• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1581-1588
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• 2011

In this study, flow characteristics in a multi-cavity injection molding process were investigated. One of main problems occurred in the multi-cavity molding is a flow imbalance among cavities since it affects physical properties and quality of products. Charge imbalance is caused by the uneven shear stress. Therefore, changes in viscosity affect the physical properties of resin and injection conditions differ in the filling imbalance phenomenon. Through, this study focus on experimental studies of flow imbalance for PC and PP resin occurring in a balanced delivery system. Experimental results were compared with CAE results. By experimental and CAE analysis, main cause for the flow imbalance is temperature distribution in cross section of runner. New runner system with a simple change of runner shape was suggested to avoid the flow imbalance. A series of simulation to confirm feasibility of Volume Runner's effects was conducted using injection molding CAE.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.5
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• pp.475-485
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• 2009

In this paper a two-step algorithm is proposed to efficiently generate rebar elements from 3D CAD data in the context of CAD/CAE data transfer. The first step is an algorithm to identify various type rebar objects and their attributes by analyzing 3D CAD data in STEP format, which is one of the international data standards. The second algorithmic step is a procedure to generate one-dimensional rebar elements from the object data made through the first step for finite element analysis or other CAE tasks. Successful rebar element data generation from real 3D CAD data for a reinforced concrete structure shows the efficacy of the proposed algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.735-741
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• 2011

In the early phase of vehicle development, CAE is conducted as tool for vehicle performance assessment. To maintain acceptable road noise performance, solution for reduced vehicle sensitivity is required. Chassis interface dynamic stiffness characteristics are key component to isolating vibration and noise of road from the vehicle interior. This research provide how to set up the optimized dynamic characteristics under noise effect through DFSS study. CAE-based DOE is performed to build prediction math model, CMS process involves DOE to achieve very fast run times while giving results very comparable. Minimized $95^{th}$ percentile of performance distribution is applied to minimize vehicle sensitivity and road noise levels variation during the optimization process. Finally, the results of optimization were reviewed for performance and robustness.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.261-268
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• 2021

Cloud computing is a computing paradigm in which users can utilize computing resources in a pay-as-you-go manner. In a cloud system, resources can be dynamically scaled up and down to the user's on-demand so that the total cost of ownership can be reduced. The Modeling and Simulation (M&S) technology is a renowned simulation-based method to obtain engineering analysis and results through CAE software without actual experimental action. In general, M&S technology is utilized in Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Multibody dynamics (MBD), and optimization fields. The work procedure through M&S is divided into pre-processing, analysis, and post-processing steps. The pre/post-processing are GPU-intensive job that consists of 3D modeling jobs via CAE software, whereas analysis is CPU or GPU intensive. Because a general-purpose desktop needs plenty of time to analyze complicated 3D models, CAE software requires a high-end CPU and GPU-based workstation that can work fluently. In other words, for executing M&S, it is absolutely required to utilize high-performance computing resources. To mitigate the cost issue from equipping such tremendous computing resources, we propose HEMOS-Cloud service, an integrated cloud and cluster computing environment. The HEMOS-Cloud service provides CAE software and computing resources to users who want to experience M&S in business sectors or academics. In this paper, the economic ripple effect of HEMOS-Cloud service was analyzed by using industry-related analysis. The estimated results of using the experts-guided coefficients are the production inducement effect of KRW 7.4 billion, the value-added effect of KRW 4.1 billion, and the employment-inducing effect of 50 persons per KRW 1 billion.

• Proceedings of the KAIS Fall Conference
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• 2006.11a
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• pp.261-264
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• 2006

본 논문에서는 다양한 곡률반경의 연속에 의하여 살 두께 차가 큰 사출성형품에 불균일한 수축으로 인한 변형이 생성되어 이를 방지하기 위한 적정 CAE 냉각설계를 수행하였다. SAN 및 PMMA 재질의 Jar용기에 대한 균일냉각구조와 최적성형조건을 금형설계에 적용하고자 사출성형의 중요인자인 사출압력, 수지온도, 금형온도, 냉각조건 등을 moldflow 프로그램을 활용하여 연구를 수행하였다. 연구결과로서, 적정 변수인 사출압력 상승, 수지온도 낮춤, 급속냉각으로 후로우 등의 불량현상을 분석하였고 변형 및 불량을 극소화시킬 수 있는 냉각구조와 사이클 시간을 단축시킬 수 있는 사출성형조건을 제시하였고 적정 냉각모듈로부터 냉각시간을 단축하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.451-452
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• 2011

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.683-684
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• 2012

제품 또는 시스템의 개발을 위해 물리현상을 정밀하게 시뮬레이션하고 그 거동을 예측하거나 평가하는 CAE 등의 엔지니어링 기술이 많이 사용된다. 한편, 가상의 공간에서 협업, 운영, 유지보수, 훈련 등의 다양한 목적을 구현하는 가상현실 시스템은 대상체 물리현상에 대한 정확한 시뮬레이션 및 데이터 신뢰도의 확보가 중요하다. 본 연구에서는 CAE 엔지니어링 데이터를 이용하여 가상현실 시스템에 정밀한 물리현상을 반영할 수 있도록 엔지니어링 데이터의 분석과 연동에 관한 연구를 수행하였으며, 간단한 결과에 대해 소개한다.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.453-462
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• 2021

As the frequency of localized heavy rainfall has increased during recent years, the importance of high-resolution radar data has also increased. This study aims to correct the bias of Dual Polarization radar that still has a spatial and temporal bias. In many studies, various statistical techniques have been attempted to correct the bias of radar rainfall. In this study, the bias correction of the S-band Dual Polarization radar used in flood forecasting of ME was implemented by a Convolutional Autoencoder (CAE) algorithm, which is a type of Convolutional Neural Network (CNN). The CAE model was trained based on radar data sets that have a 10-min temporal resolution for the July 2017 flood event in Cheongju. The results showed that the newly developed CAE model provided improved simulation results in time and space by reducing the bias of raw radar rainfall. Therefore, the CAE model, which learns the spatial relationship between each adjacent grid, can be used for real-time updates of grid-based climate data generated by radar and satellites.

• Journal of the KSME
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• v.50 no.5
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• pp.44-47
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• 2010

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.6
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• pp.683-689
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• 2014

The present study conducted a parameter effect analysis of low-frequency squeal noise using a numerical simulation. The finite element program ABAQUS was used to calculate the dynamic instability based on a complex eigenvalue analysis. A total of five parameters, including the chassis, wear, piston, material property, and contact condition, were selected to identify the factor effects on a low-frequency squeal noise between 2.5 and 3.1 kHz. The present study found the dominant level of each factor through an analysis of the means in the context of the experiment design.