• Earthquakes and Structures
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• 제16권2호
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• pp.165-175
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• 2019

Incremental dynamic analysis (IDA), as an accurate method to evaluate the parameters of structural performance levels, requires many non-linear time history analyses, using a set of ground motion records which are scaled to different intensity levels. Therefore, this method is very computationally demanding. In this study, a new method is presented to estimate the summarized (16%, 50%, and 84% fractiles) IDA curves of a first-mode dominated structure using discrete wavelet transform and bees optimization algorithm. This method reduces the number of required ground motion records for the prediction of the summarized IDA curves. At first, a subset of first list ground motion records is decomposed by means of discrete wavelet transform which have a low dispersion estimating the summarized IDA curves of equivalent SDOF system of the main structure. Then, the bees algorithm optimizes a series of factors for each level of detail coefficients in discrete wavelet transform. The applied factors change the frequency content of original ground motion records which the generated ground motions records can be utilized to reliably estimate the summarized IDA curves of the main structure. At the end, to evaluate the efficiency of the proposed method, the seismic behavior of a typical 3-story special steel moment frame, subjected to a set of twenty ground motion records is compared with this method.

• 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.

• International Journal of Fluid Machinery and Systems
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• 제2권4호
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• pp.303-314
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• 2009

The present paper shows the results of numerical and experimental modal analyses of Francis runners, which were executed in air and in still water. In its first part this paper is focused on the numerical prediction of the model parameters by means of FEM and the validation of the FEM method. Influences of different geometries on modal parameters and frequency reduction ratio (FRR), which is the ratio of the natural frequencies in water and the corresponding natural frequencies in air, are investigated for two different runners, one prototype and one model runner. The results of the analyses indicate very good agreement between experiment and simulation. Particularly the frequency reduction ratios derived from simulation are found to agree very well with the values derived from experiment. In order to identify sensitivity of the structural properties several parameters such as material properties, different model scale and different hub geometries are numerically investigated. In its second part, a harmonic response analysis is shown for a Francis runner by applying the time dependent pressure distribution resulting from an unsteady CFD simulation to the mechanical structure. Thus, the data gained by modern CFD simulation are being fully utilized for the structural design based on life time analysis. With this new approach a more precise prediction of turbine loading and its effect on turbine life cycle is possible allowing better turbine designs to be developed.

• 대한기계학회논문집
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• 제12권1호
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• pp.1-7
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• 1988

본 연구에서는 컬럼모델을 보울트로 고정할 때 접합면에 알루미늄판, 황동판, 스테인리스판 등을 삽입하고 정적강성과 동적특성을 검토하여 이것을 기초로 공작물- 주축대-공구로 형성되고 있는 사이클중에서 선반의 주축대와 베드를 연결하는 결합부에 모델실험을 사용한 게재물을 삽입하고 선반구축계의 동적특성을 검토하였다.

• Earthquakes and Structures
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• 제17권3호
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• pp.307-315
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• 2019

Building prediction and control theory have been drawing the attention of many scientists over the past few years because design and control efficiency consumes the most financial and energy. In the literature, many methods have been proposed to achieve this goal by trying different control theorems, but all of these methods face some problems in correctly solving the problem. The Evolutionary Bat (EB) Algorithm is one of the recently introduced optimization methods and providing researchers to solve different types of optimization problems. This paper applies EB to the optimization of building control design. The optimized parameter is the input to the fuzzy controller, which gives the status response as an output, which in turn changes the state of the associated actuator. The novel control criterion for guarantee of the stability of the system is also derived for the demonstration in the analysis. This systematic and simplified controller design approach is the contribution for solving complex dynamic engineering system subjected to external disturbances. The experimental results show that the method achieves effective results in the design of closed-loop system. Therefore, by establishing the stability of the closed-loop system, the behavior of the closed-loop building system can be precisely predicted and stabilized.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.896-915
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• 2023

The network function virtualization (NFV) uses virtualization technology to separate software from hardware. One of the most important challenges of NFV is the resource management of virtual network functions (VNFs). According to the dynamic nature of NFV, the resource allocation of VNFs must be changed to adapt to the variations of incoming network traffic. However, the significant delay may be happened because of the reallocation of resources. In order to balance the performance between delay and quality of service, this paper firstly made a compromise between VNF migration and energy consumption. Then, the long short-term memory (LSTM) was utilized to forecast network traffic. Also, the asymmetric loss function for LSTM (LO-LSTM) was proposed to increase the predicted value to a certain extent. Finally, an experiment was conducted to evaluate the performance of LO-LSTM. The results demonstrated that the proposed LO-LSTM can not only reduce migration times, but also make the energy consumption increment within an acceptable range.

• Smart Structures and Systems
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• 제31권1호
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• pp.69-78
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• 2023

The purpose of this paper is to develop a scalable grey predictive controller with unavoidable random delays. Grey prediction is proposed to solve problems caused by incorrect parameter selection and to eliminate the effects of dynamic coupling between degrees of freedom (DOFs) in nonlinear systems. To address the stability problem, this study develops an improved gray-predictive adaptive fuzzy controller, which can not only solve the implementation problem by determining the stability of the system, but also apply the Linear Matrix Inequality (LMI) law to calculate Fuzzy change parameters. Fuzzy logic controllers manipulate robotic systems to improve their control performance. The stability is proved using Lyapunov stability theorem. In this article, the authors compare different controllers and the proposed predictive controller can significantly reduce the vibration of offshore platforms while keeping the required control force within an ideal small range. This paper presents a robust fuzzy control design that uses a model-based approach to overcome the effects of modeling errors. To guarantee the asymptotic stability of large nonlinear systems with multiple lags, the stability criterion is derived from the direct Lyapunov method. Based on this criterion and a distributed control system, a set of model-based fuzzy controllers is synthesized to stabilize large-scale nonlinear systems with multiple delays.

• Earthquakes and Structures
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• 제25권6호
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• pp.469-479
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• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2017년도 제48회 춘계학술대회논문집
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• pp.172-178
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• 2017

열적 특성은 에너지 물질 분야에서 중요한 특성 중 하나로, 에너지 물질 분해 시 분해열을 방출하기 때문에 DSC(시차 주사 열량계, Differential Scanning Calorimetry)를 자주 사용하고 있다. 승온속도를 달리한 DSC 측정의 경우, 용융과 같은 열역학적 변화로 인해 물질의 열적 측정에 방해를 준다. 또한 kg 단위로 예측하기 때문에 mg 단위 때와는 다른 공간상의 열 변화의 변수가 생긴다. 이번 연구에서는 이 문제점을 해결하는 방안으로, 등온 조건으로 한 DSC(Differential Scanning Calorimetry) 기초 데이터로 ATKS thermokinetic 프로그램을 이용하여 열적 노화 특성을 예측한다. 그리고 g 단위로 측정하는 ARC(Accelerating Rate Calorimetry)의 데이터를 이용하여 열적 노화 특성을 예측하고 결과를 비교 할 것이다.

• Earthquakes and Structures
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• 제22권1호
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• pp.1-13
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• 2022

When a building suffers damages under moderate to severe loading condition, its physical properties such as damping and stiffness parameters will change. There are different practical methods besides various numerical procedures that have successfully detected a range of these changes. Almost all the previous proposed methods used to work with translational components of mode shapes, probably because extracting these components is more common in vibrational tests. This study set out to investigate the influence of using both rotational and translational components of mode shapes, in detecting damages in 3-D steel structures elements. Three different sets of measured components of mode shapes are examined: translational, rotational, and also rotational/translational components in all joints. In order to validate our assumptions two different steel frames with three damage scenarios are considered. An iterative model updating program is developed in the MATLAB software that uses the OpenSees as its finite element analysis engine. Extensive analysis shows that employing rotational components results in more precise prediction of damage location and its intensity. Since measuring rotational components of mode shapes still is not very convenient, modal dynamic expansion technique is applied to generate rotational components from measured translational ones. The findings indicated that the developed model updating program is really efficient in damage detection even with generated data and considering noise effects. Moreover, methods which use rotational components of mode shapes can predict damage's location and its intensity more precisely than the ones which only work with translational data.