifying enhanced tube features is quite difficult due to the intricacy of fluid flow and heat transfer processes, numerical methods are preferred to create efficient heat exchangers. Recently, machine learning algorithms have been able to analyze flow and heat transfer in improved tubes. Machine learning methods may increase heat exchanger efficiency estimates using data. In this study, the boiling pressure drop of different refrigerants in smooth and micro-fin tubes is predicted using an artificial neural network-based machine learning approach. Two different numerical models are built based on the operating conditions, geometric specifications, and dimensionless numbers employed in the two-phase flows. A dataset including 812 data points representing the flow of R12, R125, R134a, R22, R32, R32/R134a, R407c, and R410a through smooth and micro-fin pipes is used to evaluate feed-forward and backward propagation multi-layer perceptron networks. The findings demonstrate that the neural networks have an average error margin of 10 percent when predicting the pressure drop of the refrigerant flow in both smooth and micro-fin tubes. The calculated R-values for the artificial neural network’s supplementary performance factors are found above 0.99 for all models. According to the results, margins of deviations of 0.3 percent and 0.05 percent are obtained for the tested tubes in Model 1, while deviations of 0.79 percent and 0.32 percent are found for them in Model 2.

Small Modular Reactors (SMR) have attracted more and more attention. It can provide economic, stable and clean power for remote areas. It is also a reliable solution in satisfying the power supply of small and medium grids, as well as comprehensive energy supply including heat, electricity, water and steam, and distributed energy supply. Internal Fire Probabilistic Risk Assessment (PRA) is a system analysis method of assessing fire risk for a nuclear power plant, which plays a more and more important role. It can provide an overall understanding of the internal fire risk for SMR. Corresponding design optimization suggestions can be obtained through further analysis of the fire areas with major fire risk, which can help reduce the risk of internal fire and improve the safety of reactor. The paper introduces the basic information of marine SMR and development of internal fire PRA. Then the internal fire risk for marine SMR have been analyzed by this method. The results show that marine SMR have high safety with a Core Damage Frequency (CDF) for internal fire risk of less than 1.0 × 10 −7 per year.

In enclosed fire situations, the flame interacts with ceiling and its length extension take place, altering the fire severity significantly. Fire safety analysis demand a generic flame extension model. A generic reliable model is not available as the construct of flame itself had wide variation in literature. The present paper aims to develop a Machine Learning (ML) based generic model of non-dimensional flame extension as a function of non-dimensional Heat Release Rate (HRR). The non-dimensional scaling reduces the number of parameter and also provide a generic nature. Literature review was utilized to collect the data from various open literature sources. This eliminates the limitations of individual correlations and gives a best optimized model which is valid for a wide range of flow regimes and conditions as compared to a specific correlation. Various simple ML models are compared for their performance against test data and a MARS based model was finally recommended. The MARS model was tested against the data which was not used in training and also against the other reported correlation. The developed model has performed well against the test data and marked improvement over other reported correlation as a better optimized performance over an extended range of non-dimensional range. The results of the model were also conservative as compared to another model in the most of the practical requirement of NPPs. A Large Eddy Simulation (LES) based CFD code FDS was also used to generate the flame extension data for demonstrating conservative nature of the ML model.

Steam generators (SG) for Nuclear Power Plants (NPPs) are important large-sized metal consuming equipment of nuclear power installations. Efficiency of steam generator operation determines the overall service life of the whole nuclear facility. Vertical steam generators are used normally in North America, Europe and South East Asia, but horizontal steam generators are currently of great importance to reduce building heights and construction costs that govern the overall cost of new nuclear power plants. The objective of the current study is to measure the performance of nuclear steam generators in NPPs through comparative assessments of vertical and horizontal configurations. For this purpose, the vertical U-tube steam generators of a typical 4-loop Pressurized Water Reactor (PWR) was replaced by a VVER-1000 horizontal steam generators to evaluate the horizontal SG performance, at the same plant conditions and characteristics, in comparison with the vertical SG. The thermal hydraulic system code RELAP5 was used in the current work to perform the required analysis, during steady and transient scenarios.

This study presents a methodology for the Fuel Handling Accident (FHA) analysis during the long-term shutdown period with the fuels still residing in the reactor core for the Chinshan BWR/4 Nuclear Power Plant. This methodology can be used to calculate the radiation dose received by the control room personnel, assuming failure in safety systems, facilities or structures. The computer code RADTRAD is employed to perform the transport and release of radioactive inventories first, and then calculate the control room dose based on appropriate dose conversion factor for the FHA. The results show that even failure in safety systems, facilities or structures, the whole body dose, thyroid dose and beta-skin dose are well below the acceptance limits specified in the Standard Review Plan.

The paper presents a novel approach to securely store sensitive images within nuclear power stations. The suggested approach protects the secrecy, integrity, and availability of these crucial assets by integrating blockchain technology, Shamir’s secret sharing, erasure coding, and encryption. To improve data security and transaction validation, the design uses a decentralized network, smart contracts, and consensus processes. Erasure coding adds redundancy while sensitive photos are divided into hidden shares, encrypted, and disseminated strategically across hierarchical tiers. Resilience against data loss or compromise is ensured by this design. The research advances secure picture storage techniques in key infrastructures, fostering trust and dependability, by resolving security and privacy concerns through this novel technology.

This paper presents the results of a study of the use of the scaling factor method for the determination of the activities of the difficult to measure radionuclides – H-3, C-14, Ni-63, Sr-90, Pu-239/240 and Pu-214 – observed in CANDU radioactive waste. Twenty-six smear samples collected in the vault of Bruce Unit 1 during its refurbishment in the period 2009 to 2012 are used as surrogates for radioactive waste packages generated by the operation, maintenance, refurbishment and decommissioning of a CANDU reactor. A detailed analysis of the data presented in this paper shows that: (i) the activities of H-3 and C-14 cannot be reliably determined by the scaling factor method, and (ii) the activities of Ni-63, Sr-90, Pu-239/240 and Pu-214 estimated by the scaling factor method are typically in agreement with the measured activities to within only an order of magnitude of their true values. It is recommended that activities of CANDU rad-waste samples derived by the scaling factor method should be reported to only one significant figure to better reflect the limited precision and accuracy of this method.