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연소기 검증을 통한 구형으로 전파하는 SNG-공기 예혼합 화염의 층류연소속도 측정
송준호(Junho Song),조서희(Seohee Cho),이기만(Keeman Lee) 한국연소학회 2019 한국연소학회지 Vol.24 No.3
An experimental study was conducted to determine laminar burning velocity and Markstein length of SNG fuel from spherical propagating flames at normal and elevated pressure conditions. In order to make accurate results, combustion chamber was verified using methane/air mixture to find suitable extraction method and flame radius range in SNG fuel. In addition, ARAMCO 2.0 mechanism was selected to compare experimental results with numerical values. As results of this study, there were found that the laminar burning velocities and Markstein lengths of SNG fuel decreased as initial pressure rose. Pressure exponent tended to increase and decrease as the equivalence ratio increased.
송준호(Junho Song),양은정(Eunjung Yang) 한국전자파학회 2015 한국전자파학회논문지 Vol.26 No.4
대공 레이다에서 표적의 분류는 대 탄도탄 모드 수행의 가장 중요한 부분 중 하나이다. 대 탄도탄 모드에서는 항공기와 탄도탄을 분류하여 각 표적에 따른 대응 방법을 결정한다. 표적 분류의 속도와 정확도는 적의 공격에 대한 대응 능력과 직접적인 관련이 있으므로, 효율적이고 정확한 표적 분류 알고리즘이 필수적이다. 일반적으로, 레이다는 표적 분류를 위해 JEM(Jet Engine Modulation) 및 HRR(High Range Resolution), ISAR(Inverse Synthetic Array Radar) 영상 등을 사용하는데, 이러한 기법들은 표적 분류를 위한 별도의(광대역 등) 레이다 파형과 DB(Data Base) 및 분류 알고리즘을 요구한다. 본 논문은 별도의 파형 없이 실제 다기능 레이다에서 적용 가능한 표적 분류 기법을 제안한다. 특징 벡터로 추적 시 얻은 표적의 운동학적인 특징(kinematics features)을 이용하여 레이다 하드웨어 및 시간 관점에서 레이다 자원을 아끼고, 구현이 간단하여 빠르고 상대적으로 정확한 퍼지 논리(fuzzy logic)를 분류 알고리즘으로 사용하여 실제 환경에서의 적용성을 높였다. 항공기의 실측 데이터와 탄도탄의 모의 신호를 사용하여 제안한 분류 알고리즘의 성능과 적합성을 증명하였다. The target classification for ballistic target(BT) is one of the most critical issues of ballistic defence mode(BDM) in multi-function radar(MFR). Radar responds to the target according to the result of classifying BT and air breathing target(ABT) on BDM. Since the efficiency and accuracy of the classification is closely related to the capacity of the response to the ballistic missile offense, effective and accurate classification scheme is necessary. Generally, JEM(Jet Engine Modulation), HRR(High Range Resolution) and ISAR(Inverse Synthetic Array Radar) image are used for a target classification, which require specific radar waveform, data base and algorithms. In this paper, the classification method that is applicable to a MFR system in a real environment without specific waveform is proposed. The proposed classifier adopts kinematic data as a feature vector to save radar resources at the radar time and hardware point of view and is implemented by fuzzy logic of which simple implementation makes it possible to apply to the real environment. The performance of the proposed method is verified through measured data of the aircraft and simulated data of the ballistic missile.
능동학습과 연결망을 이용한 원자-거시 수준의 기계적 반응 예측
송준호(Junho Song),이재훈(JaeHoon Lee),김남중(Namjung Kim),민경민(Kyoungmin Min) 대한기계학회 2023 대한기계학회 춘추학술대회 Vol.2023 No.11
In recent years, the development of additive manufacturing has made it possible to compare the properties of a wide range of materials from the micro to the macro. To design a desired structure with the techniques, it has become important for mechanical properties to identify stress and mechanical responses even in extreme deformation. Stress-strain curve provides information to infer other mechanical properties. However, finding this curve in atomic-level materials, simulations or experiments had to be accompanied. Traditional experiments and numerical simulations are time consuming. Therefore, in recent years, there is a trend of using machine learning approaches to overcome these problems. Some cases are using machine learning to predict material and structure deformation and stress distribution. These cases are known to yield results at a faster rate while maintaining similar accuracy to the simulation method. In this study, predicting the linearity of stress-strain curve through a machine learning model.