Science and Technology of Nuclear Installations

We explored the nonlinear characteristics of energy resolution (ER) for the sealed NaI (Tl) scintillator detector by using a gamma-ray spectroscopy system and Monte Carlo simulation. Our research focused on the two primary factors of energy resolution including the photomultiplier tube (PMT) voltage and the distance between the gamma-ray sources (¹³⁷Cs and ⁶⁰Co) and the scintillator detector. The experimental results showed that energy resolution decreased when the PMT voltage increased, and the energy resolution of NaI (Tl) detectors reached a smaller value (6.92%, 6.76%, and 6.56%), especially with the PMT voltage in the range of 575–595 V. In addition, a suitable distance between the gamma-ray source and the scintillator (5 cm) can also effectively reduce the energy resolution. We established the simulation models of the experimental NaI (Tl) detectors and simulated their energy spectra. The simulation results in the peak area agreed with the experimental results. A possible better PMT voltage choice has been proposed to obtain a smaller energy resolution.

With the rapid advancement of information technology, microelectronic devices have found widespread applications in critical sectors such as nuclear power plants, aerospace equipment, and satellites. However, these devices are frequently exposed to diverse radiation environments, presenting significant challenges in mitigating radiation-induced damage. Hence, this review aims to delve into the intricate damage mechanisms of microelectronic devices within various radiation environments and highlight the latest advancements in radiation-hardening techniques. The ultimate goal is to bolster the reliability and stability of these devices under extreme conditions. The review initiates by outlining the spectrum of radiation environments that microelectronic devices may confront, encompassing space radiation, nuclear explosion radiation, laboratory radiation, and process radiation. It also delineates the potential damage types that these environments can inflict upon microelectronic devices. Furthermore, the review elaborates on the underlying mechanisms through which different radiation environments impact the performance of microelectronic devices, which includes a detailed analysis of the characteristics and fundamental mechanisms of damage when microelectronic devices are subjected to total ionizing dose effects and single-event effects. In addition, the review delves into the promising application prospects of several key radiation-hardening techniques for enhancing the radiation tolerance of microelectronic devices.

The operations of the operators are important for nuclear safety, but conventional operating experience feedback and common data-driven methods make it difficult to explicitly find valuable information hidden in these operational sequences that can help the operator to provide advice at the operational level. During the nuclear power plant (NPP) operation, a large amount of historical operating data is accumulated, which records the operational sequences of the operators and the state parameters of equipment. Therefore, this paper proposes the use of association rule techniques to mine the NPP operating data to discover the operational characteristics of operators and reveal their possible impact on the NPP operation. This work helps to improve the operational performance of operators and prevent human-factor events. To this end, the concept of state switching values for describing the operating states of NPPs is proposed to enable the proposed method to be adapted to different practical application scenarios. A sequence segmentation method is proposed to be able to transform historical NPP operating data into a sequence data set for association rule mining. Furthermore, an ensemble algorithm based on sequence pattern mining and sequence rule mining and its postprocessing method are designed. The empirical study was carried out using 20 batches of historical operating data of the cold start-up. A total of 164 original association rules are generated using the proposed method and were analyzed by experts. The recommendations were made for 4 different cases that would improve the operational performance of the operators.

On September 2021, Polish government declared that six pressurized water reactors with combined capacity of 6–9 GWe will be built by 2040 to reduce Poland’s reliance on coal. Due to the adopted schedule, construction of the first nuclear power plant will begin in 2026, with the first reactor capacity of 1–1.6 GWe to be operational in 2033. The Polish authorities announced in 2022 the selection of two locations and technologies for Poland’s first commercial nuclear power plants. Westinghouse AP1000 reactor was selected by Polish government to be built as the first plant in the location of Lubiatowo-Kopalino, on the coast of the country. In the meantime, Poland’s ZE PAK, Polska Grupa Energetyczna, and Korea Hydro & Nuclear Power have signed the letter of intent to collaborate on the project that evaluates the feasibility of building South Korean APR1400 on Pątnów site in central Poland. The objective of this study was to acquire and examine the gaseous effluents released during the standard operation of the AP1000 and APR1400 reactor technologies, with the primary goal of focusing on estimating the potential exposure of the general public. The effluents were calculated by using the GALE code based on each nuclear reactor technical specification. The obtained results were compared with those included in the Design Control Document for each reactor. Subsequently, the HotSpot software was used to calculate the radiation risk for downwind areas by utilizing GALE code results as source terms together with specific meteorological data corresponding for each localization. The results for AP1000 at Lubiatowo-Kopalino site and for APR1400 at Pątnów site were analysed and compared in the study. As the study findings were evaluated with the Polish radiation limits for the general public, all doses remained below the legal thresholds. With no previous alike studies conducted, this research begins the analysis of radiation impacts associated with the planned nuclear power plant in Poland during normal operation.

In this work, a machine learning (ML) metamodel is developed for the time-series forecasting of a typical nuclear power plant response undergoing a loss of coolant accident (LOCA). The plant model of choice is based on the APR1400 nuclear reactor. The key systems and components of APR1400 relevant to the investigated scenario are modelled using the thermal-hydraulic code, RELAP5/MOD3.4, following the description published in the design control document. The model is tested under a spectrum of initial and boundary conditions via propagation of key uncertain parameters (UPs) which are derived from the phenomena identification and ranking table (PIRT). This is achieved by loosely coupling RELAP5/MOD3.4 with the statistical tool, Dakota. The most probable nuclear power plant (NPP) response was calculated using the best estimate plus uncertainty (BEPU) approach. Next, the database generated from the NPP system response was used as an input for the ML model. The NPP system response was represented by peak cladding temperature (PCT), safety injection system (SIT), mass flow rate, reactor power, and primary system pressure. In this research, two regression models were tested with reasonably good performance, namely, the gated recurrent unit (GRU) and the long short-term memory (LSTM).

The research utilized advanced PCTRAN and RASCAL software to evaluate the potential radiological impacts of hypothetical accidents, specifically loss-of-coolant accident (LOCA) and long-term station blackout (LTSBO), at the El Dabaa Nuclear Power Plant. Over a span of ten years, comprehensive meteorological data were meticulously analyzed to assess the dispersion of radioactive substances within a 40-kilometer radius across all four seasons. The outcomes revealed that only in the case of LTSBO did the radiological levels surpass the limits set by the Environmental Protection Agency (EPA). Notably, during spring, LTSBO exhibited a maximum total effective dose equivalent (TEDE) value of 13 millisieverts (mSv) at a distance of 3.2 kilometers, and the highest thyroid dose (TD) recorded was 63 mSv at 8 kilometers. These significant findings play a crucial role in shaping strategies related to the distribution of potassium iodide (KI) and further enhance the overall preparedness and evacuation planning protocols.