• Journal of Mechanical Science and Technology
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• v.16 no.2
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• pp.203-210
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• 2002

For effective response surface modeling during sequential approximate optimization (SAO), the normalized and the augmented D-optimality criteria are presented. The normalized D-optimality criterion uses the normalized Fisher information matrix by its diagonal terms in order to obtain a balance among the linear-order and higher-order terms. Then, it is augmented to directly include other experimental designs or the pre-sampled designs. This augmentation enables the trust region managed sequential approximate optimization to directly use the pre-sampled designs in the overlapped trust regions in constructing the new response surface models. In order to show the effectiveness of the normalized and the augmented D-optimality criteria, following two comparisons are performed. First, the information surface of the normalized D-optimal design is compared with those of the original D-optimal design. Second, a trust-region managed sequential approximate optimizer having three D-optimal designs is developed and three design problems are solved. These comparisons show that the normalized D-optimal design gives more rotatable designs than the original D-optimal design, and the augmented D-optimal design can reduce the number of analyses by 30% - 40% than the original D-optimal design.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.8
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• pp.27-35
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• 2003

Geometric modeling tool and analysis tool of shell surface have been developed in the different environments and purposes. Thus they cannot be naturally fitted to each other for the integrated design and analysis. In the present study, an integrated framework of geometric modeling, analysis， and design optimization is proposed. It is based on the common representation of B-spline surface patch. In the analysis module, a geometrically-exact shell finite element is implemented. In shape optimization module, control points of the surface are selected as design variables. For the computation of shape sensitivities, semi-analytical method is used. Sequential linear programming(SLP) is adopted for the shape optimization of surfaces. The developed integrated framework should serve as a powerful tool for the geometric modeling, analysis, and shape design of surfaces.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.289-300
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• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• Ocean Systems Engineering
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• v.9 no.2
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• pp.111-133
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• 2019

Flow through a scaled horizontal axis marine current turbine was numerically simulated after validation and the turbine design was optimized. The computational fluid dynamics (CFD) code Ansys-CFX 16.1 for numerical modeling, an in-house blade element momentum (BEM) code for analytical modeling and an in-house surrogate-based optimization (SBO) code were used to find an optimal turbine design. The blade-pitch angle (${\theta}$) and the number of rotor blades (NR) were taken as design variables. A single objective optimization approach was utilized in the present work. The defined objective function was the turbine's power coefficient ($C_P$). A $3{\times}3$ full-factorial sampling technique was used to define the sample space. This sampling technique gave different turbine designs, which were further evaluated for the objective function by solving the Reynolds-Averaged Navier-Stokes equations (RANS). Finally, the SBO technique with search algorithm produced an optimal design. It is found that the optimal design has improved the objective function by 26.5%. This article presents the solution approach, analysis of the turbine flow field and the predictability of various surrogate based techniques.

• Korean Journal of Computational Design and Engineering
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• v.11 no.2
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• pp.128-137
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• 2006

In this paper, a CAD/CAE integrated optimal design system is developed, in which design and analysis process is automated using CAD/CAE softwares for a complex model in which the modeling by parametric feature is not easy to apply. Unigraphics is used for CAD modeling, in which the process is automated by using UG/Knowledge Fusion for modeling itself and UG/Open API function for the other functions respectively. Structural analyses are also carried out automatically by ANSYS using the imported parasolid model. The developed system is applied for the PLS(Plasma Lighting System) consisting of more than 20 components, which is a next generation illumination system that is used to illuminate stadium or outdoor advertizing panel. The analyses include responses by static, wind and impact loads. As a result of analyses, tilt assembly, which is a link between upper and lower body, is found to be the most critical component bearing higher stresses. Experiment is conducted using MTS to validate the analysis result. Optimization is carried out using the software Visual DOC for the tilt assembly to minimize material volume while maintaining allowable stress level. As a result of optimization, the maximum stress is reduced by 57% from the existing design, though the material volume has increased by 21%.

• Wind and Structures
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• v.16 no.6
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• pp.541-560
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• 2013

The aim of the paper is to use optimization and advanced numerical computation of a sail fiber-reinforced composite model to increase the performance of a yacht under wind action. Designing a composite-shell system against the wind is a very complex problem, which only in the last two decades has been approached by advanced modeling, optimization and computer fluid dynamics (CFDs) based methods. A sail is a tensile structure hoisted on the rig of a yacht, inflated by wind pressure. Our objective is the multiple criteria optimization of a sail, the engine of a yacht, in order to obtain the maximum thrust force for a given load distribution. We will compute the best possible yarn thickness orientation and distribution in order to minimize the total fiber volume with some displacement constraints and in order to leave the most uniform stress distribution over the whole structure. In this paper our attention will be focused on computer simulation, modeling and optimization of a sail-shape mathematical model in different regatta and wind conditions, with the purpose of improving maneuverability and speed made good.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1132-1142
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• 2001

The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods 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 training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

• Korean Journal of Computational Design and Engineering
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• v.12 no.3
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• pp.163-170
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• 2007

A torque converter, connected to a transmission/transaxle input shaft, connects, multiplies and interrupts the flow of engine torque into the transmission. Damper springs are usually equipped in a torque converter to convert stably the torque power supplied from engine. Damper Springs generally have the most flexible design variables among vehicle transmission parts, so that they could be effective design factors to improve the entire vehicle's performance. Damper spring, however, has geometric complexity after it equipped in a torque converter. For that reason, modeling a damper spring requires expert's knowledge to determine many design parameters and satisfy the functional requirements at the same time. In this paper, we introduce an optimum design method applied in detailed-design stage to reduce design process and financial loss caused by adequate design. Many design variables have to be classified and structuralized for Optimization. This also could make designer concentrate on functional requirements of damper spring, not on design possibility. In addition, modeling an assembled spring has technical restriction with primitives of the current major CAD solutions because of complexity of assembled spring shape. Thus, one of modeling solution presented in this paper since detailed and exact modeling is important for CAE or DMU.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.658-668
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• 2009

Earth-moving operation has a great impact on the overall budget and schedule of any heavy civil projects. More often than not, the operational decisions are made largely based on field personnel's experience and judgment. In particular, decisions on earth moving operations by scraper-dozer fleets have been heavily influenced by the following belief: "The longer a dozer pushes a scraper for loading, the better earth-moving productivity is gained by the fleet." Even though there is some truth to this notion, scraper-dozer earth moving operations involve a much complex process that requires a systematic analysis for predicting the maximum production. To this end, this paper presents a spreadsheet-based scraper-dozer fleet operation model for its production optimization. Various optimization techniques, including a genetic-algorithm method, are presented for comparison and each technique's pros and cons are discussed.

• Structural Engineering and Mechanics
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• v.61 no.2
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• pp.283-293
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• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.