친환경절연가스 중의 연면방전특성에 관한 연구

이정환 영남대학교 대학원 2010 국내석사

본 논문은 SF6용 GIS를 대체할 친환경가스절연개폐장치(EGIS)의 절연매질 중 질소(N2) 및 산소(O2)의 혼합비에 따른 테플론 수지에서의 연면방전특성을 구명하여 SF6가스의 특성과 비교하였다. 이에 본 논문은 모의 GIS내 질소(N2)와 산소(O2)의 체적혼합비율이 각각 100[%]:0[%]인 (N2), 80[%]:20[%]인 (I-Air), 60[%]:40[%]인 (M-AirⅠ) 및 40[%]:60[%]인 (M-AirⅡ)의 압력 및 갭 변화에 따른 연면방전특성을 구명하였다. 그 결과 Knife-Knife형 전극 하에서 절연내력은 SF6 > I-Air > M-AirⅠ > M-AirⅡ > N2의 순으로 평가되었다. 친환경가스 중 가장 절연내력이 좋은 I-Air와 SF6 가스의 절연내력을 비교한 결과 연면절연파괴전압 및 연면방전전계강도가 SF6의 약 50%로 평가되었다. 또한, 본 논문은 I-Air의 절연특성을 평가함으로서 각종 전력응용 설비의 절연설계에 SF6 대신 친환경가스의 대체가능성을 평가하였다. This paper presents the Surface Discharge characteristic at the Teflon resin according to the mixture gas of the nitrogen (N2) and oxygen (O2) in which it includes in insulator of the Environment-Friendly Gas Insulated Switchgear (EGIS) in order to replace SF6 the use of GIS and it compared with the characteristic of the SF6 gas. Also this paper studied the Surface Discharge characteristic at the volume mixture ratio of the nitrogen (N2) and oxygen (O2), as 100[%]:0[%] (N2), 80[%]:20[%] (I-Air), 60[%]:40[%] (M-AirⅠ) and 40[%]:60[%] (M-AirⅡ) according to the gas pressure and gap change in the experimental GIS. As a result, the dielectric strength under the Knife-Knife electrode was evaluated as SF6 > I-Air > M-AirⅠ > M-AirⅡ > N2. The Surface Discharge voltage and Surface Discharge electric field intensity were evaluated about 50% of SF6 as a result of comparing the insulation strength of the I-Air in which the dielectric strength is the best among the environment-friendly gas. Moreover, the insulation property of the I-Air was evaluated, this paper evaluated the interchange possibility of the environment-friendly gas at the insulation design of all kinds of power application facility instead of SF6.

고층 주거건물의 공기유동에 의한 실내 미세먼지 농도 형성 특성 분석

옹드람 성균관대학교 일반대학원 2020 국내석사

국내 미세먼지 농도는 2010년 이후 8년 연속 경제협력개발기구(OECD) 국가 중에 가장 높은 수치를 기록하였다. 미세먼지는 기관지를 통해 인체에 유입되며 기관지 염증 발생, 심혈관계 질환 등 여러 질병을 유발시키는 원인이 될 수 있다. 따라서 세계보건기구(WHO)에서 2013년에 미세먼지를 인간에게 암을 일으키는 1군 발암물질로 분류하였다. 국내 미세먼지 농도가 특히 봄철, 겨울철에 가장 높게 형성되며, 특히 겨울철 미세먼지는 초미세먼지(PM2.5)의 비중이 높기 때문에 건강에 더 해로울 수 있다. 국내에서 주거건물의 대부분을 공동주택이 차지하며 1990년대부터 주거건물에서 고층화가 이루어지기 시작하였다. 고층건물의 경우 실내외 온도차가 큰 겨울철에 연돌효과로 인한 공기유동 현상이 발생하게 된다. 따라서 겨울철에 고층 주거건물의 이러한 공기유동에 의하여 외부 고농도의 미세먼지가 실내로 유입 및 확산되어 거주자의 건강에 안 좋은 영향을 미칠 수 있다. 따라서 고층 주거건물의 경우 공기유동 특성을 고려하여 실내 미세먼지 농도를 분석할 필요가 있다. 국내에서 공동주택의 실내 미세먼지 농도에 대해 분석한 연구는 거의 진행되지 않았으며 주로 건물의 한 세대를 대상으로 실내 미세먼지 농도를 측정하여 건물 전체에 대한 미세먼지를 평가하고 있다. 고층 주거건물의 경우 건물의 공기유동 특성이 일반 건물과 다르게 형성되기 때문에 공기유동에 따라 건물의 수직적 미세먼지 농도 형성이 상이하게 나타날 수 있다. 본 논문에서는 겨울철 고층 주거건물의 공기유동에 의한 실내 미세먼지 농도 형성 특성에 대해 분석하고자 현장 실측을 통하여 고층 주거건물의 겨울철 공기유동을 분석하였다. 또한 선행연구 고찰을 통하여 고층 주거건물의 기밀성능 측정 데이터를 수집하여 기밀성능 수준 별 공기유동에 의한 실내 미세먼지 농도 및 그 영향인자에 대해 검토하였다. 논문의 결과를 요약하면 다음과 같다. (1) 대상건물의 공기유동 특성을 분석한 결과, 중성대 하층부에서 외기가 실내로 유입되고 중성대 상층부에서 외부로 유출되어 이론상의 공기유동과 일치하는 것으로 나타났다. 수직샤프트의 경우 E/V 샤프트를 통한 공기유동이 가장 크게 나타났으며, 외부 출입문을 통한 외기의 유입이 가장 높은 것으로 나타났다. (2) 건물의 수직적 공기유동에 의한 실내 미세먼지 농도 형성 특성을 분석한 결과, 중성대 하층부의 경우 외피를 통한 직접 외기에 의해 형성되고 상층부는 복도에서 세대로 유입되는 간접외기에 의해 형성되는 것으로 나타났다. 또한 건물의 공기유입량이 감소함에 따라 I/O ratio도 감소하는 것으로 나타났으며 패시브 수준으로 기밀성능을 향상할 경우 미세먼지 농도는 크게 저감되는 것으로 나타났다. (3) 요리에 의한 미세먼지의 발생을 고려하였을 때 건물의 기밀성능이 향상될수록 실내 미세먼지 농도에 대한 요리 발생 미세먼지의 기여율이 높아지는 것으로 나타났다. 주차장의 미세먼지 발생을 고려하였을 때 건물 중성대 상층부 세대에서 실내 미세먼지 농도는 주차장의 미세먼지의 유입으로 인해 높게 형성되는 것을 확인하였다. 풍압에 의한 영향을 고려하였을 때 세대 미세먼지 농도는 직접외기 및 간접외기에 복합적으로 영향을 받아 형성되는 것으로 나타났으며 기밀성능이 향상될수록 풍압의 영향을 적게 받는 것을 확인할 수 있었다.

전고체 전지용 황화물계 고체전해질의 ZnO의 도핑 효과에 관한 전기화학적 성능 연구

장금지 울산대학교 대학원 2023 국내석사

All-solid-state batteries remain problems to be overcome due to the high-temperature heat-treatment process, and the compatibility problem with Li metal as an anode. In this work, glass-ceramic Li7P2S8I (LPSI) and argyrodite Li6PS5Cl (LPSCl) solid electrolyte with high ionic conductivity is prepared using a high-energy dry ball milling process with a low-temperature (200 °C) and high temperature (550°C) heat-treatment process. Then, ZnO are doped with LPSI and LPSCl solid electrolyte, particularly Zn at the Li site and O at the S site, by our optimized synthesis process. The ZnO co-doping is confirmed by powder X-ray diffraction (XRD), Laser–Raman, field emission scanning electron microscopy (FE-SEM), and solid-state nuclear magnetic resonance (NMR) spectroscopy analysis. The ionic conductivity value of the prepared solid electrolytes is measured by electrochemical impedance spectroscopy analysis, and the prepared LPSI and Li6.9Zn0.05P2S7.95O0.05I solid electrolytes exhibit an ionic conductivity of (4.4 and 4.2) mS·cm−1, respectively, at room temperature. The prepared Li6PS5Cl and Li5.95Zn0.025PS4.975O0.025Cl solid electrolytes exhibited ionic conductivities of 4.55 and 4.08 mS·cm−1, respectively at 30°C. To evaluate the electrochemical stability of the prepared solid electrolyte, we perform cyclic voltammetry and galvanostatic discharge/charge voltage profiles analysis. In addition, the fabricated all-solid-state battery exhibits a high specific capacity of 165 mAh·g−1 (0.1 C), and a high-capacity retention rate of 95.2 % for LPSI-0.05ZnO. And the initial discharge capacity of the assembled all-solid-state battery showed a specific capacity of 149 mAh g-1 (0.1 C) and a high capacity retention rate of 99.7 % for LPSCl-0.025ZnO. Interestingly, ZnO co-doped LPSI and LPSCl solid electrolyte exhibits longer air-stability than the undoped LPSI and LPSCl solid electrolyte in dry air with 10 % humidity.

내측마그넷과 외측마그넷으로 구성된 고자장용 이중고온초전도마그넷의 특성

구명환 순천향대학교 대학원 2010 국내석사

p-i-n 구조의 무기 CsPbI2Br 페로브스카이트 태양전지(PSC) 소자에 대한 연구

김리나 청주대학교 대학원 2023 국내석사

본 연구에서 장기 안정성이 낮은 유기 물질을 대체하여 수분, 고온, 빛 환경에서 장기 안정성이 높은 무기 물질 Cs+ 이온을 이용한 CsPbX3 페로브스카이트 태양전지에서 CsPbI2Br 페로브스카이트 물질을 이용하였다. CsPbI2Br 페로브스카이트 물질로 태양전지를 제작하기 위하여 전자, 정공 수송층을 고밀도, 균일한 성막이 가능한 Thermal-ALD로 이용하여 제작하기 위해서 태양전지의 구조를 p-i-n 구조로 선정하였고 p-i-n 구조는 SLG/ITO/NiO/CsPbI2Br/SnO2/Au 혹은 Ag 전극으로 구성하였다. 먼저, CsPbI2Br 페로브스카이트 흡수층을 제작하기 위해서 CsPbI2Br 용액은 Cesium iodide (CsI), Lead (Ⅱ) bromide (PbBr2), Lead (Ⅱ) iodide (PbI2)의 용질과 N,N-dimethylformamide (DMF)와 Dimethly sulfoxide (DMSO)의 용매를 이용하였다. CsPbI2Br 용액을 1.2M의 농도로 용매 (DMF:DMSO)의 비율을 3:7 v/v%, 4:1 v/v% 로 제조하였다. 그리고 흡수층 제작은 hot-air 공정을 이용하지 않은 스핀 코팅과 hot-air 공정을 이용한 스핀 코팅으로 나누어 진행하였다. CsPbI2Br 층은 hot-air 공정을 이용하지 않은 조건보다 hot-air 공정을 이용한 조건이 균일하게 코팅되었고, 높은 광학적 흡수율이 나왔으며, 광학적 밴드갭이 1.91 eV로 CsPbI2Br의 이상적인 밴드갭 (1.91 eV)과 근접하게 측정되었다. 이를 통해 hot-air 공정이 적용된 CsPbI2Br 층을 p-i-n 구조로 적용하기 위해서 Thermal-ALD로 H2O 산화제를 이용하여 SnO2 층을 증착하였다. 그 결과, 용매 (DMF:DMSO)의 비율이 3:7 v/v%로 제작된 CsPbI2Br 층이 산화되지 않았으며, Black α-Cubic 상을 유지하였다. 그리고 제작된 무기 CsPbI2Br 페로브스카이트 태양전지의 XRD 분석 결과 CsPbI2Br Black α-Cubic 상을 확인하였다. 그리고 솔라 시뮬레이터를 이용하여 Au 혹은 Ag 전극을 이용한 태양전지의 단락전류밀도-전압 곡선을 측정한 결과 Au 전극을 이용한 태양전지의 전력변환효율은 2.75% (순방향), 5.11%(역방향)으로 측정되었으며, Ag 전극을 이용한 태양전지의 전력변환효율이 4.73% (순방향), 6.79% (역방향)으로 측정되었다. This research used the CsPbI2Br perovskite material in the CsPbX3 perovskite solar cell using the inorganic material Cs+ ion with high long-term stability in moisture, high temperature, and light environment by replacing the organic material with low long-term stability. The structure of the solar cell was selected as a p-i-n structure to manufacture the solar cell using Thermal-ALD of high density and uniform film formation to manufacture the solar cell with CsPbI2Br perovskite material, and the p-i-n structure was composed of SLG/ITO/NiO/CsPbI2Br/SnO2/electrode(Au or Ag). First, in order to fabricate the CsPbI2Br perovskite absorption layer, the CsPbI2Br solution used the solute of Cesium iodid e (CsI), Lead (II) bromide (CsBr2), Lead (II) iodide (PbI2), and N,N-dimethylformamide (DMF), and Dimethyl sulfoxide (DMSO). The CsPbI2Br solution was prepared in a ratio of a solvent (DMF:DMSO) of 3:7 v/v% and 4:1 v/v% at a concentration of 1.2M. In addition, the production of the absorption layer was divided into spin coating without using a hot-air process and spin coating using a hot-air process. The CsPbI2Br layer was coated uniformly under conditions using the hot-air process rather than without the hot-air process, had a high optical absorption rate, and measured close to the ideal band gap (1.91 eV) of CsPbI2Br with an optical band gap of 1.91 eV. Accordingly, in order to apply the CsPbI2Br layer to which the hot-air process is applied in a p-i-n structure, a SnO2 layer was deposited using an H2O oxidant by Thermal-ALD. As a result, the CsPbI2Br layer manufactured with a solvent (DMF:DMSO) ratio of 3:7 v/v% was not oxidized, and the Black α-Cubic phase was maintained. And as a result of XRD analysis of the manufactured inorganic CsPbI2Br perovskite solar cell, the CsPbI2Br Black α-Cubic phase was confirmed. And as a result of measuring the short circuit current density-voltage curve of the solar cell using the Au or Ag electrode using a solar simulator, the power conversion efficiency of the solar cell using the Au electrode was measured as 2.75% (forward) and 5.11% (reverse). And the power conversion efficiency of the solar cell using the Ag electrode was measured as 4.73% (forward) and 6.79% (reverse).

발파로 인한 환경문제 및 암석파괴물성 예측에 ANN 알고리즘의 적용

나퓨 전북대학교 일반대학원 2023 국내박사

The exponential and continuous growth of the human population has led to increasing demand for mineral resources, the utilization of underground spaces, and improved amenities worldwide. These demands have led to increasing pressure and challenges for geomechanical, rock, and geotechnical engineers and policymakers to devise practical solutions to the prevailing sustainability and environmental problems resulting from the extraction of minerals and the development of various geo-structures for human use. Rock blasting and the characterization of rock geomechanical properties are the two main means of achieving practical engineering solutions. Blasting is the most economical and flexible method for rock excavation and has been applied in various geomechanics fields, including surface and underground mining, civil engineering construction, and tunneling. Nevertheless, the confined explosive energy used in rock blasting generates not only fragments of the rock but also many unfriendly and deleterious environmental problems, such as ground vibration (PPV), air blast overpressure (AOp), and fly rock. These deleterious environmental impacts of blasting often result in higher cost overruns for mining and construction companies, and severe safety concerns for nearby human settlements. The characterization of rock geomechanical properties is a crucial task in geomechanics because the parameters of rock properties are required for the successful and efficient design of important geomechanical structures. However, the accurate characterization of rock geomechanical properties is challenging owing to the variability and inhomogeneous characteristics of rock materials and the nonuniformity of testing conditions, leading to measurement errors. An inaccurate estimation of these properties may lead to unexpected failures of geomechanical structures, safety issues, and high-budget overruns. Therefore, there is a need to develop robust prediction and characterization models to address these problems. Several studies have been undertaken with the aim of proposing prediction models for estimating PPV and AOp and characterizing the geomechanical properties of rocks. These studies utilized different techniques, including empirical equations and analytical, theoretical, and numerical methods, with promising results. However, the techniques employed by previous researchers for the prediction of PPV and AOp were developed for mine production blasts and a single site and may not be suitable for construction excavation and tunneling projects proceeded from multiple sites and used fewer explosives but shorter distances to human settlements. In addition, most geomechanical rock property prediction models used in engineering designs and analyses consider static rock behavior but neglect the dynamic nature of most geomechanical structures and events like rock bursts. This study aims to address these challenges by developing an ANN-based approach for blast-induced PPV and AOp prediction in construction and tunnel excavations, as well as rock geomechanical property characterization under static and dynamic conditions. An ANN-based approach was first developed for the prediction of blast-induced PPV and AOp from ten different construction excavation sites and geological terrains in South Korea. The ANN-based framework utilized 115 blast event records and geological conditions of all ten study sites as input parameters. The selected input parameters included burden (B), spacing (S), hole diameter (D), hole depth (L), stemming length (T), monitoring distance (DIS), charge per delay (Q), powder factor (PF), and rock mass rating (RMR), while both blast-induced PPV and AOp were the targeted outputs. For the blast-induced PPV, the best-performing models were the grasshopper algorithm and slime mold algorithm-optimized ANNs, ANN-GOA, and ANN-SMA with R2 = 0.9955 and 0.9820, respectively, for the validation datasets. For the blast-induced AOp, the best-performing models were the slime mold algorithm and multiverse optimized ANNs, ANN-SMA, and ANN-MVO, with R2 = 0.8670 and 0.8300, respectively, for the testing datasets. The PPV model was tested with 20 new blasting event records from two construction excavation sites not used in the validation and showed high testing results of R2 = 0.985 and 0.975 for ANN-GOA and ANN-SMA, respectively. The proposed ANN-based framework explicitly addresses the black-box, site-specific, and non-large-scale deployment of existing blast-induced PPV and AOp models by transforming the models into engineer-friendly closed-form equations for the easy estimation of PPV and AOp from rock excavation. The RMR was found to be the most sensitive parameter for the generation of PPV and AOp, and this showed the site-specific problem of the previous models. The PF and Q values of 0.25, 0.30, and 0.35 kg/m3, and between 0.25 and 4 kg, respectively, were suggested to be optimum blast design parameters to reduce PPV (mm/s) and AOp (dB) in bench blasting at the shortest distance of 10–40 m to the blast location. A robust prediction model was developed to predict blast-induced PPV during tunnel excavation at five different tunnel sites with different cross-sectional areas and geological terrain in South Korea. 221 field databases comprising seven effective parameters, including charge per delay (Q), number of holes (n), monitoring distance (DIS), hole depth (L), rock mass rating (RMR), total charge (QT), and tunnel cross-sectional area, were used for the model development. The ANN-GOA 7-17-1 model was optimal for the developed models with R2 = 0.99062, MSE = 0.00161, and VAF = 99.0544% for the test datasets. The variable importance analysis of the input parameters showed that the total charge, rock mass rating, and tunnel cross-sectional area are important parameters for the prediction of PPV in tunnel blasting. Next, the suitability of the developed ANN-based prediction framework was assessed for the prediction of the high strain-rate loading-dependent UCS of rocks. An ANN-based model was developed using a database of 94 direct laboratory measurements comprising six effective input parameters: rock disc sample diameter (D), rock disc sample thickness (L), strain rate (Ɛ), rock bulk density (ρ), static UCS (σc), and P-wave velocity (PWV). The salp swarm algorithm hybrid ANN (ANN-SSA 6-10-1) model was determined to be the optimum of the developed models, with the lowest error metrics and highest coefficient of correlation of R = 0.99270, RRMSE = 0.07384, VAF = 98.49%, and a20-index = 1.0000 for the testing dataset. To ensure an easy and large-scale implementation of the optimum ANN-SSA 6-10-1 and other developed ANN-based models, the models were transformed into an intuitive closed-form equation. The static UCS, strain rate, and rock disc sample diameter were found to be the most sensitive parameters to the dynamic UCS. The ANN-based framework was extended to study the fracture behavior of rock. An ANN-based prediction model framework was developed for the ISRM-suggested Semi-Circular Bend (SCB) specimen Mode-I fracture toughness (KIc) using 121 experimental data points obtained from the fracture toughness tests on SCB rock specimens. Four effective parameters affecting KIc, namely the Brazilian tensile strength (σt), disc specimen radius (R), thickness (T), and crack length (a), were selected as the input parameters. The ANN-GOA 4-9-1 model was determined to be the best-performing model based on the error metrics, with R = 0.98498, MSE = 0.0036, VAF = 97.02%, and a20-index = 0.96694 for the overall datasets. To ensure easy implementation of the optimum ANN-GOA 4-9-1, the model was transformed into a tractable closed-form explicit equation. The Brazilian tensile strength was found to be the most sensitive parameter for KIc. The ANN-based framework and closed-form equations proposed in this thesis provide an engineer-friendly, non-time-consuming, and reliable method for predicting blast-induced PPV and AOp in rock excavation and tunneling and for the accurate characterization of rock properties that can be integrated into geomechanical design and analysis frameworks.