• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2616-2628
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• 2021

The results of effective irradiation swelling in a wide range of burnup levels are numerically obtained for an inert matrix fuel, which are verified with DART model. The fission gas swelling of fuel particles is calculated with a mechanistic model, which depends on the external hydrostatic pressure. Additionally, irradiation and thermal creep effects are included in the inert matrix. The effects of matrix creep strains, external hydrostatic pressure and temperature on the effective irradiation swelling are investigated. The research results indicate that (1) the above effects are coupled with each other; (2) the matrix creep effects at high temperatures should be involved; and (3) ranged from 0 to 300 MPa, a remarkable dependence of external hydrostatic pressure can be found. Furthermore, an explicit multi-variable mathematic model is established for the effective irradiation swelling, as a function of particle volume fraction, temperature, external hydrostatic pressure and fuel particle fission density, which can well reproduce the finite element results. The mathematic model for the current volume fraction of fuel particles can help establish other effective performance models.

• Nuclear Engineering and Technology
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• 제52권12호
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• pp.2699-2708
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• 2020

Helical-coil steam generator (HCSG) technology is a major design candidate for small modular reactors due to its compactness and capability to produce superheated steam with high generation efficiency. In this paper, we investigate the feasibility of the passively autonomous power maneuvering by coupling the 3-D transient multi-physics of a soluble-boron-free (SBF) core with a time-dependent HCSG model. The predictor corrector quasi-static method was used to reduce the cost of the transient 3-D neutronic solution. In the numerical system simulations, the feedwater flow rate to the secondary of the HCSGs is adjusted to extract the demanded power from the primary loop. This varies the coolant temperature at the inlet of the SBF core, which governs the passively autonomous power maneuvering due to the strongly negative coolant reactivity feedback. Here, we simulate a 100-50-100 load-follow operation with a 5%/minute power ramping speed to investigate the feasibility of the passively autonomous load-follow in a 450 MW_th SBF PWR. In addition, the passively autonomous frequency control operation is investigated. The various system models are coupled, and they are solved by an in-house Fortran-95 code. The results of this work demonstrate constant steam temperature in the secondary side and limited variation of the primary coolant temperature. Meanwhile, the variations of the core axial shape index and the core power peaking are sufficiently small.

• Mass Spectrometry Letters
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• 제12권4호
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• pp.200-205
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• 2021

Polynemoraline C, a pyridocoumarin alkaloid, exhibits anticholinergic, anti-inflammatory, antitumor, and antimicrobial activities. A sensitive analytical method of polynemoraline C in mouse plasma was developed and validated using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Polynemoraline C and ¹³C-caffeine (internal standard) in mouse plasma were extracted using a liquid-liquid extraction method coupled with ethyl acetate. This extraction method resulted in high and reproducible extraction recovery in the range of 73.49%-77.31% with no interfering peaks around the peak retention time of polynemoraline C and ¹³C-caffeine. The standard calibration curves for polynemoraline C were linear over the range of 0.5-200 ng/mL with r² > 0.985. The accuracy, precision, and the stability of the data were within acceptable limits on the FDA guideline. After intravenous and oral administration of polynemoraline C at doses of 5 and 30 mg/kg, respectively, the present method was successfully applied to the pharmacokinetic study of polynemoraline C. Polynemoraline C in mouse plasma showed a multi-exponential elimination pattern with a high volume of distribution values. This compound's absolute oral bioavailability was found to be 17.0%. Polynemoraline C's newly developed LC-MS/MS method can be used for further studies on the efficacy, toxicity, and biopharmaceutics of polynemoraline C, as well as its pharmacokinetic studies.

• Tribology and Lubricants
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• 제35권1호
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• pp.77-86
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• 2019

The present study focuses on the effect of staggered integration factor (SIF) on Morton effect simulation results. The Morton effect is a synchronous rotordynamic instability problem caused by the temperature differential across the journal in fluid film bearings. Convection and conduction of heat in the thin film displaces the hot spot, which is the hottest circumferential position in the thin film, from -20 to 40 degrees ahead of the high spot, where the minimum film clearance is experienced. The temperature differential across the journal causes a bending moment and the corresponding thermal bow in the rotating frame acts like a distributed synchronous excitation in the fixed frame. This thermal bow may cause increased vibrations and continued growth of the synchronous orbit into a limit cycle. The SIF is developed assuming that the response of the rotor-lubricant-bearing dynamic system is much quicker than that of the bearing-journal thermal system, and it is defined as the ratio between the simulation time of the thermal system and the rotor-spinning period. The use of the SIF is unavoidable for efficient computing. The value of the SIF is chosen empirically by the software users as a value between 100 and 400. However, the effect of the SIF on Morton effect simulation results has not been investigated. This research produces simulation results with different values of SIF.

• 환경분석과 독성보건
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• 제21권4호
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• pp.237-242
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• 2018

Strontium (Sr) commonly exists in rock, groundwater, soil, plants, and animals. The Sr isotope ratio offers important information as a tracer on nature because the Sr isotopic composition is not fractionated by any biological process in these ecosystems. Hence, Sr isotope ratio has been used in several studies on tracing the Sr source for contaminated sites and human migration. In this study, we developed a separation method for Sr content, and then improved Sr isotope analysis using multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). A powdered rock standard (NIST 2710a) was used to determine the removal of interference elements (Rb and Ca) and the recovery rate of Sr content. The results ranged from 98% to 106%. Additionally, three standard samples (NBS 987, IAPSO and NIST 1486) were analyzed to evaluate the precision and accuracy of the results. The measured $^{87}Sr/^{86}Sr$ ratio for all the samples were consistent with the reported values, within an error. These results indicate that our established Sr separation and Sr isotope measurement methods are reliable and can hence be useful in the fields of environmental and forensic sciences.

• 대기
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• 제31권5호
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• Geomechanics and Engineering
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• 제28권4호
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• pp.385-396
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• 2022

The accurate determination of the subgrade reaction modulus (K_s) of soil is an important factor for geotechnical engineers. This study estimated the K_s of soft soil improved with floating deep cement mixing (DCM) columns. A novel prediction model was developed that emphasizes the accuracy of identifying the most significant parameters of K_s. Several multi-layer perceptron (MLP) models that were trained using the Levenberg Marquardt (LM) backpropagation method were developed to estimate K_s. The models were trained using a reliable database containing the results of 36 physical modelling tests. The input parameters were the undrained shear strength of the DCM columns, undrained shear strength of soft soil, area improvement ratio and length-to-diameter ratio of the DCM columns. Grey wolf optimization (GWO) was coupled with the MLPs to improve the performance indices of the MLPs. Sensitivity tests were carried out to determine the importance of the input parameters for prediction of K_s. The results showed that both the MLP-LM and MLP-GWO methods showed high ability to predict K_s. However, it was shown that MLP-GWO (R = 0.9917, MSE = 0.28 (MN/m²/m)) performed better than MLP-LM (R =0.9126, MSE =6.1916 (MN/m²/m)). This proves the greater reliability of the proposed hybrid model of MLP-GWO in approximating the subgrade reaction modulus of soft soil improved with floating DCM columns. The results revealed that the undrained shear strength of the soil was the most effective factor for estimation of K_s.

• Advances in nano research
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• 제12권5호
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• pp.529-547
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• 2022

Graphene is one of the strongest, stiffest, and lightest nanoscale materials known to date, making it a potentially viable and attractive candidate for developing lightweight structural composites to prevent high-velocity ballistic impact, as commonly encountered in defense and space sectors. In-plane twist in bilayer graphene has recently revealed unprecedented electronic properties like superconductivity, which has now started attracting the attention for other multi-physical properties of such twisted structures. For example, the latest studies show that twisting can enhance the strength and stiffness of graphene by many folds, which in turn creates a strong rationale for their prospective exploitation in high-velocity impact. The present article investigates the ballistic performance of twisted bilayer graphene (tBLG) nanostructures. We have employed molecular dynamics (MD) simulations, augmented further by coupling gaussian process-based machine learning, for the nanoscale characterization of various tBLG structures with varying relative rotation angle (RRA). Spherical diamond impactors (with a diameter of 25Å) are enforced with high initial velocity (V_i) in the range of 1 km/s to 6.5 km/s to observe the ballistic performance of tBLG nanostructures. The specific penetration energy (E_p^*) of the impacted nanostructures and residual velocity (V_r) of the impactor are considered as the quantities of interest, wherein the effect of stochastic system parameters is computationally captured based on an efficient Gaussian process regression (GPR) based Monte Carlo simulation approach. A data-driven sensitivity analysis is carried out to quantify the relative importance of different critical system parameters. As an integral part of this study, we have deterministically investigated the resonant behaviour of graphene nanostructures, wherein the high-velocity impact is used as the initial actuation mechanism. The comprehensive dynamic investigation of bilayer graphene under the ballistic impact, as presented in this paper including the effect of twisting and random disorder for their prospective exploitation, would lead to the development of improved impact-resistant lightweight materials.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3544-3564
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• 2022

With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.419-419
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• 2023

하천의 최종 유출부와 해양이 만나는 지점을 하구라고 하며, 우리나라는 주로 서해안 지역에 하구 방조제 건설에 따른 담수호가 조성되어 다양한 목적으로 수자원이 활용되고 있다. 이러한 하구 담수호는 바다로 유입되기 직전의 물을 저류시켜 수자원 확보에 긍정적이나, 일반적으로 유역의 최하류에 위치해 있어 오염물질 유입, 부영양화, 염분 침출로 인한 오염물질 용출 등에 취약하다. 따라서 담수호의 회복탄력성 향상과 지속가능한 수자원 관리를 위해서는 미래 기후변화에 따른 영향 분석이 필수적이다. 특히 기후변화는 거대규모의 홍수과 같은 자연재난, 농업가뭄 및 식생가뭄 등의 증가로 이어질 수 있으므로, 이에 효과적으로 대비하기 위해서는 미래 기후조건에 따른 하천의 미래 유출량 변화 예측이 수행되어야 한다. 본 연구에서는 불확실한 미래 수문변화를 예측하기 위해 CMIP6(Coupled Model Intercomparison Project Phase 6) GCMs(Global Climate Models)의 SSP(Shared Socioeconomic Pathways) 시나리오를 유역 유출모델에 적용하여 기후변화에 따른 미래 유출특성의 변화를 예측하였다. 충청남도 서산시에 위치한 간월호 유역을 대상유역으로 선정하고, HSPF(Hydrological Simulation Program-FORTRAN) 모형을 적용하여 상류유역의 과거 및 미래 장기유출량 모의를 수행하였다. 모의된 시나리오별 유출량을 기반으로 최빈유량곡선법을 적용하여 미래의 기준유량 발생시점 및 지속기간의 변화를 분석하였으며, CVDs(Center-of-volume dates)의 변화를 통해 기후변화에 따른 홍수기의 시기적 변화 양상을 파악하고자 하였다. 본 연구의 결과는 미래 유역 환경변화를 고려한 담수호의 수자원 보전관리계획 수립에 있어 기초자료로 활용될 수 있을 것으로 기대된다.