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초임계 압력 조건에서 스월 인젝터 내 케로신 대체 모델의 열역학 물성 특성 연구
김국진(Kukjin Kim),허준영(Junyoung Heo),성홍계(Honggye Sung) 한국추진공학회 2012 한국추진공학회 학술대회논문집 Vol.2012 No.11
초임계 압력 조건에서 상태 방정식(이상 기체, SRK, PR, RK-PR)과 케로신 대체 모델에 따른 열역학 상태량과 스월 인젝터에서의 열역학 물성 특성을 연구하였다. 난류 수치 모델은 large eddy simulation을 사용하였으며 전달 물성치의 계산을 위해 Chung의 기법을 포함하고 있다. 열역학 상태량 비교를 통하여 매우 높은 정확도를 나타내는 실 기체 상태 방정식과 케로신 대체 모델을 도출하였다. 또한 초임계 스월 인젝터 내에서 열역학 물성(밀도, 음속, 점성 계수, 열 전도도)을 비교 조사하여 상태량 예측 정확도가 초임계 스월 인젝터에서의 동적 분사 특성에 큰 영향을 미치는 것이 관찰되었다. Thermophysical properties of kerosene surrogates in a swirl injector operating at supercritical pressure condition have been investigated using various equations of state(ideal gas, SRK, PR, RK-PR). Large eddy simulation has been applied for turbulent numerical model including Chung"s model for the calculation of transport properties. Based on the comparison of thermophysical properties such as density, speed of sound, viscosity, and thermal conductivity, the real gas equation of state, RK-PR, and a surrogate model consisting of 4 components are proposed to be very high accuracy. The thermophysical properties have fairly influence on the dynamic characteristics in supercritical swirl injector.
초임계 스월 인젝터에서의 케로신 Surrogate 모델에 대한 수치적 연구
김국진(Kukjin Kim),허준영(Junyoung Heo),성홍계(Honggye Sung) 한국추진공학회 2010 한국추진공학회 학술대회논문집 Vol.2010 No.11
초임계 환경에서 작동하는 액체 로켓 엔진의 케로신 스월 인젝터에서 케로신 물성치에 따른 인젝터 내외에서의 분사 특성을 연구하였다. 케로신의 물성치를 계산하기 위해 surrogate 모델이 적용되었다. 난류 수치 모델은 large eddy simulation을 기반으로 하였으며 SRK 상태 방정식, Chung의 기법을 포함하고 있다. 초임계 환경의 수치 해석 결과는 천이 임계 조건의 결과와 비교되었으며 스월 인젝터 내부의 액막과 중심부 사이의 밀도 및 점성 계수 분포의 차이가 관찰되었다. Injection characteristics of a kerosene swirl injector of liquid rocket engine operating at supercritical environment have been investigated. Kerosene surrogate models are proposed to model the kerosene properties. Turbulent numerical model is based on large eddy simulation and contains Soave modification of Redlich-Kwong equation of state and Chung"s model. Numerical analysis results at supercritical environment are compared with the one at transcritical condition. Differences of density and viscosity are analyzed at both liquid film and core gas in the swirl injector.
아이템 네트워크를 활용한기술 중심 사업 다각화 기회 탐색 지원 방법론
배국진(Kukjin Bae),김지은(Ji-Eun Kim),김남규(Namgyu Kim) 한국IT서비스학회 2020 한국IT서비스학회지 Vol.19 No.3
Recently, various attempts have been made to discover promising items and technologies. However, there are very few data-driven approaches to support business diversification by companies with specific technologies. Therefore, there is a need for a methodology that can detect items related to a specific technology and recommend highly marketable items among them as business diversification targets. In this paper, we devise Labeled Item Network for Business Diversification Consulting Support System. Our research is performed with three sub-studies. In Sub-study 1, we find the proper source documents to build the item network and construct item dictionary. In Sub-study 2, we derive the Labeled Item Network and devise four index for item evaluation. Finally, we introduce the application scenario of our methodology and describe the result of real-case analysis in Sub-study 3. The Labeled Item Network, one of the main outcome of this study, can identify the relationships between items as well as the meaning of the relationship. We expect that more specific business item diversification opportunities can be found with the Labeled Item Network. The proposed methodology can help many SMEs diversify their business on the basis of their technology.
압입 시험과 머신러닝을 통한 초탄성 소재 모델 계수 예측 기법 개발
두국진(Kukjin Doo),김진현(Jinhyun Kim) 제어로봇시스템학회 2020 제어·로봇·시스템학회 논문지 Vol.26 No.11
In this paper, a hyper-elastic model coefficients prediction algorithm is developed to simplify the experiment to derive the hyper-elastic model coefficients needed for nonlinear finite element analysis (FEA). In the simulations, the correlation between the hyper-elastic model coefficients and the selected measurement data is analyzed through the replicate simulation. A predictive flow graph using TensorFlow is obtained using the acquired data and machine learning techniques. Using these predictive flow graphs, the random hyper-elastic model coefficients are predicted. In addition, the model coefficients of real hyper-elastic materials are predicted using the developed algorithm. Although the accuracy of the prediction is decreased, the model coefficient prediction techniques using manipulator and machine learning algorithms show great potential. An improvement to the pressure test will be attempted in the future to increase the probability of the measuring field.