• Nuclear Engineering and Technology
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• v.18 no.2
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• pp.78-84
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• 1986

An equation of material number density sensitivity coefficient is derived using first-order perturbation theory. The beginning of cycle of Super-Phenix I is taken as the reference system for this study. Effective multiplication factor of the reference system is defined as system response function and fuel enrichment and fuel effective density are chosen for the variation of reference input data since they are described by material number density which is a component of Boltzmann operator. The nuclear computational code system (KAERI-26 group cross section library/1DX/2DB/PERT-V) is employed for this calculation. Sensitivity coefficient of fuel enrichment on effective multiplication factor is 4.576 and sensitivity coefficient of effective fuel density on effective multiplication factor is 0.0756. This work shows that sensitivity methodology is lesser timeconsuming and gives more informations on important design parameters in comparison with the direct iterative calulation through large computer codes.

• Advances in aircraft and spacecraft science
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• v.4 no.2
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• pp.145-167
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• 2017

The presented paper gives an overview of several projects addressing the experimental characterization and control of the buffet phenomenon on 3D turbulent wings in transonic flow conditions. This aerodynamic instability induces strong wall pressure fluctuations and therefore limits flight domain. Consequently, to enlarge the latter but also to provide more flexibility during the design phase, it is interesting to try to delay the buffet onset. This paper summarizes the main investigations leading to the achievement of open and closed-loop buffet control and its experimental demonstration. Several wind tunnel tests campaigns, performed on a 3D half wing/fuselage body, enabled to characterize the buffet aerodynamic instability and to study the efficiency of innovative fluidic control devices designed and manufactured by ONERA. The analysis of the open-loop databases demonstrated the effects on the usual buffet characteristics, especially on the shock location and the separation areas on the wing suction side. Using these results, a closed-loop control methodology based on a quasi-steady approach was defined and several architectures were tested for various parameters such as the input signal, the objective function, the tuning of the feedback gain. All closed-loop methods were implemented on a dSPACE device able to estimate in real time the fluidic actuators command calculated mainly from the unsteady pressure sensors data. The efficiency of delaying the buffet onset or limiting its effects was demonstrated using the quasi-steady closed-loop approach and tested in both research and industrial wind tunnel environments.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.6
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• pp.751-760
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• 2015

Management of renewable organic resources is important in attaining the sustainability of agricultural production. However, nutrient management with organic resources is more complex than fertilization with chemical fertilizer because the composition of the organic input or the environmental condition will influence organic matter decomposition and nutrient release. One of the most effective methods for estimating nutrient release from organic amendment is the use of N mineralization models. The present study aimed at parameterizing N mineralization models for a number of organic amendments being used as a nutrient source for crop production. Laboratory incubation experiment was conducted in aerobic condition. N mineralization was investigated for nineteen organic amendments in sandy soil and clay soil at $20^{\circ}C$, $25^{\circ}C$, and $30^{\circ}C$. N mineralization was facilitated at higher temperature condition. Negative correlation was observed between mineralized N and C:N ratio of organic amendments. N mineralization process was slower in clay soil than in sandy soil and this was mainly due to the delayed nitrification. The single and the double exponential models were used to estimate N mineralization of the organic amendments. N mineralization potential $N_p$ and mineralization rate k were estimated in different temperature and soil conditions. Estimated $N_p$ ranged from 28.8 to 228.1 and k from 0.0066 to 0.6932. The double exponential model showed better prediction of N mineralization compared with the single exponential model, particularly for organic amendments with high C:N ratio. It is expected that the model parameters estimated based on the incubation experiment could be used to design nutrient management planning in environment-friendly agriculture.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.47-64
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• 2021

In this study, the annual and monthly groundwater recharge for the Sapgyo-cheon upstream basin in Chungnam Province was evaluated by water balance analysis utilizing WetSpass-M model. The modeling input data such as topography, climate parameters, LAI (Leaf Area Index), land use, and soil characteristics were established using ArcGIS, QGIS, and Python programs. The results showed that the annual average groundwater recharge in 2001 - 2020 was 251 mm, while the monthly groundwater recharge significantly varied over time, fluctuating between 1 and 47 mm. The variation was high in summer, and relatively low in winter. Variation in groundwater recharge was the largest in July in which precipitation was heavily concentrated, and the variation was closely associated with several factors including the total amount of precipitation, the number of days of the precipitation, and the daily average precipitation. This suggests the extent of groundwater recharge is greatly influenced not only by quantity of precipitation but also the precipitation pattern. Since climate condition has a profound effect on the monthly groundwater recharge, evaluation of monthly groundwater recharge need to be carried out by considering both seasonal and regional variability for better groundwater usage and management. In addition, the mathematical tools for groundwater recharge analysis need to be improved for more accurate prediction of groundwater recharge.

• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.1-8
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• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.

• Wind and Structures
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• v.32 no.4
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• pp.355-369
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• 2021

Shape optimization of tall buildings is an efficient approach to mitigate wind-induced effects. Several studies have demonstrated the potential of shape modifications to improve the building's aerodynamic properties. On the other hand, it is well-known that the cross-section geometry has a direct impact in the floor area availability and subsequently in the building's profitability. Hence, it is of interest for the designers to find the balance between these two design criteria that may require contradictory design strategies. This study proposes a surrogate-based multi-objective optimization framework to tackle this design problem. Closed-form equations provided by the Eurocode are used to obtain the wind-induced responses for several wind directions, seeking to develop an industry-oriented approach. CFD-based surrogates emulate the aerodynamic response of the building cross-section, using as input parameters the cross-section geometry and the wind angle of attack. The definition of the building's modified plan shapes is done adopting the reduced basis approach, advancing the current strategies currently adopted in aerodynamic optimization of civil engineering structures. The multi-objective optimization problem is solved with both the classical weighted Sum Method and the Weighted Min-Max approach, which enables obtaining the complete Pareto front in both convex and non-convex regions. Two application examples are presented in this study to demonstrate the feasibility of the proposed strategy, which permits the identification of Pareto optima from which the designer can choose the most adequate design balancing profitability and occupant comfort.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• Steel and Composite Structures
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• v.31 no.6
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• pp.575-589
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• 2019

The main focus of this study is to numerically investigate the influence of strong earthquake and tsunami-induced wave impact on the response and behavior of a cable-stayed steel bridge with large caisson foundations, by assuming that the earthquake and the tsunami come from the same fault motion. For this purpose, a series of numerical simulations were carried out. First of all, the tsunami-induced flow speed, direction and tsunami height were determined by conducting a two-dimensional (2D) tsunami propagation analysis in a large area, and then these parameters obtained from tsunami propagation analysis were employed in a detailed three-dimensional (3D) fluid analysis to obtain tsunami-induced wave impact force. Furthermore, a fiber model, which is commonly used in the seismic analysis of steel bridge structures, was adopted considering material and geometric nonlinearity. The residual stresses induced by the earthquake were applied into the numerical model during the following finite element analysis as the initial stress state, in which the acquired tsunami forces were input to a whole bridge system. Based on the analytical results, it can be seen that the foundation sliding was not observed although the caisson foundation came floating slightly, and the damage arising during the earthquake did not expand when the tsunami-induced wave impact is applied to the steel bridge. It is concluded that the influence of tsunami-induced wave force is relatively small for such steel bridge with large caisson foundations. Besides, a numerical procedure is proposed for quantitatively estimating the accumulative damage induced by the earthquake and the tsunami in the whole bridge system with large caisson foundations.

• Composites Research
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• v.32 no.2
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• pp.120-126
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• 2019

Advanced fiber fabrics have been utilized in not only anti-stabbing and bullet-proofing for body armor but also various industrial fields including vehicular armor and spacecraft structure. Furthermore, there have been a number of research to improve the ballistic performance of advanced fabrics introducing many computational approaches. In our research, an advanced fabric, Heracron manufactured in South Korea was modelled firstly using Autodyn, a commercial software specializing in impact and explosion phenomenon. The sensitivity of the input parameters was also confirmed by conducting simulations. To verify the numerical modelling, we measured and compared the simulation results with velocity decrements after impact involving one, three, and five layers of Heracron under 200-500 m/s impacts by an aluminum spherical projectile. The Heracron fabric was successfully modelled using Autodyn.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.55-63
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• 2019

In engineering problems, many random variables have correlation, and the correlation of input random variables has a great influence on reliability analysis results of the mechanical systems. However, correlated variables are often treated as independent variables or modeled by specific parametric joint distributions due to difficulty in modeling joint distributions. Especially, when there are insufficient correlated data, it becomes more difficult to correctly model the joint distribution. In this study, multivariate kernel density estimation with bounded data is proposed to estimate various types of joint distributions with highly nonlinearity. Since it combines given data with bounded data, which are generated from confidence intervals of uniform distribution parameters for given data, it is less sensitive to data quality and number of data. Thus, it yields conservative statistical modeling and reliability analysis results, and its performance is verified through statistical simulation and engineering examples.