When storing spent fuel in a dry condition, it becomes essential to ensure that any remaining moisture bound to the canister and spent fuel is effectively removed and stored within an inert gas environment. This is crucial for preserving the integrity of the spent fuel. According to the NRC- 02-07-C-006 report, it is advised to reduce pressure gradually or in incremental stages to prevent the formation of ice. In the context of vacuum drying, it is desirable to perform testing using a prototype model; however, utilizing a prototype model can be difficult due to budget constraints. To address this limitation, we designed and constructed a laboratory-scale vacuum drying apparatus. Our aim was to assess the impact of vacuum pump capacity on the drying process, as well as to evaluate the influence of canister volume on drying efficiency. The vacuum drying tests were carried out until the surface temperature of the water inside reached 0.1°C. In the tests focusing on vacuum pump capacity, vacuum pumps with capacities of 100, 200, 400, and 600 liters were employed. The outcomes of these tests indicated that smaller vacuum pump capacities resulted in increased evaporation rates but also prolonged drying times. In the case of drying tests based on canister volume, canisters with volumes of approximately 100 and 200 liters were utilized. The results revealed that larger canister volumes led to longer drying times and lower rates of evaporation. Consequently, if we were to employ an actual dry storage cask for vacuum drying the interior of the canister, it is anticipated that the process would require a substantial amount of time due to the considerably larger volume involved.

The saturation of wet storage facilities constructed and operated within nuclear power plant sites has magnified the significance of research concerning the dry storage of spent nuclear fuel. Not only do wet storage facilities incur higher operational and maintenance costs compared to dry storage facilities, but long-term storage of metal-clad fuel assemblies submerged in aqueous tanks is deemed unsuitable. Consequently, dry storage is anticipated to gain prominence in the future. Nevertheless, it is widely acknowledged that quantitatively assessing the residual water content remains elusive even when employing the apparatus and procedures utilized in the existing dry storage processes. The presence of residual water can only be inferred from damage or structural alterations to the spent nuclear fuel during its dry storage, making precise prediction of this element crucial, as it can be a significant contributor to potential deformations and deterioration. The aforementioned challenges compound the issue of retrievability, as substantial complexities emerge when attempting to retrieve spent nuclear fuel for permanent disposal in the future. Consequently, our research team has established a laboratory-scale vacuum drying facility to investigate the sensitivity of various parameters, including canister volume, pump capacity, water surface area, and water temperature, which can exert thermohydraulic influences on residual water content. Moreover, we have conducted dimensional analysis to quantify the thermohydraulic effects of these parameters and express them as dimensionless numbers. These analytical approaches will subsequently be integrated into predictive models for residual water content, which will be further developed and validated at pilot or full-scale levels. Furthermore, our research team is actively engaged in experimental investigations aimed at fine-tuning the duration of the pressure-holding phase while optimizing the evaporation process under conditions designed to avert the formation of ice caused by abrupt temperature fluctuations. Given that the canister is constructed from acrylic material, we are able to identify, from a phenomenological perspective, the specific juncture at which the boiling phenomenon becomes manifest during the vacuum drying process.

There is a need to develop a quantitative residual water measurement method to reduce the measurement uncertainty of the amount of residual water inside the canister after the end of vacuum drying. Therefore, a lab-scale vacuum drying apparatus was fabricated and its characteristics were evaluated by performing vacuum drying experiments based on the amount of residual water, vacuum drying experiments based on the surface area of residual water, and vacuum drying experiments based on the energy of residual water using the lab-scale vacuum drying apparatus. As a result of the vacuum drying experiments, if the surface area of water is the same, the greater the amount of water, the greater the energy of the water, so more energy is transferred to the surface of the water. Therefore, more water evaporated, and the average temperature of the remaining water was higher. The larger the surface area of the water, the more energy it takes to vaporize it, so the faster it dries and the faster the drying time. Before ice formed, energy was actively transferred by conduction heat transfer from the top, center, and bottom of the water to provide the energy needed for the water to evaporate from the surface. However, no energy was transferred from the water just before it turned into ice. When vacuum drying water, you can dry more water if you dry it slowly over a longer period of time. Therefore, by using a vacuum pump with a low flow rate, the pressure can be lowered slowly to prevent ice from freezing, thereby improving the drying quantity. It was evaluated that there was a good agreement between the energy used when water evaporated and the energy absorbed from the surroundings to within about 4%. Therefore, if the energy absorbed from the surroundings is known, it is possible to evaluate the amount of water evaporated in vacuum drying.

In order to monitor the long-term condition of structures in nuclear waste disposal system and evaluate the degree of damage, it is necessary to secure quantitative monitoring, diagnosis, and prediction technology. However, at present, only simple monitoring or deterioration evaluation of the structure is being performed. Recently, there is a trend to develop monitoring systems using artificial intelligence algorithms, such as to introduce artificial intelligence-based failure diagnosis technology in nuclear power plant facilities. An artificial intelligence algorithm was applied to distinguish the noise signal and the destructive signal collected in the field. This can minimize false alarms in the monitoring system. However, it is difficult to apply artificial intelligence to industrial sites only by learning through laboratory data. Therefore, a database of noise signals and destructive signals was constructed through laboratory data, and signals effective for quantitative soundness determination of structures were separated and learned. In addition, an adaptive artificial intelligence algorithm was developed to enable additional learning and adaptive learning using field data, and its performance was verified through experiments.