Chan Yik Park

Bio: Chan Yik Park is an academic researcher. The author has contributed to research in topics: Inertia & Hard landing. The author has an hindex of 1, co-authored 1 publications receiving 11 citations.

Hard-landing Simulation by a Hierarchical Aircraft Landing Model and an Extended Inertia Relief Technique

Abstract: In this work, an efficient aircraft landing simulation strategy is proposed to develop an efficient and reliable hard-landing monitoring procedure. Landing stage is the most dangerous moment during operation cycle of aircraft and it may cause structural damage when hard-landing occurs. Therefore, the occurrence of hard-landing should be reported accurately to guarantee the structural integrity of aircraft. In order to accurately determine whether hard-landing occurs or not from given landing conditions, full nonlinear structural dynamic simulation can be performed, but this approach is highly timeconsuming. Thus, a more efficient approach for aircraft landing simulation which uses a hierarchical aircraft landing model and an extended inertia relief technique is proposed. The proposed aircraft landing model is composed of a multi-body dynamics model equipped with landing gear and tire models to extract the impact force and inertia force at touch-down and a linear dynamic structural model with an extended inertia relief method to analyze the structural response subject to the prescribed rigid body motion and the forces extracted from the multi-body dynamics model. The numerical examples show the efficiency and practical advantages of the proposed landing model as an essential component of aircraft hard-landing monitoring procedure.

Multibody Systems Approach To Vehicle Dynamics

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Estimation of Maximum Strains and Loads in Aircraft Landing Using Artificial Neural Network

Abstract: Hard landings account for a large proportion of aircraft accidents and are generally judged by the intuition of pilots. For this reason, there are frequent false judgments that lead to unnecessary and costly ground inspections. False judgments can be reduced significantly if detailed load information is available, such as strains or loads on critical areas of aircraft structures. This study used an artificial neural network (ANN) to develop a numerical model that can estimate the maximum strains for areas of interest and landing loads from basic flight parameters. The results can be used to provide the required detailed load information. An efficient and accurate landing simulation model was constructed and used to build reliable datasets for training. Basic flight parameters from immediately after touchdown were used as input data for training, and the corresponding maximum values of strains and landing loads were obtained from the landing simulation model as target data for training. This information was used to train the ANNs with the Levenberg–Marquardt backpropagation algorithm. A performance evaluation using test data confirmed that the trained ANNs can successfully estimate strains and landing loads with sufficient accuracy.

항공기 올레오식 2중 완충기 종류에 따른 특성 비교 연구 -Part II. 수치해석 및 비교

Abstract: 본 연구에서는 세 가지 종류의 올레오식 2중 완충기 특성 비교를 위해 첫 단계 연구에서 제안한 수학적 모델을 이용하여 수치해석을 수행하고 완충 특성을 비교하였다. 각 올레오식 2중 완충기 모델에 대해 수치해석 알고리듬을 제시하였으며, 이를 MSC/ADAMS 상용 다물체 동역학 해석 소프트웨어의 사용자 서브루틴으로 구...