Earticle

안정적인 수돗물 공급을 목적으로 하는 상수도 시설은 설계비용과 에너지 비용 등 총 비용을 저감할 수 있도록 계획되어야 한다. 상수관망 설계 시 관경에 따라 설계비용이 달라지고 마찰 손실 에너지가 달라지기 때문에 적절한 관경을 결정하는 것이 효과적인 관망 관리에 도움이 된다. 본 연구에서는 설계비 용의 최소화와 마찰 손실 에너지의 최소화를 목적으로 하는 다목적 최적화 연구를 수행하기 위해 비지배 정렬 유전자 알고리즘(NSGA-Ⅱ)과 상수관망 프로그램인 EPANET을 연계하여 적용하였다. 대상지역은 인천시 청라지역 3개의 동으로 하였고, 마찰 손실 에너지를 산출하기 위한 관 내의 유량과 마찰 손실 수두는 EPANET 모델 결과를 활용하였다. 연구를 통하여 대상지역 상수관망 내 수요량과 압력을 충족시 키면서 설계비용과 마찰 손실 에너지의 파레토 프런트를 구성하였다. 또한, 비지배 정렬 유전자를 이용하 여 두 목적함수를 만족시키는 관망의 최적 설계 연구가 가능하다는 것을 제시하였다.

Water supply system that provide safe water to customers should be designed to reduce construction cost and management cost. Pipe diameter is a key aspect of water distribution system since it affects design cost and friction loss. This study applied multi-objective optimization algorithm coupled with EPANET hydraulic solver for minimizing design cost and friction loss energy to water distribution system. Non-dominated sorting genetic algorithm (NSGA-Ⅱ) was used as an optimization technique. The hydraulic results such as head loss and flow in the pipeline were obtained from EPANET program. The test bed was the Cheongna water distribution system in Incheon, Korea. The findings in this study provided Pareto front of minimizing design cost and friction loss energy with simultaneously satisfying the water demand and hydraulic pressure. This study suggests that NSGA-Ⅱ can be used to design optimal water distribution system that satisfy the multi-objective options.

도시 내에서 발생한 화재와 같은 재난은 도시 내 광범위한 지역에서 다수의 인명, 경제적 피해를 야기한다. 이러한 피해를 최소화하기 위하여 강화학습을 활용한 효과적인 재난 대응 팀의 배치 방안이 주로 연구되었으나, 기존 방안은 재난 대응팀의 관할 지역의 크기가 증가될 때 효과적인 재난 대응을 기대하기 어렵다. 따라서, 본 연구팀은 대규모의 재난 상황에서 기존 강화학습 기반 대응팀 배치 방식의 문제를 해결하기 위해 상충되는 목표들 간의 가중치를 조절하는 새로운 강화학습 기법과 각 대응 목표들의 정의를 화재 재난 상황을 기반으로 설계하고 제안한다.

In various machines used in industrial sites and transportation equipment, fastening structures of bolts and nuts are widely employed. However, conventional Steel sockets, classified as non-explosion-proof materials, have a high likelihood of generating sparks due to friction with components, which can lead to explosions or large-scale fires. To address this issue, this study developed a lightweight explosion-protection socket using AL-7075-T6 aluminum alloy, which is known for its excellent explosion-proof properties. However, due to the inherent characteristics of aluminum, it has lower rigidity compared to Steel, requiring the use of more expensive alloy materials. Therefore, our research team utilized Finite Element Analysis (FEA) and Multi-Objective Genetic Algorithm (MOGA) to optimize the mass and safety factor of the socket, proposing a design that simultaneously achieves both weight reduction and structural stability. The socket developed in this study is approximately 30% lighter than traditional Steel-based sockets while maintaining a safety factor of 1.2 or higher, significantly enhancing operational safety in explosive environments. This research sets a new standard in the design and manufacturing process of explosion-proof sockets and is expected to contribute to the optimization of various explosion-proof equipment in the future.

이 연구는 CO2 수송-주입-저장 통합 시스템의 기본 설계 단계에서 다목적 최적화에 기반한 설비및 운영 인자 선정 방법을 제안한다. CO2 지중저장 통합 시스템의 설계 인자에 대한 다목적 최적화를 수행하여 최적의 비지배 해집합인 파레토 최적면을 탐색함으로써 상호 비지배 관계인 등가의설계안을 다수 도출한다. 방법론의 성능은 대염수층 대상 CO2 지중저장 통합 시스템의 기본 설계에 적용하여 검증하였다. 결정변수는 CO2 송출 압력, 수송관 내경, 주입정 튜빙 내경, 주입정 상단에서의 CO2 온도 4종류를 설정하였고, 목적함수는 CO2 저장량, 주입정 공저압, 편익비용비 3종류를 설정하였다. 연구 결과, 전체 탐색공간 대비 약 5% 수준의 계산 비용으로 파레토 최적면을 도출하여 설계 인자 범위를 확인하였다. 또한, 최적화 목적함수에 대한 사전 선호도와 사후 선호도 적용영향을 비교 분석하였다. 제안하는 방법론은 향후 CO2 지중저장 통합 시스템 설계 과정에서 검토할 수 있는 우수해를 다수 제공할 수 있음을 확인하였다.

This study proposes a method based on multi-objective optimization to select facility and operational parameters for the basic design of an integrated CO2 transportation-injection-storage system. Ｍulti- objective optimization was conducted to explore a Pareto-optimal front composed of non-dominated design solutions. The performance of the proposed approach was validated by applying it to CO2 storage at a saline aquifer. We set four decision variables (CO2 source pressure, pipeline inner diameter, injection well tubing inner diameter, and CO2 temperature at wellhead) and three objective functions (CO2 storage capacity, injection pressure, and benefit-cost ratio). The Pareto-optimal front was derived at approximately 5% of the computational cost compared to the exhaustive search. A comparative analysis was conducted to analyze the effects of prior and posterior preferences toward objectives on optimization performance. This method demonstrates the potential to provide effective solutions for designing an integrated CCS (carbon capture and storage) system.

Due to the multi-objective process of complex product scheme design, the multi-objective design optimization method of product scheme design is studied, an improved decision-making method of multi-objective extension design based on the extension theory is put forward, and a decision-making method of multi-objective extension design model based on the extensible relational degree is presented. By using the method and the model, the characteristic indexes are processed integrated, the computation model of extension degree of association is founded to process the uncertainty design information, and the decision design is achieved based on the optimum degree of the multi-objective design scheme. Finally, a case is provided to prove its feasibility.

This paper considers the problem of designing a Multilayer Survivable Optical Network for the customers’ Demands Problem called MSONDP. The network is modelled by two graphs: an undirected graph G1 = (V1, E1) and a complete undirected and weighted graph G2 = (V2, E2, c). The goal objective of this problem is to design connections based on customers’ demands with the smallest a minimum network cost to protect the network against all failures. This paper introduces a multi-objective approach for MSONDP. These objectives are to minimize the network cost (totalCost) and the maximum number of connections passing over a link (maxConn). Further, this paper also proposes a multi-objective genetic algorithm to solve this problem. The eExperimental results on real world and random instances are reported to show the efficiencyefficacy, in terms of minimizing the network cost, of the proposed algorithm comparing compared to the single genetic PGAMSONDP.

A new approach, Multi-Objective Design Exploration (MODE), is presented to address Multidisciplinary Design Optimization (MDO) problems by CFD-CSD coupling. MODE reveals the structure of the design space from the trade-off information and visualizes it as a panorama for Decision Maker. The present form of MODE consists of Kriging Model, Adaptive Range Multi Objective Genetic Algorithms, Analysis of Variance and Self-Organizing Map. The main emphasis of this approach is visual data mining. An MDO system using high fidelity simulation codes, Navier-Stokes solver and NASTRAN, has been developed and applied to a regional-jet wing design. Because the optimization system becomes very computationally expensive, only brief exploration of the design space has been performed. However, data mining result demonstrates that design knowledge can produce a good design even from the brief design exploration.

This study aims to develop efficient composite laminates for buckling load enhancement, interlaminar shear stress minimization, and weight reduction. This goal is achieved through cover-skin lay-ups around skins and stiffeners, which amplify bending stiffness and defer delamination by means of effective stress distribution. The design problem is formulated as multi-objective optimization that maximizes buckling load capability while minimizing both maximum out-of-plane shear stress and panel weight. For efficient optimization, response surface methodology is employed for buckling load, two out-of-plane shear stresses, and panel weight with respect to one ply thickness, six fiber orientations of a skin, and four stiffener heights. Numerical results show that skin-covered composite stiffened panels can be devised for maximum buckling load and minimum interlaminar shear stresses under compressive load. In addition, the effects of different material properties are investigated and compared. The obtained results reveal that the composite stiffened panel with Kevlar material is the most effective design.

Multi-objective design exploration (MODE) and its applications are reviewed as an attempt to utilize numerical simulation in aerospace engineering design. MODE reveals the structure of the design space based on trade-off information. A self-organizing map (SOM) is incorporated into MODE as a visual data mining tool for the design space. SOM divides the design space into clusters with specific design features. This article reviews existing visual data mining techniques applied to engineering problems. Then, we discuss three applications of MODE: multidisciplinary design optimization for a regional-jet wing, silent supersonic technology demonstrator and centrifugal diffusers.

Recently, a direct data-driven synthesis of a proportional integral derivative(PID) controller for a linear time-invariant(LTI) plant was presented in [1]. The authors showed that a complete set of PID controllers achieving robust performance and stability can be calculated directly from frequency response(FR) data without an identified transfer function model. However, it is not convenient to use this method because it requires complicated numerical algorithms to find specific frequencies which are solutions of an identical equation. The method also requires determination of the boundary of the controller's parameters from a finite set of FR data. In this paper, we present the development of a user-friendly Matlab toolbox based on the method in [1]. This toolbox allows us to obtain a complete three-dimensional(3-D) graphical solution of PID controllers that meet multiple design objectives. Several examples are given to demonstrate the use of the toolbox.

In this paper, multi-objective optimization of a multi-bubble pressure cabin in the underwater glider with Blended-Wing-Body (BWB) is carried out using Kriging and the Non-dominated Sorting Genetic Algorithm (NSGA-II). Two objective functions are considered: buoyancy-weight ratio and internal volume. Multi-bubble pressure cabin has a strong compressive capacity, and makes full use of the fuselage space. Parametric modeling of the multi-bubble pressure cabin structure is automatic generated using UG secondary development. Finite Element Analysis (FEA) is employed to study the structural performance using the commercial software ANSYS. The weight of the primary structure is determined from the volume of the Finite Element Structure (FES). The stress limit is taken into account as the constraint condition. Finally, Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) method is used to find some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. The best solution is compared with the initial design results to prove the efficiency and applicability of this optimization method.

This paper presents a multi-objective optimization approach to design rotor slot geometry of three-phase squirrel cage induction machine to achieve NEMA design D torque-speed (T-S) characteristics with high efficiency. The multi-objective Particle Swarm Optimization (MOPSO) algorithm combined with the adaptive response surface method and Latin hypercube sampling strategy is applied to obtain the Pareto optimal designs. In order to demonstrate the validity of the suggested optimal algorithm, an application to rotor slot design of three-phase induction motor is presented.

A new Pareto-optimal design algorithm, requiring least computational work, is proposed for a single phase AC solenoid actuator with multi-design-objectives: maximizing holding force and minimizing eddy current loss simultaneously. In the algorithm, the design space is successively reduced by a suitable factor, as iteration repeats, with the center of pseudo-optimal point. At each iteration, the objective functions are approximated to a simple second-order response surface with the CCD sampling points generated within the reduced design space, and Pareto-optimal solutions are obtained by applying($1+{\lambda}$) evolution strategy with the fitness values of Pareto strength.

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.

It is necessary to develop an efficient optimization technique to optimize engineering structures that have given design spaces, discrete design values, and several design goals. In this study, an optimum algorithm based on the genetic algorithm was applied to the multi-object problem to obtain an optimum solution that simultaneously minimizes the structural weight and construction cost of panel blocks in ship structures. The cost model was used in this study, which includes the cost of adjusting the weld-induced deformation and applying the deformation control methods, in addition to the cost of the material and the welding cost usually included in the normal cost model. By using the proposed cost model, more realistic optimum design results can be expected.

텐세그리티 구조물의 설계를 위한 다목적 최적화 기법이 제시되었다. 구조물의 기하가 먼저 주어지며, 설계변수는 부재력이다. 목적함수는 최대 강성매트릭스에 대한 최저 고유치와 찾고자 하는 목표값으로부터 가장 근접하게 일치하는 부재력이다. 복수의 목적함수 문제가 구속조건을 도입하여 일련의 단일 목적함수 문제로 전환되었다. 본 논문의 타당성을 알아보기 위해 텐세그리티 그리드에 대한 최적해를 구해 보았다.

A multi-objective optimization approach is presented for force design of tensegrity structures. The geometry of the structure is given a priori. The design variables are the member forces, and the objective functions are the lowest eigenvalue of the tangent stiffness matrix that is to be maximized, and the deviation of the member forces from the target values that is to be minimized. The multi-objective programming problem is converted to a series of single-objective programming problems by using the constraint approach. A set of Pareto optimal solutions are generated for a tensegrity grid to demonstrate the validity of the proposed method.

This paper presents decision making method of structural multi-objective fuzzy optimum problem. The data and behavior of many engineering systems are not know precisely and the designer is required to design the system in the presence of fuzziness in the multi-goals, constraints and consequences of possible actions. In this paper, in order to find a satisfactory solution, the membership functions are constructed for the fuzzy objectives subject to the fuzzy constraints, and two approaches are presented by using the different types of fuzzy decision making. Thus, multi-objective fuzzy optimum problem can be converted into single objective non-fuzzy optimum problem and satisfactory solution of the multi-objective fuzzy optimum problem can be found with general optimum programming. Illustrative numerical example of the ten bar truss for minimum weight and minimum deflection is provided to demonstrate the process of finding the solution and the results are discussed.

Error correcting codes는 일반적으로 soft error를 막기 위해서 사용된다. single error의 수정과 double error의 검출(SEC-DED) 코드들은 이런 목적으로 사용된다. 본 논문에서는 이러한 회로의 크기, 지연시간, 전력 소비를 선택적으로 최소로 하는 SEC-DED의 설계방법을 제안한다. 이러한 SEC-DED의 설계는 비선형 최적화 문제로 포함되는데 우리는 다목적 유전자 알고리즘을 이용하여 이 문제를 해결한다. 제안하는 방법은 여러 가지 SEC-DED code들을 제공하여 사용자의 환경에 따라 알맞은 회로를 선택할 수 있도록 한다. 제안하는 방법을 효율적인 ECC코드로 알려져 있는 odd-column weight Hsiao code에 적용하여 그 효율성을 입증하였다.

Error correcting codes (ECCs) are commonly used to protect against the soft errors. Single error correcting and double error detecting (SEC-DED) codes are generally used for this purpose. The proposed approach in this paper selectively reduced power consumption, delay, and area in single-error correcting, double error-detecting checker circuits that perform memory error correction. The multi-objective genetic algorithm is employed to solve the non -linear optimization problem. The proposed method allows that user can choose one of different non-dominated solutions depending on which consideration is important among them. Because we use multi-objective genetic algorithm, we can find various dominated solutions. Therefore, we can choose the ECC according to the important factor of the power, delay and area. The method is applied to odd-column weight Hsiao code which is well- known ECC code and experiments were performed to show the performance of the proposed method.

It is necessary to develop an efficient optimization technique to perform optimum designs which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of ship structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points well by spreading points randomly entire the design spaces. In this paper, Pareto Strategy (PS) multi-objective function method is developed by considering the search direction based on Pareto optimal points, the step size, the convergence limit and the random number generation. The success points between just before and current Pareto optimal points are considered. PS method can also apply to the single objective function problems, and can consider the discrete design variables such as plate thickness, longitudinal space, web height and web space. The optimum design results are compared with existing Random Search (RS) multi-objective function method and Evolutionary Strategy (ES) multi-objective function method by performing the optimum designs of double bottom structure and double hull tanker which have discrete design values. Its superiority and effectiveness are shown by comparing the optimum results with those of RS method and ES method.