격납건물은 원자력 발전소의 중대 사고 발생시 방사성 물질의 외부 방출을 막는 심층 방어 체계 중 마지막 방벽이다. 중대사고 발생시 격납건물 내부에선 노심 융해와 수소 발생으로 인한 내압 상승과 증기 폭발로 인한 구조적 손상이 일어나며, 이에 대한 구조적 건전성을 평가하기 위해 격납건물에 대 한 극한 내압 성능 평가를 실시한다. 극한 내압 성능 평가 방법 중 확률론적 평가시 현실적인 제약으 로 인해 고신뢰도 유한요소해석 모델을 이용하며 이때에 불확실성 인자들의 확률 분포 특성을 고려한 데이터 셋을 샘플링 기법을 이용하여 구성한 후 비선형 해석을 실시한다. 도출된 비선형 해석 결과는 취약도 곡선을 도출에 사용되며, 취약도 곡선을 이용하여 확률론적인 평가가 실시된다. 샘플링 기법에 따라 적절한 표본 크기가 아닌 데이터셋을 구성하게 되면 통계적 불확실성으로 인한 취약성 분석의 오차가 증대된다. 하지만 유한요소해석시 발생하는 막대한 계산 비용으로 인하여 기존의 방식은 적절 한 샘플링 크기 선정 및 부적절한 샘플링 크기 선정으로 인한 확률론적인 성능평가에 대한 영향에 대 한 정량화 및 평가를 제한적으로 수행하였다. 따라서 본 연구에서는 격납건물의 재료적 특성 및 내압 으로 인한 변위 데이터를 기반으로 생성한 인공신경망 모델을 통해 유한요소 해석에 대한 대리모델을 생성한다. 이후 생성한 대리모델을 기반으로 일반적인 불확실성 분포 샘플링에 사용되는 Monte Carlo method, latin hypercube sampling, Sobol sequence을 이용하여 표본 크기에 따른 격납건물 확률론적 인 극한내압성능 평가에 대한 영향을 정량화 및 평가를 실시하겠다. 이를 통해 제한적으로 탐색되었던 불확실성 공간에 대하여, 그 통계적 불확실성 및 전방위적인 탐색이 가능해 질것으로 기대한다.

노면 마찰력은 포장 표면과 타이어의 마찰력으로 인해 발생하는 현상으로 높은 노면의 마찰력은 제동 중 차량의 안정성과 조종성을 향상시킨다. 노면 마찰력이 증가함에 따라 교통사고 횟수가 감소하는 것으로 알려져 있으며 습윤 상태의 노면에서 교통사고가 증가하 는 것으로 알려져있다. 따라서 교통사고 발생 억제와 도로 안전의 확보를 위해서는 적정 수준의 노면 마찰력, 특히 습윤 상태의 노면 마찰력을 확보하는 것이 중요하다. 노면 마찰력은 adhesion과 hysteresis로 분류되며 특히 습윤상태 도로에서 hysteresis가 중요한 역 할을 한다. hysteresis는 고무의 변형에 의해 발생하기 때문에 고무 변형에 영향을 미치는 노면 조직 변수를 선정하여 노면 마찰력을 예측하고자 한다. 노면 마찰력은 노면 조직 특성과 밀접한 관련이 있으며, 이에 따라 노면 조직 특성을 나타내는 지수 중 하나인 MTD(Mean Texture Depth)가 노면 마찰력 예측을 위한 인자로 사용되고 있는 실정이다. 하지만 MTD는 노면 조직 깊이만을 평가하 는 인자로 다양한 요소가 결합되어 있는 노면 조직 특성을 모두 설명할 수 없으며, 노면 마찰력 예측을 위해서는 복잡한 노면 조직을 설명할 수 있는 추가 변수의 선정이 요구된다. 본 연구에서는 노면 마찰력의 메커니즘 분석을 토대로 노면 마찰력에 영향을 미치는 노면 조직 특성을 분석하였고, wave-length와 노면 조직의 형태, 노면 조직 깊이가 노면 마찰력에 미치는 영향이 클 것으로 예상하였 다. 이를 검증하기 위해서는 3가지 노면 조직 특성이 노면 마찰력에 미치는 영향에 대한 검토가 요구되나 실제 도로의 노면은 노면 조직이 불규칙하게 형성되어 있어 노면 조직 특성의 개별적 영향을 검토하기 어렵다. 이를 위해서는 선정한 노면 조직 특성의 정량적 형성이 요구되며 3D 프린팅 시편을 제작해 노면 조직을 인위적으로 형성함으로써 실제 도로 노면 조직의 불규칙성을 개선하였다. 노 면 조직 특성을 시편에 반영하기 위해 노면 조직 깊이는 MTD, wave-length는 노출 골재의 개수를 뜻하는 EAN을 변수로 설정하였 다. 또한 EAN(Exposed Aggregate Number)은 노출 골재의 형성이 필수적이므로 골재의 형상을 제어하여 노면 조직의 형태를 시편에 반영하였으며 골재 형상과 노면 마찰력의 통계학적 분석을 위해 형상 지수를 산출하여 분석하였다. 3D 프린팅 시편은 크기에 제한이 있어 좁은 영역에서 측정이 용이한 BPT(British Pendulum Test)를 사용해 노면 마찰력을 측정하였고, 습윤한 노면에서는 수막으로 인해 노면 마찰력이 크게 감소하여 노면 조직의 영향이 커지므로 습윤 상태에서 노면 마찰력을 측정하였다. 측정 데이터를 통한 분석 결과 노면 조직 변수인 MTD가 증가할수록 BPN(wet)이 선형적으로 증가하는 것이 확인되었으며, EAN에 따라서 BPN이 증가했다가 감소하는 경향이 나타났다. 이는 EAN이 과도하게 많아지면 고무가 침투할 공간이 줄어들어 hysteresis가 감소하기 때문으로 사료된 다. 또한 골재 형상에 따라 노면 마찰력의 최댓값과 optuimum EAN의 변화가 있었다. 이는 골재 형상에 따른 고무 침투 부피의 변화 에 의한 것으로 사료된다. 위의 결과를 통해 MTD, EAN, 골재 형상과 BPN(wet)의 관계를 통계학적으로 분석하여 BPN(wet) 예측 모 델을 제안하였다.

Reported positive ion fragmentation of phenolic acid derivatives in rice (Oryza sativa L.) were summarized based on the literature. A total of eight phenolic acids (4 derivatives of ferulic acid, 3 derivatives of sinapic acid and p-coumaric acid) were isolated and identified from rice (raw and steamed) using UPLC-DAD-QToF/MS. Results revealed that 6-O-feruloylsurose was the major component with 3'-O-sinapoylsucorse being tentatively identified in Oryza sativa L. for the first time as a new hydroxycinnamoyl derivative in rice grains. In our study, raw brown rice had the highest phenolic acid contents with Samkwang showing higher phenolic acid content than Saeilmi and Sindongjin (12.41 vs. 7.89 and 3.10 mg/100 g dry weight, respectively). Of all varieties, brown rice had higher phenolic acid contents than white rice. These contents decreased considerably when rice was steamed whereas, p-coumaric acid and ferulic acid contents were increased. Additionally, contents of rice (raw and steamed) can be used as a fundamental report for new rice varieties.

In our previous study, we developed a CFD thermal analysis model for a CANDU spent fuel dry storage silo. The purpose of this model is to reasonably predict the thermal behavior within the silo, particularly Peak Cladding Temperature (PCT), from a safety perspective. The model was developed via two steps, considering optimal thermal analysis and computational efficiency. In the first step, we simplified the complex geometry of the storage basket, which stored 2,220 fuel rods, by replacing it with an equivalent heat conductor with effective thermal conductivity. Detailed CFD analysis results were utilized during this step. In the second step, we derived a thermal analysis model that realistically considered the design and heat transfer mechanisms within the silo. We developed an uncertainty quantification method rooted in the widely adopted Best Estimate Plus Uncertainty (BEPU) method in the nuclear industry. The primary objective of this method is to derive the 95/95 tolerance limits of uncertainty for critical analysis outcomes. We initiated by assessing the uncertainty associated with the CFD input mesh and the physical model applied in thermal analysis. And then, we identified key parameters related to the heat transfer mechanism in the silo, such as thermal conductivity, surface emissivity, viscosity, etc., and determined their mean values and Probability Density Functions (PDFs). Using these derived parameters, we generated CFD inputs for uncertainty quantification, following the principles of the 3rd order Wilks’ formula. By calculating inputs, A database could be constructed based on the results. And this comprehensive database allowed us not only to quantify uncertainty, but also to evaluate the most conservative estimates and assess the influence of parameters. Through the aforementioned method, we quantified the uncertainty and evaluated the most conservative estimates for both PCT and MCT. Additionally, we conducted a quantitative evaluation of parameter influences on both. The entire process from input generation to data analysis took a relatively short period of time, approximately 5 days, which shows that the developed method is efficient. In conclusion, our developed method is effective and efficient tool for quantifying uncertainty and gaining insights into the behavior of silo temperatures under various conditions.

Measuring the amount of water remaining in the canister after drying is critical to ensuring the integrity of Dry Storage. There are many ways to measure residual moisture, but dew point sensors are typically used to measure residual moisture after drying the canister. Because the dew point temperature inside the canister depends on the water vapor partial pressure, the water vapor partial pressure present in the canister can be determined using the dew point temperature. The British Standard (BS1336) proposes a formula for converting dew point temperature into vapor partial pressure. It is possible to validate changes in residual water concentration throughout drying and at the end of drying. It has around 500 ppmv when the dew point temperature hits -73°C at 3 torr. Nuclear Regulatory Commission (US NRC) presented at 3 torr for 30 minutes as a criterion for the suitability of spent nuclear fuel drying. When the canister’s internal pressure is around 1,000 torr and the dryness criteria are met, the moisture concentration for this value is around 3,000 ppmv. We conducted a vacuum drying test of a 57 liter test vessel. It is filled with helium after vacuum drying was completed, and the concentration of residual water is measured by AquaVolt Moisture Analyzer (AMA) connected by a sample flow line. After the vacuum pressure of 1.5 torr was reached, the test vessel was filled to a pressure of 1,140 torr of helium after 30 minutes. The average temperature inside the basket inside the test vessel is 50°C, the dew point temperature is below -70°C, the pressure of test vessel is around 1,000 torr, and the measurement results of the AMA connected to the sample line showed less than 200 ppmv. From these results, we can evaluate that the residual moisture in the test vessel is about 0.01 gram.

The characterization of nuclear materials is crucial for global nuclear safeguards efforts, as these materials can potentially be used for illicit purposes. In this study, we evaluated the applicability and performance of the In-Situ Object Counting System (ISOCS) equipment for the characterization and quantification of uranium, including uranium pellets and radioactive wastes. Our methodology involved using ISOCS to measure samples with different enrichments and total amounts of uranium, and to analyze the results in order to evaluate the ISOCS’s effectiveness in accurately characterizing the various uranium samples. To this end, we compared the ISOCS results with those of the Multi-Group Analysis for Uranium (MGAU) system, which is currently used in the field of international safeguards. The results of this study showed that the ISOCS was sensitive enough to analyze small amounts of uranium pellet, with %differences ranging from -0.7% to 19%. However, when analyzing shielded nuclear materials like in concrete waste, the uncertainty was relatively high, with %differences ranging from 11% to 67%. On the other hand, the MGAU system was unable to analyze uranium for the same spectrum, indicating the superiority of the ISOCS in terms of usability. The ISOCS instrument was also found to be effective in analyzing uranium in various types of samples without the need of standard sources. Overall, the findings of this study have important implications for the development of more effective safeguards strategies for the characterization of nuclear materials. The ISOCS instrument could be a reliable tool for analyzing nuclear materials, contributing to global safeguards efforts to reduce the risk of nuclear proliferation.

For Dry Storage of Spent Nuclear Fuel (SNF), all moisture must be removed from the dry storage canister through subjected to a drying process to ensure the long-term integrity. In NUREG-1536, the evacuation of most water contained within the canister is recommended a pressure of 0.4 kPa (3 torr) to be held in the canister for at least 30 minutes while isolated from active vacuum pumping as a measure of sufficient dryness in the canister. In the existing drying process, the determination of drying end point was determined using a dew point sensor indirectly. Various methods are being studied to quantify the moisture content remaining inside the canister. We presented a moisture quantification method using the drying process variables, like as temperature, pressure, and relative humidity operation data. During the drying process, it exists in the form of a mixed gas of water vapor and air inside the canister. At this time, if the density of water vapor in the mixed gas discharged out of the canister by the vacuum pump is known, the mass of water removed by vacuum drying can be calculated. The canister is equipped with a pressure gauge, thermometer and dew point sensor. The density of water vapor is calculated using the pressure, temperature and relative humidity of the gas obtained from these sensors. First, calculate the saturated water vapor pressure, and then calculate the humidity ratio. The humidity ratio refers to the ratio of water vapor mass to the dry air mass. After calculating the density of dry gas, multiply the density by the humidity ratio to calculate the density of water vapor (kg/m3). Multiply the water vapor density by the volume flow (m3/s) to obtain the mass value of water (kg). The calculated mass value is the mass value obtained per second since it is calculated through the flow data obtained every second, and the amount of water removed can be obtained by summing all the mass values. By comparing this value with the initial moisture content, the amount of moisture remaining inside the canister can be estimated. The validity of the calculations will be verified through an experimental test in the near future. We plan to conduct various research and development to quantify residual water, which is important to ensure the safety of the drying process for dry storage.

Molten salt reactors and pyroprocessing are widely considered for various nuclear applications. The main challenges for monitoring these systems are high temperature and strong radiation. Two harsh environments make the monitoring system needs to measure nuclides at a long distance with sufficient resolution for discriminating many different elements simultaneously. Among available methodologies, laser-induced breakdown spectroscopy (LIBS) has been the most studied. The LIBS method can provide the required stand-off and desired multi-elemental measurable ability. However, the change of the level for molten salts induces uncertainty in measuring the concentration of the nuclides for LIBS analysis. The spectra could change by focusing points due to the different laser fluence and plasma shape. In this study, to prepare for such uncertainties, we evaluated a LIBS monitoring system with machine learning technology. While the machine learning technology cannot use academic knowledge of the atomic spectrum, this technique finds the new variable as a vector from any data including the noise, target spectrum, standard deviation, etc. Herein, the partial least squares (PLS) and artificial neural network (ANN) were studied because these methods represent linear and nonlinear machine learning methods respectively. The Sr (580–7200 ppm) and Mo (480–4700 ppm) as fission products were investigated for constructing the prediction model. For acquiring the data, the experiments were conducted at 550°C in LiCl-KCl using a glassy carbon crucible. The LIBS technique was used for accumulating spectra data. In these works, we successfully obtained a reasonable prediction model and compared each other. The high linearities of the prediction model were recorded. The R2 values are over 0.98. In addition, the root means square of the calibration and cross-validation were used for evaluating the prediction model quantitatively.

To dry storage of spent nuclear fuel withdrawn the wet storage, all moisture inside the dry storage container must be removed to ensure the long-term integrity and retrievability. Substantial amounts of residual water in dry storage container may have potential impacts on the fuel, cladding, and other components in the dry storage system, such as fuel degradation and cladding corrosion, embrittlement, and breaching. The drying could perform as a vacuum drying process or a forced helium dehydration process. In NUREG-1536, the evacuation of most water contained within the canister is recommended a pressure of 0.4 kPa (3 torr) to be held in the canister for at least 30 minutes while isolated from active vacuum pumping as a measure of sufficient dryness in the canister. Monitoring the moisture content in gas removed from the canister is considered as a means of evaluating adequate dryness. Dew point monitoring and special techniques could be used to evaluate this adequacy. Various studies are continuing for quantitative evaluation of residual moisture inside the dry storage system. Andrawes proposed a methodology for determining trace water contents in gaseous mixtures, utilizing gas chromatography together with a helium ionization source. A microwave plasma source and emission spectrometry were utilized to determine trace amounts of bound water in solid samples using peak areas of atomic oxygen (O) and hydrogen (H) emissions. Bryans measured the gas samples taken from the High Burn-Up Demonstration Cask at three intervals: 5 hours, 5 days, and 12 days after the completion of drying and backfilling in the North Anna power Station. To measure water content, a Vaisala humidity probe was used. Final results indicated that the cask gas water content built up over 12 days to a value of 17,400 ppmv ±10%, equivalent to approximately 100 g of water within the entire cask gas phase. Tahiyats also proposed a methodology that involves a direct current (dc) driven plasma discharge and optical emission spectroscopy for detecting and quantifying water vapor in a flowing gas stream under both trace and high water vapor loading conditions. For detecting water vapor concentration, the emission from H at 656.2 nm was employed. The H emission is the red visible spectral line generated by a hydrogen atom when an electron falls from the third lowest to the second lowest energy level, this suggests that the normalized H intensity can be used as a marker for water vapor detection and quantification. Several of the attempts are continuing to quantify water contents in dry storage system. Lessons learned by Case studies would be provided insights into how to improve future measurements.

정전용량 수분측정 센서는 수경용 배지 양쪽에 구리 및 테플론으로 절연된 전극판(30cm×10cm)을 부착하 여 배지의 넓은 부분에 걸쳐 측정하도록 개발되었다. 본 연구는 콘덴서형 정전용량 센서로부터 출력되는 정전용량 값을 배지 함수량으로 변환하는 것이다. 정량화 실험은 양액을 공급하면서 배지 물무게와 정전용량 변화를 측정하고 그 값을 비교하는 방식으로 수행되었다. 배지 함수량과 정전용량은 본 연구를 위해 특별히 개발된 소프트웨어와 함께 센서와 로드셀을 사용하여 20~30초마다 측정되었다. 상용 curve-fitting 프로그램을 이용하여 배지 함수량과 정전용량을 변수로 정전용량 값으로 배지 함수량을 추정하였다. 공급하는 물의 양이 증가하면 정전용량도 증가하는 경향을 보였다. 배지 내 물무게에 따른 정전용량에 대한 변동계수(coefficient of variation, cv)는 배지 내 물무게가 1.0kg 수준에서 다른 무게에 비해 높아 함수량 보정은 물무게를 1.7~6.0kg 수준에서 수행하였다. 정전용량과 물무게 사이의 상관 계수는 0.996이었고 보정식에 의해 정전용량으로 추정된 함수량은 로드셀로 측정한 배지 함수량과 비교하였다.

Widespread consumption of fresh-cut vegetables without cooking results in ingestion of major foodborne pathogens including Bacillus cereus. In this study, we aimed to develop a method to rapidly detect B. cereus in fresh-cut vegetables by combining commercial PCR analysis with enrichment of the pathogenic levels. A mixture of B. cereus strains (KCTC1013, KCTC1014, KCTC1092, KCTC1094, and KCTC3624) was inoculated on the surface of fresh-cut cabbage lettuce (20 g) and baby leafy vegetables (10 g) to concentration 1, 2, 3, 4, and 5 log CFU/g. Eighty milliliters of TSB with 0.15% polymyxin B was used for cabbage lettuce, and 90 mL of medium was used for baby leafy vegetables and incubated at 42oC for 0, 2, 3, 4, 5, 6, and 7 h. One milliliter of the enriched media was plated on mannitol-egg yolk-polymyxin agar for quantification, and another 1 mL was used for DNA extraction for PCR analysis. Additionally, the minimum number of sub-samples to be tested from a pack of fresh-cut vegetable samples was determined using 5 sub-samples. The results from this study showed that for detecting B. cereus in fresh-cut cabbage lettuce, 3, 4, 5, 6, and 7 h enrichment were required to at least detect 5, 4, 3, 2, and 1 log CFU/g of B. cereus, respectively. B. cereus in fresh-cut baby leafy vegetables could be detected after 2, 3, 4, 5, and 6 h of enrichment at 5, 4, 3, 2, and 1 log CFU/g, respectively, using a combination of enrichment and PCR analysis. To determine if a pack of fresh-cut vegetable is positive, the minimum number of sub-samples should be 3. These results can be used to develop a rapid detection method to semi-quantify B. cereus in fresh-cut vegetable samples combining enrichment and PCR.