The design of demountable parts is based on the ALARA principle, and the design of the correct part helps to ensure safety and reduce costs. Wright's learning-forgetting model is used to estimate a worker's skill level and calculate the reduction in working hours.
Introduction
In this study, it is intended to evaluate the radiological safety of the worker by considering the dynamic characteristics in the D&D process of the NPP. The assessment was performed during dose assessment reflecting dynamic changes.
Literature survey
The composition of Bio-shield is shown in table 2.1, and the geometric data is assumed as shown in figure 2.2. VISIPLAN codes were used for external exposure assessment, and dose rates were evaluated as shown in figure 2.10.
Methods
Target analysis
Source data
Activity is the mean of the distribution with height to determine the radioactivity relative to. The evaluation results show that neutrons lose energy, which is mostly absorbed up to 437 cm, before which the radiation level drops sharply. According to a study by Kim et al (2008), the strongly coupled 3H response of 6Li to the weakly coupled 3H is caused by HTO diffusion due to HTO expansion in the reactor core20.
In the study, the change in concentration due to tritium proliferation is not considered important because most of them are strongly coupled.
Working Scenario
Once the overall decommissioning scenario is determined, it is necessary to decide on the detailed task to perform the actual task. The work order was assumed as 4 stages of preparation stage, drilling stage, cutting stage, lifting and clearing stage. The internal dose of the worker by inhalation is closely related to the amount of dust generated by the breakdown of the bio-shield. Equipment costs were calculated by multiplying the unit cost of the equipment by the time each piece of equipment took in the dismantling process.
Dose rate data were analyzed as shown in Figures 4.1-4.3 for the distribution of dose rates immediately after decommissioning the facility, after 8.5 years and after 13.5 years, depending on the decommissioning process. The doses of the decommissioning workers are assessed based on the derived time and dose rate distribution and shown in table 4.6-4.7. Cheon, C.-S.; Kim, C.-L., The dismantling and disposal strategy of a biological shield for minimizing radioactive concrete waste during nuclear power plant decommissioning.
Methodology of dose assessment
Learning analysis
Many studies have been conducted in the traditional Wright learning curve, resulting in various forms of learning curve based on the real learning curve. By default, the calculation for work is equation 5, which reduces the work time by the index of the learning index of the task repeated in the initial work hours. The average time required to work on a cycle based on the previously derived scenario was derived, based on what the unskilled work time was derived, which is shown in table 3.9.
They calculated that the working hours of the unskilled and the skilled differ by about two times, and in wet cutting it is estimated that an unskilled person does 6.50 hours of work for 3.06 hours of work. When decommissioning nuclear power plants, the nature of the task makes it difficult for completely untrained people to participate. The training time required for the issuance of a license is specified in the by-laws of the Construction Machinery Management Act.
Dose reduction methods
The minimum level of training is the minimum level of training required before a worker starts working, using the training time specified in the law. The difference in cutting time between the two scenarios is due to the difference in the cutting order of the interface between activated and unactivated concrete. Reducing the area required for cutting has the effect of reducing cutting time, thereby reducing worker exposure.
In this method, additional cutting methods can be considered in the process of transporting and packing cut concrete. In the case of 0.5 mmPb, which is commonly used, protection is more than 95% for low radioactivity. In the case of total dose costs, they were calculated by multiplying the monetary value by the calculated total dose.
Results and discussions
Dose rate analysis
Comparing before and after cooling, it appears that the dose rate in the working area has decreased significantly. The dose rate distribution shows the highest value near 2 to 3 m and tends to decrease exponentially with height. This characteristic results in the same form of dose rate distribution in low-radioactivity zones with a point source in the center of the high-radioactivity zone, whereby the dose rate decreases as it approaches.
Of the three radionuclides evaluated, 60Co had the highest effect, accounting for 99.7% of the dose rate; in contrast, 152Eu and. The dose rate distribution was calculated as shown in figure 4.6-4.8 to check the dose rate distribution after the elimination of high radiation concrete directly exposed by radiation. Comparing the dose rates before and after cutting the inner zones, the dose rate in figure 4.1-4.3 is significantly lower than in Figure 4.6-4.8 because most of the radiation from the concrete occurs within 1 m.
Dose assessment analysis
The maximum dose was at 3 m, where the dose rate was the highest, the dose at this point was 50 mSv, which could reach the annual dose limit even if only this part were dismantled. For the inner zone, the total internal dose of the dismantling worker was 0.503 mSv for wet cutting and 1.44 mSv for dry cutting, which was lower than the annual dose limit. Dry cutting showed a higher dose compared to wet cutting, which is apparently due to the difference in working time and dust generation due to the relatively low cutting speed.
To see the differences in doses received from different nuclides, the inhalation dose and the oral dose. Protective measures are necessary because external exposure doses are very high compared to internal exposure, and all results exceed the limit annual dose. For internal expansion and external expansion, dry cutting was calculated as a higher dose than wet cutting due to differences in working time due to differences in working speed.
Learning analysis
Short-term Learning effect in wet cutting from the point of view of change x x represents the workload, and the sum of x for each step represents the total amount of work. When you restart the task, count back from 0 and x continues to increment until the next break. Short-term The learning effect in wet cutting in terms of change u u is the experience that is remembered for the task and the task is performed with some degree of learning because the basic training was performed before the disassembly task.
Therefore, the u-value increases most during the morning work, when the workload is high, and the afternoon work indicates an increase in the u-value, which is smaller. As time goes on, the decline in working hours decreases and shows a curve in the chart. Considering that the learning curve is in the form of an exponential function, it can be considered acceptable and the derivative function converges to 2.98 hours to complete a work unit as time increases.
Training optimization
The time required for the team to work can then be calculated using the previously derived learning curve and the resulting total work unit. Since the previously derived working hours are assumed to be the work of a professional worker, the comparison of efficiency with a primary school worker can be shown in table 4.13. For this cost-effective optimization, it can be derived by comparing the cost value with the unit price of the task as shown in Table 4.14.
The methods for estimating these training dates can be applied to the previously derived dose reduction methods and differences can be identified. Based on these differences and the previously derived training dates, optimized personnel designs can be performed for each method and the collective dose received can be calculated, which is summarized as shown in Table 4.15. If this method is applied, the collective dose can be reduced by 44%, and the task time can also be reduced.
Conclusions
항상 저의 연구를 지도해 주시고 지도해 주시는 김희령 교수님께 항상 감사드립니다. 람다연구소의 다른 연구실 선배, 후배들에게 늘 빚을 지고 있다는 생각이 듭니다. 연구실 선배님들이 좋은 기초를 다지고 방향을 제시해주셔서 혼자서는 감당하기 어려웠던 어려운 과제를 해결할 수 있었습니다.
연구실에 있는 아이들도 잘 자라고 있어서 믿고 맡길 수 있었어요. 앞으로도 새로운 길을 걸어가며 더 나은 사람이 되도록 노력하겠습니다. 다른 원자력공학과 연구원들도 저에게 많은 도움을 주었습니다.
