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- 저자 : 김양균(Yangkyun Kim) (검색결과 44 건)
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김양균,이제겸,이승원,Kim, Yangkyun,Lee, Je-Kyum,Lee, Sean Seungwon 한국터널지하공간학회 2022 한국터널지하공간학회논문집 Vol.24 No.5
As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.
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개방 공간에서 발생하는 수소-공기 혼합 가스 폭연에 대한 실험적/해석적 연구
김양균,박병직,Kim, Yangkyun,Park, Byoung Jik 한국안전학회 2021 한국안전학회지 Vol.36 No.1
Experimental and analytical investigations are performed to explore the explosion characteristics of a hydrogen-air mixture in open atmosphere. A hydrogen-air mixture tent of total volume of 27 m<sup>3</sup>, with 40% hydrogen volume, is used to observe overpressure at a distance from the ignition source. Vapor cloud explosion analyses are performed using the TNO multi-energy model and Baker-Strehlow-Tang model. The results of these analyses are compared with experiment done from this study and references. The experimental results with and without obstacles indicate that the overpressure values measured at a distance of 4.5-21.5 m from the ignition source are about 9.4-3.6 kPa and 6.5-2 kPa, respectively. This implies that the overpressure with obstacles is approximately 1.7 times greater than that without obstacles. Analytical observation indicates that the results obtained with the Baker-Strehlow-Tang model with M<sub>f</sub> = 0.2-0.35 are in good agreement with those of most of the previous studies, including that obtained from this study. Moreover, the TNO multi-energy model with a volume of 27 m<sup>3</sup> well predicts the overpressure obtained from this study. Further studies should considered explosions in semi-confined spaces, which is more suitable for hydrogen refueling stations.
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수소충전소 사고대응 표준작전절차서(SOP)에 대한 고찰
김양균(Yangkyun Kim),박병직(Byoungjik Park) 한국화재소방학회 2021 한국화재소방학회 학술대회 논문집 Vol.2021 No.추계
탄소중립 실현을 위해 수소 활용에 대한 사회적인 요구가 늘어남에 따라 정부는 수소경제활성화로드맵(‘19)을 발표하고 수소사회 진입을 시도하고 있다. 로드맵 이행을 위해 국토 내 수소충전소 등의 인프라와 수소를 동력원으로 하는 자동차, 버스, 트럭 등의 모빌리티가 급격히 증가하는 추세이며, 이에 따른 크고 작은 사고가 발생할 것으로 예상된다. 소방청에서는 이러한 사고를 적적히 대응하고 인적/물적 피해를 최소화 할 수 있는 방법을 모색하고자 수소충전시설에서 발생하는 사고 대응 표준작전절차서(Standard Operating Procedure)를 개발하고 있다. 본 논문에서 미국과 유럽의 수소사고대응 표준작전절차를 알아보고 현재 개발되고 있는 국내 수소사고대응 표준작전절차를 소개하고자 한다. 본 수소사고대응 표준작전절차는 현재 소방청에서 운영중인 재난현장 표준작전절차와 마찬가지로 ‘긴급구조대응활동 및 현장지휘에 관한 규칙’ 제10조에 의거 수소충전소에서 발생하는 사고현장에서 현장지휘체계를 확립하고 신속⋅효율적인 현장 대응을 위해 요구되는 기본적이고 일반적인 방침사항을 기술한다. 특히 현장안전관리표준지침(Standard Safety Guidlines) 내 SSG 1-1(현장활동 주의사항), SSG 1-2(임무별 안전관리), SSG 1-3(유형별 안전관리)을 준수하고 SOP 105(위험구역진입절차 및 대원긴급탈출절차), 202(화재 시 특수현상 대응절차), 204-14(차량화재 대응절차), 204-18(석유화학단지 화재), 306(차량사고), 307(하이브리드차량사고), 310(유해화학물질사고), 312(폭발물사고), 317(위험물시설물 안전조치)에서 소개하는 기존 대응절차를 반영하여 기존에 없던 수소 인프라 사고에 대한 표준작전절차를 제시하고자 한다.
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김양균(Yangkyun Kim),김성초(Sungcho Kim),김정수(Jeong Soo Kim),이기만(Kee Man Ree),진학수(Haksu Jin) 대한기계학회 2007 대한기계학회 춘추학술대회 Vol.2007 No.5
Flow and aerodynamic characteristics were analyzed numerically for a commercial passenger airplane, Boeing 747-400, flying in the cruising condition. The model geometry with 100:1 in scale was obtained by the photo scanning measurement with the maximum error of 1.4% comparing with the real airplane dimension. The three-dimensional inviscid steady compressible governing equations were solved by the finite volume method in the unstructured grid system. The convective terms were treated by the Crank-Nicholson and first-order upwind schemes. In the computational results, the strong wing-tip vortices were clearly observed and the pressure contours on the airplane surface were suggested. The lift and drag forces in the wing with engines increase by 1.49% and 3.9%, respectively compared with the case without engines. The aerodynamic forces were estimated quantitatively for each element which consists of the airplane.
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김양균(Yangkyun Kim) 한국암반공학회 2021 터널과지하공간 Vol.31 No.1
4차 산업혁명 시대의 도래에 따라 암반공학분야에서도 인공지능을 활용한 연구가 점차 증가하고 있다. 본 논문에서는 인공지능에 대한 이해와 그 활용도를 더욱 증진시키기 위하여, 암반공학기술의 주된 적용대상인 터널, 발파, 광산과 관련된 최근의 국내외 연구 중 인공지능이 활용된 논문들에서 그 알고리즘의 종류와 적용방법을 분석하였다. 터널에서는 암반분류, TBM굴진율 및 막장전방 지질 예측, 발파에서는 암반의 파쇄도 및 비산거리, 광산에서는 폐광의 침하가능성 예측을 위해 주로 활용되고 있으며, 기계학습의 다양한 알고리즘 중 인공신경망이 압도적으로 많이 활용되고 있는 것으로 나타났다. 연구결과의 정확도와 신뢰성 제고를 위해 사용하고자 하는 인공지능 알고리즘에 대한 정확하고 상세한 이해가 필수적이며, 현재는 접근이나 분석이 난해한 암반공학 분야의 다양한 문제해결을 위해 기계학습뿐 아니라 CNN 또는 RNN과 같은 딥러닝을 활용한 연구 아이디어들이 점차 증가될 것으로 기대된다. As the era of Industry 4.0 arrives, the researches using artificial intelligence in the field of rock engineering as well have increased. For a better understanding and availability of AI, this paper analyzed the types of algorithms and how to apply them to the research papers where AI is applied among domestic and international studies related to tunnels, blasting and mines that are major objects in which rock engineering techniques are applied. The analysis results show that the main specific fields in which AI is applied are rock mass classification and prediction of TBM advance rate as well as geological condition ahead of TBM in a tunnel field, prediction of fragmentation and flyrock in a blasting field, and the evaluation of subsidence risk in abandoned mines. Of various AI algorithms, an artificial neural network is overwhelmingly applied among investigated fields. To enhance the credibility and accuracy of a study result, an accurate and thorough understanding on AI algorithms that a researcher wants to use is essential, and it is expected that to solve various problems in the rock engineering fields which have difficulty in approaching or analyzing at present, research ideas using not only machine learning but also deep learning such as CNN or RNN will increase.
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