• 대한수학회보
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• 제58권5호
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• pp.1109-1127
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• 2021

We study the long-term dynamics for a family of incompressible three-dimensional Leray-α-like models that employ the spectral fractional Laplacian operators. This family of equations interpolates between incompressible hyperviscous Navier-Stokes equations and the Leray-α model when varying two nonnegative parameters 𝜃₁ and 𝜃₂. We prove the existence of a finite-dimensional global attractor for the continuous semigroup associated to these models. We also show that an operator which projects the weak solution of Leray-α-like models into a finite-dimensional space is determining if it annihilates the difference of two "nearby" weak solutions asymptotically, and if it satisfies an approximation inequality.

• 천문학회지
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• 제56권2호
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• pp.187-194
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• 2023

The remote sensing technique of measuring the magnetic field was applied first to sunspots by Hale (1908). Later Babcock (1961) showed that the solar surface magnetic field on a global scale is a dipole in first-order approximation and that this dipole field reverses once every solar cycle. The Wilcox Solar Observatory (WSO) supplies the spherical harmonics coefficients of the solar corona magnetic field of each Carrington Rotation, calculated based on the remotely-sensed photospheric magnetic field of the solar surface. To infer the internal current system producing the global solar coronal magnetic field structure and evolution of the Sun, we calculate the multipole components of the solar magnetic field using the WSO data from 1976 to 2019. The prominent cycle components over the last 4 solar activity cycles are axis-symmetric fields of the dipole and octupole. This implies that the current inversion driving the solar magnetic field reversal originates from the equatorial region and spreads to the whole globe. Thus, a more accurate solar dynamo model must include an explanation of the origin and evolution of such solar internal current dynamics.

• Structural Engineering and Mechanics
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• 제13권4호
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• pp.369-385
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• 2002

Ductility capacity is comprehensively studied for steel moment-resisting frames. Local, story and global ductility are being considered. An appropriate measure of global ductility is suggested. A time domain nonlinear seismic response algorithm is used to evaluate several definitions of ductility. It is observed that for one-story structures, resembling a single degree of freedom (SDOF) system, all definitions of global ductility seem to give reasonable values. However, for complex structures it may give unreasonable values. It indicates that using SDOF systems to estimate the ductility capacity may be a very crude approximation. For multi degree of freedom (MDOF) systems some definitions may not be appropriate, even though they are used in the profession. Results also indicate that the structural global ductility of 4, commonly used for moment-resisting steel frames, cannot be justified based on this study. The ductility of MDOF structural systems and the corresponding equivalent SDOF systems is studied. The global ductility values are very different for the two representations. The ductility reduction factor $F_{\mu}$ is also estimated. For a given frame, the values of the $F_{\mu}$ parameter significantly vary from one earthquake to another, even though the maximum deformation in terms of the interstory displacement is roughly the same for all earthquakes. This is because the $F_{\mu}$ values depend on the amount of dissipated energy, which in turn depends on the plastic mechanism, formed in the frames as well as on the loading, unloading and reloading process at plastic hinges. Based on the results of this study, the Newmark and Hall procedure to relate the ductility reduction factor and the ductility parameter cannot be justified. The reason for this is that SDOF systems were used to model real frames in these studies. Higher mode effects were neglected and energy dissipation was not explicitly considered. In addition, it is not possible to observe the formation of a collapse mechanism in the equivalent SDOF systems. Therefore, the ductility parameter and the force reduction factor should be estimated by using the MDOF representation.

• 대한지리학회지
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• 제43권2호
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• pp.253-262
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• 2008

이 논문의 주요 목적은 정규성 가정 하에 일변량 공간 연관성 측도의 첫 번째 네 적률을 구해내는 일반화된 추출 절차를 정식화하고, 그것을 바탕으로 각 측도의 가설 검정을 위해 정규근사가 갖는 가능성과 한계를 평가하는 것이다. 중요 연구 결과는 다음과 같다. 첫째, 이전의 연구에 기반함으로써, 정규성 가정 하에 전역적 측도와 국지적 측도에 모두 적용될 수 있는 일반화된 적률 추출절차가 도출되었다. 개별 공간 연관성 측도를 위한 필수적인 메트릭스가 적절히 정의되었을 때, 일반화된 유의성 검정 방법은 각 공간 연관성 측도의 기대값과 분산은 물론 첨도와 왜도를 효과적으로 산출하였다. 둘째, 첫 번째 두 적률에 근거한 정규근사 방법은 전역적 통계량에 대해서는 유효한 것으로 판명되었지만, 국지적 통계량에 대해서는 매우 높은 왜도와 첨도로 말미암아 그 유효성이 현저히 떨어지는 것으로 드러났다.

• 대한기계학회논문집A
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• 제25권3호
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• pp.537-547
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• 2001

The paper proposes the fuzzy logic global approximate optimization strategies in optimal sizing of automotive A-pillar trim with rib structures for occupant head protection. Two different strategies referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the inherent nonlinearity in analysis model should be accommodated over the entire design space and the training data is not sufficiently provided. The objective of structural design is to determine the dimensions of rib in A-pillar, minimizing the equivalent head injury criterion HIC(d). The paper describes the head-form modeling and head impact simulation using LS-DYNA3D, and the approximation procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and subsequently presents their generalization capabilities in terms of number of fuzzy rules and training data.

• International Journal of Aeronautical and Space Sciences
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• 제18권4호
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• pp.662-674
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• 2017

This paper describes the multidisciplinary design optimization (MDO) process of a tailless unmanned combat aerial vehicle (UCAV) using global variable fidelity aerodynamic analysis. The developed tailless UAV design framework combines multiple disciplines that are based on low-fidelity and empirical analysis methods. An automated high-fidelity aerodynamic analysis is efficiently integrated into the MDO framework. Global variable fidelity modeling algorithm manages the use of the high-fidelity analysis to enhance the overall accuracy of the MDO by providing the initial sampling of the design space with iterative refinement of the approximation model in the neighborhood of the optimum solution. A design formulation was established considering a specific aerodynamic, stability and control design features of a tailless aircraft configuration with a UCAV specific mission profile. Design optimization problems with low-fidelity and variable fidelity analyses were successfully solved. The objective function improvement is 14.5% and 15.9% with low and variable fidelity optimization respectively. Results also indicate that low-fidelity analysis overestimates the value of lift-to-drag ratio by 3-5%, while the variable fidelity results are equal to the high-fidelity analysis results by algorithm definition.

• 한국CDE학회논문집
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• 제17권5호
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• pp.375-386
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• 2012

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• 한국항공우주학회지
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• 제34권1호
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• pp.42-48
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• 2006

연구에서는 다분야 통합 최적설계를 위한 시스템 근사화 기법으로 RRSET (Repetitive Response Surface Enhancement Technique)를 제안하였다. 2차 다항식만으로는 어려운 반응면의 표현을 위해 RRSET는 설계공간을 변형할 수 있는 스트레칭 함수를 도입하고 전역 최적화 알고리즘인 담금질 모사기법을 이용하여 반응면을 최적화 하였다. 도출된 최적점은 반복적으로 다음 순기의 반응면의 구성에 이용하여 반응면의 신뢰도를 더욱 높일 수 있었다. 제안된 기법을 수치예제 등에 적용한 결과, 비교적 적은 수의 실험 회수로 비선형적인 반응면을 잘 표현하고 최적 설계점을 도출해낼 수 있음이 확인되었다. 정밀한 근사화 기법의 중요성이 강화되고 있는 현재, 본 연구에서 제시된 근사화 기법은 차후의 연구에서 다분야 통합 최적화 기법에의 적용이 가능하리라 사료된다.

• International Journal of Fluid Machinery and Systems
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• 제10권3호
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• pp.240-253
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• 2017

Latin hypercube sampling is widely used design-of-experiment technique to select design points for simulation which are then used to construct a surrogate model. The exploration/exploitation properties of surrogate models depend on the size and distribution of design points in the chosen design space. The present study aimed at evaluating the performance characteristics of various surrogate models depending on the Latin hypercube sampling (LHS) procedure (sample size and spatial distribution) for a diverse set of optimization problems. The analysis was carried out for two types of problems: (1) thermal-fluid design problems (optimizations of convergent-divergent micromixer coupled with pulsatile flow and boot-shaped ribs), and (2) analytical test functions (six-hump camel back, Branin-Hoo, Hartman 3, and Hartman 6 functions). The three surrogate models, namely, response surface approximation, Kriging, and radial basis neural networks were tested. The important findings are illustrated using Box-plots. The surrogate models were analyzed in terms of global exploration (accuracy over the domain space) and local exploitation (ease of finding the global optimum point). Radial basis neural networks showed the best overall performance in global exploration characteristics as well as tendency to find the approximate optimal solution for the majority of tested problems. To build a surrogate model, it is recommended to use an initial sample size equal to 15 times the number of design variables. The study will provide useful guidelines on the effect of initial sample size and distribution on surrogate construction and subsequent optimization using LHS sampling plan.