Analysis of the Effect of Applied Load on Crevice Corrosion Behavior
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Science and Technology of Nuclear Installations publishes research on issues related to the nuclear industry, particularly the installations of nuclear technology, and aims to promote development in the area of nuclear sciences and technologies.
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Professor Michael I. Ojovan is the Chief Editor of the journal, and is currently based at the University of Sheffield, UK. He is known for many innovations in nuclear research, including metallic and glass-composite materials for nuclear waste immobilisation.
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More articlesAcceptable Level of Acceptance and the Affecting Factors: What Is the Acceptable Public Acceptance of Building a Nuclear Power Plant
This research determines the Acceptable Level of Acceptance (ALA) based on the countries with active Nuclear Power Plant (NPP). The ALA is a particular value of public acceptance of NPP, indicating public support and participation in the program. If the public acceptance level is lower than the ALA, then the probability of public resistance against the program is relatively high and would harm the NPP. There is no correlation between the number of populations. This research uses four categories to classify public acceptance: (1) low, (2) moderate, (3) high, and (4) very high. Based on these categories, this research suggests that the moderate ALA is 27.5% of the acceptance level.
Modelling and Validation of CANDU Shim Operation Using Coupled TRACE/PARCS with Regulating System Response
In CANDU reactors, shim operation is used when the online refuelling capability becomes temporarily unavailable. Adjuster rods, normally in-core to provide flux flattening, are withdrawn in sequence to provide additional reactivity as the fuel is depleted. In a CANDU 900 reactor, up to three of the eight adjuster banks may be withdrawn, with the power derated accordingly. In this study, the shim operation was modelled using a combination of TRACE_Mac1.1, PARCS_Mac1.1, and scripts modelling the reactor regulating system, all running as a single coupled simulation. A driver script simulated the operation as a sequence of steady-state, depletion, and transient models. The results were compared to operational data from a nuclear power plant, evaluating the key figures of merit. The simulation was extended beyond the operational data by reducing the power to 59% FP and withdrawing the remaining adjusters, to observe the behaviour of the simulated reactor for a deeper reactivity-driven transient. Sensitivity cases, including adjuster rod depletion and nuclear data uncertainty, were also evaluated. This study was able to successfully reproduce the general results of the shim operation. Some discrepancies were observed between the simulation and dataset for the duration of the shim, particularly for the one bank out phase of the shim. Several potential causes for the early phase behaviour were identified. When the simulation was extended, the model predicted that a power reduction below 60% FP would lead to xenon poison out when the adjusters were depleted, with the timing sensitive to the adjuster depletion. Nodalisation of the PARCS model also had a significant impact, due to the effect on adjuster nodalisation and its area-of-effect with respect to the actual adjuster locations. Nuclear data uncertainty had a lesser but still noticeable effect. Other parameters, such as the distribution of fuel burnups in the core, only had a small effect on the shim operation.
Steam-Jet Evaluation for Predicting Leakage Behavior and Interpretation of Experimental Verification
Owing to pipe thinning, fatigue damage, and aging, pipes, valves, and devices installed in the primary and secondary systems of nuclear power plants may leak high-temperature/high-pressure reactor coolant. Thus, a system must be developed to determine if the leakage is exceeding the operating limit of the nuclear power plant, thereby mitigating any loss of life or economic loss in such cases. In this study, a validated numerical analysis method was established to initially simulate the leakage behavior and subsequently to evaluate the small amount of leakage in the compartment. For this purpose, a vapor-jet collision test in the compartment and a vapor-jet test in the pipe were performed; numerical analysis was conducted, and comparative analysis was performed to verify the validity of the established method. The evaluation results suggested that the proposed numerical analysis method could optimally simulate the flow characteristics of the steam jet. Notably, compared to the existing evaluation method, the proposed approach simulated a more detailed behavior of the jet formed at the leakage point. In future research, the results of this study (data) will be used to inform the design of the second phase of the leak-capture system and will be served as the foundation for a performance-optimization study on the capture system.
Assessing the Impact of Common Cause Failures on Site Risk within Level 1 Multi-Unit PSA
Common cause failures (CCFs) may lead to the simultaneous unavailability or failure of numerous components in the nuclear power plant because of the existence of a shared cause when an initiating event disrupts the normal functioning of nuclear power plants. The presence of common cause failures (intra-unit and inter-unit) can be recognized in a multi-unit probabilistic safety assessment (MUPSA) as a crucial dependency factor that can influence accident scenarios and the core damage frequency (CDF), as CCF may affect the availability and proper operation of mitigating systems. Since such failures are likely to significantly undermine the benefits of the concept of redundancy in nuclear power plant systems, it is necessary to identify the CCFs that contribute to the core damage in a multi-unit site and analyse their overall quantitative magnitude and qualitative proportions. In this study, a twin-unit generic pressurized water reactor (PWR) nuclear plant is modeled using the AIMS-PSA software. For the loss-of-offsite-power (LOOP) and station blackout (SBO) events, the site CDF was calculated, and the cut-sets produced by this quantification were examined for the modeled CCF basic events in the fault trees. The quantitative and qualitative contributions of the CCFs to the frequency of site core damage were examined. CCFs in the modeled fault trees contributed to 4.58% to the site CDF of the combined LOOP followed by SBO event. In the LOOP event alone that leads to core damage, the CCF contributed 4.58% to the site CDF while CCFs contributed 17.19% to the site CDF in the SBO event alone that leads to core damage. With CCF events considered in the modeling process, the site CDF estimated with CCF events increased by 7.53% in the combined LOOP followed by SBO event. In the LOOP event alone that leads to core damage, inclusion of CCF events in the modeling increased the site CDF by 7.42%. A 15.66% increase in site CDF was recorded in the SBO event alone that leads to core damage as compared to modeling without CCF events. The results show how crucial the common cause failure contribution is to site CDF. The safety of the nuclear plant at a site is impacted by an increase in site CDF when common cause failures are considered. The various CCF fundamental event compositions and their percentage contributions were explicitly examined by the minimal cut-sets which leads to core damage in the units. In conclusion, this study’s findings can help us better understand how CCFs increase multi-unit site risk and can also act as a starting point for future studies on the qualitative and quantitative categorizations of CCF effects within MUPSA.
Prediction of Automatic Scram during Abnormal Conditions of Nuclear Power Plants Based on Long Short-Term Memory (LSTM) and Dropout
A deep-learning model was proposed for predicting the remaining time to automatic scram during abnormal conditions of nuclear power plants (NPPs) based on long short-term memory (LSTM) and dropout. The proposed model was trained by simulated condition data of abnormal conditions; the input of the model was the deviation of the monitoring parameters from the normal operating state, and the output was the remaining time from the current moment to the upcoming reactor trip. The predicted remaining time to the reactor trip decreases with the development of abnormal conditions; thus, the output of the proposed model generates a predicted countdown to the reactor trip. The proposed prediction model showed better prediction performance than the Elman neural network model in the experiments but encountered an overfitting problem for testing data containing noise. Therefore, dropout was applied to further improve the generalization ability of the prediction model based on LSTM. The proposed automatic scram prediction model can provide NPP operators with an alert to the automatic scram during abnormal conditions.
Simulations of Core Damage Progression for TMI-2 Severe Accident Using CINEMA Computer Code
As an integrated computer code development for severe accident sequence analysis in Korea, CINEMA has been developing from an initiation event to a containment failure. The CINEMA computer code is composed of CSPACE, SACAP, and SIRIUS, which are capable of simulating core melt progression with thermal hydraulic analysis of the RCS (reactor coolant system), severe accident analysis of the containment, and fission product analysis in the vessel and the containment, respectively. The severe accident progression in TMI unit 2 has been analyzed as a part of a validation of the CINEMA computer code. This analysis has been performed to validate CINEMA models on the core melt progression, in particular, RCS thermal hydraulic behavior during core melt progression, fuel cladding oxidation with hydrogen generation, and fuel melting with relocation to the lower part of the core. The CINEMA results on main parameters, such as RCS pressure and an integrated hydrogen generation mass are compared with the TMI-2 data. The CINEMA results have shown that the RCS pressure is very similar to the TMI-2 data. The CINEMA results and measured total hydrogen production are very similar, which were approximately 465 kg and 460 kg, respectively.