Effective mixing of different-sized aggregates in mobile asphalt plant dryers is crucial for ensuring high-quality, consistent asphalt production. This study explores the application of spatial analysis techniques, particularly the Discrete Element Method (DEM), to understand and optimize the mixing process of aggregates in drum dryers. The research emphasizes the importance of proper mixing to achieve uniform moisture removal and heating across various aggregate sizes. Larger aggregates heat more slowly, while finer particles risk overheating or being carried away by air currents, necessitating careful management of the mixing process. Using LIGGGHTS, an open-source simulation framework, we conducted DEM simulations to analyze the spatial distribution and behavior of aggregates within a 3D model of a drum dryer. The study considered multiple factors affecting mixing efficiency, including drum inclination, rotational speed, and aggregate feeding frequency. Results indicate that the rotational speed of the drum dryer has the most significant impact on mixing effectiveness. The DEM simulations provided valuable insights into particle movement, heat transfer, and potential segregation issues within the dryer. Further investigations into additional factors that may influence aggregate mixing in drum dryers is recommended, paving the way for improved efficiency and quality in asphalt manufacturing.

Electric-propulsion systems for ships, also known as electric propulsion devices, represent the current direction of development for maritime power. Issues concerning the environment and fuel economy have compelled the maritime transport sector to seek solutions that reduce emissions and improve fuel efficiency. In this process, power electronics technology plays a significant role in the propulsion systems of ships. Selecting an efficient battery system is of great importance for enhancing the cruising range of yachts and minimizing environmental impact. The battery model is crucial for revealing the working principles of batteries, and it is extremely critical for the application and development of battery technology. The Battery Management System (BMS) serves a crucial regulatory function, optimizing both the safety and performance of battery cells. Central to its operation is the precise estimation of the battery's State of Charge (SOC), a process dependent on an exacting battery model. This system not only enhances longevity and reliability but also ensures that energy storage solutions meet high standards of efficacy. This study focused on testing the impedance characteristics of lithium-sulfur batteries (LSB) at various SOC points and establishing first- and second-order RC equivalent circuit models. The model parameters were identified through experimental data. Subsequently, a simulation platform was constructed using MATLAB/Simulink to simulate the behavior of LSB under a constant current discharge condition. The simulation results showed that the second-order RC model had significantly lower errors than the first-order model, demonstrating higher accuracy. These achievements can provide technical support for the research of energy storage systems in the green aviation and maritime industries.

To ensure the safety of disposal facilities for radioactive waste, it is essential to quantitatively evaluate the performance of the waste disposal facilities by using safety assessment models. This paper addresses the development of the safety assessment model for the underground silo of Wolseong Low-and Immediate-Level Waste (LILW) disposal facility in Korea. As the simulated result, the nuclides diffused from the waste were kept inside the silo without the leakage of those while the integrity of the concrete is maintained. After the degradation of concrete, radionuclides migrate in the same direction as the groundwater flow by mainly advection mechanism. The release of radionuclides has a positive linear relationship with a half-life in the range of medium half-life. Additionally, the solidified waste form delays and reduces the migration of radionuclides through the interaction between the nuclides and the solidified medium. Herein, the phenomenon of this delay was implemented with the mass transfer coefficient of the flux node at numerical modeling. The solidification effects, which are delaying and reducing the leakage of nuclides, were maintained the integrity of the nuclides. This effect was decreased by increasing the half-life and the mass transfer coefficient of radionuclides.

태풍 매미 통과 시에 부산연안에서 폭풍해일고를 해석하기 위한 수치실험을 수행하였다. 태풍 매미는 2003년 9월 12일 21:00에 중심기압 950hPa로 우리나라 남해안에 상륙하였으며, 지난 수십년 간에 걸쳐 최악의 해안재해로 기록되었다. 태풍 매미 통과시 부산항, 여 수항, 통영항, 마산항, 제주항 및 서귀포항에서 관측된 폭풍해일고와 계산된 해일고의 시계열을 비교하였으며, 계산결과와 관측결과는 잘 일치하였다. 태풍해일고는 마산항에서 약 230 cm로 가장 크게 나타났으며, 여수항과 통영항에서는 약 200 cm, 부산항에서는 약 75 cm로 나 타났다. 폭풍해일 수치실험결과, 부산 동부 연안에서 해일고는 52∼55 cm 범위이고, 외해로 갈수록 해일고는 감소하였다. 따라서 반폐쇄된 만에서는 해일고의 상승으로 인한 연안 침수범람 피해가 크게 발생할 것으로 사료되며, 본 수치실험결과는 태풍으로 인한 연안재해 저감 을 위한 중요한 자료로 사용될 수 있다.

The safety of deep geological disposal systems has to be ensured to guarantee the isolation of radionuclides from human and related environments for over a million years. Over such a long timeframe, disposal systems can be influenced by climate change, leading to significant long-term impacts on the hydrogeological condition, including changes in temperature, precipitation and sea levels. These changes can affect groundwater flow, alter geochemical conditions, and directly/ indirectly impact the stability of the repository. Hence, it is essential to conduct a safety assessment that considers the long-term evolution induced by climate change. In this context, the Korea Atomic Energy Research Institute (KAERI) is developing the Adaptive Process-based total system performance assessment framework for a geological disposal system (APro). Currently, numerical modules for APro are under development to account for the longterm evolution that can influence groundwater flow and radionuclide transport in the far-field of the disposal system. This study focuses on the development of two numerical modules designed to model permafrost formation and buoyance force due to relative density changes. Permafrost is defined as a ground in which temperature remains below zero-isotherm (0°C) continuously for more than two consecutive years. In regions where permafrost forms, the relative permeability of porous media is significantly reduced. The changes in permeability due to permafrost formation are modelled by calculating the unfrozen fluid content within a porous medium. Meanwhile, buoyancy force can occur when there is a difference in density at the boundary of two distinct water groups, such as seawater (salt water) and freshwater. Sea level change associated with climate change can alter the boundary between seawater and freshwater, resulting in changes in groundwater flow. The buoyancy force due to relative density is modelled by adjusting concentration boundary conditions. Using the developed numerical modules, we evaluated the long-term evolution’s effects by analyzing radionuclide transport in the far-field of the disposal system. Incorporating permafrost and buoyancy force modelling into the APro framework will contribute valuable insights into the complex interactions between geological and climatic factors, enhancing our ability to ensure the secure isolation of radionuclides for extended periods.

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier’s abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

South Korea has been storing UNF in spent fuel pool dry storage facility within Nuclear Power Plants. The dry storage facility of used nuclear fuel (UNF) is essential to sustain safety and sustain stable operation of a nuclear power plant. Most abroad countries have attempted to develop a variety of dry storage facility for used nuclear fuel in order to retain the safe restoration. Many studies have been conducting to safety evaluation for the dry storage facility. However, there is not a ventilation evaluation in the wake of fire event that could influence of the thermal effect on the dry storage facility, even though it will likely to occur fire events such as wildfire, air craft crash. In practice, it happened to catastrophic disaster due to the wild fire adjacent to ul-jin mountain. Also, it happened to fire accident near to the Japonia NPP in Ukraine territory caused of military air plane missile. It has not mostly been studied on the ventilation evaluation considered to thermal safety in the dry storage facility excepted for some researches. It could need the mechanical ventilation systems such as HVAC system in the dry storage system, so that thermal effect can be reduced. In this study, we conducted to the ventilation control modelling by using fire modelling tool (Fire Dynamic Simulator v.6.7). The ventilation scenarios made up for 3 case that can compare flowrate variation with ventilation control. As a result of modelling, there is no differentiation between ventilation control using performance curve with not using performance curve even though the pressure fluctuation would be increased, compared with the case of considering performance curve. Second, it evaluated that the mode for fraction control would occur to pressure rise in the state of controlling the ventilation system flowrate. However, sensitivity of flowrate control was more decreased below less than 5 seconds. Third, in the case of on/off control system revealed more higher resolution than other cases caused by flowrate variation. These results could be considered as the design guidelines for the development dry storage facility to improve the thermal performance that can reduce thermal risk. Furthermore, the study results would expect HVAC system installed in dry storage to help automatic ventilation control relevant to dry storage safety increased.

A deep geological disposal system, which consists of the engineered and natural barrier components, is the most proven and widely adopted concept for a permanent disposal of the high level radioactive waste (HLW) thus far. The clay-based engineered barrier is designed to not only absorb mechanical stress caused by the geological activities, but also prevent inflow of groundwater to canister and outflow of radionuclides by providing abundant sorption sites. The principal mineralogical constituent of the clay material is montmorillonite, which is a 2:1 phyllosilicate having two tetrahedral sheets of SiO2 sandwiching an octahedral sheet of Al2O3. The stacking of SiO2 and Al2O3 sheets form the layered structures, and ion-exchange and water uptake reactions occur in the interlayer space. In order to reliably assess the radionuclide retention capacity of engineered barrier under wide geochemical conditions relevant to the geological disposal environments, sorption mechanisms between montmorillonite and radionuclides should be explicitly investigated in advance. Thus far, sorption behavior of mineral adsorbents with radionuclides has been quantified by the sorption-desorption distribution coefficient (Kd), which is simply defined as the ratio of radionuclide concentration in the solid phase to that in the equilibrium solution; the Kd value is conditional, and there have been scientific efforts to develop geochemically robust bases for parameterizing the sorption phenomena more reliably. In this framework, application of thermodynamic sorption model (TSM), which is theoretically based on the concept of widely accepted equilibrium models for aquatic chemistry, offers the potential to improve confidence in demonstration of radionuclide sorption reactions on the mineral adsorbents. Specifically, it is generally regarded in the TSM that coordination of radionuclides on montmorillonite takes place at the surficial aluminol and silanol groups while their ion-exchange reactions occur in the interlayer space also. The effects of electrical charge on the surface reactions are additionally corrected in accordance with the numerous theories of electrochemical interface. The present work provides an overview of the current status of application of TSM for quantifying sorption behaviors of radionuclides on montmorillonite and experimental results for physical separation and characterization of Ca-montmorillonite from the newly adopted reference bentonite (Bentonil- WRK) by means of XRD, BET, FTIR, CEC measurement, and acid-base titration. The determined mineralogical and chemical properties of the montmorillonite obtained will be used as input parameters for further sorption studies of radionuclides with the Bentonil-WRK montmorillonite.

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

본 연구는 시설토경에 적합한 간단관개의 1회 관개량, 관개주기 및 최대 관개횟수를 구명하기 위해 토양 수직단면 상에서의 수분이동을 모델링하였다. 간단관개는 특정양의 물을 관개할 때, 소량의 물을 일정한 시간 간격으로 여러 번 나누어 급수하여 토양표면의 용수 손실을 줄이고, 근권부까지 수분이 원활히 침투되도록 하기 위한 관개 방법이다. 토양 내부의 수분이동 특성은 관개방법, 토성 등에 따라 다양하게 변화한다. 반면, 현장에서 간단관개는 토마토 시설토경 재배의 경우 40분 간격으로 1회 4분간 최대 4회 관개, 사과의 경우 110분 간격으로 1회 5분간 최대 3∼5회 등 경험에 따라 관개하고 있다. 본 연구에서는 국내의 대표적인 밭토성인 사질양토, 양토, 양질사토에 대해 10, 20, 40분 간격으로 관개깊이 5㎜의 용수를 1.0, 1.7, 2.5, 5.0㎜씩 각 1∼5회로 나누어 관개하는 조건을 모의하였다. 모의할 토층단면은 폭 10㎝, 깊이 30㎝로 설정하고, 모의 프로그램은 Hydrus- 2D를 이용하였다. 기존 관련 연구의 실측 자료를 이용해 모델을 보정하고, 토층단면에 걸쳐 시간 경과에 따른 토양 내 수분 변화를 2차원 토양수분 패턴도로 작성하였다. 간단관개 방법에 따라 토양표면 및 근권부 토층에서의 수분 변화를 분석한 결과 모든 조건에서 연속관개 보다는 간단관개가 근권부의 수분전달에 유리하고, 양토, 사질양토, 양질사토의 순으로 관개용수의 침투가 원활한 것으로 파악되었다. 1회 관개량 1.7㎜ 이상, 간단시간 40분 이상인 경우에 토양표면에서의 수분 침투가 원활해지는 것으로 나타났으며, 근권부의 토양수분 전달에 가장 유리한 것은 1회 관개량 1.7㎜, 3회 간단관개하는 경우로 분석되었다. 토성에 따른 토양수분이동의 특성 차이는 크지 않은 것으로 나타났다. 본 연구 결과 시설토경 관개에서 토양표면에서의 용수손실을 감소시키면서, 근권부 수분공급에 적합한 간단관개법을 구명할 수 있었으며, 이를 향후 자동관개 제어 시스템에 적용한다면 용수손실을 최소화하면서 토양수분을 작물생육에 최적의 상태로 제어할 수 있을 것으로 사료된다.