• Journal of Mechanical Science and Technology
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• 제17권10호
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• pp.1450-1457
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• 2003

Structural integrity of either a passenger car or a light truck is one of the basic requirements for a full vehicle engineering and development program. The results of the vehicle product performance are measured in terms of durability, noise/vibration/harshness (NVH), crashworthiness and passenger safety. The level of performance of a vehicle directly affects the marketability, profitability and, most importantly, the future of the automobile manufacturer. In this study, we used the Virtual Proving Ground (VPG) approach for obtaining the dynamic stress or strain history and distribution. The VPG uses a nonlinear, dynamic, finite element code (LS-DYNA) which expands the application boundary outside classic linear, static assumptions. The VPG approach also uses realistic boundary conditions of tire/road surface interactions. To verify the predicted dynamic stress and fatigue critical region, a single bump run test, road load simulation, and field test have been performed. The prediction results were compared with experimental results, and the feasibility of the integrated life prediction methodology was verified.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2011년도 춘계학술대회 초록집
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• pp.59.2-59.2
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• 2011

Although there are many activities on the construction of wind farm to produce amount of power from the wind, in practice power productions are not as much as its expected capabilities. This is because a lack of both the prediction of wind resources and the aerodynamic analysis on turbines with far wake effects. In far wake region, there are velocity deficits and increases of the turbulence intensity which lead to the power losses of the next turbine and the increases of dynamic loadings which could reduce system's life. The analysis on power losses and the increases of fatigue loadings in the wind farm is needed to prevent these unwanted consequences. Therefore, in this study velocity deficits have been predicted and aerodynamic analysis on turbines in the far wake is carried out from these velocity profiles. Ainslie's eddy viscosity wake model is adopted to determine a wake velocity and aerodynamic analysis on wind turbines is predicted by the numerical methods such as blade element momentum theory(BEMT) and vortex lattice method(VLM). The results show that velocity recovery is more rapid in the wake region with higher turbulence intensity. Since the velocity deficit is larger when the turbine has higher thrust coefficient, there is a huge aerodynamic power loss at the downstream turbine.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2010년도 춘계학술대회 논문집
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• pp.315-323
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• 2010

장대레일 교체주기는 자갈궤도 레일두부 표면요철과 레일 휨피로의 상관관계 분석을 통해 산정되었다. 본 연구에서는 국내 도시철도(서울메트로) 콘크리트궤도에서 레일두부 표면요철량에 따른 레일 휨응력을 측정하였다. 또한, 콘크리트궤도 레일두부 표면요철, 속도와 레일 휨응력의 상관관계를 분석하였다. 결론적으로, 중회귀분석을 통해 운행속도(U), 표면요철(v, w)에 따른 레일 휨응력(Y) 예측식을 도출하였다. 본 연구결과는 콘크리트궤도 장대레일 교체주기를 수립하기 위한 기초데이터로 활용이 가능할 것이다.

• 대한기계학회논문집A
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• 제33권8호
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• pp.767-772
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• 2009

Laser shock peening(LSP) is proving to be better surface treatment than conventional one such as shot peening. The LSP process has a compressive residual stress into a metal alloy and a significant improvement in fatigue life. Our research is focused on applying finite element method to the prediction of residual stress through the LSP processing in some LSP conditions such as pressure and spot size induced by laser. Two analysis methods are considered to calculating the compressive residual stress. But the explicit solution and the static one after partially explicit solving are almost same. In LSP, because of very high strain rate($10^6s^{-1}$), HEL(Hugoniot Elastic Limit) is the most important parameter in material behavior modeling. As the circular laser spot is considered, 2-D axisymmetric elements are used and the infinite elements are applied to boundaries for no reflection. The relations of material properties and the LSP are also important parts in this study.

• Journal of Welding and Joining
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• 제13권2호
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• pp.96-105
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• 1995

본 연구에서는 종래의 미소피로균열 성장속도 해석방법에 대한 수정안을 제시하고 수정 후의 방 법에 의해서 계산한 값들과 S4SC 탄소강에 대한 Nisitani와 Goto의 실험결과를 비교하여 계산한 값과 실험데이터 사이에 양호한 일치가 있음을 보였다. 이미 제시된 피로균열성장속도 식에는 하한계수준과 피로한도를 연관시키는 재료상수와 탄소성 거동에 대한 수정 및 균열닫힘효과를 나타내는 방법이 포함되어 있다. 본 연구에서 행한 수정중의 하나는 기하학적인 상수대신에 퍼만(Forman)의 탄성응력 강도계수 범위식을 이용하는 것이고, 다른 하나는 균열이 성장함에 따라 편심형단면으로 되면서 모멘트에 기인해 발생되는 굽힘효과를 고려하는 것이다. 이 방 법을 수명예측에 사용하면 용접구조물은 물론 기계구조물의 보다 정확한 수명예측이 가능할 것 이다.

• 대한기계학회논문집
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• 제16권1호
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• pp.53-62
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• 1992

본 연구에서는 약 10년 정도 사용하여 경년 열화가 되었다고 예상되는 실구조 물의 일부를 입수하였으며 열화재의 특성과 비교하기 위하여 열처리에 의해 충격치를 회복시킨 재료를 회복재로 하여 두가지 재료에 대해 시험편을 제작하였따.열확현상 을 파악하기 위하여 평활재로 피로과정, 즉 미소 균열의 발생, 진전 및 복수 균열이 간섭합체하여 파단에 달하는 과정에 대하여 파괴역학적 견지에서 열화재와 회복재를 해석하고 이결과로 부터 확율변수를 추정하여 통계학적인 수명예측방법의 하나를 제시 하여 실구조물에 적용하는 방법에 대해 시도해 보았다.

• 대한기계학회논문집
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• 제16권9호
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• pp.1605-1614
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• 1992

본 연구에서는 고장력 7075-T73 알루미늄합금에 대하여 변동하중의 기보파형 인 단일과대하중과 고-저(high-low) 블럭하중하의 지연거동에 미치는 과대하중비 %O.L., 기준응력확대계수범위 .DELTA. $K_{b}$ 및 무차원 균열깊이 a/W의 영향을 규명하였 으며, 또한 Wheeler모델의 수정에 의한 예측피로수명을 실험치와 함께 검토하였다.다.

• 한국자동차공학회논문집
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• 제16권6호
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• pp.33-38
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• 2008

Most structures of vehicle are composed of many substructures connected to one another by various types of mechanical joints. In vehicle engineering, it is important to study these jointed structures under random frequency vibration for the evaluations of fatigue life and stress concentration exactly. It is rarely obtained the accurate load history of specified positions in a jointed structure because of the errors such as modeling, measurement, and etc. In the beginning of design, exact load data are actually necessary for the fatigue strength and life analysis to minimize the cost and time of designing. In this paper, the hybrid method of practical dynamic load determination is developed by the combination of the principal stresses from F. E. Analysis and test of a jointed structure. Least square pseudo inverse matrix is adopted to obtain an inverse matrix of analyzed stresses matrix. The error minimization method utilizes the inaccurate measured error and the shifting error that the whole data is stiffed over real data. The least square criterion is adopted to avoid these errors. Finally, to verify the proposed system, a heavy-duty bus is analyzed. This measurement and prediction technology can be extended to the different jointed structures.

• 한국학교보건학회지
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• 제36권3호
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• pp.113-125
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• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.

• Structural Engineering and Mechanics
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• 제90권1호
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• pp.27-40
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• 2024

The prediction of VIV amplitude is essential for the design and fatigue life estimation of steel tubes in tubular transmission towers. Limited to costly and time-consuming traditional experimental and computational fluid dynamics (CFD) methods, a machine learning (ML)-based method is proposed to efficiently predict the VIV amplitude of steel tubes in transmission towers. Firstly, by introducing the first-order mode shape to the two-dimensional CFD method, a simplified response analysis method (SRAM) is presented to calculate the VIV amplitude of steel tubes in transmission towers, which enables to build a dataset for training ML models. Then, by taking mass ratio M^*, damping ratio ξ, and reduced velocity U^* as the input variables, a Kriging-based prediction method (KPM) is further proposed to estimate the VIV amplitude of steel tubes in transmission towers by combining the SRAM with the Kriging-based ML model. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by using three full-scale steel tubes with C-shaped, Cross-shaped, and Flange-plate joints, respectively. The results show that the SRAM can reasonably calculate the VIV amplitude, in which the relative errors of VIV maximum amplitude in three examples are less than 6%. Meanwhile, the KPM can well predict the VIV amplitude of steel tubes in transmission towers within the studied range of M^*, ξ and U^*. Particularly, the KPM presents an excellent capability in estimating the VIV maximum amplitude by using the reduced damping parameter S_G.