• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.46-51
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• 2014

The most important process of engineering system optimal design is to identify the relationship between the design variables and system response. In case of the system optimization, Response Surface Method (RSM) is widely used. The optimization process of RSM generates the design space using the typical alternative candidates and finds the optimal design point in the generated design space. By changing the optimal point depending on the configuration of the design space, it is important to generate the design space. Therefor in this study, the design space is generated by using the relationship between design variables and system response based on Neuro-Response Surface Method (NRSM). And I try to construct the framework for optimal shape design based on NRSM that the optimum shape can be predicted using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) within the generated design space. In order to verify the usefulness of the constructed framework, we applied the nonlinear mathematical function problem. In this study, we can solve the constraints of time in the optimization process for the engineering problem and effective to determine the optimal design was possible. by using the generated framework for optimal shape design based on NRSM. In the future research, we try to apply the optimization problem for Naval Architectural & Ocean Engineering based on the results of this study.

• Earthquakes and Structures
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• v.11 no.6
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• pp.1101-1121
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• 2016

Base isolation, one of the popular seismic protection approaches proven to be effective in practical applications, has been widely applied worldwide during the past few decades. As the techniques mature, it has been recognised that, the biggest issue faced in base isolation technique is the challenge of great base displacement demand, which leads to the potential of overturning of the structure, instability and permanent damage of the isolators. Meanwhile, drain, ventilation and regular maintenance at the base isolation level are quite difficult and rather time- and fund- consuming, especially in the highly populated areas. To address these challenges, a number of efforts have been dedicated to propose new isolation systems, including segmental building, additional storey isolation (ASI) and mid-storey isolation system, etc. However, such techniques have their own flaws, among which whipping effect is the most obvious one. Moreover, due to their inherent passive nature, all these techniques, including traditional base isolation system, show incapability to cope with the unpredictable and diverse nature of earthquakes. The solution for the aforementioned challenge is to develop an innovative vibration isolation system to realise variable structural stiffness to maximise the adaptability and controllability of the system. Recently, advances on the development of an adaptive magneto-rheological elastomer (MRE) vibration isolator has enlightened the development of adaptive base isolation systems due to its ability to alter stiffness by changing applied electrical current. In this study, an innovative semi-active storey isolation system inserting such novel MRE isolators between each floor is proposed. The stiffness of each level in the proposed isolation system can thus be changed according to characteristics of the MRE isolators. Non-dominated sorting genetic algorithm type II (NSGA-II) with dynamic crowding distance (DCD) is utilised for the optimisation of the parameters at isolation level in the system. Extensive comparative simulation studies have been conducted using 5-storey benchmark model to evaluate the performance of the proposed isolation system under different earthquake excitations. Simulation results compare the seismic responses of bare building, building with passive controlled MRE base isolation system, building with passive-controlled MRE storey isolation system and building with optimised storey isolation system.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2125-2138
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• 2023

The printed circuit heat exchanger (PCHE) with airfoil fins has the benefits of high compactness, high efficiency and superior heat transfer performance. A novel multi-objective optimization approach is presented to design the airfoil fin PCHE in this paper. Three optimization design variables (the vertical number, the horizontal number and the staggered number) are obtained by means of dimensionless airfoil fin arrangement parameters. And the optimization objective is to maximize the Nusselt number (Nu) and minimize the Fanning friction factor (f). Firstly, in order to investigate the impact of design variables on the thermal-hydraulic performance, a parametric study via the design of experiments is proposed. Subsequently, the relationships between three optimization design variables and two objective functions (Nu and f) are characterized by an improved particle swarm optimization-backpropagation artificial neural network. Finally, a multi-objective optimization is used to construct the Pareto optimal front, in which the non-dominated sorting genetic algorithm II is used. The comprehensive performance is found to be the best when the airfoil fins are completely staggered arrangement. And the best compromise solution based on the TOPSIS method is identified as the optimal solution, which can achieve the requirement of high heat transfer performance and low flow resistance.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.2
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• pp.186-191
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• 2015

Presently, rapidly changing and unstable global economic environments demand engineers. Products should be designed to increase profits by lowering costs and provide distinguished performance compared with competitors. This study aims to optimize the design of the power-transmission drive shaft. The mass is reduced as an objective function, and the stress is constrained under a constant value. To reduce the number of experiments, CCD (central composite design) and D-Optimal are used for the experimental design. RSM (response surface methodology) is employed to construct a regression model for the objective functions and constraint function. In this problem, there is only one objective function for the mass. The other objective function gives 1; thus, NSGA-II is used.

• Advances in aircraft and spacecraft science
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• v.4 no.6
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• pp.701-709
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• 2017

One of the components that used in the satellite thermal control subsystem is the Mechanically Pumped Fluid Loop (MPFL) system; this system mostly used in geosynchronous orbit (GEO) satellites, and can transfer heat from a hot point to a cold point using the fluid which circulated in a closed loop. Heat radiates to the deep space at the cold plate to cool down the fluid temperature. In this research, the radiative heatexchanger (RHX) for a MPFL system is optimized. The genetic algorithm has been used for minimizing the total mass and pressure drop by considering a constant transferred heat rate at the heat exchanger. The optimization has been done in two cases. In case I, two parameters are considered as a goal function, so optimization is performed using NSGA-II method. Results of optimization are shown in the pareto diagram. In case II, the diameter of pipe is considered constant, so the optimized value for distances of the parallel pipes is obtained by using the genetic algorithm, in which the system has the least total mass. Results show that in the RHX, by increasing the pipe diameter, pressure drop decreases and total mass increases. Also by considering a constant value for pipe diameter, an optimum distance between pipes and pipe length are obtained in which the system has a minimum mass.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.6
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• pp.661-669
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• 2018

Engineering system problems consist of multi-objective optimisation and the performance analysis is generally time consuming. To optimise the system concerning its performance, many researchers perform the optimisation using an approximation model. The Response Surface Method (RSM) is usually used to predict the system performance in many research fields, but it shows prediction errors for highly nonlinear problems. To create an appropriate metamodel for marine systems, Lee (2015) compares the prediction accuracy of the approximation model, and multi-objective optimal design framework is proposed based on a confirmed approximation model. The proposed framework is composed of three parts: definition of geometry, generation of approximation model, and optimisation. The major objective of this paper is to confirm the applicability/usability of the proposed optimal design framework and evaluate the prediction accuracy based on sensitivity analysis. We have evaluated the proposed framework applicability in derrick structure optimisation considering its structural performance.

• Advances in Computational Design
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• v.1 no.1
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• pp.87-103
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• 2016

This paper adopts and investigates the non-dominated sorting approach for extending the single-objective Cuckoo Search (CS) into a multi-objective framework. The proposed approach uses an archive composed of primary and secondary population to select and keep the non-dominated solutions at each generation instead of pairwise analogy used in the original Multi-objective Cuckoo Search (MOCS). Our simulations show that such a low computational complexity approach can enrich CS to incorporate multi-objective needs instead of considering multiple eggs for cuckoos used in the original MOCS. The proposed MOCS is tested on a set of multi-objective optimization problems and two well-studied engineering design optimization problems. Compared to MOCS and some other available multi-objective algorithms such as NSGA-II, our approach is found to be competitive while benefiting simplicity. Moreover, the proposed approach is simpler and is capable of finding a wide spread of solutions with good coverage and convergence to true Pareto optimal fronts.

• Structural Engineering and Mechanics
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• v.70 no.3
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• pp.367-380
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• 2019

The paper concerns topology and geometry optimization of statically determinate beams with arbitrary number of supports. The optimization problem is treated as a bi-criteria one, with the objectives of minimizing the absolute maximum bending moment and the maximum deflection for a uniform gravity load. The problem is formulated and solved using the Pareto optimality concept and the lexicographic ordering of the objectives. The non-dominated sorting genetic algorithm NSGA-II and the local search method are used for the optimization in the Pareto sense, whereas the genetic algorithm and the exhaustive search method for the lexicographic optimization. Trade-offs between objectives are examined and sets of Pareto-optimal solutions are provided for different topologies. Lexicographically optimal beams are found assuming that the maximum moment is a more important criterion. Exact formulas for locations and values of the maximum deflection are given for all lexicographically optimal beams of any topology and any number of supports. Topologies with lexicographically optimal geometries are classified into equivalence classes, and specific features of these classes are discussed. A qualitative principle of the division of topologies equivalent in terms of the maximum moment into topologies better and worse in terms of the maximum deflection is found.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.256-265
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• 2014

Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using non-dominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

• Journal of Ocean Engineering and Technology
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• v.25 no.4
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• pp.42-47
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• 2011

In this paper, wake equalizing duct (WED) form optimization was carried out using computational fluid dynamics (CFD) techniques. A WED is a ring-shaped flow vane with a foil-type cross-section fitted to a hull in front of the upper propeller area. The main advantage of a WED is the power savings resulting from the uniformity of the velocity distribution on the propeller plane, a reduction in the flow separation at the aft-body, and lift generation with a forward force component on the foil section. This paper intends to evaluate these functions and find an optimized WED form for minimizing the viscous resistance and equalizing the wake distribution. In the optimization process, the study uses four WED parameters: the angle of the section, longitudinal location, and angles of the axes for the half rings against the longitudinal and transverse planes of the ship. KRISO 300K VLCC2 (KVLCC2) is chosen as an example ship to demonstrate the WED optimization. The optimization procedure uses genetic algorithms (GAs), a gradient-based optimizer for the refinement of the solution, and Non-dominated Sorting GA-II(NSGA-II) for Multiobjective Optimization. The results show that the optimized WED can reduce the viscous resistance at the expense of the uniformity of the wake distribution.